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Anyplace, a startup aimed at giving 'digital nomads’ a comfortable place to work, nearly doubles valuation

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Blueground In The News 📰 Expansión chatted with our General Manager – Spain, Francisco Juliusberger , about what makes Blueground so great , as well as our recent growth.

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THE PENTATOMÆ AND THEIR EGGS

245 Tools & Resources for Travelers and Digital Nomads

It’s difficult to produce and sell quality items when you don’t know where your assets are located at a given time. Or when your inventory is constantly running out of essential materials. Or when equipment unexpectedly breaks down despite scheduled inspection rounds. And on top of this, your supplier doesn’t bother to inform you about shipment delays or delivers spoiled items because the cooling system broke down and no one noticed. Are you looking to take full control over your manufacturing process and monitor your supply chain? Then an might be exactly what you need. Industrial Internet of Things solution Keep reading this article to discover top Industrial IoT applications that will ensure the unfortunate situations above don’t compromise your operations. What is Industrial IoT, and how does it work? The Industrial Internet of Things (IIoT) is the utilization of sensors to gather and process data from connected devices with the goal of understanding and improving the industrial processes. IIoT can also use actuators and programmable controllers to communicate with devices and make them act in a certain way. The number of IoT devices is steadily growing worldwide. Statista . And this number is expected to surpass 29 billion by 2030 as the demand for IoT grows. Ericsson states that for a factory to be considered “smart,” it . reported 11.28 billion devices in 2021 needs to deploy 0.5 IoT devices per square meter So, how do IoT devices work together? And what do you need to know to use them at your organization? The anatomy of IoT IoT architecture consists of software and hardware components that interact together to aggregate, transmit, and analyze data coming from different devices. Below, you will find a brief description of the five main IoT layers. For a detailed explanation, check out our . detailed guide on IoT architecture . Covers various IoT devices equipped with sensors and actuators. Sensors gather data from the corresponding devices and transmit it as a signal to a processing unit. Actuators, on the contrary, receive commands — i.e., signals — from processing centers and make devices act upon them. Device layer . Spans communication technologies, such as Bluetooth, 4G/5G, and Wi-Fi, to mention a few examples. Network layer . Comprises intermediary devices between IoT units and cloud servers. These devices process critical data locally and send the remaining information to the cloud at scheduled intervals, thus reducing infrastructure costs. While edge computing devices aren’t part of the traditional IoT architecture, they’re getting increasingly popular with the Internet of Things adopters. Edge computing layer . Receives, processes, and stores data gathered from IoT sensors. Device and application support layer . Covers different applications that work on IoT data. This includes analytical algorithms, data visualization tools, software that issues commands to actuators, etc. Application layer While the “traditional” IoT is customer-centric and captures how people interact with their smart devices, Industrial IoT focuses on enterprise-wide applications and implies large-scale deployments. IIoT platforms for even greater benefits IoT is not a new concept, and many industrial organizations use IoT sensors to gather data from connected devices. However, IoT is often for each Industrial IoT application, which limits data sharing and restricts the benefits. deployed in isolated silos To mitigate this, organizations can benefit from the to build IIoT platforms that span the entire enterprise. This setup will break isolated IoT pipelines and establish a centralized point for managing IoT devices across different use cases. It will also allow you to integrate more data and have a unified view on all operations. Internet of Things development services Top 10 Industrial IoT applications Here are 10 key Industrial Internet of Things examples that will help you save time and money without compromising quality: Predictive maintenance Quality control Supply chain management Asset tracking Inventory monitoring Remote equipment and production site control Energy management Automation Process optimization and advanced analytics Safety control Predictive maintenance Manufacturers , which is equivalent to 15 hours a week. Another research shows that unplanned device failure can . go through 800 hours of equipment downtime annually cost a company as much as $260,000 per hour Fortunately, Industrial IoT can be applied to prevent equipment failure and cut down on associated expenses. IoT sensors placed on equipment and vehicles can gather data on its performance and condition, such as vibrations, voltage, and temperature. These sensors detect and report any suspicious changes and slight performance degradations, giving technicians enough time to interfere and fix the issue before it impacts the production process. Volvo, one of the leading car manufacturers, outfits its trucks with IoT sensors that capture vehicle data and send it for analysis. Their comprehensive IoT platform can process millions of data records instantly. This tactic enables Volvo to and diagnostic time by a whopping 70%. reduce track repair time by 25% Quality control their products at any stage, starting with raw materials, and all the way as the item moves along the production line. This even includes the amount of waste emitted during the manufacturing process. IoT sensors can continue gathering data even after the item is purchased. They can transmit information on customer satisfaction and keep track of any issues the client encounters with the product. IoT allows manufacturers to monitor Recently, a research team deployed IoT sensors together with convolutional neural networks to identify defects in Printed Circuit Boards (PCBs) manufacturing before the products reached the final production stages. As a result, they could . spot defected PCBs with 97% accuracy There are many benefits of combining IoT and AI, like in the IIoT example above. To learn more, check out this from our innovation analysts. Artificial Intelligence of Things (AIoT) blog post Supply chain management Industrial IoT systems allow managers to track different events across the supply chain. They can access real-time information on the location of materials in transit and get a notification if there is an unforeseen delay, accompanied with the supplier’s justification. Sensors also help monitor temperature, humidity, and other relevant conditions of transported goods. In May 2021, DHL to over 120 countries worldwide. The company used IoT sensors to monitor transportation conditions and ensure the temperature did not exceed the threshold, which would result in spoilage. transported around 200 million COVID-19 vaccines In another real-life IIoT application, California-based Golden State Foods uses IBM’s IoT trackers to monitor storage conditions while transporting meat. Furthermore, deploying IoT in supply chains can help in logistics optimization as companies can identify shorter, less crowded routes. Enhanced with AI, such systems can predict traffic congestions, take into account weather conditions and accidents on the transportation route and make the necessary changes. Consider for comprehensive . consulting artificial intelligence services vendors logistics optimization solutions An interesting example of combining AI and IIoT comes from a logistics innovation company. The client turned to ITRex to develop an that allows companies to monitor cargo in real time, calculate shipping costs based on 60 parameters, fill in paperless documents, and more. Currently, this IIoT platform has over 25,000 users, including large players in the transportation sector, such as DHL and Maersk. AI-powered IoT system Asset tracking Large facilities typically have too many assets that employees can’t keep track of. For instance, studies show that searching for equipment. This problem is not limited to healthcare. The same goes for large factories, stores, etc. nurses waste around 10% of their time One of the Industrial IoT applications is that it can automate asset finding and monitoring. Companies can outfit their assets, such as finished goods, raw materials, tools, and packages, with radio-frequency identification (RFID) tags. These tags are scanned with RFID readers to display information on the corresponding item. Another option is to use equipment sensors that transmit signals over Bluetooth. If power consumption is an issue and you need long-range connectivity, opt for narrowband IoT (NB-IoT), which relies on low-power wide area network (LPWAN) technology. In addition to locating items, IIoT-powered asset tracking can help you protect raw materials and avoid waste. For instance, if employees are working with large paint barrels, it’s tempting to grab the barrel that is the closest to the entrance point instead of walking through the barrel storage unit checking for expiration dates. With IoT tags, employees will be notified about the location of the barrels nearing their expiration date and receive a warning if they still grab the closest item. There are many vendors who have their own IoT tracking systems. For instance, Bosch built a cloud-based , which relies on Bluetooth to transmit signals. This system was designed in collaboration with construction firms and can be integrated with popular enterprise-wide software used in manufacturing. Bosch claims the tags can withstand water and dust and their batteries can last up to two years. This system captures detailed asset-related information. For instance, it can understand when a tool was dropped. And if an item goes missing, the system can show where it was last seen. Bluehound asset tracking solution In another example, Volvo in its large truck plants, where they assemble around 72 vehicles per day. uses IoT-powered trackers to track vehicles https://www.youtube.com/watch?v=M1TUEzcc5Q0&embedable=true Inventory monitoring Another Internet of Things industrial application is fitting the inventory with IoT sensors that will transmit its status in real time. For example, if materials or manufactured products are overheated in storage, smart sensors will ask an IoT-enabled thermostat to lower the temperature in the storage unit. Companies can combine Industrial IoT and analytics to rearrange inventory in an optimal way. For instance, the tools and raw materials that are used the most frequently will be moved closer to access points. IIoT can monitor inventory levels and automatically place an order if stock decreases below a predetermined threshold. This eliminates the need for manual checks. If you are a retailer owning a store with electronic shelves, you can deploy e-labels. Powered by Bluetooth beacons, these tags allow customers to place orders simply by pressing on them. Suppliers will receive order alerts and can either confirm or decline. The e-label can also display “on-order” tag to prevent customers from placing repetitive orders. Levi Strauss, a clothing company, deploys . This enables the company to know how many items they still have left of each model and when the new arrivals are expected. RFID tags to monitor stock levels Remote equipment and production site control Giving managers control over remote equipment is another important Industrial IoT application. Sensors can monitor machinery and allow human operators to activate equipment, shut it down, or change the settings. For instance, if water pump leakage is detected, the technician can deactivate the pump without having to access it physically. This use case is particularly convenient for machinery installed in hard-to-reach locations. Speaking of remote locations, IIoT can be deployed to control tower beacon lights. These lights have very specific requirements, and any organization that owns towers, like the telecom sector, has to conform to these standards. IoT sensors can make sure the lights are located at the right height, and have the required color and brightness, and notify the operator immediately in the case of violations. IIoT is also helpful in monitoring remote sites. For example, the British oil and gas company Shell, to install IoT sensors in their wells. The sensors are supposed to gather information on the wells’ pressure and temperature to minimize human intervention. This is not the only IoT industrial application that Shell deployed. The oil and gas giant also used the technology . The company claims to have in its oilfields. partnered with an IIoT startup Hiber to protect its pipelines saved $1 million deploying IoT sensors Energy management Companies can attach IoT sensors to its infrastructure to monitor the environment and cut on energy consumption when possible. For instance, sensors can turn off the lights when everyone leaves the building. They can also gather and analyze data on the heating and ventilation system to find the optimal performance. Another application of Industrial IoT is detecting when one piece of equipment starts to excessively consume electricity. This is often an indication that the machine is damaged and needs closer inspection. The Chinese company Chengdu Xihui Water Environmental Co., Ltd relied on MindSphere, the Industrial IoT as a service solution from Siemens, to save on energy consumption. The company operates wastewater treatment stations, and energy bills account for almost half of their operational costs. Chengdu Xihui deployed IoT sensors on its equipment to gather real-time data. After thorough analysis, the company understood when the equipment is at its highest energy consumption rate and managed to match this with off-peak electricity prices, . reducing the expenses by 10% Automation With the help of IIoT and robotics, it’s possible to that are typically performed by human employees. automate tedious tasks One example of this Industrial IoT application comes from Amazon. The retail giant uses several types of robots at its warehouses to automate tedious and even dangerous tasks. For instance, , read their label with the help of AI and , and place the right package into a GoCart to move packages within the facility. Cardinal can easily lift heavy packages that can cause injuries in human employees. Cardinal robot can lift packages computer vision https://www.youtube.com/watch?v=3dB-YHAP5RM&embedable=true Proteus is another robot that can move GoCarts through order fulfillment centers while navigating around people and robots. https://www.youtube.com/watch?v=AmmEbYkYfHY&embedable=true IoT-based automation is not limited to warehouse-related activities. Robots can also take over production tasks. For example, Lear Corporation, a car seat manufacturer, to assemble car seats. UR5 works alongside human employees and can perform tasks, such as screwdriving, lifting, and placing seats. relies on UR5 robots Robots can also replace humans in toxic environments, like painting facilities with poisonous emissions. Process optimization and advanced analytics Another Industrial IoT application example is spotting opportunities for process and product optimization with the help of analytics. IoT enables manufacturers to identify and predict bottlenecks in production processes and address the issue preemptively. The technology can also help update performance benchmarks. These production standards, such as the amount of time it takes to produce a product part, are set and rarely updated. Tools like allow managers to gather performance data and optimize job standards to set realistic deadlines and prices of new products. Machine Metrics Applying advanced analytics to data incoming from purchased products helps companies understand how customers actually utilize the items, and update the warranty policy accordingly. IoT sensors attached to packages enable companies to monitor the products during transportation and optimize the packaging to minimize item damage. Safety control IoT can contribute to workers’ safety by monitoring the environment, employee behavior, and compliance with safety protocols. Employees working at high altitudes or operating heavy machinery can wear sensors that will evaluate their body temperature, perspiration, and working technique, and alert managers if they detect a likelihood of a hazardous scenario. Given that musculoskeletal disorders are , these sensors can significantly enhance safety. responsible for 33% of workplace injuries For instance, a workplace wearables firm called Kinetic the size of a smartphone. It can detect posture and movements that are highly dangerous on the job and notify the employee immediately through light vibrations. built a smart device Another example of IIoT comes from Uruguay, where a biofuel plant . It contains sensors that track people’s movements and detect if someone falls or spends too much time in an area with noxious gasses. supplies all workers with a smart helmet Factories can also enhance safety by deploying environmental sensors. These devices can monitor the surroundings for dangers. For instance, they can detect extreme temperatures that can result in a fire and spot any hazardous gas leakage, and alert workers. On a final note Industrial IoT has many exciting applications. It can decrease time and costs associated with equipment failure, take over tedious tasks that pose health risks for human employees, make sure the stock is always replenished so that you don’t get stuck waiting for raw materials. And these are just a few of its benefits. However, there are challenges associated with deploying this technology. The main problem with IoT applications in industry is . As more information is aggregated, processed, and stored, the higher the risk of cyberattacks. Another challenge is the . Keep these aspects in mind when choosing your IIoT vendor. We can help you equip your devices with sensors, build a platform that enables you to manage the IoT devices, develop tailored to your business needs. maintaining data security costs associated with IoT solutions custom big data analytics solutions Whether you want to build IIoT infrastructure from scratch, improve your existing initiatives, or supplement IoT with AI and advanced analytics, ! We will work with you to produce a customized solution that meets your unique business needs. drop us a line Also published . here

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Ten years ago, the idea of DevOps sprang from the minds of Andrew Shafer and Patrick Debois ( ). A year later they actually named it “DevOps”. This tenth anniversary marks a good time to look at where DevOps has taken us as an industry and where we are heading. source This write up is not about vendors, commercial, open source or otherwise. At least not exclusively. This post looks back at the history of DevOps and CI/CD practices and digs into what patterns have evolved over the years and what patterns we see coming over the horizon. An Agile birth At the Agile 2008 conference in Toronto, Andrew Shafer did a talk that became the seed for the whole DevOps movement. This is now “ancient history” in the tech world but it’s worth reminding ourselves that DevOps is an idea that was sprouted by, or at least in the context of, the Agile movement. Now, the 2001 is a bit light on specifics but it pretty clear on… Agile Manifesto is “continuous delivery of quality software” “delivering working software frequently” “harnessing change … as a competitive advantage” It is not a surprise then that DevOps and the practice of CI/CD struck a chord with the Agile people. From manual to codification The principal idea that caught the imagination of almost everyone in IT and related professions was the idea of codifying not just business logic, but also the supporting infrastructure around it. is in essence a codified manual test and code management process. Continuous integration and cloud resources with an API are codified manual installation and configuration processes. Infrastructure as code and deployment are in essence codified manual release activities. Continuous delivery Without codification, the other big promises of DevOps and Agile are pies in the sky. Codification has two main applications: Testing an application, setting up a server or installing an application can be all pretty time consuming activities. Abstracting the process into code and re-using that code is almost always a net gain in productivity and drop in lead times. Reduced lead times. . Even if you are not the quickest and Agile of the bunch, codifying helps address the inherent danger of “changing things” and amps up the predictability of releasing quality software. Risk aversion Automated testing catches bugs earlier and faster. Infrastructure as code prevents configuration drift. Automated rollbacks provide a safety valve for botched releases. All of the above are less prone to human error when automated. Not a goal, but certainly a huge side benefit is that code creates a shared language, platform and understanding between developers and sys admins. This eponymous 2009 presentation by John Allspaw and Paul Hammond from Flickr catches that spirit completely From “codifying all the things” sprang forth an entire industry of products and services, hosted, SaaS, open source, commercial and everything in between: Heroku, Cloud Foundry, AWS Beanstalk ,TravisCI, Jenkins, CodeShip, Bamboo, Puppet, Ansible, Terraform. The list goes on and on. From codification to experimentation The first wave of DevOps practices ticked the “continuous”, “frequent” and “working software” boxes of the Agile manifesto. However, it turns out that that other box, “harnessing change … as a competitive advantage”, is a bit of an enigma. What changes actually to our advantage? How do we know when they are not? What do customers actually want? are These types of questions have always been the domain of . A/B and multi-variate testing, test audiences and short-lived experimental features are typical weapons found in the field. The base assumption is “we don’t know the answers. Let’s experiment and find out.” marketing, UX and product management people This experimentation mindset is now making its way into the domain of the back end. And it was high time. Software engineers, architects and sys admins are by nature more predictors than adapters. and build our code base and infrastructure to get that future job done as best as possible. We call it “specification” or “estimation.” We predict the future There’s a snag though. Raise your hand if you’ve done these types of predictions and it turned out your we’re wildly off target after things went live. ✋ Experimentation patterns, tools and services Ditching predictions and estimates can be a profoundly powerful thing. But the story doesn’t end there of course. Over the last few years many patterns, techniques, tools and services have popped up to assist engineers in turning “I don’t know” into “I have learned. I know now”. 1. Blue / green deployments The granddaddy of them all, and by far the most crude but wide spread pattern is the blue/green deployment. Arguably, many engineers implemented this pattern long before DevOps saw the light of day. source: https://martinfowler.com/bliki/BlueGreenDeployment.html A typical blue/green deployment scenario involves two identical environments and some form of switch to route traffic to either environment. Come release time, you deploy to one environment and direct traffic at it. You keep the other environment for fallback in case things go south. 2. Canary releasing A lot less crude than blue/green deployment is canary releasing. The principle is the same, but the granularity is a lot higher. With canary releasing you can actually start running proper experiments without putting your production environment at risk. source: https://martinfowler.com/bliki/CanaryRelease.html Your routing component needs to be “smart” to do canary releasing because you want to target only a sliver of your total traffic and direct that traffic to the new version of your app. Canary releasing is still very much a pattern. The implementation and execution is left as an exercise to the reader. There are however products that already offer this as a first class citizen. With we allow targeting on many different traffic aspects like user agents, devices, geo location etc. and have support built in to the UI. AWS offers canary releasing in their API Gateway services https://vamp.io AWS Api Gateway Canary Releasing 3. A/B testing A/B testing is canary releasing with a statistical framework around it. By and large it offers similar targeting options as canary releasing but it adds goals to the mix. This helps determining if and how much a different or new version of your app contributes to a specific goal. source: https://www.optimizely.com/optimization-glossary/ab-testing/ A/B testing is offered by vendors like Optimizely, Visual Website Optimizer (VWO), Google Optimize and is implemented as feature in many other services like Unbounce, Mailchimp and Kissmetrics. Companies also build it themselves, like and . Pinterest Booking However, all of these services and implementations work strictly on the front end (web pages, email etc.). This means you can mostly test visual and textual variations of an existing application. What if you need to experiment on other parts of your application stack? Product and services are appearing in this arena. is a full service solution, covering many languages and run times. In the open source world we have by Facebook, by Intuit and by Wix. Optimizely X Full Stack Planout Wasabi Petri 4. Feature toggling Feature toggling is more of a parallel pattern. It integrates aspects of canary releasing and A/B testing into a pattern focused on toggles. These toggles turn application features on and off. Either in general or for target audiences. Feature toggling’s main virtue is that you can decouple the moment of deployment from the moment of release. Services offering feature toggling are for instance and . Open source alternative are for the Java platform and for Ruby. LaunchDarkly Split.io Togglz Flipper 5. Shadow traffic Also know as “dark traffic”, shadow traffic is the practice of syphoning of traffic from the production flow and exposing it to an experimental version of your app, without the end user being directly involved. This pattern is firmly rooted in the back end and can be used for load & performance testing or as a general sanity check. Implementation at this moment seems to be completely custom, as of the time of writing I could not find any SaaS service that offers shadow traffic. Open source, middleware-type projects like (for the Kubernetes stack) and are relatively new. Istio GoReplay All implementations need to overcome some thorny issues with how network traffic works at the lowest level with regard to client responses, encryption etc. However, the potential specifically as part of an integrated platform like Kubernetes is high. Towards an experimentation platform As experimentation patterns are adopted across the full IT stack, the granularity of experiments and scale at which they are deployed rise. This gives way to a whole set of new problems. How to deal with the data output of the experiments? Not every company has a dedicated data science team. How to match the infrastructure to the experiments need? Successful experiments might need more/new/different resources quickly. How to do all of this at scale and keep your sanity? Many of these problems are automation problems and fall within the scope of DevOps practices. We can see the first products and services appearing that aim to tackle these issues. 1. Machine learning & AI Using machine learning and artificial intelligence to make sense of the deluge of logs and metrics a typical modern IT stack generates is still in its infancy, but looks really promising. Pattern recognition, filtering and predictive analysis are all well understood mathematical problems. We can see these techniques appear in DevOps oriented products and services like and . Both services promises to cut down the noise inherent in data streams and help teams focus on finding the nuggets of gold buried among the scraps. Signifai Logz ElasticSearch, a popular open source search engine used by many to analyse log data, to its feature set. This gives users a relatively easy path into using machine learning to grog their data. just added machine learning predictive analysis in ElasticSearch ML, source: https://www.elastic.co/guide/en/x-pack/current/ml-overview.html Leveraging ML and AI to assist in decision making with regard to deployments, scaling, feature toggling and running experiments in general is still largely unexplored terrain. 2. Smart orchestration 2.0™ Modern container platforms like Kubernetes, Docker Swarm and Mesosphere’s DC/OS fit neatly into the new and emerging patterns of DevOps. Dubbed “Cloud Native”, these platform provide portability, scalability and rapid, standardized deployment patterns. In this context, they are the ideal base infrastructure for running an experiment based IT infrastructure as they tick almost all of the boxes needed for such a system: Codified and standardised run times, network and storage through (Docker) containers and related tech. Codified deployment process through the platform’s API. Smart routing through overlay networks, integrated API gateways and service discovery / meshes. OCI, CSI and CNI aim to standardise the container, storage and network layers of cloud native platforms. The move to cloud native infrastructures is already happening and already we can see new applications enabled by the cloud native paradigm appear. Products and frameworks like and take to a level where routing decisions become real time malleable application attributes. Coupled with a monitoring solution, smart routing solutions are becoming the back bone of A/B testing and shadow traffic patterns implemented on DC/OS and Kubernetes. Istio Vamp smart routing is a technique where many application processes share a physical host right up to its capacity as to not let unused capacity go to waste. Both Kubernetes and DC/OS already do this. Bin packing takes it to another level of cost efficiency. Interesting advances can be made here by tying that cost level to higher level business goals set, for example, in an A/B test. Many companies have internal cost accounting and attribution to structure their IT cost. This could tremendously help in making the business case for an experimental application or service. Combining this with something like AWS Spot instances Want to increase innovation? Lower the cost of failure — Joi Ito, Director MIT Media Lab If you liked this article, please show your appreciation by 👏 below! clapping Tim is a product advocate for , the smart & stress free application releasing for modern cloud platforms. https://vamp.io

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