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Drones like these allow military personnel to remotely conduct surveillance and occasional combat operations if the UAV has onboard weaponry, while minimizing the risks to their soldiers. Although UAVs were initially used for military applications, they have since expanded into various civilian sectors. When people hear the term UAV (Unmanned Aerial Vehicle), the first thing that comes to mind are the drones used by the military, such as the or the RQ-12 Wasp. MQ-1 Predator 1.Delivery One such use for drones is the use in delivery services. By using UAVs for deliveries, companies such as Amazon could save money on shipping costs, which would reduce costs for consumers, who would receive their packages more quickly and reliably. . In November 2016, Domino’s delivered an order of a Peri-Peri Chicken Pizza and a Chicken and Cranberry Pizza to a customer in Whangaparaoa, New Zealand. The first successful delivery with a UAV was a pizza delivery Delivery drones have also been used to such as blood products, vaccines, pharmaceuticals, and medical samples. Whereas ground vehicles may not be able to reach a remote destination due to difficult terrain, a UAV can make the delivery with ease. These drones have been credited with saving lives by , as well as personal protective equipment and COVID-19 tests during the pandemic in the United States, Israel, and Ghana. transport medicinal products delivering blood in Rwanda 2.Journalism In recent years, UAVs have provided journalists with a unique and cost-effective way to capture footage and images from hard-to-reach places. Using a drone, news reporters can cover important events from various angles that would be impossible for a hand-held camera to capture. Additionally, can be used to capture footage from dangerous or inaccessible locations, such as war zones or natural disasters. drones It’s important to note that the use of drones in these situations must comply with , as well as sports league, venue, and local law enforcement agency rules. Federal Aviation Administration (FAA) regulations 3.Search and Rescue Search and rescue operations are often tricky and unpredictable. Time is paramount, as the missing person is most likely trapped in a life-threatening situation. And depending on the location, the terrain can make things exponentially more difficult for the rescue team. Using UAVs, the rescue team can cover more ground faster than if they were using ground-based vehicles. These drones can also provide an aerial view of the area and carry various sensors to identify specific objects and heat signatures on the ground, providing much-needed situational awareness. in June 2017, two hikers were reported missing in Pike National Forest in Colorado. Search and rescue teams, comprising twenty-five volunteers, searched on foot, with ATVs, and with the aid of a K-9 team. But it was the team using a UAV that found the two hikers in an area spanning thousands of acres in just two hours. In one such case https://www.youtube.com/watch?v=Y1VPZ5jt5Xw&embedable=true 4.Disaster Response By utilizing drones in disaster relief efforts, dangerous situations can be assessed quickly without placing risks on personnel. For example, During the Fukushima Daiichi nuclear disaster in 2011, without risking the lives of the relief workers. They have also proven excellent first responders since they can be deployed fast and easily compared to manned aerial vehicles. drones were used to survey the extent of the disaster As previously mentioned, drones are excellent at providing an aerial view of the area and can be equipped with infrared (IR) cameras to detect heat signatures. This is vital not only in search and rescue operations but also in surveying unstable areas. When dealing with the aftermath of an earthquake, a partially damaged building or a civic structure requires a thorough inspection before it can be deemed safe for humans to go there. Drones with sensors like IR cameras can survey these sites easily and safely. 5.Firefighting UAVs can also be a massive asset to the men and women who risk their lives fighting fires. They can be immensely useful in providing situational awareness about the fire and its surroundings. By using drones equipped with thermal cameras, firefighters can determine the extent of the fire and identify its source, which can greatly aid them in bringing the situation under control and conducting search and rescue operations. UAVs have also been used as effective tools in monitoring and suppressing wildfires. By using real-time data, firefighters can determine where a fire will move next, allowing them to make a plan for movement and evacuation quickly. In addition to providing important tactical data, UAVs are more maneuverable than helicopters or planes. 6.Agriculture Some of the more common agricultural applications for UAVs include pest control, plant health monitoring, livestock management, soil analysis, and aerial survey. Farmers must maintain constant vigilance, as their crops are susceptible to disease, fungus, and bugs. They also need to be aware of the carbon levels and nitrates in the soil, which can impact a plant’s productivity. Traditionally, farmers must monitor these factors by conducting visual inspections and sending soil samples to a laboratory. By using UAVs, however, these factors can be measured in real-time by utilizing a combination of RGB cameras, thermal imaging, and multi-spectral imaging. The Future of UAVs In this day and age, where technology is rapidly evolving to meet new demands, UAVs could fulfill a number of roles in current and future job sectors. Additionally, many people operate drones for various hobbies, such as photography and drone racing. However, no matter how UAVs get used, operators must ensure that they comply with any rules regarding drone operation in their country. For instance, drone operators in the United States need to obtain a from the FAA before flying a UAV. Remote Pilot Certificate What do you think about these use cases? Do you own a drone yourself? Let me know what you think in the comments below!

6 Important Use Cases for UAVs (Unmanned Aerial Vehicles)

6 usos comunes de los UAV (vehículos aéreos no tripulados)

6 utilisations courantes des UAV (véhicules aériens sans pilote)

यूएवी (मानवरहित हवाई वाहन) के लिए 6 सामान्य उपयोग

UAV (無人航空機) の 6 つの一般的な用途

6 Usos Comuns para UAVs (Veículos Aéreos Não Tripulados)

6 Cách sử dụng phổ biến cho UAV (Máy bay không người lái)

UAV（无人驾驶飞行器）的 6 种常见用途

Infrastructure drift, unmanaged resources, ghost assets, these are all well-known “silent killers” in our clouds.  Whether AWS, GCP, Kubernetes, Azure or anything else, when deploying our services to multiple clouds, we know that a unified inventory and management of our cloud resources is complicated, and there are many great tools out there that are looking to help solve this growing complexity. ‍ One thing that is often overlooked though is where our SaaS tooling comes into the mix.  When we adopt SaaS tools, we tend to see them as just that–tools, and not what they eventually actually are: additional siloed, unmanaged clouds with their own proprietary inventory of services, objects and resources. ‍ A phenomenon we have encountered often, when helping companies overcome drift, is a common neglect of cloud infrastructure tooling, such as CloudFlare, Okta, Mongo Atlas, Datadog, Git and many other popular SaaS platforms and tools that are part of our core operations.  How can we make these SaaS clouds immutable, versioned, scalable and monitored if these extensions aren't codified?  Is state drift in Okta less troubling than drift in your IAM roles, for example? How can we guarantee proper monitoring if our Datadog dashboards make it possible for anyone to cause drifts? ‍ These are just some of the questions that come to mind when we see this recurring anti-pattern in cloud operations today. But you may ask, why does this matter? ‍ If a growing understanding is dawning on DevOps engineers that it is much safer and less error prone to codify cloud resources, including the inherent benefits of managing these resources like all other code––whether the git history, peer reviews, PR automation and policy enforcement, it seems that SaaS service have not yet undergone a similar evolution and epiphany.  If clickops for cloud configuration has mostly been abandoned for IaC practices, SaaS tooling is predominantly still configured manually via the UI with minimal codification. Not surprisingly this leads to many similar problems you’d find in your cloud operations. Putting the Git in GitOps When it comes to Kubernetes and cloud native systems, that are so commonly associated with GitOps practices, which is considered the best practice and modern way for managing complex Kubernetes operations––the git part of gitops is all but neglected when it comes to managing these systems.  I’ll explain. ‍ If we look at the top downloaded Terraform providers for SaaS applications that are not clouds, the list and data is extremely compelling: ‍ DataDog/datadog 32.4M+ integrations/github 16.9M+ cloudflare/cloudflare 16.8M+ newrelic/newrelic 12.5M+ hashicorp/consul 9.7M+ PagerDuty/pagerduty 8.6M+ grafana/grafana 5.4M+ gitlabhq/gitlab 4.8M+ mongodb/mongodbatlas 4.2M+ okta/okta 4M+ elastic/ec 3.2M+ ‍ While there is an increasing trend towards codifying these resources through Terraform providers, if we take the number of downloads of the AWS Terraform provider or the next most popular cloud Azure, these are 750M+ and 127M+ respectively, placing the next most popular non-cloud provider at about 5% adoption (and ultimately codification). ‍ This is because not only would this codification need to be done for each SaaS tool individually, once these tools are configured through clickops, just translating this to IaC is an extremely complex undertaking (particularly in large organizations with multiple dashboards, clouds, services and other dependencies and resources). ‍ If we come back to thinking about how we convert our git operations to be GitOps native, we’d likely need to follow a post similar to this one that walks you through the process of .  And this is just one tool of many in a huge stack of SaaS tooling that would need to undergo a similar transformation, this is another example of a post that walks you through a .  And the list goes on.  Now imagine having 10s of tools, in large organizations––multiple teams and clouds.  The task is daunting just to think about.  Until now. managing your Github organization with Terraform similar transition for Datadog Codifying Your SaaS When thinking about the critical aspects of codifying your SaaS, there are a few angles it was important for Firefly to focus on to make this transition truly valuable for all DevOps teams.  The first layer of value is in the unified inventory of both cloud assets and SaaS assets in a single place. Just this alone enables DevOps teams to search, understand, and classify assets across all clouds––the operational ones or the tooling clouds.  Something that wasn’t possible before from a single dashboard or tool. ‍ The next aspect is actually getting all of these tools and assets managed as code.  If this has become the cloud standard, it’s not clear why this hasn’t happened for SaaS apps too.  We’ve spoken about the benefits of managing everything as code, but eventually once managed as code with the relevant guidelines and internal engineering practices applied, these can then be automated as part of CI/CD processes and the relevant gating & guardrails applied here too. ‍ Doing so manually would require engineers to translate all of their manual configurations (that are not always found in a single place in the UI, across the many layers of their application) into the relevant code configurations, and usually many times over if there are multiple applications, dashboards or tools.  This is now possible at the click of a single button, for all SaaS tools, in one place. ‍ If we take a look at a typical Firefly dashboard, we can see that typical SaaS tools have as low as 20%+ of codified resources, vs. 50% in cloud service providers. ‍ The companies that have flipped this number and codified these resources, were able to enjoy the IaC advantages of faster deployment cycles and standby configuration templates for disaster recovery scenarios. We hope you found this useful - feel free to drop any questions you may have in the comments. ‍

Codify your SaaS Apps: The Answer to the Unmanaged SaaS Jungle

Alibaba’s the Tmall Future Store shows how technology can help offline merchants thrive in the e-commerce age If you’ve ever waited in line impatiently to pay for groceries, chances are you’ve wished you could escape the typical service experience we all tend to encounter as shoppers. While delivery and e-commerce services offer one alternative for people hoping to avoid the hassle, a more recent effort at Alibaba is making it likely that you’ll soon be shopping in stores that let you walk in, grab what you need, and pay automatically as you walk out, simplifying retail for shoppers and merchants alike. To showcase its progress, Alibaba recently unveiled an experimental 80-square meter installation of its new Tmall Future Store at the 2018 Yunqi Computing Conference, inviting attendees to explore a convenience store without a single employee on staff. Inside, they were able to browse for food, snacks, and beverages while getting help as needed from a digital T-guide assistant, then settle their bills by simply exiting the premises. Visitors line up outside Alibaba’s Tmall Future Store at the 2018 Yunqi Computing Conference To further highlight this new mode of shopping, senior technical expert Shi Wei of Alibaba’s Taobao Technology Department addressed the conference with a speech covering where the emerging field of new retail has been so far and where it is going next. Inside, we look at highlights of his speech and explore the technology that is helping make unmanned retail a crucial part of that evolution today. From New Retail to Unmanned Retail While Tmall’s unmanned Future Store presents an entirely new paradigm for shopping, it is only one part of a broader movement to upgrade offline shopping through new technologies, business models, and channels, known together as ‘new retail’. At the most basic level, new retail ranges from vending machines that accept mobile payments to the cheap, open-shelved mini shops many workplaces now offer thanks to digitalized business models that expedite stocking and reduce overhead. On a larger scale, it also encompasses supermarkets like Alibaba’s Hema that combine features of online and offline commerce, as well as cooperative ventures with independent retailers like Starbucks. At the heart of new retail is data, and ways it can be used to reduce pain points for consumers and merchants alike in traditional retail settings. Market analysis indicates that early efforts to remove human workers from retail have been met with mixed reactions from consumers. Nevertheless, the output value for such ventures in China is predicted to rise from RMB 20 billion in 2017 to RMB 65 billion in 2020, driven mainly by rising labor costs, offline operating costs, and consumer demand. With technology now able to support the truly futuristic experience the Tmall Future Store captures, unmanned retail can focus on giving consumers a whole new way to shop, rather than simply upgrading existing options. According to an iResearch study of the Chinese market, 8.5 percent of the country’s consumers prefer to make purchases from human cashiers, while 23.7 percent prefer to scan a code as they leave the store. Beyond this, 29.8 percent prefer to pick up goods from a smart settlement basket that they retrieve at the exit, while an overwhelming 39 percent would prefer to simply walk out with their groceries in a form of automatic settlement. Having cleared the initial hurdle of cultivating demand for experiences like the Tmall Future Store, Alibaba has since been able to focus on the ways its new model can enhance business for its partners, as well as the efficiency of labor in the broader marketplace. In one instance, moving the group’s Alicool souvenir shop to a completely automated framework increased annual turnover by 75 percent and sales volume by 20 percent, attracting 56 percent more consumers than during the previous year. At just 120 square meters, the store is now able to handle as many as 2,300 visitors per day, largely owing to the average 4.5 seconds it takes shoppers to complete automatic settlement when exiting. In another example, Alibaba collaboratively renovated the Zhida Bookstore in Shanghai to enable automatic facial recognition-based settlement, greatly increasing the store’s daily turnover. Notably, Alibaba was able to apply findings from previous efforts to complete the installation of the new facilities in just 53 days, raising the store’s daily turnover by 78.3 percent. A Look Inside the Tmall Future Store With the Tmall Future Store, Alibaba has entered a phase it calls ‘unmanned retail 2.0’, significantly expanding on the capabilities Alicool and the Zhida Bookstore demonstrate. As well as a technical upgrade, the new phase involves a shift of emphasis from impressing customers to attracting existing merchants to the concept. Technically, the key advance captured in the Tmall Future Store is computer vision. Upon first entering the shop, customers scan a code with their phones to establish their virtual presence there. Once they have done so, a series of roughly 100 screens will respond as they move through the store, making personalized recommendations and providing directions to help locate items. At shelves, the system uses gravity sensors and built-in cameras to identify and add goods to customers’ virtual shopping carts. This enables customers to simply leave when ready, as the system will automatically collect payment for any items which have not been returned as they exit. To document these transactions, the system generates a virtual bill which appears in customers’ Alipay or Mobile Taobao accounts, and the items in the virtual bill will also include links for repurchasing if they are available online. A view of customer guidance systems inside the Tmall Future Store As a whole, this user experience traces the interaction of three core systemic capabilities: global tracking, product identification, and user-item matching. The last of these is by far the most critical, as any confusion that results in a payment being deducted from the wrong customer can invalidate the idea that automation can replace human services. In an example of how this can happen, visitors to the store at the conference occasionally crossed arms reaching for items, swapped items, or offered to help each other return items, challenging the algorithm to sort out conflicting digital impressions. The best solution to such problems is prevention through the effective digitalization of offline behavior, presenting an ongoing challenge for developers as they seek to make these novel capabilities fully marketable. Continuing Development for the Future In its present state, the Tmall Future Store introduces a coherent “6+1” technical architecture, in which five processing ends and a local gateway interact together with a coherent set of business priorities — namely transaction, processing, operating, and data storage in the cloud. While anyone hoping to open a store could work from this architecture, preparing it for widespread marketing still requires the targeted improvement of algorithm capabilities, reduction of hardware costs, and reduction of deployment costs. An overview of Alibaba’s most recent architecture for unmanned retail environments, now undergoing further development One current example of an algorithmic limitation is the need for customers to cooperate with sensors by standing still while selecting items or completing settlement. Ideally, scanners should be equipped to handle some amount of motion or other variations, presenting a complex deep learning problem for optimization. Further, the system’s facial recognition component uses an initial scan image of each customer as a “one-off” point of reference. As even human observers may struggle to tell whether two images from different angles show the same person’s face, refining the recognition algorithm poses formidable challenges. Another outstanding challenge is to make algorithms more adaptable across environments, as each new unmanned store requires deploying and training the model to work within its specific dimensions and conditions. The key to doing so is separating the tuning of specific parameters from the model, which can be done using information gathered through the repeated deployment of its environmental sensors in different environments. In terms of market entry barriers, hardware costs remain preventive, not because they must be high but because the processing challenges developers currently face place heavy demands on equipment. Moreover, transforming a traditional brick-and-mortar shop into an unmanned store takes up a great deal of time, as engineers need to work on a by-case basis to update devices and systems one by one. To reduce costs, Alibaba is working to narrow down the functionality of devices. Because the cost of increasing computing power is not linear, boosting central server power to cover more end functions is not viable in the age of edge computing. To counter this, cameras and screens deployed in unmanned stores will need upgrades to their own computing power. Another cost reduction approach in the works is device standardization. By making all devices replaceable, repairable, and consistent, developers hope deployment and maintenance processes can be streamlined and standard procedures enabled. This would allow for a robotic approach to off-hours maintenance and inspection for both hardware and AI. With the ultimate goal of establishing a device management system, developers are seeking applications for an Alibaba Cloud product called Linkedge, which can be used to develop a hot-swappable HAL layer for the framework that uploads and downloads device information for monitoring. In effect, this means the system will be remotely upgradable, deployable across different devices, and primed for the addition of new functions — in short, perfectly suited to the mobile era. Looking Ahead To summarize the importance of the Tmall Future Store, Alibaba’s Shi Wei described it as an offline Taobao designed to be as interactive as the mobile phones that shoppers carry in with them. Like the first wave of online commerce that Taobao emerged with, it is offering new ways to study people’s habits and preferences as shoppers, making stores both a laboratory for understanding economic activity and a setting for putting that knowledge to use. Some of the many ways data from unmanned retail stores can help merchants thrive With the rise of e-commerce, many have voiced fears that platforms like Taobao will eventually make offline retail obsolete. With its Tmall Future Store, Alibaba is showing how the technology enabling it might instead be used to improve business for offline merchants, helping them to make smarter choices and better returns on investment. As a first step in unmanned retail, its completion should provide a valuable foundation for developments to come. Alibaba Tech First hand and in-depth information about Alibaba’s latest technology → Facebook: . Twitter: . “Alibaba Tech” “AlibabaTech”

New Meaning to Grab and Go: Alibaba Unveils its Unmanned Store

How to Scale a Startup: 7 Proven Strategies for Explosive Growth

Authors:
(1) Benzar Glen S. Grepon, Northern Bukidnon State College, Philippines ben.it2c@gmail.com (corresponding author);
(2) JC P. Margallo, Northern Bukidnon State College, Philippines;
(3) Jonathan B. Maserin, Northern Bukidnon State College, Philippines;
(4) Rio Al-Di A. Dompol, Northern Bukidnon State College, Philippines. Table of Links Abstract and Intro Literature Review Methodology Results Discussions Conclusions Recommendations Research Implications Practical Implications Declarations References and Author's Biography CONCLUSIONS The developed web application meets the objectives to provide the users. The web application is well-recommended and acceptable by the users. The researchers provided cheaper premium web hosting every year for its renewal. The researchers will incorporate ads for additional revenue for the web application and a registration form for the users to get an acknowledgment of their contribution to the post and to improve the web application. Agencies such as DPWH, RTA, and the like should be able to contribute accurate reports using the web application to ensure reliable and well-grounded information is delivered to the community. RECOMMENDATIONS It is recommended that the future development expand the coverage of the road updates, including the entire Philippine archipelago, for long-drive commuters and drivers to be more updated regarding road updates. Also, include using mobile applications for more user-friendly design and interactions. RESEARCH IMPLICATIONS The study allows viewing and reporting road updates with attachments as proof, informing commuters about what to expect when they travel. The system has been implemented in the municipality of Bukidnon and has been updated now and then for continuous quality improvement. PRACTICAL IMPLICATIONS The main benefit that the Road Updates Information System can do is to inform people who are constantly traveling to be updated on the current situation of a specific road by accessing the system and checking the map on areas to travel and see for  themselves the photos and description of a place where road construction, accidents and other causes that delay the traveling time of passengers and travelers. Aside from being informed of the road updates, citizens can also participate by uploading photos and descriptions of what is still going on the roads to the system and be viewed by people who try to access the web portal. DECLARATIONS Conflict of Interest No author has disclosed any conflicts of interest. Informed Consent The study did not involve humans as participants and only used scene text datasets that are already available online hence this is not applicable. Ethics Approval The conducted research uses only scene text datasets, which are already available online, and did not include humans as participants; hence this is not applicable. REFERENCES Altvater, A. (2023) What is agile methodology? tools, best practices & more, Stackify. Retrieved from https://stackify.com/agile-methodology Amado, T. and Dela Cruz,J.C. (2022).Localized Intelligent Traffic Monitoring System through Ontological Modelling. 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Boracay Island, Philippines, pp. 1-5, doi: 10.1109/HNICEM57413.2022.10109419. Balahadia, F. F., Fabito, B. S., & Fernando, M. C. G. (2015). E-Participation: Incident mapping portal for local government units. In 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). IEEE. Dadvar, S., Lee, Y. J., Shin, H. S., & Khodaparasti, H. (2020). Integrated Highway Safety Information System (HSIS) datasets by means of a Roadway Safety Data Integrator (RSDI) tool. Data in Brief, 32, 106154. https://doi.org/10.1016/j.dib.2020.106154. ISO 25010. (n.d.). ISO 25000 PORTAL. https://iso25000.com/index.php/en/iso-25000- standards/iso-25010 Khan, M. E., Shadab, S. G. M., & Khan, F. (2020). Empirical study of software development life cycle and its various models. International Journal of Software Engineering, 8, 16-26. Khan, M., Shadab, S., & Khan, F. (2020). Empirical Study of Software Development LifeCycle and its Various Models. International Journal of Software Engineering (IJSE),8(2), 16-26. Khoo, H.L., Ong, G.P. (2013). Evaluating perceived quality of traffic information system using structural equation modeling. KSCE Journal of Civil Engineering, 17(4), 837-849. ScholarBank@NUS Repository. https://doi.org/10.1007/s12205-013-0248-6 Li, H. & Zhijian, L. (2010). The study and implementation of mobile GPS navigation system based on Google Maps. 2010 International Conference on Computer and Information Application, Tianjin, China, pp. 87-90, doi: 10.1109/ICCIA.2010.6141544. Luthfi, A. M., Karna, N., & Mayasari, R. (2019). Google maps API implementation on IOT platform for tracking an object using GPS. In 2019 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) (pp. 126-131). IEEE. Macfarlane, J. (2023). Your navigation app is making traffic unmanageable. IEEE Spectrum. Retrieved April 17, 2023, from https://spectrum.ieee.org/your-navigation-app-ismaking-traffic-unmanageable McCall, M. (2021). Participatory Mapping and PGIS: Secerning Facts and Values, Representation and Representativity. International Journal of E-Planning Research (IJEPR), 10(3), 105-123. http://doi.org/10.4018/IJEPR.20210701.oa7 Markus Löchtefeld. (2019). DetourNavigator - Using Google Location History to Generate Unfamiliar Personal Routes. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). Association for Computing Machinery, New York, NY, USA, Paper LBW1117, 1–6. https://doi.org/10.1145/3290607.3313047 Novikov, A., Borovskoy, A., Gorbun, Y., Terentyev A. and Pletney,M. (2021). Geographic Information Systems to Improve Road Safety. Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 2021, pp. 1-6, doi: 10.1109/IEEECONF51389.2021.9416096. Slamet, C., Rahman, A., Sutedi, A., Darmalaksana, W., Ramdhani, M. A., & Maylawati, D. S. A. (2018). Social media-based identifier for natural disaster. In IOP conference series: materials science and engineering (Vol. 288, No. 1, p. 012039). IOP Publishing. Srivastava, A., Bhardwaj, S., & Saraswat, S. (2017). SCRUM model for agile methodology. In P. N. Astya, A.Swaroop, V. Sharma, M. Singh, & K. Gupta (Eds.), Proceedings of the International Conference on Computing, Communication, and Automation (ICCCA). Paper presented at the 2017 International Conference on Computing, Communication, and Automation, India (pp. 864-869). India: IEEE. Thalluri, L., Veeravalli, H., Rekapalli, D., Katragadda, S., Teja, T., & Sundar, C., (2022). Smart Vehicle Tracking System with Collision Avoidance. 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 462-467). IEEE. Wörndl, W., Herzog, D. (2020). Mobile Applications for e-Tourism. In Xiang, Z., Fuchs, M., Gretzel, U., Höpken, W. (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_17-1 Xu, R., Zhang, S., Xiong, P., Lin, A. and Ma, J. (2022). Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, pp. 3004-3009, doi: 10.1109/ITSC55140.2022.9922545. Yang, S. Y., & Hsu, C. L. (2016). A location-based services and Google maps-based information master system for tour guiding. Computers & Electrical Engineering, 54, 87-105. Yamsaengsung, S., & Papasratorn, B. (2018). Towards Improving User Interaction with Navigation Apps: an Information Quality Perspective. KnE Social Sciences, 3(1), 119- 131.https://doi.org/10.18502/kss.v3i1.1401 Authors’ Biography Benzar Glen S. Grepon is the Director of the Information Management Office and the previous IT Program Head of Northern Bukidnon State College, specializing in research, Programming, Project Management, Management Information Systems, Network Management, and Administration JC P. Margallo is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. Jonathan B. Maserin is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. Rio Al-Di A. Dompol is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College and a computer programmer in the Information Management Office of NBSC, specializing in Web development, programming, Database Management and Mobile Design and Development. This paper is available on arxiv under CC 4.0 license. Authors: (1) Benzar Glen S. Grepon, Northern Bukidnon State College, Philippines ben.it2c@gmail.com (corresponding author); (2) JC P. Margallo, Northern Bukidnon State College, Philippines; (3) Jonathan B. Maserin, Northern Bukidnon State College, Philippines; (4) Rio Al-Di A. Dompol, Northern Bukidnon State College, Philippines. Authors: Authors: (1) Benzar Glen S. Grepon, Northern Bukidnon State College, Philippines ben.it2c@gmail.com (corresponding author); (2) JC P. Margallo, Northern Bukidnon State College, Philippines; (3) Jonathan B. Maserin, Northern Bukidnon State College, Philippines; (4) Rio Al-Di A. Dompol, Northern Bukidnon State College, Philippines. Table of Links Abstract and Intro Abstract and Intro Literature Review Literature Review Methodology Methodology Results Results Discussions Discussions Conclusions Conclusions Recommendations Recommendations Research Implications Research Implications Practical Implications Practical Implications Declarations Declarations References and Author's Biography References and Author's Biography CONCLUSIONS The developed web application meets the objectives to provide the users. The web application is well-recommended and acceptable by the users. The researchers provided cheaper premium web hosting every year for its renewal. The researchers will incorporate ads for additional revenue for the web application and a registration form for the users to get an acknowledgment of their contribution to the post and to improve the web application. Agencies such as DPWH, RTA, and the like should be able to contribute accurate reports using the web application to ensure reliable and well-grounded information is delivered to the community. RECOMMENDATIONS It is recommended that the future development expand the coverage of the road updates, including the entire Philippine archipelago, for long-drive commuters and drivers to be more updated regarding road updates. Also, include using mobile applications for more user-friendly design and interactions. RESEARCH IMPLICATIONS The study allows viewing and reporting road updates with attachments as proof, informing commuters about what to expect when they travel. The system has been implemented in the municipality of Bukidnon and has been updated now and then for continuous quality improvement. PRACTICAL IMPLICATIONS The main benefit that the Road Updates Information System can do is to inform people who are constantly traveling to be updated on the current situation of a specific road by accessing the system and checking the map on areas to travel and see for  themselves the photos and description of a place where road construction, accidents and other causes that delay the traveling time of passengers and travelers. Aside from being informed of the road updates, citizens can also participate by uploading photos and descriptions of what is still going on the roads to the system and be viewed by people who try to access the web portal. DECLARATIONS Conflict of Interest Conflict of Interest Conflict of Interest No author has disclosed any conflicts of interest. Informed Consent Informed Consent Informed Consent The study did not involve humans as participants and only used scene text datasets that are already available online hence this is not applicable. Ethics Approval Ethics Approval Ethics Approval The conducted research uses only scene text datasets, which are already available online, and did not include humans as participants; hence this is not applicable. REFERENCES Altvater, A. (2023) What is agile methodology? tools, best practices & more, Stackify. Retrieved from https://stackify.com/agile-methodology Amado, T. and Dela Cruz,J.C. (2022).Localized Intelligent Traffic Monitoring System through Ontological Modelling. 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Boracay Island, Philippines, pp. 1-5, doi: 10.1109/HNICEM57413.2022.10109419. Balahadia, F. F., Fabito, B. S., & Fernando, M. C. G. (2015). E-Participation: Incident mapping portal for local government units. In 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). IEEE. Dadvar, S., Lee, Y. J., Shin, H. S., & Khodaparasti, H. (2020). Integrated Highway Safety Information System (HSIS) datasets by means of a Roadway Safety Data Integrator (RSDI) tool. Data in Brief, 32, 106154. https://doi.org/10.1016/j.dib.2020.106154. ISO 25010. (n.d.). ISO 25000 PORTAL. https://iso25000.com/index.php/en/iso-25000- standards/iso-25010 Khan, M. E., Shadab, S. G. M., & Khan, F. (2020). Empirical study of software development life cycle and its various models. International Journal of Software Engineering, 8, 16-26. Khan, M., Shadab, S., & Khan, F. (2020). Empirical Study of Software Development LifeCycle and its Various Models. International Journal of Software Engineering (IJSE),8(2), 16-26. Khoo, H.L., Ong, G.P. (2013). Evaluating perceived quality of traffic information system using structural equation modeling. KSCE Journal of Civil Engineering, 17(4), 837-849. ScholarBank@NUS Repository. https://doi.org/10.1007/s12205-013-0248-6 Li, H. & Zhijian, L. (2010). The study and implementation of mobile GPS navigation system based on Google Maps. 2010 International Conference on Computer and Information Application, Tianjin, China, pp. 87-90, doi: 10.1109/ICCIA.2010.6141544. Luthfi, A. M., Karna, N., & Mayasari, R. (2019). Google maps API implementation on IOT platform for tracking an object using GPS. In 2019 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) (pp. 126-131). IEEE. Macfarlane, J. (2023). Your navigation app is making traffic unmanageable. IEEE Spectrum. Retrieved April 17, 2023, from https://spectrum.ieee.org/your-navigation-app-ismaking-traffic-unmanageable McCall, M. (2021). Participatory Mapping and PGIS: Secerning Facts and Values, Representation and Representativity. International Journal of E-Planning Research (IJEPR), 10(3), 105-123. http://doi.org/10.4018/IJEPR.20210701.oa7 Markus Löchtefeld. (2019). DetourNavigator - Using Google Location History to Generate Unfamiliar Personal Routes. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). Association for Computing Machinery, New York, NY, USA, Paper LBW1117, 1–6. https://doi.org/10.1145/3290607.3313047 Novikov, A., Borovskoy, A., Gorbun, Y., Terentyev A. and Pletney,M. (2021). Geographic Information Systems to Improve Road Safety. Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 2021, pp. 1-6, doi: 10.1109/IEEECONF51389.2021.9416096. Slamet, C., Rahman, A., Sutedi, A., Darmalaksana, W., Ramdhani, M. A., & Maylawati, D. S. A. (2018). Social media-based identifier for natural disaster. In IOP conference series: materials science and engineering (Vol. 288, No. 1, p. 012039). IOP Publishing. Srivastava, A., Bhardwaj, S., & Saraswat, S. (2017). SCRUM model for agile methodology. In P. N. Astya, A.Swaroop, V. Sharma, M. Singh, & K. Gupta (Eds.), Proceedings of the International Conference on Computing, Communication, and Automation (ICCCA). Paper presented at the 2017 International Conference on Computing, Communication, and Automation, India (pp. 864-869). India: IEEE. Thalluri, L., Veeravalli, H., Rekapalli, D., Katragadda, S., Teja, T., & Sundar, C., (2022). Smart Vehicle Tracking System with Collision Avoidance. 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 462-467). IEEE. Wörndl, W., Herzog, D. (2020). Mobile Applications for e-Tourism. In Xiang, Z., Fuchs, M., Gretzel, U., Höpken, W. (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_17-1 Xu, R., Zhang, S., Xiong, P., Lin, A. and Ma, J. (2022). Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, pp. 3004-3009, doi: 10.1109/ITSC55140.2022.9922545. Yang, S. Y., & Hsu, C. L. (2016). A location-based services and Google maps-based information master system for tour guiding. Computers & Electrical Engineering, 54, 87-105. Yamsaengsung, S., & Papasratorn, B. (2018). Towards Improving User Interaction with Navigation Apps: an Information Quality Perspective. KnE Social Sciences, 3(1), 119- 131.https://doi.org/10.18502/kss.v3i1.1401 Authors’ Biography Benzar Glen S. Grepon is the Director of the Information Management Office and the previous IT Program Head of Northern Bukidnon State College, specializing in research, Programming, Project Management, Management Information Systems, Network Management, and Administration Benzar Glen S. Grepon JC P. Margallo is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. JC P. Margallo Jonathan B. Maserin is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College, specializing in Web development and programming. Jonathan B. Maserin Rio Al-Di A. Dompol is a Bachelor of Science in Information Technology graduate College at Northern Bukidnon State College and a computer programmer in the Information Management Office of NBSC, specializing in Web development, programming, Database Management and Mobile Design and Development. Rio Al-Di A. Dompol This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Impactful Road Update Systems: Practical and Research Implications for Commuters and Agencies

প্রভাবশালী রাস্তা আপডেট সিস্টেম: যাত্রী এবং সংস্থাগুলির জন্য ব্যবহারিক এবং গবেষণার প্রভাব

Wirkungsvolle Straßenaktualisierungssysteme: Praktische und forschungsbezogene Auswirkungen für Pendler und Behörden

Sistemas impactantes de actualización de carreteras: implicaciones prácticas y de investigación para viajeros y agencias

Systèmes de mise à jour des routes percutants : implications pratiques et de recherche pour les navetteurs et les agences

प्रभावशाली सड़क अद्यतन प्रणालियाँ: यात्रियों और एजेंसियों के लिए व्यावहारिक और अनुसंधान निहितार्थ

効果的な道路更新システム: 通勤者と機関に対する実践的および研究的な影響

영향력 있는 도로 업데이트 시스템: 통근자와 기관을 위한 실제적 및 연구적 시사점

Sistemas impactantes de atualização rodoviária: implicações práticas e de pesquisa para passageiros e agências

Эффективные системы обновления дорог: практические и исследовательские последствия для пассажиров и агентств

Etkili Yol Güncelleme Sistemleri: İşe gidip gelenler ve kurumlar için Pratik ve Araştırma Uygulamaları

Hệ thống cập nhật đường bộ có tác động: Ý nghĩa nghiên cứu và thực tiễn đối với người đi làm và các cơ quan

有效的道路更新系统：对通勤者和机构的实践和研究意义

Drones on the frontlines of Ukraine’s ongoing fight against the Russian incursion have showcased in real-time the unprecedented changes drone warfare brings to the modern battlefield. Emphasis rests on the failure of the West to adapt its drone systems adequately for future combat, analysts warn, explaining that the described failings are overt and damaging over the long term. ‍ Drones and  Power Competition A recent publication from the Wall Street Journal highlighted how American drones have failed to make a significant impact on the battlefield in Ukraine. The piece points out that American drones have failed to “perform in combat” and that this is a bad sign for the Pentagon, “which needs a reliable supply of thousands of small, unmanned aircraft.” The adoption challenges in combat settings extend beyond American weapons systems, stretching to North Atlantic Treaty Organization partners. For example, the Turkish-made Bayraktar TB2 drones, which initially helped Ukraine achieve military successes, especially in the first months of the war, have seen a significant decrease in use as the war has progressed. Rather, Ukraine’s defense forces have turned to the use of China-made drones, a factor that has geopolitical implications for the power competition between the Western-led NATO military alliance and China-aligned nations. A Systemic Weapons System Issue The issue Western-made drone adoption faces in combat has been linked by thought leaders to a systemic issue in the weapons industry. In a recent interview with Emily Chang of Bloomberg, Palmer Lucky, founder of Anduril Industries, warned that American weapons systems have not necessarily adapted to conflicts of the future and are not “necessarily” causing China to “quake in their boots.” Lucky noted that, in his view, a lot of current American weapons systems are quite “bad” and overly “expensive.” Cost-prohibitive systems reflect down to drones, making the induction of drone systems into warfighting strategy fall behind the pace of warfare, which is evolving underneath rapid innovation.  In prolonged, high-intensity conflicts such as the war between Russia and Ukraine, warfighters note strategic shifts directly linked to the rapid introduction of 21st-century technological enhancement. Electronic Warfare As far back as December 2023, The Telegraph noted that Russia's electronic warfare tactics are contributing to its gaining ground against Ukraine. Soldiers are making incremental improvements daily, seeking to gain an advantage over the adversary. The production of drones and their timely deployment to the battlefield are critical. Any significant delay in development risks rendering them obsolete and ineffective by the time they reach the front lines. Drone Costs Changing Dynamics With Russia enhancing its air and electronic defenses, Ukraine has had to adjust its drone strategy accordingly. In an interview with Frontsight media, Norman, a drone unit commander from the 109th Separate Territorial Defense Brigade, said that at the start of the war, Ukrainian soldiers were able to deploy drones wherever they wanted. Norman noted that things were disorganized and soldiers could deploy drones, standing anywhere. But now, operations must be highly organized to effectively operate with electronic warfare. This shift has, as the Washington Post noted, seen  “cheap drones” deployed in Ukraine which have subsequently transformed modern warfare — and initially gave Ukrainian troops an advantage on a battlefield where they are perpetually outnumbered and outgunned.” Eyewitness Accounts Dmytro Lysenko, the late drone pilot from the 109th Brigade who died in May fighting on the front in Donetsk Oblast, shared that the most optimal drone has been the Chinese-made DJI Mavic 3 Pro drone. The Mavic 3 is affordable at around $2000-3000 dollars per drone (depending on where it is sourced from), and it has a good set of versatile functions from having a quality camera with a “good zoom” that can be used for reconnaissance to being able to drop explosives. Lysenko shared with Frontsight Media shortly before his death that, at the peak of the battle for Avdiivka, Ukrainian pilots often had to fly over 8 kilometers around the battlefield in order to circle back in to avoid Russia’s electronic warfare systems that would jam the drones. He further pointed out that Ukrainian units often lack drones and are easily lost in battle, sometimes due to jamming or poor weather. The Royal United Services Institute think tank estimated in 2023 that Ukraine was losing around 10,000 drones per month. Drones are considered so precious to Ukrainian soldiers that they will often walk through an active battlefield or even a minefield to try and recover a drone that crashed in the field, eyewitnesses related to Frontsight Media. Braving Minefields For Drones Vasyl Shyshola, a commander of an aerial reconnaissance unit (128th Separate Mountain Assault Brigade), explained to Frontsight contributor David Kirichenko, in an analysis written for The Kyiv Post, that he and his men would “often walk through a minefield to try and recover a drone because of how few they have.” Shyshola shared how if Ukrainian soldiers are storming a Russian trench, there is immense pressure on the drone pilot to not make a mistake and lose the drone. “Commanders overseeing a storming of a trench line will be watching the drone feed and giving commands to the soldiers in the middle of the battle,” said Shyshola. If the drone is lost, the soldiers on the ground will be left without guidance and are more or less blind. He further remarked that sometimes the drone unit would not have any more drones in reserve, but if they do, they can quickly redeploy a drone to restore visibility but explained that this is never guaranteed. ‘Drones Over Funding,’ One Operator Prefers When asked about making costly modifications to improve a drone*,* Oleksiy Tymofeev, a drone unit commander from the 108th Separate Territorial Defense Brigade fighting in Zaporizhzhia Oblast, said he would prefer to receive another drone instead of investing money to make expensive modifications because of quantity plays a more important role. “You can make modifications such as adding tech to the drone to have AI software increase the accuracy, but that doesn’t mean it will not easily get lose signal once jammed out there, and the higher costs will make it more painful to lose,” said Oleksiy, in an interview with Frontsight Media. Therefore, he prefers to have multiple drones that are cheaper and that will improve his odds of hitting a target rather than relying on a more expensive drone. Due to Russian jamming, Ukrainian drones will often only be able to move one mile before their screen goes gray and they lose signal. Crowdfunding Multiple units that Frontsight contributor David Kirichenko spoke with on the Eastern and Southern fronts informed him that they get the overwhelming majority of their drones from foundations and volunteers. A top Ukrainian military commander, Lt Col Pavlo Kurylenko, was quoted in the Telegraphstating, “We’re only holding back the Russians with crowdfunded drones.” With drones in such short supply, demand far outstrips supply. Resulting To Psyops For Funding In the "Russia Contingency'' podcast hosted by Mike Kofman and Rob Lee, the hosts pointed out how drones have become such a contingent part of Ukrainian defense strategy that units rely on psyops to procure them if they can’t finance them by other means. Because some units have to crowdfund their drone procurement with help from volunteers and even rely on donations to purchase drones, there's an increased emphasis on producing impressive video footage from drone attacks. This focus on visuals doesn't necessarily correlate with hitting the most strategic targets. Shyshola’s drone unit was more recently formed in the fall of 2023. The group quickly realized that they needed to set up a social media presence to share videos of their strikes to try and raise funds to buy more drones. So, some of the soldiers have to spend a lot of time editing videos and managing their social media presence because this will mean they can procure drones that they desperately need. Lessons For the West As the WSJ pointed out, “Made-in-America drones tend to be expensive, glitchy, and hard to repair.” American drones are reportedly unable to fend off “Russian jamming and GPS blackout technology.” As a result, these drones will continue to be ineffective on the battlefield in Ukraine. Costs must be lower, and the tech must be quickly adaptable, as operators and defenders have noted. Ukrainian units need to constantly tinker and make changes to the drones, operators explained. If the parts are expensive to replace, the drones won’t be usable. If Ukraine was forced to use more expensive drones, it wouldn’t be able to sustain the 10,000 rate per month losses it is currently sustaining. Analysts note that American drone makers will need to be able to both bring down drone costs and manufacture them at a greater rate. If they are able to achieve this, then American drone suppliers would have a significant impact on the drone element of the war effort and the drone power competition overall, showcasing their prowess as elements of large-scale warfighting strategy and game-changers of individual battles. One Ukrainian drone commander who spoke with Kirichenko explained that macro-to-micro scale best: “If I had more drones, I would use them in batches to conduct a swarm attack to overwhelm the enemy." Drones on the frontlines of Ukraine’s ongoing fight against the Russian incursion have showcased in real-time the unprecedented changes drone warfare brings to the modern battlefield. Emphasis rests on the failure of the West to adapt its drone systems adequately for future combat, analysts warn, explaining that the described failings are overt and damaging over the long term. ‍ frontlines ‍ Drones and  Power Competition Drones and  Power Competition A recent publication from the Wall Street Journal highlighted how American drones have failed to make a significant impact on the battlefield in Ukraine. The piece points out that American drones have failed to “perform in combat” and that this is a bad sign for the Pentagon, “which needs a reliable supply of thousands of small, unmanned aircraft.” Wall Street Journal The adoption challenges in combat settings extend beyond American weapons systems, stretching to North Atlantic Treaty Organization partners. For example, the Turkish-made Bayraktar TB2 drones , which initially helped Ukraine achieve military successes, especially in the first months of the war , have seen a significant decrease in use as the war has progressed. Rather, Ukraine’s defense forces have turned to the use of China-made drones, a factor that has geopolitical implications for the power competition between the Western-led NATO military alliance and China-aligned nations. drones war A Systemic Weapons System Issue A Systemic Weapons System Issue The issue Western-made drone adoption faces in combat has been linked by thought leaders to a systemic issue in the weapons industry. In a recent interview with Emily Chang of Bloomberg , Palmer Lucky, founder of Anduril Industries, warned that American weapons systems have not necessarily adapted to conflicts of the future and are not “necessarily” causing China to “quake in their boots.” Bloomberg Lucky noted that, in his view, a lot of current American weapons systems are quite “bad” and overly “expensive.” Cost-prohibitive systems reflect down to drones, making the induction of drone systems into warfighting strategy fall behind the pace of warfare, which is evolving underneath rapid innovation.  In prolonged, high-intensity conflicts such as the war between Russia and Ukraine, warfighters note strategic shifts directly linked to the rapid introduction of 21st-century technological enhancement. evolving warfighters Electronic Warfare Electronic Warfare As far back as December 2023, The Telegraph noted that Russia's electronic warfare tactics are contributing to its gaining ground against Ukraine. Soldiers are making incremental improvements daily, seeking to gain an advantage over the adversary. The production of drones and their timely deployment to the battlefield are critical. Any significant delay in development risks rendering them obsolete and ineffective by the time they reach the front lines. The Telegraph advantage Drone Costs Changing Dynamics Drone Costs Changing Dynamics With Russia enhancing its air and electronic defenses, Ukraine has had to adjust its drone strategy accordingly. In an interview with Frontsight media, Norman, a drone unit commander from the 109th Separate Territorial Defense Brigade, said that at the start of the war, Ukrainian soldiers were able to deploy drones wherever they wanted. Norman noted that things were disorganized and soldiers could deploy drones, standing anywhere. But now, operations must be highly organized to effectively operate with electronic warfare. This shift has, as the Washington Post noted, seen  “cheap drones” deployed in Ukraine which have subsequently transformed modern warfare — and initially gave Ukrainian troops an advantage on a battlefield where they are perpetually outnumbered and outgunned.” Washington Post Eyewitness Accounts Eyewitness Accounts Dmytro Lysenko , the late drone pilot from the 109th Brigade who died in May fighting on the front in Donetsk Oblast, shared that the most optimal drone has been the Chinese-made DJI Mavic 3 Pro drone. The Mavic 3 is affordable at around $2000-3000 dollars per drone (depending on where it is sourced from), and it has a good set of versatile functions from having a quality camera with a “good zoom” that can be used for reconnaissance to being able to drop explosives. Dmytro Lysenko The Mavic 3 Lysenko shared with Frontsight Media shortly before his death that, at the peak of the battle for Avdiivka, Ukrainian pilots often had to fly over 8 kilometers around the battlefield in order to circle back in to avoid Russia’s electronic warfare systems that would jam the drones. He further pointed out that Ukrainian units often lack drones and are easily lost in battle, sometimes due to jamming or poor weather. The Royal United Services Institute think tank estimated in 2023 that Ukraine was losing around 10,000 drones per month. Drones are considered so precious to Ukrainian soldiers that they will often walk through an active battlefield or even a minefield to try and recover a drone that crashed in the field, eyewitnesses related to Frontsight Media. estimated so precious Braving Minefields For Drones Braving Minefields For Drones Vasyl Shyshola, a commander of an aerial reconnaissance unit (128th Separate Mountain Assault Brigade), explained to Frontsight contributor David Kirichenko, in an analysis written for The Kyiv Post, that he and his men would “often walk through a minefield to try and recover a drone because of how few they have.” The Kyiv Post, Shyshola shared how if Ukrainian soldiers are storming a Russian trench, there is immense pressure on the drone pilot to not make a mistake and lose the drone. “Commanders overseeing a storming of a trench line will be watching the drone feed and giving commands to the soldiers in the middle of the battle,” said Shyshola. If the drone is lost, the soldiers on the ground will be left without guidance and are more or less blind. He further remarked that sometimes the drone unit would not have any more drones in reserve, but if they do, they can quickly redeploy a drone to restore visibility but explained that this is never guaranteed. ‘Drones Over Funding,’ One Operator Prefers ‘Drones Over Funding,’ One Operator Prefers When asked about making costly modifications to improve a drone*,* Oleksiy Tymofeev, a drone unit commander from the 108th Separate Territorial Defense Brigade fighting in Zaporizhzhia Oblast, said he would prefer to receive another drone instead of investing money to make expensive modifications because of quantity plays a more important role. “You can make modifications such as adding tech to the drone to have AI software increase the accuracy, but that doesn’t mean it will not easily get lose signal once jammed out there, and the higher costs will make it more painful to lose,” said Oleksiy, in an interview with Frontsight Media. Therefore, he prefers to have multiple drones that are cheaper and that will improve his odds of hitting a target rather than relying on a more expensive drone. Due to Russian jamming , Ukrainian drones will often only be able to move one mile before their screen goes gray and they lose signal. jamming Crowdfunding Crowdfunding Multiple units that Frontsight contributor David Kirichenko spoke with on the Eastern and Southern fronts informed him that they get the overwhelming majority of their drones from foundations and volunteers. A top Ukrainian military commander, Lt Col Pavlo Kurylenko, was quoted in the Telegraph stating, “We’re only holding back the Russians with crowdfunded drones.” With drones in such short supply, demand far outstrips supply. Multiple units Telegraph Resulting To Psyops For Funding Resulting To Psyops For Funding In the " Russia Contingency '' podcast hosted by Mike Kofman and Rob Lee, the hosts pointed out how drones have become such a contingent part of Ukrainian defense strategy that units rely on psyops to procure them if they can’t finance them by other means. Russia Contingency Because some units have to crowdfund their drone procurement with help from volunteers and even rely on donations to purchase drones, there's an increased emphasis on producing impressive video footage from drone attacks. This focus on visuals doesn't necessarily correlate with hitting the most strategic targets. Shyshola’s drone unit was more recently formed in the fall of 2023. The group quickly realized that they needed to set up a social media presence to share videos of their strikes to try and raise funds to buy more drones. So, some of the soldiers have to spend a lot of time editing videos and managing their social media presence because this will mean they can procure drones that they desperately need. Lessons For the West Lessons For the West As the WSJ pointed out, “Made-in-America drones tend to be expensive, glitchy, and hard to repair.” American drones are reportedly unable to fend off “Russian jamming and GPS blackout technology.” As a result, these drones will continue to be ineffective on the battlefield in Ukraine. WSJ Costs must be lower, and the tech must be quickly adaptable, as operators and defenders have noted. Ukrainian units need to constantly tinker and make changes to the drones, operators explained. If the parts are expensive to replace, the drones won’t be usable. If Ukraine was forced to use more expensive drones, it wouldn’t be able to sustain the 10,000 rate per month losses it is currently sustaining. Analysts note that American drone makers will need to be able to both bring down drone costs and manufacture them at a greater rate. If they are able to achieve this, then American drone suppliers would have a significant impact on the drone element of the war effort and the drone power competition overall, showcasing their prowess as elements of large-scale warfighting strategy and game-changers of individual battles. One Ukrainian drone commander who spoke with Kirichenko explained that macro-to-micro scale best: “If I had more drones, I would use them in batches to conduct a swarm attack to overwhelm the enemy."

Adapting on the Fly: The Impact of Cost-Effective, Customizable Drones for Ukrainian Forces

Authors:
(1) Ruochu Yang;
(2) Chad Lembke;
(3) Fumin Zhang;
(4) Catherine Edwards. Table of Links Abstract and Intro Anomaly Detection Algorithm Experimental Evaluation Conclusion and References IV. CONCLUSION In this paper, we apply an anomaly detection algorithm to four real glider missions supported by the Skidaway Institute of Oceanography in the University of Georgia and the University of South Florida. On one side of generality, the algorithm is capable of detecting anomalies like remora attachment and shark hit in diverse real-world deployments based on high-resolution DBD data. On the other side of realtime performance, we simulate the online detection on subsetted SBD data. It utilizes generic data of glider trajectory and heading angle to estimate glider speed and flow speed. Anomalies can be identified by comparing the estimated glider speed with the normal speed range. False alarms can be minimized by comparing the algorithm-estimated flow speed with the glider-estimated flow speed. The algorithm achieves real-time estimation through a model-based framework by continuously updating estimates based on ongoing deployment feedback. Future work will enhance estimation accuracy by incorporating large amount of glider data into a data-driven framework. It is also worth taking into account the impact of the anomaly on the estimated flow speed, aiding in the process of determining false alarms. REFERENCES [1] F. Zhang, D. M. Fratantoni, D. A. Paley, J. M. Lund, and N. E. Leonard, “Control of coordinated patterns for ocean sampling,” International Journal of Control, vol. 80, no. 7, pp. 1186–1199, 2007. [2] D. A. Paley, F. Zhang, and N. E. Leonard, “Cooperative control for ocean sampling: The glider coordinated control system,” IEEE Transactions on Control Systems Technology, vol. 16, no. 4, pp. 735– 744, 2008. [3] M. Hou, S. Cho, H. Zhou, C. R. Edwards, and F. Zhang, “Bounded cost path planning for underwater vehicles assisted by a time-invariant partitioned flow field model,” Frontiers in Robotics and AI, vol. 8, 2021. [4] J. Nicholson and A. Healey, “The present state of autonomous underwater vehicle (auv) applications and technologies,” Marine Technology Society Journal, vol. 42, no. 1, pp. 44–51, 2008. [5] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb et al., “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473– 485, 2007. [6] M. J. Stanway, B. Kieft, T. Hoover, B. Hobson, D. Klimov, J. Erickson, B. Y. Raanan, D. A. Ebert, and J. Bellingham, “White shark strike on a long-range auv in monterey bay,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–7. [7] J. E. McCosker and R. N. Lea, “White shark attacks upon humans in california and oregon, 1993-2003,” Proceedings-California Academy Of Sciences, vol. 57, no. 12/24, p. 479, 2006. [8] L. G. Baehr, “Swimming with sharks: An underwater robot learns how to track great whites,” Oceanus, vol. 50, no. 2, pp. 42–49, 2013. [9] J. J. Gertler, Fault detection and diagnosis in engineering systems. CRC press, 2017. [10] J. Chen and R. J. Patton, Robust model-based fault diagnosis for dynamic systems. Springer Science & Business Media, 2012, vol. 3. [11] K. Aslansefat, G. Latif-Shabgahi, and M. Kamarlouei, “A strategy for reliability evaluation and fault diagnosis of autonomous underwater gliding robot based on its fault tree,” International Journal of Advances in Science Engineering and Technology, vol. 2, no. 4, pp. 83–89, 2014. [12] X. Wang, “Active fault tolerant control for unmanned underwater vehicle with sensor faults,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 12, pp. 9485–9495, 2020. [13] B. Y. Raanan, J. Bellingham, Y. Zhang, B. Kieft, M. J. Stanway, R. McEwen, and B. Hobson, “A real-time vertical plane flight anomaly detection system for a long range autonomous underwater vehicle,” in OCEANS 2015 - MTS/IEEE Washington, 2015, pp. 1–6. [14] E. Anderlini, C. A. Harris, G. Salavasidis, A. Lorenzo, A. B. Phillips, and G. Thomas, “Autonomous detection of the loss of a wing for underwater gliders,” in 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV). IEEE, 2020, pp. 1–6. [15] G. Fagogenis, V. De Carolis, and D. M. Lane, “Online fault detection and model adaptation for underwater vehicles in the case of thruster failures,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 2625–2630. [16] Y.-s. Sun, X.-r. Ran, Y.-m. Li, G.-c. Zhang, and Y.-h. Zhang, “Thruster fault diagnosis method based on gaussian particle filter for autonomous underwater vehicles,” International Journal of Naval Architecture and Ocean Engineering, vol. 8, no. 3, pp. 243–251, 2016. [17] A. Caiti, F. Di Corato, F. Fabiani, D. Fenucci, S. Grechi, and F. Pacini, “Enhancing autonomy: Fault detection, identification and optimal reaction for over—actuated auvs,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–6. [18] V. Asalapuram, I. Khan, and K. Rao, “A novel architecture for condition based machinery health monitoring on marine vessels using deep learning and edge computing,” in 2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR). IEEE, 2019, pp. C1–3. [19] P. Wu, C. A. Harris, G. Salavasidis, I. Kamarudzaman, A. B. Phillips, G. Thomas, and E. Anderlini, “Anomaly detection and fault diagnostics for underwater gliders using deep learning,” in OCEANS 2021: San Diego–Porto. IEEE, 2021, pp. 1–6. [20] Z. Bedja-Johnson, P. Wu, D. Grande, and E. Anderlini, “Smart anomaly detection for slocum underwater gliders with a variational autoencoder with long short-term memory networks,” Applied Ocean Research, vol. 120, p. 103030, 2022. [21] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM computing surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009. [22] W. Yassin, N. I. Udzir, Z. Muda, and M. N. Sulaiman, “Anomaly-based intrusion detection through k-means clustering and naives bayes classification,” International Conference on Computing and Informatics, p. 49, 2013. [23] K. Szwaykowska and F. Zhang, “Controlled lagrangian particle tracking: error growth under feedback control,” IEEE Transactions on Control Systems Technology, vol. 26, no. 3, pp. 874–889, 2017. [24] S. Cho, F. Zhang, and C. R. Edwards, “Learning and detecting abnormal speed of marine robots,” International Journal of Advanced Robotic Systems, vol. 18, no. 2, p. 1729881421999268, 2021. [25] R. Yang, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Anomaly detection of underwater gliders verified by deployment data,” in 2023 IEEE Underwater Technology (UT). IEEE, 2023, pp. 1–10. [26] Y. Ruochu, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Real-time autonomous glider navigation software,” arXiv preprint arXiv:2304.13096, 2023. [27] X. Liang, W. Wu, D. Chang, and F. Zhang, “Real-time modelling of tidal current for navigating underwater glider sensing networks,” Procedia Computer Science, vol. 10, pp. 1121–1126, 2012. [28] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb, and S. Glenn, “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473–485, 2007. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ruochu Yang; (2) Chad Lembke; (3) Fumin Zhang; (4) Catherine Edwards. Authors: Authors: (1) Ruochu Yang; (2) Chad Lembke; (3) Fumin Zhang; (4) Catherine Edwards. Table of Links Abstract and Intro Abstract and Intro Anomaly Detection Algorithm Anomaly Detection Algorithm Experimental Evaluation Experimental Evaluation Conclusion and References Conclusion and References IV. CONCLUSION In this paper, we apply an anomaly detection algorithm to four real glider missions supported by the Skidaway Institute of Oceanography in the University of Georgia and the University of South Florida. On one side of generality, the algorithm is capable of detecting anomalies like remora attachment and shark hit in diverse real-world deployments based on high-resolution DBD data. On the other side of realtime performance, we simulate the online detection on subsetted SBD data. It utilizes generic data of glider trajectory and heading angle to estimate glider speed and flow speed. Anomalies can be identified by comparing the estimated glider speed with the normal speed range. False alarms can be minimized by comparing the algorithm-estimated flow speed with the glider-estimated flow speed. The algorithm achieves real-time estimation through a model-based framework by continuously updating estimates based on ongoing deployment feedback. Future work will enhance estimation accuracy by incorporating large amount of glider data into a data-driven framework. It is also worth taking into account the impact of the anomaly on the estimated flow speed, aiding in the process of determining false alarms. REFERENCES [1] F. Zhang, D. M. Fratantoni, D. A. Paley, J. M. Lund, and N. E. Leonard, “Control of coordinated patterns for ocean sampling,” International Journal of Control, vol. 80, no. 7, pp. 1186–1199, 2007. [2] D. A. Paley, F. Zhang, and N. E. Leonard, “Cooperative control for ocean sampling: The glider coordinated control system,” IEEE Transactions on Control Systems Technology, vol. 16, no. 4, pp. 735– 744, 2008. [3] M. Hou, S. Cho, H. Zhou, C. R. Edwards, and F. Zhang, “Bounded cost path planning for underwater vehicles assisted by a time-invariant partitioned flow field model,” Frontiers in Robotics and AI, vol. 8, 2021. [4] J. Nicholson and A. Healey, “The present state of autonomous underwater vehicle (auv) applications and technologies,” Marine Technology Society Journal, vol. 42, no. 1, pp. 44–51, 2008. [5] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb et al., “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473– 485, 2007. [6] M. J. Stanway, B. Kieft, T. Hoover, B. Hobson, D. Klimov, J. Erickson, B. Y. Raanan, D. A. Ebert, and J. Bellingham, “White shark strike on a long-range auv in monterey bay,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–7. [7] J. E. McCosker and R. N. Lea, “White shark attacks upon humans in california and oregon, 1993-2003,” Proceedings-California Academy Of Sciences, vol. 57, no. 12/24, p. 479, 2006. [8] L. G. Baehr, “Swimming with sharks: An underwater robot learns how to track great whites,” Oceanus, vol. 50, no. 2, pp. 42–49, 2013. [9] J. J. Gertler, Fault detection and diagnosis in engineering systems. CRC press, 2017. [10] J. Chen and R. J. Patton, Robust model-based fault diagnosis for dynamic systems. Springer Science & Business Media, 2012, vol. 3. [11] K. Aslansefat, G. Latif-Shabgahi, and M. Kamarlouei, “A strategy for reliability evaluation and fault diagnosis of autonomous underwater gliding robot based on its fault tree,” International Journal of Advances in Science Engineering and Technology, vol. 2, no. 4, pp. 83–89, 2014. [12] X. Wang, “Active fault tolerant control for unmanned underwater vehicle with sensor faults,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 12, pp. 9485–9495, 2020. [13] B. Y. Raanan, J. Bellingham, Y. Zhang, B. Kieft, M. J. Stanway, R. McEwen, and B. Hobson, “A real-time vertical plane flight anomaly detection system for a long range autonomous underwater vehicle,” in OCEANS 2015 - MTS/IEEE Washington, 2015, pp. 1–6. [14] E. Anderlini, C. A. Harris, G. Salavasidis, A. Lorenzo, A. B. Phillips, and G. Thomas, “Autonomous detection of the loss of a wing for underwater gliders,” in 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV). IEEE, 2020, pp. 1–6. [15] G. Fagogenis, V. De Carolis, and D. M. Lane, “Online fault detection and model adaptation for underwater vehicles in the case of thruster failures,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 2625–2630. [16] Y.-s. Sun, X.-r. Ran, Y.-m. Li, G.-c. Zhang, and Y.-h. Zhang, “Thruster fault diagnosis method based on gaussian particle filter for autonomous underwater vehicles,” International Journal of Naval Architecture and Ocean Engineering, vol. 8, no. 3, pp. 243–251, 2016. [17] A. Caiti, F. Di Corato, F. Fabiani, D. Fenucci, S. Grechi, and F. Pacini, “Enhancing autonomy: Fault detection, identification and optimal reaction for over—actuated auvs,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–6. [18] V. Asalapuram, I. Khan, and K. Rao, “A novel architecture for condition based machinery health monitoring on marine vessels using deep learning and edge computing,” in 2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR). IEEE, 2019, pp. C1–3. [19] P. Wu, C. A. Harris, G. Salavasidis, I. Kamarudzaman, A. B. Phillips, G. Thomas, and E. Anderlini, “Anomaly detection and fault diagnostics for underwater gliders using deep learning,” in OCEANS 2021: San Diego–Porto. IEEE, 2021, pp. 1–6. [20] Z. Bedja-Johnson, P. Wu, D. Grande, and E. Anderlini, “Smart anomaly detection for slocum underwater gliders with a variational autoencoder with long short-term memory networks,” Applied Ocean Research, vol. 120, p. 103030, 2022. [21] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM computing surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009. [22] W. Yassin, N. I. Udzir, Z. Muda, and M. N. Sulaiman, “Anomaly-based intrusion detection through k-means clustering and naives bayes classification,” International Conference on Computing and Informatics, p. 49, 2013. [23] K. Szwaykowska and F. Zhang, “Controlled lagrangian particle tracking: error growth under feedback control,” IEEE Transactions on Control Systems Technology, vol. 26, no. 3, pp. 874–889, 2017. [24] S. Cho, F. Zhang, and C. R. Edwards, “Learning and detecting abnormal speed of marine robots,” International Journal of Advanced Robotic Systems, vol. 18, no. 2, p. 1729881421999268, 2021. [25] R. Yang, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Anomaly detection of underwater gliders verified by deployment data,” in 2023 IEEE Underwater Technology (UT). IEEE, 2023, pp. 1–10. [26] Y. Ruochu, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Real-time autonomous glider navigation software,” arXiv preprint arXiv:2304.13096, 2023. [27] X. Liang, W. Wu, D. Chang, and F. Zhang, “Real-time modelling of tidal current for navigating underwater glider sensing networks,” Procedia Computer Science, vol. 10, pp. 1121–1126, 2012. [28] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb, and S. Glenn, “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473–485, 2007. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv available on arxiv

Real-Time Anomaly Detection in Underwater Gliders: Conclusion and References

Previously… https://awstip.com/reducing-eks-windows-node-5-min-start-time-to-90s-eks-windows-bootstrapper-4314f72367a0?embedable=true I threw in some tricks I used to make our Windows EKS nodes start in 90s rather than the 5 minutes a standard EKS Windows node takes. I introduced you to the git repo eks-windows-bootstrapper - the application replacing the AWS-provided configuration scripts for EKS Windows. https://github.com/atg-cloudops/eks-windows-bootstrapper?embedable=true In today’s story, I will walk you through how to make your Windows nodes take ~30s to start! Recap In the previous story, we got to the point where our start times looked like this: Let’s take a closer look at our latencies now: In order for us to get better launch times, we now have 2 areas where we can improve timing Windows Startup Configure Network/Start K8s Services Improving Windows Startup Performance As we are using the AWS Image Builder (https://aws.amazon.com/image-builder/) to build our custom AMIs as discussed in the previous story, any changes we make to our image need to be made here. Outside AWS Fast Launch - there really isn’t much we can do to help with the Windows boot process, if you are able - you can uninstall Windows Defender which will massively improve start performance. Side note - I found in our use case we do not actually need Fast Launch as we do not actually need to run the OOBE process. We can set our culture using Set-Culture, etc. inside one of our components, and as long as we remove the sections in Unattend.xml related to OOBE, Windows will skip much of the setup process that EC2 Fast Launch deals with. When you start the output AMI, it will start as quickly as a Fast Launch AMI, but without the added expense of pre-provisioning EBS snapshots. The downside to this approach is that you won’t be able to configure Windows settings on the first boot, what you bake into the AMI is what you will start with - So make sure you set culture/timezone information correctly somewhere in one of your Image Builder components. For reference: So with all avenues to make Windows do less stuff on boot exhausted, there was only one choice left… Changes were made to the EKS Windows Bootstrapper code so it could be run as a Windows Service. The idea behind this is that we could squeeze a few more seconds out if we could run our code as Windows is starting services on boot instead of waiting for the userdata script to fire. This worked - however, it did break the AWS SSM Agent as a result as it also starts early. In the installation script included in the repo, we work around this by setting the SSM agent startup type to Automatic (Delayed). Faster Configure Network/Start K8s Services This basically involved translating the remainder of the code in the standard AWS bootstrapping scripts in EKS-StartupTask.ps1 Setup Container Network
        ... 
           [DllImport("vmcompute.dll")]
            static extern void HNSCall([MarshalAs(UnmanagedType.LPWStr)] string method,
                           [MarshalAs(UnmanagedType.LPWStr)] string path,
                           [MarshalAs(UnmanagedType.LPWStr)] string request,
                           [MarshalAs(UnmanagedType.LPWStr)] out string response);
            HNSCall("POST", "/networks", jsonString, out response);
        ...

https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L514 Add routes to created NIC
        ...
          routeAddCommands.Append($"route ADD {ipAddrs[i]} MASK 255.255.255.255 0.0.0.0 IF {vNICIndex}");
        ...
        Process process = new Process();
        process.StartInfo.FileName = "cmd.exe";
        process.StartInfo.Arguments = $"/C {routeAddCommands}";
        process.StartInfo.RedirectStandardOutput = true;
        process.StartInfo.UseShellExecute = false;
        process.StartInfo.CreateNoWindow = true;
        process.Start();
        await process.WaitForExitAsync();

https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L586


Start Kube-proxy and Kubelet in parallel once all configuration files have been written (all the previously converted code) Installation To install the bootstrapper, add this to a component in Image Builder: - name: InstallEksWindowsBootstrapper
        action: ExecutePowerShell
        inputs:
          commands:
            - |
              Invoke-WebRequest -Uri 'https://github.com/atg-cloudops/eks-windows-bootstrapper/releases/download/v1.29.1/Install-Service.ps1' -OutFile 'Install-Service.ps1'; 
              .\Install-Service.ps1; 
              Remove-Item 'Install-Service.ps1'; Putting everything together, I tested the output image using these settings: EC2 Instance

m4.xlarge (4 Core)
EBS


GP3 - 120GB/600Mbps/3600 IOPS Testing on these settings got me start times around 55s! We are finally under a minute, maybe spot instances are now viable for Windows EKS Nodes now that the 5-minute penalty is now 1 minute. Hold On… You Said A 30s Start Time! You Said as Fast as Linux! A 30s start time is possible! It just costs a lot. 😢 I ended up testing how fast the sum of all the software was by eliminating as many hardware bottlenecks as I could. Switched from GP3 → IO2 30K IOPS Switched from m4.xlarge → c6a.4xlarge Require Nitro Hypervisor It’s quite easy to change in Karpenter; simply change the Ec2NodeClass to change storage options, and then add the relevant keys in NodePool, e.g. blockDeviceMappings:
    - deviceName: /dev/sda1
      ebs:
        iops: 30000
        volumeSize: 120Gi
        volumeType: io2 - key: karpenter.k8s.aws/instance-hypervisor
          operator: In
          values:
            - 'nitro'
        - key: karpenter.k8s.aws/instance-cpu
          operator: In
          values:
            - '8' The beast that was produced by Karpenter was visible in K8s in 32s. It would be sub-30s with antivirus disabled. Our final tuning produces nodes in around 45s (Using GP3). You can tune for cost/performance yourself for your own scenario. Looking for Help EKS Windows bootstrapper is a simple app that writes config and starts Kubernetes services. It is currently set up for EKS 1.29. As Kubernetes goes through 1.30, 1.31, 1.32… it will need to be kept up to date… I am looking for help with this. 🙂 Previously… https://awstip.com/reducing-eks-windows-node-5-min-start-time-to-90s-eks-windows-bootstrapper-4314f72367a0?embedable=true https://awstip.com/reducing-eks-windows-node-5-min-start-time-to-90s-eks-windows-bootstrapper-4314f72367a0?embedable=true I threw in some tricks I used to make our Windows EKS nodes start in 90s rather than the 5 minutes a standard EKS Windows node takes. I introduced you to the git repo eks-windows-bootstrapper - the application replacing the AWS-provided configuration scripts for EKS Windows. https://github.com/atg-cloudops/eks-windows-bootstrapper?embedable=true https://github.com/atg-cloudops/eks-windows-bootstrapper?embedable=true In today’s story, I will walk you through how to make your Windows nodes take ~30s to start! Recap In the previous story, we got to the point where our start times looked like this: Let’s take a closer look at our latencies now: In order for us to get better launch times, we now have 2 areas where we can improve timing Windows Startup Windows Startup Configure Network/Start K8s Services Configure Network/Start K8s Services Improving Windows Startup Performance As we are using the AWS Image Builder ( https://aws.amazon.com/image-builder/ ) to build our custom AMIs as discussed in the previous story, any changes we make to our image need to be made here. Outside AWS Fast Launch - there really isn’t much we can do to help with the Windows boot process, if you are able - you can uninstall Windows Defender which will massively improve start performance. https://aws.amazon.com/image-builder/ Side note - I found in our use case we do not actually need Fast Launch as we do not actually need to run the OOBE process. We can set our culture using Set-Culture , etc. inside one of our components, and as long as we remove the sections in Unattend.xml related to OOBE, Windows will skip much of the setup process that EC2 Fast Launch deals with. When you start the output AMI, it will start as quickly as a Fast Launch AMI, but without the added expense of pre-provisioning EBS snapshots. Set-Culture Unattend.xml The downside to this approach is that you won’t be able to configure Windows settings on the first boot, what you bake into the AMI is what you will start with - So make sure you set culture/timezone information correctly somewhere in one of your Image Builder components. For reference: So with all avenues to make Windows do less stuff on boot exhausted, there was only one choice left… Changes were made to the EKS Windows Bootstrapper code so it could be run as a Windows Service. The idea behind this is that we could squeeze a few more seconds out if we could run our code as Windows is starting services on boot instead of waiting for the userdata script to fire. This worked - however, it did break the AWS SSM Agent as a result as it also starts early. In the installation script included in the repo, we work around this by setting the SSM agent startup type to Automatic (Delayed) . userdata Automatic (Delayed) Faster Configure Network/Start K8s Services This basically involved translating the remainder of the code in the standard AWS bootstrapping scripts in EKS-StartupTask.ps1 EKS-StartupTask.ps1 Setup Container Network
        ... 
           [DllImport("vmcompute.dll")]
            static extern void HNSCall([MarshalAs(UnmanagedType.LPWStr)] string method,
                           [MarshalAs(UnmanagedType.LPWStr)] string path,
                           [MarshalAs(UnmanagedType.LPWStr)] string request,
                           [MarshalAs(UnmanagedType.LPWStr)] out string response);
            HNSCall("POST", "/networks", jsonString, out response);
        ...

https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L514 Setup Container Network ... 
           [DllImport("vmcompute.dll")]
            static extern void HNSCall([MarshalAs(UnmanagedType.LPWStr)] string method,
                           [MarshalAs(UnmanagedType.LPWStr)] string path,
                           [MarshalAs(UnmanagedType.LPWStr)] string request,
                           [MarshalAs(UnmanagedType.LPWStr)] out string response);
            HNSCall("POST", "/networks", jsonString, out response);
        ... https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L514 Setup Container Network ... 
           [DllImport("vmcompute.dll")]
            static extern void HNSCall([MarshalAs(UnmanagedType.LPWStr)] string method,
                           [MarshalAs(UnmanagedType.LPWStr)] string path,
                           [MarshalAs(UnmanagedType.LPWStr)] string request,
                           [MarshalAs(UnmanagedType.LPWStr)] out string response);
            HNSCall("POST", "/networks", jsonString, out response);
        ... ... 
           [DllImport("vmcompute.dll")]
            static extern void HNSCall([MarshalAs(UnmanagedType.LPWStr)] string method,
                           [MarshalAs(UnmanagedType.LPWStr)] string path,
                           [MarshalAs(UnmanagedType.LPWStr)] string request,
                           [MarshalAs(UnmanagedType.LPWStr)] out string response);
            HNSCall("POST", "/networks", jsonString, out response);
        ... https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L514 https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L514 Add routes to created NIC
        ...
          routeAddCommands.Append($"route ADD {ipAddrs[i]} MASK 255.255.255.255 0.0.0.0 IF {vNICIndex}");
        ...
        Process process = new Process();
        process.StartInfo.FileName = "cmd.exe";
        process.StartInfo.Arguments = $"/C {routeAddCommands}";
        process.StartInfo.RedirectStandardOutput = true;
        process.StartInfo.UseShellExecute = false;
        process.StartInfo.CreateNoWindow = true;
        process.Start();
        await process.WaitForExitAsync();

https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L586 Start Kube-proxy and Kubelet in parallel once all configuration files have been written (all the previously converted code) Add routes to created NIC ...
          routeAddCommands.Append($"route ADD {ipAddrs[i]} MASK 255.255.255.255 0.0.0.0 IF {vNICIndex}");
        ...
        Process process = new Process();
        process.StartInfo.FileName = "cmd.exe";
        process.StartInfo.Arguments = $"/C {routeAddCommands}";
        process.StartInfo.RedirectStandardOutput = true;
        process.StartInfo.UseShellExecute = false;
        process.StartInfo.CreateNoWindow = true;
        process.Start();
        await process.WaitForExitAsync(); https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L586 Add routes to created NIC ...
          routeAddCommands.Append($"route ADD {ipAddrs[i]} MASK 255.255.255.255 0.0.0.0 IF {vNICIndex}");
        ...
        Process process = new Process();
        process.StartInfo.FileName = "cmd.exe";
        process.StartInfo.Arguments = $"/C {routeAddCommands}";
        process.StartInfo.RedirectStandardOutput = true;
        process.StartInfo.UseShellExecute = false;
        process.StartInfo.CreateNoWindow = true;
        process.Start();
        await process.WaitForExitAsync(); ...
          routeAddCommands.Append($"route ADD {ipAddrs[i]} MASK 255.255.255.255 0.0.0.0 IF {vNICIndex}");
        ...
        Process process = new Process();
        process.StartInfo.FileName = "cmd.exe";
        process.StartInfo.Arguments = $"/C {routeAddCommands}";
        process.StartInfo.RedirectStandardOutput = true;
        process.StartInfo.UseShellExecute = false;
        process.StartInfo.CreateNoWindow = true;
        process.Start();
        await process.WaitForExitAsync(); https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L586 https://github.com/atg-cloudops/eks-windows-bootstrapper/blob/8f519953209e3caea590652832acc74585ab1a45/BootstrapperService.cs#L586 Start Kube-proxy and Kubelet in parallel once all configuration files have been written (all the previously converted code) Start Kube-proxy and Kubelet in parallel once all configuration files have been written (all the previously converted code) Installation To install the bootstrapper, add this to a component in Image Builder: - name: InstallEksWindowsBootstrapper
        action: ExecutePowerShell
        inputs:
          commands:
            - |
              Invoke-WebRequest -Uri 'https://github.com/atg-cloudops/eks-windows-bootstrapper/releases/download/v1.29.1/Install-Service.ps1' -OutFile 'Install-Service.ps1'; 
              .\Install-Service.ps1; 
              Remove-Item 'Install-Service.ps1'; - name: InstallEksWindowsBootstrapper
        action: ExecutePowerShell
        inputs:
          commands:
            - |
              Invoke-WebRequest -Uri 'https://github.com/atg-cloudops/eks-windows-bootstrapper/releases/download/v1.29.1/Install-Service.ps1' -OutFile 'Install-Service.ps1'; 
              .\Install-Service.ps1; 
              Remove-Item 'Install-Service.ps1'; Putting everything together, I tested the output image using these settings: EC2 Instance

m4.xlarge (4 Core)
EBS


GP3 - 120GB/600Mbps/3600 IOPS EC2 Instance m4.xlarge (4 Core)
EBS


GP3 - 120GB/600Mbps/3600 IOPS m4.xlarge (4 Core) EBS


GP3 - 120GB/600Mbps/3600 IOPS m4.xlarge (4 Core) EBS GP3 - 120GB/600Mbps/3600 IOPS GP3 - 120GB/600Mbps/3600 IOPS GP3 - 120GB/600Mbps/3600 IOPS GP3 - 120GB/600Mbps/3600 IOPS Testing on these settings got me start times around 55s! We are finally under a minute, maybe spot instances are now viable for Windows EKS Nodes now that the 5-minute penalty is now 1 minute. Hold On… You Said A 30s Start Time! You Said as Fast as Linux! A 30s start time is possible! It just costs a lot. 😢 I ended up testing how fast the sum of all the software was by eliminating as many hardware bottlenecks as I could. Switched from GP3 → IO2 30K IOPS Switched from m4.xlarge → c6a.4xlarge Require Nitro Hypervisor It’s quite easy to change in Karpenter; simply change the Ec2NodeClass to change storage options, and then add the relevant keys in NodePool, e.g. blockDeviceMappings:
    - deviceName: /dev/sda1
      ebs:
        iops: 30000
        volumeSize: 120Gi
        volumeType: io2 blockDeviceMappings:
    - deviceName: /dev/sda1
      ebs:
        iops: 30000
        volumeSize: 120Gi
        volumeType: io2 - key: karpenter.k8s.aws/instance-hypervisor
          operator: In
          values:
            - 'nitro'
        - key: karpenter.k8s.aws/instance-cpu
          operator: In
          values:
            - '8' - key: karpenter.k8s.aws/instance-hypervisor
          operator: In
          values:
            - 'nitro'
        - key: karpenter.k8s.aws/instance-cpu
          operator: In
          values:
            - '8' The beast that was produced by Karpenter was visible in K8s in 32s. It would be sub-30s with antivirus disabled. Our final tuning produces nodes in around 45s (Using GP3). You can tune for cost/performance yourself for your own scenario. Looking for Help EKS Windows bootstrapper is a simple app that writes config and starts Kubernetes services. It is currently set up for EKS 1.29. As Kubernetes goes through 1.30, 1.31, 1.32… it will need to be kept up to date… I am looking for help with this. 🙂

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