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Data Communications and Flow: Focus on What You Need

Illustration: © IoT For All

When visualizing a new IoT application; carefully consider not what data flows you want, but what data flows your application needs to be successful.

Data flows are one of the key constraints in the design of any IoT application. Data flows drive not just communications cost, but also indirectly control communication technology selection, power needs, and the actual paradigm of an application’s functionality. As you begin visualizing your new IoT application, think carefully about the data and communication patterns that support your planned features.

IoT is a confluence of smart and connected in a remote device. I assume that if you’re reading this you have a “device” side and a “user” application side that you are thinking about connecting.  Your devices could be in near proximity of your user, their home/office, or anywhere. The user application where the device data lands initially could be a smartphone, or in many cases a cloud platform. Throughout the discussion, chances are the connection will be over a wireless link. This is by far the predominant pattern for typical IoT (non-IIOT/non-manufacturing use cases).

First, let’s differentiate between “data” and “data flows.

Data: what is measured

Data Flows: what is communicated.

Sure, increased data is likely to ramp up the amount of data sent to/from your device, but more data is not a linear predictor of how much data an application needs to communicate with a user. As the power of IoT device MCU chips increases, there is a steady ability to do more processing on the device and only communicate a summary of relevant events and periodic data points.

Communications are power-hungry compared with computation and memory on an IoT device. The more you can keep your radio turned off, the more battery life that remains. There are power light wireless technologies like Bluetooth Low Energy (BLE) for near distance communications, but what if your device is far away? Radios vary in their performance profile and there are numerous articles out there about WiFi vs. LoRa vs. LTE. Know your communications stack. Next, I lay out some concepts that should be considered regardless of which type of radio is in your device.

Most IoT projects fall into two broad categories. These two patterns dictate many aspects of the data flows your application will need to perform and when your communications hardware needs to be turned on.


Interactive applications place the user and device in virtual proximity, with physical distance ranging from a few feet to miles to wherever. The communication flows, bridge that physical distance. This application pattern is the most demanding from a communications perspective.

Communications that are interactive require that a device radio stays on to listen for user input. This could be constrained to a specific interval of interest, rather than 24 hours a day. Maybe the communications channel can be predictably enabled during “business hours” or only during predicted device “usage” times.  The key point, radio on all the time increases power consumption considerably. This in turn increases challenges for off-grid or solar applications to have enough power harvesting and storage.

Being that the interactive application pattern is so demanding that you may find variations necessary to make things work. Consider maybe delaying user input by minutes or even hours, opening times for user control of the device.

Remote Monitoring

From a communication and power budget perspective, this application pattern is much easier to implement. Devices can wake up occasionally, gather and locally store data, assess the situation, and then decide if communication is required. The radio stays off until it is needed to send data to the user before it’s back to sleep for the communications.

The remote monitoring pattern can be integrated with an interactive application by making use of when the radio is already turned on. When you periodically send data, check for user directives. This approach is standardized in LoRaWAN Class A devices which listens for user input 1 and 2 seconds after transmitting its data.

IoT applications typically use protocols such as MQTT or HTTP to package their data while in transit. MQTT, HTTP, AMQP and other IoT communication protocols add protocol data to the total amount of IoT device payload data being transmitted between the device and the user. The amount of data communicated typically increases in two ways: framing overhead and keepalives.

Framing overhead is the extra data that is sent along with an application’s data to make communications more robust and reliable. Think of protocol framing as the envelope you put your physical correspondence in. In the case of MQTT, to send data, the overhead is 6 characters + the MQTT topic name your device is publishing to. This can add up and, in some cases, exceed the size of the payload data you are sending to the user side. It is important to note that while MQTT transmits these extra characters with your message payload, MQTT is more efficient than AMQP and HTTP; which is why MQTT is so often used in IoT systems.

The other protocol tax is keepalive messaging (sometimes referred to as heartbeats). MQTT implementations typically perform a keepalive action every 1-4 minutes, this time period is referred to as the keepalive interval. Keepalives are not required if data transmission has been performed recently. To keep communications active, MQTT sends a 2-character long PING when the keepalive interval ends. The keepalive interval is reset with each transmission, for either a PING or payload data.

Most implementations afford the ability to lengthen the keepalive interval (reduce the number of keepalives sent), each system will typically impart some upper limit for the keepalive interval. Azure IoT Hub uses MQTT extensively and limits the keepalive interval to a maximum of 1177 seconds or once every 19 minutes, 37 seconds (Understand Azure IoT Hub MQTT  Support).

When reviewing data and deciding what to send back and forth, think about ways to eliminate or reduce application data flows. When reviewing data flows, take note of how big each one is, how often data is sent, and what is going on with your communications channel when nothing is happening.

There are tools online (IoT Bandwidth Estimation Tool) to help visualize your data budget and be proactive in planning your data communications.

Time adds up… fast! Sending 500 characters of data every 20 seconds:

180 times / hour                           90KB / hour

4,320 times / day                          2,160MB / day

30,240 times / week                   17,120MB / week

129,600 times / month              64,800MB / month

Remember every character you send can increase costs and draws down your device’s battery.

Some Ideas…

  • After sampling remote data, look for ways to summarize prior to sending. For example: consider sending maximum, minimum, average, and number of data points over a specific period.
  • Similarly, once a maximum and minimum are established, consider sending data only when a new outlying maximum or minimum has been observed.
  • For remote sensing consider only sending data once a day or even once a week. But send data events when something significant has been observed at the device.
  • Consider building normal limits in your device software. When the data being sensed leaves these limits, then communicate and report the event to the user.
  • Log your data locally on the device and send a block of data (a day or weeks’ worth) at one time. Once the radio is on using it, then shut it off. Every time the radio is turned on/off, power is wasted before/after when data is sent.

Only you can determine when a piece of data being sent is valuable. Is that piece of data something you want… or is it something you need?

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Advantages and Disadvantages of Implementing IoT in Healthcare

Illustration: © IoT For AllThe Internet of Things (IoT) is quickly gaining popularity in all spheres of life, healthcare systems in particular. In a nutshell, the technology allows multiple connected devices to collect and share information with each other. What does this mean for healthcare?In fact, the applications are …

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7 IoT Certifications to Enhance Your Career Prospects in 2020

Illustration: © IoT For AllAs a software engineer, computer engineer, full-stack developer or any other IT professional, new career opportunities mean better income, perks, growth and a more satisfying job experience. But some opportunities don’t come easily unless you have the right certifications.In 2017, Gartner forecasted the creation of 1.4 …

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Verisilicon and MicroEJ Join Forces to Accelerate Hardware IP Innovation, Thanks to Software Virtualization Leveraging 10 Million Software Engineers Worldwide

Nuremberg, February 25, 2020 – Verisilicon, a Silicon Platform as a Service (SiPaaS) company, and MicroEJ, a  leader in trusted virtual execution environments for cost effective SoC/MCU/MPU, today announces their collaboration on providing software and hardware IP, with related tools, to manufacturers and silicon players willing to develop their own SoC or MCU. Together, the two partners make it possible to run legacy complex software on new integrated Verisilicon IPs based hardware, thanks to complete secure virtualization provided by MicroEJ Virtual Execution Environment (VEE). As soon the MicroEJ VEE secure container runs on the new chip, all the previous software assets run the same, transparently leveraging the new hardware innovation from Verisilicon IP built in the new chip.

The Graphics Processing Unit Vivante GCNanoLite-V IP is the first IP that benefits from the partnership: MicroEJ VEE extendable embedded UI/UX open-source multi-languages library (C, Java, Javascript, etc.) enables to run new and legacy UI applications, instantly. The first MCU powered by MicroEJ and Verisilicon is the i.MX-RT595 by NXP, targeting wearable, appliances, home and industrial markets.

MicroEJ VEE bridges Software innovation with Hardware innovations, enabling full scalability from conception to the development of a whole new line of products thanks to a joint software and hardware components mindset: assembling reusable assets, both software and hardware, thanks to secure virtualization. This solution is specifically well suited for very large volume markets, where Bill Of Material is a matter while still providing rich User Interfaces Experiences on resource-constrained embedded devices.

With its incredible growth, the small electronics industry is increasingly looking for both low consuming GUI combined with impressive performance on a very low footprint. When NXP had chosen our Vivante GCNanoLite-V IP to build their next gen i.MX-RT595 MCU, MicroEJ VEE was the obvious natural one-device platform, as our combined technologies follow the same cost oriented logic and address the same markets” said Jarmon David, Sr. VP Worldwide Sales and Business Development at Verisilicon.

Teaming with Verisilicon to provided cutting edge graphical user interface libraries for impeccable looking user experiences, with security at execution level, is the essence of MicroEJ VEE, often described as “the tiny sibling of Android” : making as easy as possible for the mass of the software engineers to leverage hardware innovation” said Fred Rivard, PhD, CEO of MicroEJ.

About MicroEJ

MicroEJ is a software vendor of cost-driven solutions for embedded and IoT devices. We are focused on providing device manufacturers with secure products in markets where software applications require high performance, compact size, energy efficiency, and cost-effective development.

Today more than 120+ companies in the world with currently over 37 million products sold, have already chosen MicroEJ to design electronic product applications in a large variety of industries, including smart home, wearables, healthcare, industrial automation, retail, telecommunications, smart city, building automation, transportation, etc.

For more info:

About VeriSilicon

VeriSilicon Microelectronics (Shanghai) Co., Ltd. (VeriSilicon) is committed to providing customers with platform-based, all-round, one-stop custom silicon services and semiconductor IP licensing services leveraging its in-house semiconductor IP. Under the unique “Silicon Platform as a Service” (SiPaaS) business model, depending on the comprehensive IP portfolio, VeriSilicon can create silicon products from definition to test and package in a short period of time, and provides high performance and cost-efficient semiconductor alternative products for IDM, Fabless, system vendors (OEM/ODM) and large Internet companies, etc. VeriSilicon’s business covers consumer electronics, automotive electronics, computer and peripheral, industry, data processing, Internet of Things and other applications. VeriSilicon presents a variety of customized silicon solutions, including high-definition video, high-definition audio and voice, In-Vehicle Infotainment, video surveillance, IoT connectivity, data center, etc. In addition, VeriSilicon has five types of in-house processor IPs, namely GPU IP, NPU IP, VPU, DSP IP and ISP IP, and more than 1,400 analog and mixed signal IPs and RF IPs. Founded in 2001 and headquartered in Shanghai, China, VeriSilicon has 5 design and R&D centers in China and the United States, as well as 10 sales and customer service offices worldwide. VeriSilicon currently has more than 800 employees.

For more info:

Media Contact
[email protected]

Source: IoT For All

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Why You Need Contextual IoT Device Management

Illustration: © IoT For All

In my previous post, I gave an explanation of basic IoT device management and why it’s insufficient for certain kinds of massive-scale IoT deployments. In addition to basic IoT device management, contextual IoT device management is necessary to ensure success when dealing with IoT solutions involving thousands to millions of devices.

In this post, I explore some of the key aspects of contextual IoT device management, with real examples that demonstrate why you need to manage devices contextually if you’re building, buying, and/or implementing massive-scale IoT solutions.

Identify and Address Issues

When building, buying and/or implementing IoT solutions, it’s critical to assume that devices are going to experience issues. The world is imperfect. Trying to make an IoT solution perfect is an impossible and foolhardy endeavor. If you have one million devices, all it takes is a failure rate of 0.1 percent, and you already have 1000 devices that will fail. For IoT solutions with devices expected to last for years in harsh conditions, the percentage of devices that will experience issues at some point increases dramatically.

Issues with devices can take many forms, including hardware defects in manufacturing, firmware bugs, device failures due to extreme temperatures or weather conditions, degraded performance from wear-and-tear, dying batteries—the list goes on. It’s therefore essential first to be able automatically to identify, and then be equipped to address issues, with IoT devices.

Let’s look at an example in which you have thousands to millions of devices in an agricultural setting. What happens when a device stops communicating? If there’s an issue with the device, you’ll want to replace that device with a new device. Alternatively, if the device’s battery is simply drained, you’ll want to replenish the battery in that device.

In either case, you’ll first need to know where the device is located. If the devices don’t have a GPS module, this means that when the devices are installed out in the fields, you’ll probably need to record their coordinates (latitude/longitude) as part of the installation process, so that you can visualize them in a user interface (UI). These installation and visualization capabilities would need to be included as part of the IoT solution.

In addition, it would be costly to replace or replenish individual devices, so instead, you should probably hunt down and address multiple devices at a time. Rather than replenishing drained batteries after they’ve already died (and thus have ceased to provide you with data), you’ll want to know which devices are getting close to being fully drained. But what does “close” mean? Where’s the threshold? Devices may be able to report the percentage of battery life remaining, but what does “10 percent battery life” translate into? A year? A few months? A few weeks? A day?

The rate of battery drain depends on usage, and usage depends on the use case. For a use case like agricultural IoT, the battery usage will likely be relatively consistent and, therefore, predictable. However, in asset tracking use cases, the usage can be dependent on how much the assets are moving, which is inherently difficult to predict with high accuracy. To make such a prediction, the IoT solution will need to determine what usage is “normal” and use that as a baseline for predicting battery life remaining for devices.

As you can see, the ability to automatically identify and then be equipped to address potential issues with devices is heavily dependent on the context of the use case and the business needs that inspired it. This is why you need contextual IoT device management.

Classify Devices into Contextual States

The previous section focused on issues with devices, whether that’s an issue such as a defect that prevents a device from communicating, or simply that the device’s battery has drained and is in need of replacement/replenishment. However, there can be situations in which a device is operating as expected without any issues, but there’s certain information that’s important to highlight for users.

Let’s return to the agricultural example, in which you might have devices attached to mobile agricultural equipment (e.g. a tractor) that enable that equipment to be tracked. The devices will likely be using GPS to get location data for the tractor, but unfortunately, GPS doesn’t work effectively when vehicles are inside buildings (here’s how GPS works). If all you show on the UI is the last known location of the tractor, and that tractor is now being stored inside a building, the GPS location won’t reflect the actual location of the tractor and can confuse users and/or geofences.

In this scenario, there are no issues with the device itself. However, the contextual state that the device is in (i.e. inside a building) is important to the functionality of the IoT solution. Therefore, you can use contextual data to classify the device—and by extension, the tractor—into a state (e.g. “indoors”) that provides helpful information to users so they’re not confused when the device can’t get an accurate GPS position inside a building.

However, the device itself can’t tell when it’s inside. All the device knows is that it isn’t getting a good signal from GPS satellites and therefore can’t acquire an accurate GPS position. Is it because the GPS satellite constellation just happens to be in a bad position at this particular moment? Is it because of adverse weather conditions? Both? Or maybe neither, and the vehicle is in fact indoors?


The above example from an agricultural IoT scenario is just one of many. The key takeaway is that contextual IoT device management is both essential and difficult. It’s essential because every IoT solution is different, and even the same IoT solution can be different when implemented within different businesses and contexts. It’s therefore difficult because this means that there isn’t a one-size-fits-all answer for effectively managing devices when you’re dealing with devices numbering in the thousands to millions.

I’m optimistic though! While we’ll never find a one-size-fits-all answer, we’ll continue to build the platforms and tools that will enable us to adapt IoT solutions quickly to varying businesses and contexts, unlocking the true potential of the Internet of Things.

Source: IoT For All

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MachNation Launches MIT-E Pf, Performance Testing Software for IoT solutions

MachNation has launched MIT-E Performance, the world’s first software built exclusively for performance and scalability testing of IoT platforms and solutions. MIT-E Performance (MIT-E Pf) can verify platform performance up to 10 million IoT devices and 1 million IoT messages per second.

MachNation MIT-E Pf Architecture

MIT-E Pf meets all requirements for IoT platform performance testing.

  • Purpose-built for IoT. MIT-E Pf simulates real-world IoT message flows while identifying and tracking every message.
  • Scalable, cloud native, and API based. MIT-E Pf scales to match customer needs while systematically orchestrating the spin-up and spin-down processes.
  • Template driven. Customers can use MachNation’s pre-designed, best-in-class templates or easily design custom test templates in MIT-E Pf.
  • Open data and analytics. MachNation developers and data scientists have built a MIT-E Pf reporting and big data engine that delivers native analytics reports and easily makes data available for customers’ BI tools.
  • Operated and managed by IoT experts at MachNation. MIT-E Pf is supported by developers and testers at MachNation who use IoT platforms daily.

Enterprises, service providers, and IoT platform vendors now have an independent, accurate, and reproducible way to measure IoT performance without impacting their deployments in the field. MachNation designed MIT-E Pf to deliver groundbreaking accuracy while ensuring that enterprise production deployments are isolated from the testing process.

“MachNation, a company known for exceptional technology skills and fact-based insights, is entering a new phase by launching performance testing software for IoT platforms and solutions. MIT-E Pf will revolutionize the way enterprises design, procure, and test IoT platforms,” said Steve Hilton, President, MachNation. “No longer will enterprises need to rely on IoT vendors’ claims about the scalability of their solutions.”

MachNation MIT-E Pf sample output

Designed using the newest industry-standard software and development methodologies, MachNation can deploy MIT-E Pf on-premises or in the cloud. Leveraging serverless microservices and containerized load generation, MachNation has created an infrastructure as code (IaC) tool that can test even the most dynamic and scalable IoT platform.

“We have designed MIT-E Pf from the ground up with the IoT user in mind. We saw a huge void in the market for IoT-centric performance testing software, so we started with a clean sheet of paper and have built a tool that is template-based, simulates real-world IoT message flows, and is API driven,” said Samuel Hale, Technology Analyst and MIT-E Pf head architect, MachNation.

MachNation is continuing to add new functionality, testing templates, and metrics to MIT-E Pf. With 4 performance categories, 20 performance tests, and 67 metrics per load schedule, MIT-E Pf sets the industry standard for IoT-first performance testing software.

Learn more about MIT-E Pf

Source: IoT For All

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Making IoT Scalable, Simpler and SAFEr

Illustration: © IoT For All

The growth of connected devices is unlocking new services across M2M and consumer IoT use-cases. ABI Research predicts annual revenues from IoT services will hit $460 billion by 2026.

IoT services are enabled by devices collecting, processing and sending data, quite often sensitive or personal, to the cloud. A key factor in the widespread deployment of IoT services is the ability for key stakeholders – end-users and service providers – to trust that the data gathered and transmitted to the IoT cloud is done so securely, in order to protect its integrity and the resulting integrity of the service.

Global authorities, industry bodies, governments and regulators are therefore working collaboratively towards defined IoT guidelines and mandates. This activity is particularly advanced in Europe. The General Data Protection Regulation (GDPR) defines strict penalties for device manufacturers and service providers who do not protect consumer privacy. A robust certification framework has also emerged, with the ENISA Cybersecurity Act and Eurosmart IoT Certification Scheme requiring IoT devices to undergo penetration testing from state-of-the-art independent security laboratories prior to deployment.

The challenges of remotely provisioning, managing and updating credentials across millions of different devices throughout their entire lifecycle to ensure this security and privacy are myriad. It’s the ability to protect IoT data communications in a simple, standardized manner at scale, however, that has emerged as a key industry challenge.

Market Fragmentation – A Key Challenge

Leveraging a hardware secure element (SE) as a “Root of Trust” to execute security services and store security credentials is an essential step in the development lifecycle to guarantee end-to-end security for IoT products and services. It’s also a key recommendation of the GSMA IoT Security Guidelines.

There are several proprietary hardware SE solutions available to deliver this root of trust, but market fragmentation introduces a key challenge. Connected devices must be modified to access security services from different SE providers, which creates significant design issues and is unsustainable at scale given the ever-increasing size and diversity of the IoT ecosystem.

The SIM on the other hand, in combination with supporting over-the-air provisioning and management infrastructure, is fully standardized. When used as the hardware Root of Trust in an IoT device, it promotes interoperability across different vendors and more consistent use by IoT device makers and service providers.

An additional advantage is that the SIM has advanced security and cryptographic features, including a securely designed central processing unit (CPU) and dedicated secure memory to store operating system programs, keys and certificate data. This protects IoT devices from various hacking scenarios, such as cloning, physical attacks to a single device and remote attacks from the internet to numerous devices.

Although this advanced functionality and infrastructure means the SIM can effectively function as the hardware Root of Trust in an IoT device, the fragmentation challenge isn’t completely removed. This is because device middleware must still be modified to enable access to the SIM services.

It was apparent, therefore, that removing this design hurdle was critical to the development of a scalable, secure IoT ecosystem.

Introducing IoT SAFE

It’s widely recognized that industry collaboration is key to promoting a secure, interoperable connected future. To further extend the capability of the SIM, GSMA and SIMalliance have partnered on IoT SAFE (IoT SIM Applet For Secure End-2-End Communication).

IoT SAFE directly addresses the significant challenge of industry fragmentation. By specifying a common API and defining a standardized way to leverage the SIM to securely perform mutual authentication between IoT device applications and the cloud, it’s far easier for IoT device makers to execute security services and manage credentials across millions of devices.

And as all of the critical security functions are executed on the SIM rather than untrusted areas of the device, the robustness of the mutual authentication is assured. Also, a remote interface enables the management of the secure IoT applet throughout its lifecycle.

Delivering Flexibility and Maximizing Investments

The benefits of IoT SAFE go beyond scalability and security. For example, as security functions can be delegated to the SIM, device makers aren’t solely dependent on cloud provider services to secure their products and solutions.

In addition, SIMs are already widely deployed to ensure trusted connectivity across the mobile ecosystem.

“For over 25 years the SIM has been the ‘Root of Trust’ for the mobile industry, its security constantly evolving over this period so that today the SIM secures over 9.4 billion cellular-connected devices[1],” said Graham Trickey, Head of IoT, GSMA. “IoT SAFE extends the security capabilities of the SIM even further to secure new IoT services end-to-end, underpinning a new generation of IoT services and billions of new IoT devices.”

An estimated 5.6 billion SIMs were shipped in 2018 alone, with estimated total shipments from 2013 to 2018 hitting 32 billion. This can be leveraged to deliver enhanced security for devices with minimal additional investment.

IoT SAFE enables all ecosystem players to homogenously leverage the advanced features of the SIM and the supporting infrastructure to deliver enhanced security at scale, increasing flexibility and maximizing investments. To find out more about IoT SAFE and delivering privacy and security by design, click here and contact SIMalliance.

Written by Remy Cricco, Chairman of the SIMalliance
Source: IoT For All

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Cultivating a Healthier IoT Technology Ecosystem

Illustration: © IoT For All

Although the global number of Internet of Things, or IoT, implementations is soaring, there’s more to a flourishing network than merely hooking things up. Your choice of IoT technology is just as important as what you do with it, but selecting the right components and software may be tricky. For your framework to do its job and thrive under pressure, you need to consider how its aspects interact with each other and impact your mission.

This concept is similar to building a strong business team — when hiring new employees, you probably take pains to find individuals who aren’t only talented but also suitable matches for your corporate culture. While you might not expect an IoT sensor or gateway to base its actions on the moral ethos that drives your enterprise, the principle still stands. Healthy IoT ecosystems revolve around frameworks that maintain sufficient self-awareness to serve your organizational needs even as the parameters change.

Defining IoT Technology Health

Setting performance criteria is integral to monitoring and maintaining the health of any IoT system. Leaders who want to generate meaningful data that reflects the realities of running an enterprise must be willing to overcome a few hurdles that stand between them and rigorous oversight.

Getting Precise

Internet of Things implementations are anything but static, and this can result in imprecision. Here are three examples of how IoT applications might get confusing:

• A city creates a litter-monitoring system to track how well its garbage services clean the streets. Upon realizing that this approach exposes it to enormous blind spots, it decides to expand its IoT by transitioning from closed-circuit cameras to onboard rubbish truck sensors.

• A manufacturing firm employs an automated fabrication process to build its turbine engines. It monitors plant performance by connecting the programmable logic controllers, or PLCs, that run its machinery to a bespoke IoT setup. As safety regulations change, however, it’s forced to add new data points to its monitoring practices, increasing the amount of information that flows through its networks and into its data silos.

• A university seeks to control its on-campus utility costs by monitoring occupancy and automating building lighting. From there, it might eventually hit upon the ingenious idea to start tracking in-room temperatures and heating system power consumption. Or it might gradually migrate from monitoring large common spaces all at once to placing sensors near entryways, windows, vents and other notorious HVAC troublespots.

These hypotheticals demonstrate the importance of implementing flexible ecosystem health metrics. Overarching IoT technology frameworks, also known as application enablement platforms or AEPs, help enterprises construct end-to-end perspectives that keep them plugged in without risking tunnel vision.

Selecting IoT Health Rubrics

The AEP-powered approach makes IoT technology oversight a bit more robust. AEPs that include supervisory tools are conveniently able to monitor their own performance, so you don’t have to worry about who’s watching the watchers.

That being said, you should hone in on trend-monitoring practices that apply to your business. Here are a few universally relevant examples:

• Security oversight involves not only monitoring who has access to the system but also tracking where the information it produces ends up. Risk analysis and privacy governance practices come with unique health indicators, such as incident response histories and breach event scopes, that you’ll want to include in your routine IoT checkups.

• Network performance monitoring should be familiar to anyone who’s ever dealt with IT administration. When data starts flowing at full bore, it’s good to be able to adjust loads, examine transmission losses and keep tabs on resource parameters such as power consumption. Industry-standard network performance factors must figure into your health assessments if you want your framework to achieve its inner potential.

• Data value tracking practices help you determine how beneficial specific aspects of your IoT truly are. Organically evolving IoT implementations make it crystal clear how information that powers valuable business insights one day might prove far less useful the next. On top of monitoring data streams from your operational activities, it’s wise to conduct regular meta-analyses of the end results. For instance, you might trace the decision-making processes informed by the raw evidence as well as the real-world effects of acting on it. Maintaining an awareness of the ROI associated with your data methodologies also makes it far simpler to steer clear of devastatingly poor KPIs.

• Operational cost supervision should be a given for any budget-concerned enterprise. Your IoT framework not only needs money to run but also demands an investment of time and effort to launch and master. While choosing a superior AEP takes a lot of the sting out of the overhead, wise users still institute their own cost monitoring practices.

Each implementation deserves its own approach to health upkeep. Your framework might also benefit from closer attention in some areas than others or completely forego specific rubrics. Unlike many generic business tools, everything revolves around the implementation and your goals.

Three Smart Strategies for Enhanced IoT Technology Stack Health

You’ve chosen the specific well-being trends you wish to track. Now, you’ll need a system that exposes their nuances. This generally means looking beyond the fresh-out-of-the-box solutions that many IoT providers offer in favor of custom implementations and communication tools.

1. Keep Your Data Cleaner

An IoT technology stack could include sensors from a variety of manufacturers. The business processes or machinery you’re monitoring might all be uniform, such as in a factory that was outfitted in one fell swoop. Alternatively, it might be wildly disparate, such as in plants that underwent partial renovations or ownership changes. Your health monitoring practices must unify such sources if you ever hope to reveal unbiased insights.

Going back to the employee hiring analogy, most companies onboard their new workers with uniform training programs and continuing professional education. When dealing with IoT technology, similar homogenization can be achieved through practices such as data sanitization and validating, filtering and organizing inputs for further consumption.

Sanitizing your IoT health data makes your life easier when it comes time to feed the info into dashboards and analysis tools. It also shields you from drawing incorrect conclusions based on outliers and process-induced irregularities.

2. Simulate Performance

You don’t have to naively await disasters to learn how healthy your framework is. Creating a digital twin can reveal problems before they cause real-world havoc by showing you how your IoT technology stack might perform under ideal — and undesirable — conditions.

Simulation is also an illuminating practice for understanding prior incidents. By running mock-ups, you can analyze critical events that preceded known problems and identify alternative avoidance strategies.

3. Establish Better Baselines

The Internet of Things makes it extremely easy to generate operational baselines that place later analyses in a more useful context. On the other hand, it’d be nice not to have to repeat mistakes that others have already suffered through.

This is where choosing a well-supported, exhaustive AEP saves the day. An IoT technology stack that’s demonstrated its ability to adapt to a variety of business models and data-gathering strategies grants you the benefit of prior experience even if you’re a complete novice. Since IoT health monitoring is indispensable to the well-being of your enterprise, it pays to pick total ecosystem solutions that give you a head start.

Written by Brian McGlynn, President and Co-Founder at Davra

Source: IoT For All

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Sustainability Process Blueprint

Illustration: © IoT For All

Good business and environmental stewardship go hand-in-hand with sustainability as Wall Street investors elevate the value of companies that demonstrate improving environmental and social governance performance. Essentially, what’s good for the environment is also good for consumers and the bottom line.

But because technology is changing the way companies integrate sustainability into their business strategies, the tool with which progress in this area has traditionally been measured—the sustainability scorecard—may no longer be the right one for the job.

IoT and analytics enable an innovative approach to a sustainability scorecard by using IoT devices to remotely monitor, measure and catalog quantifiable metrics around energy, air and water relevant to sustainability. We define it as the sustainability process blueprint approach.

Keep reading to find out why the scorecard falls short, and how a different approach can actually be more indicative of your company’s progress on the road to sustainability.

Where the Sustainability Scorecard Falls Short  

Sustainability information is important to both investors and customers, but few environmental sustainability reporting mechanisms (and corporate social responsibility scorecards in general) provide specific and accurate information about a company’s sustainability practices. In general, scorecards provide a qualitative view of performance, evaluating the level of commitment in terms of broad categories like company culture and engagement, corporate vision and strategic planning. In other words, are you fostering effective corporate governance and including the right decision-makers on your team to make real progress? (Here’s an example of a sustainability scorecard that rates corporate progress with broad brushstrokes.)

In addition, legacy scorecards measure progress only at a specific point in time. That limits the notion of success to the evaluation of a single moment and leaves little room for characterizing and analyzing a company’s ongoing efforts in a useful way.

This traditional approach to sustainability reporting may help outsiders to understand the mindset of corporate governance, but it does little to provide the support needed by investors and consumers to trust that leaders’ intentions are, in fact, coming to fruition. Both this lack of transparency and the sustainability scorecard’s static method of reporting leave much to be desired from a credibility standpoint.

The New Scorecard: The Sustainability Process Blueprint     

Why just talk about strategies your company is implementing to improve when you could be providing data around actual improvements?   The sustainability process blueprint employs a dynamic approach focusing on key metrics from which remotely monitored data is measured and cataloged in a format that provides insight as well as the ability to benchmark progress towards goals and objectives and comparisons to peers.  Sensor data and analytics provide the framework to offer visual understanding, context and perspective of progress towards sustainability.

The sustainability process blueprint approach answers three key questions about your commitment to sustainability: 1) Do you have a plan, or blueprint, for improving your sustainability over time? 2) Are your sustainability efforts actually having an impact? 3) How are you performing in comparison to other companies in your industry?

This approach is possible using the Internet of Things (IoT). IoT sensors allow you to measure almost every aspect of your business environment, facility and operations around the clock, from energy usage to water and air quality to leakage detection and more. (See these seven metrics for ideas about what your company could measure with regard to sustainability.) The granular data you collect—and that your IoT analytics platform helps you analyze—leads to better sustainability reporting for several reasons:

  1. It offers a more dynamic approach to sustainability than traditional scorecards. Forget about “point in time” assessments. Now you have actual data that can be examined over time. This data acts as a feedback mechanism, allowing you to actually “see” the impact of your efforts.
  2. It offers more details than legacy scorecards. Instead of viewing sustainability from general, high-level perspectives, you can see it from the ground-up—how much energy even a single piece of equipment actually uses, for example, or the actual levels of volatile organic compounds present in your building’s air.  
  3. It allows you to perform data analysis for the purpose of devising smart sustainability strategies. Otherwise “invisible” building characteristics can be transformed into quantifiable data points that can be used in a statistical or analytical model for context. For instance, strategies can be implemented to directly address equipment using excess energy, or the root causes of air pollution.
  4. It allows you to benchmark your company’s performance against others in your industry. Benchmarking can help you see if you’re moving in the right direction and on the right initiatives. It can also highlight specific areas for improvement going forward.

A process blueprint represents a new approach to sustainability reporting: one that produces a quantitative, metric-specific and dynamic process showing your progress toward the ultimate goal of sustainability.

Source: IoT For All

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The Top Five Reasons IoT Devices Will Malfunction in 2020

Illustration: © IoT For All

According to Juniper Research, the number of IoT (Internet of Things) connected devices will number 38.5 billion in 2020, up from 13.4 billion in 2015: a rise of over 285 percent. Consumer IoT, especially as it relates to the smart home, has received significant attention, especially because of the prevalence of online gaming, video streaming, home audio and home video security systems. With the new year on the horizon and smart home devices set to remain among the top purchases in 2020, this article focuses on the top reasons that devices are expected to malfunction over the next 12 months.

IoT is rapidly becoming a transformative force, delivering the digital lifestyle to billions of people. Integrating an amazing array of smart devices with internet connectivity, the IoT market already includes more than 25 billion devices in use. Smart home devices include products, including smart speakers, smart displays, smart plugs, smart light bulbs, smart thermostats, web-connected home security systems and literally thousands of other products.

As consumers acquire and implement interconnected IoT devices, the number of malfunctions is growing, which has been an unresolved problem. If only 1 percent of devices suffered a malfunction annually, that would be 250 million failures this year alone. But, 1 percent is far below the actual failure rate; almost two-thirds of IoT-technology consumers already report having experienced device failures. On average, consumers experience 1.5 digital-performance problems on a daily basis. That’s an overpowering message to tech support organizations as customer experience will be negatively impacted unless this issue is addressed.

The Top Five Reasons for Device Failures

After intensive corroboration with top research firms, five distinct factors are projected to increasingly contribute to the malfunctioning of smart devices in 2020, all of which can be considered detrimental by both manufacturers and users. Until the recent availability of technologies to diagnose the causes of IoT/smart device failures, these problems have required manual diagnosis and repair:

  1. Operating environment: The wide range of operating environments will be a key factor in smart device functionality. This includes issues with IoT uptime caused by environmental conditions, including extreme temperatures, rough device handling, WIFI availability/signal blockage, etc.
  2. Integration problems: Many new smart home devices require their own app that may or may not integrate with various routers, smart hubs and other systems in the home. Popular apps and services may only be available on specific devices. As the number and variety of devices proliferate, consumers in 2020 can expect to see higher malfunction rates.
  3. Device configuration: Smart device configuration should be very user-friendly. However, many devices still require manual intervention. The requirement for AI-based configuration is obvious in this situation in order to ensure a fast and effective setup for devices. The ability to auto-configure such devices will be critical for smart device/home enablement as consumers bring a broader range of more complex smart devices into their homes.
  4. Connectivity: Smart device connectivity (or lack thereof) will be a significant contributor to device malfunctioning in 2020. The problems include a lack of signaling or bidirectional communication between devices for collection and routing purposes. There’s also the issue of presence detection, where the smart hub/router must be able to detect when a smart device drops offline and when it rejoins the network. This gives the ability to monitor the device and fix any problems that may arise.
  5. Device load: Device load and bandwidth limitations are other challenges expected to increase in 2020. As the device load increases and the volume of devices rise and project activity volumes to the service provider, this requires a large-scale server farm to handle the large amount of data. Instead, enhanced management and processing will allow for the seamless transfer of data between devices and servers.

Malfunctions Wear Many Disguises

When devices were simple, it was easy to address malfunctions. If a music speaker failed to deliver sound, the problem was usually with the speaker wire or speaker. The problem was either fixed or a new item was purchased. However, in the era of integrated smart-device systems, the actual cause of a malfunction can be difficult to identify. Just like in many human medical cases, the symptom might disguise the underlying cause and lead to a misdiagnosis.

For example, if a smart garage door opener isn’t responding to a remote “close” command from your mobile phone while you’re at work, the fault could be a mechanical problem with the door mechanism. Or perhaps there’s an electrical problem in the motor. Or even a general electrical issue like a blown circuit breaker in the fuse box. Maybe the mobile phone app has a bug or has been infiltrated by a cyber virus. Maybe the signal to the opener is blocked because of radio frequency bandwidth overload or a transient environment condition. The cause could be in the garage door itself—maybe last night’s ice storm is preventing the door from freeing itself from the ground. What if the user looked at all of those conditions and none of them seem to fix the problem? How about the internet router or in-house hub? The situation can become quite complicated. Multiply this simple garage-door opener example by the tens of other connected devices in the home, and it’s easy to understand how confusing it can be to properly diagnose, let alone fix, a malfunction.

Service and Support Are the New Success Factors

In the face of so many inevitable malfunctions, the ability to quickly detect, analyze and repair problems will determine success for device manufacturers, integrators and service teams. Device manufacturers will need to provide warranties and software updates along with a helpful support center. ISPs and integrators will have to take on responsibility for the performance of a very wide and growing variety of complex devices. Company IT departments will be inundated with hundreds of new devices to support. Most of all, billions of individual consumers will turn to efficient service desks when the inevitable problems occur.

Written by Amir Kotler, CEO, Veego
Source: IoT For All