IoT Agenda


November 13, 2019  12:59 PM

Can trusted data exchanges help grow ethical AI?

Chiaren Cushing Profile: Chiaren Cushing
Artificial intelligence, Data privacy, ethics, iot, IoT and AI, IoT data, IoT data management, iot security

AI is transforming the world as we know it. Contextual awareness paired with AI is opening the door to many positive solutions for healthcare, environmental protection, conservation, smart cities and public safety. Enterprise AI applications also proliferate in marketing and sales, HR and recruiting, security, autonomous operations and financial services. On the other hand, the rapid advancement of AI also raises questions and concerns around data ethics, which are only beginning to be addressed.

As a case in point, the New York Police Department (NYPD) has been challenged by AI bias concerns for its new crime analysis AI tool. The tool is intended to help identify crime patterns for faster response and crime prevention. To avoid bias, the NYPD removed race, gender and location attributes from the historical data that was used to train the tool. However, as one analyst noted, if racial or gender bias played any role in past police actions, even if it’s not explicitly captured in the data, the tool’s predictions will still be impacted by race and gender. As the AI market continues to explode, clarifying an ethical approach to scenarios like these will be paramount.

How fast is the AI market exploding?

IDC predicts that global spending on AI systems will reach $79.2 billion by 2022 at a compound annual growth rate of 38% between 2018 and 2022. For a good visual reference of just how fast the AI topography is evolving, just compare the AI business landscape from five years ago to the current2019 AI landscape. AI topography and the number of AI inventions follows similar paths of acceleration, as this visualization of AI patents illustrates.

What’s to fear? The rise in AI ethics

If AI can really help us make the world better, then what’s the problem? Consider this example: A city has a network of smart traffic lights to reduce congestion. AI algorithms time the lights to minimize traffic buildup and capture license plate images of any vehicles that fail to stop for a red light. The system automatically matches license plate numbers to vehicle owners, enabling the city to process traffic violations faster and more effectively.

In this scenario, you may not be surprised to receive a traffic ticket in the mail. But what if your insurance company notifies you that your rates are going up as a result? While that raises questions around citizen privacy, addressing AI bias may be an even bigger challenge. For instance, what if AI model that determined your initial insurance rate was trained on historical data that contained bias with respect to your race, gender, or education level? In fact, IBM researchers working to mitigate AI bias have identified and classified more than 180 human biases that could affect AI models.

Most AI ethics concerns can be generally categorized into these general areas:

  • Human and machine interaction. Machines impersonating or fooling humans; autonomy gone wrong in weapons, accidents or rogue robots, and human abuse of machines.
  • Data collection. Collecting more data than is needed; collecting data without permission; surveillance, selling or connecting different data sets without permission and perpetuating bias and human error contained in data used to train AI.
  • Data use. Deep fake videos, fake news, social manipulation, invasion of privacy, social grading based on insurance, credit or jobs and discrimination.

Fortunately, many governments and companies with a stake in AI are aware of the potential for bias and working to implement ethical approaches. For example, The European Union published a set of guidelines for ethical AI for companies and governments. The U.S. Department of Defense is hiring an AI ethicist. Industry consortiums such as The Partnership on AI and the IEEE have developed guidelines on AI ethics. The Partnership on AI, which includes 100 corporate and nonprofit members, also recently announced a research fellowship to advance diversity and inclusion in AI. Many tech giants, including Google, Microsoft, IBM have developed their own AI ethics guidelines, and Amazon has committed $10 million to AI fairness research.

Growing ethical AI is bigger than just one stakeholder or policy, but typically include principles of fairness, inclusion, transparency, privacy, security, accountability and societal benefit. Data is at the heart of many of these areas as it really boils down to how data is collected and used to train AI models.

How trusted data exchanges could help

AI models are as good as the data used to train them. Larger sample sizes with diverse characteristics that have been cleaned of bias and errors will result in fairer, more accurate AI models. Additional data sources added over time can also help improve AI models. For example, Siemens built an AI model that analyzes sensor data from trains paired with historical data to predict faulty components. In the future, the company could add data from other sources — such as route-based weather forecasts — to further improve the model.

Data exchanges are emerging as one way that organizations can better use the data they have, including monetization, as well as tap into data from other sources. Combining data from different sources across IoT data exchanges can improve AI models, yield deeper insights and open the door to new services. Trust is an essential building block.

Data exchanges built with the principles of a data trust are better equipped to address data privacy concerns. By directly and securely interconnecting IoT ecosystem participants, data, and algorithms, a trusted data exchange can generate maximum shared value while keeping data private and safe in transit on low-latency, high bandwidth infrastructure.

As Jeni Tennison, CEO of The Open Data Institute put it, “We only unlock the full value of data when it gets used, so we really need to find good ways to share data more widely without putting people at risk.”

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

November 12, 2019  3:39 PM

The emergence of autonomous irrigation technology streamlines farming operations

Marc Pegulu Profile: Marc Pegulu
Agriculture, Internet of Things, iot, IoT automation, IoT benefits, IoT connectivity, IoT devices, IoT use cases, IoT verticals

The agriculture industry is facing challenges as the global population continues to grow and the amount of arable land decreases. As a result, farmers are using technology to streamline operational processes, influence the way food is being produced and to leave a longstanding positive impact on the environment.

Just about everyone understands the importance of watering plants to ensure they grow, but what if irrigation could not only ensure optimal yield, but save costs on labor and inputs? Precision agricultural irrigation companies, such as WaterBit, think of this when they set out to use IoT technology to make  smart irrigation systems.

Farmers are seeking solutions for data that enables them to make pin-pointed irrigation and fertilization decisions, which help them optimize their crop yields. Water and soil are the main factors that drive crop yields, and when farmer has more control over these factors they have more control over their outcomes.

For example, when developing their smart irrigation system, WaterBit’s goal was to create a reliable networking product in agriculture. WaterBit’s irrigation solution has streamlined farming operations and increased overall productivity where deployed. With LoRa devices and the LoRaWAN protocol, this solution provides two-way communications in response to the primary needs of irrigation and fertilization.

By implementing LoRa, batteries in production units can be eliminated and duplex communication is enabled. Automatic identification systems enable growers to maximize yield across soil types, all while optimizing the use of labor and other input resources. This solution can be used across crops such as tree nuts, corn, cotton and berries.

As the demand for increased crop yields continues to grow, commercial farmers are closely monitoring overhead costs, and turning to technology to minimize expenditures, grow their businesses and feed a growing population.

We’re seeing more LoRa use cases pop up in agriculture given its long range and low power capabilities, which are suited well for the rural environments in which devices are deployed. It’s enabling growers to act based upon events as they happen on the field. Many farmers are also turning to companies such as Semtech to deploy scalable automatic identification systems that are reliable and provide real-time data and information.

The agriculture industry faces many challenges that they cannot control easily, such as the weather. However, technology is enabling farmers to take more of the control back into their own hands through better automation and the ability to streamline their agricultural operations.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.


November 11, 2019  3:07 PM

How IoT connectivity will tackle society’s biggest challenges

Suresh Ram Profile: Suresh Ram
cellular IoT, Connected Data, Internet of Things, iot, IoT connectivity, IOT Network, Network, smart factory

IoT is coming of age.

In recent years, the technologies behind IoT connectivity have powered new products, new market categories and new ideas while impacting business decisions, consumer behaviors and economic trends. And now, from health trackers to real-time video surveillance, IoT products and services are not just changing lives, they’re also gaining traction.

Now, as IoT technologies continue to break new ground, the opportunity to apply IoT-based solutions to some of society’s greatest challenges becomes more realistic. Consider how efficient transportation networks or smart factories might affect energy consumption, lower pollution levels and boost regional economies.

To better understand how IoT will revolutionize industries and markets and regions, it’s important to look at how the technologies not only power the IoT ecosystem but how they also work together to open doors to new possibilities.

Unleashing technology

Consider the value that three important connectivity technologies — cellular, short-range radio and positioning — provide on their own.

When it comes to device tracking or remote monitoring — perhaps a medical monitoring device or a smart streetlight — cellular technology is what allows data to be shared with a central location. But with a range of demands and use cases, the levels of cellular connectivity needed can vary based on a number of factors.

Short-range radio technologies such as Wi-Fi and Bluetooth may be widely recognized for their consumer applications, but the ability for these technologies to enhance digital experiences within defined physical locations — think corporate campuses, airport terminals or manufacturing facilities — can change the way IoT applications are adopted in the workplace or public venues.

Positioning technologies, which are most recognized for their impact on the advancement of autonomous vehicles initiatives, are a critical piece of the IoT equation. In large part, its importance is centered around the accuracy that it needs to deliver while taking into consideration that latency issues that must be tackled to deliver precise results.

Emphasis on reliability

As we’ve learned with other technology product categories, not all products are created equal. For any number of reasons, some products will be inferior to others and experiences could be sub-par. But it shouldn’t be because of the technology.

When it comes to the technology, reliability — the ability to access the device and get the data when it’s needed most — is key, no matter the product, the manufacturer or the vendor. A smart streetlight, for example, might only need to report data about its energy usage or illumination levels on monthly basis, but that doesn’t mean it shouldn’t be able to transmit a maintenance issue outside of the pre-scheduled update window. Compare that to a heart-monitoring device that needs constant connectivity, not only to monitor the patient and transmit medical data but also to monitor the device’s power consumption.

Increasingly, issues like power management and security — elements that traditionally had been part of the device itself — are relegated to the technology, too. When the security technologies are implemented at the chip level, the device has the highest level of security and will generate the secure keys that are needed for the application.

What’s next

The demand for IoT solutions continues to grow, and along the way, it’s unveiling some unique problems that need to be solved, not necessarily by a single approach but more by a multi-pronged approach that includes custom solutions developed through a mixture of technologies. Perhaps the solution involves the need for more data, advanced sensor capabilities or enhanced machine learning. Perhaps it needs a combination of them.

That’s what’s important to remember about the next wave of IoT and the technologies that are powering it. As we’re able to introduce a greater range of IoT-powered solutions, solutions can be designed to address specific problems by implementing the right mixture of connectivity technologies.

When it comes to IoT, the limitations are shrinking while the opportunities continue to grow.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.


November 11, 2019  11:30 AM

What can 5G really deliver?

Tim Sherwood Tim Sherwood Profile: Tim Sherwood
5G adoption, 5G and IoT, 5G network, 5G use cases, commercial IoT, Internet of Things, iot, IoT collaboration, IoT connectivity, IOT Network

There has been a tremendous amount of excitement around the arrival of 5G over the last several years. The hype is now finally giving way to progress as proof of concepts, field trials, buildouts and early deployments emerge. But despite the many recognized advantages of 5G, some mobile network operators are still looking for concrete evidence of ROI.

The reason for this is many mobile network operators are not yet prepared to offer 5G at scale. When operators launch a new service, the first phase typically involves building out and scaling up within their own networks. Only after the internal build up is done do they start exploring interoperability and interworking between networks.

While there has been a lot of interest around the rollout of 5G networks by major carriers such as AT&T, Verizon and T-Mobile, the fact remains that operators are very focused on their own networks. They haven’t even started to talk about how to make that available to other partners or asked how to support sharing across partners within the network.

So, what difference will 5G make?

A look at 5G

The adoption of 5G in its early stages is going to be critical to fully realize the vision of a mobile-first, connected workforce that has access to optimal bandwidth speeds at the mobile edge, which will help improve collaboration functionality such as augmented reality and virtual reality.

5G will enable an explosion of more advanced IoT use cases that will consume greater bandwidth and remote surveillance, and will increase interactions across different IoT endpoints leveraging AI and machine learning, especially in the realm of autonomous vehicles. These higher bandwidths use cases require a faster and more responsive network on-demand to realize their full potential.

In some cases, 5G will provide more flexibility in access to technology. Similarly, 5G will also leverage software-defined network capabilities to better manage underlying access for the particular use cases that organizations have.

5G in the automotive industry

Arguably, the most 5G-centric use case is autonomous driving. If you examine a car closely, you’ll find that most of a car’s operations are controlled by technology, not the driver. 5G is critical to ensure the vehicle can effectively make intelligent and secure decisions based on the vast amounts of data shared between the vehicle’s system components, other vehicles and roadway infrastructure elements.

A robust 5G mobile network will enable more decentralization, but for autonomous cars to really thrive, a completely seamless mobile experience is critical to keeping cars constantly connected. The challenge will be to design IT architecture that can be deployed globally while still allowing for localized technology to cater to different regions.

Coverage, reliability and scalability must be optimized, and seamless access to multiple mobile networks will require a unified management policy to ensure consistent standards. It’s important that operators, vehicle makers and other ecosystem players, such as transportation authorities, telematics control unit providers and fleet management companies, work together to ensure 5G capabilities are seamless, efficient and effective.

Patience is key when dealing with 5G

The key thing to remember when it comes to these use cases — and 5G in general — is that patience is a virtue. Ultimately, it’s going to take some time for 5G to reach a similar level of availability as 3G and 4G. It is vital that the mobile ecosystem appreciates the limitations associated with frequency allocation, network investment, regulatory restrictions and the availability of funds for investment.

The investment by mobile operators and providers isn’t a small one. In fact, it will likely amount to billions of dollars in new network equipment, licenses, deployment, time and collaboration.

There is no question that we are at the beginning of a new era of global connectivity, with speeds we’ve never seen before. If various parties, including the mobile operators, government and network equipment companies, work together to identify commercially viable and desirable customer solutions, 5G can fulfill the vast potential ascribed to it these past years.

But it’s important to keep things in perspective. We are at the very beginning, and there’s still a long way to go.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.


November 8, 2019  12:52 PM

A new era of continuous streaming IoT analytics

Mark Palmer Profile: Mark Palmer
Business Intelligence, Data streaming, Internet of Things, iot, IoT analytics, IoT strategy, streaming analytics

Using traditional analytics to steer IoT systems is like trying to steer your car while only looking through your rearview mirror. A new class of IoT tools is flipping the conventional school of analytics thought on its head.

Old-school business intelligence (BI) and data science tools are designed to look only at historical data, or a recording of IoT sensor data, like a DVR of an old football game that’s already been played. This architecture helps analysts understand what has already happened, not what’s about to happen next, while the outcome is yet to be determined.

Looking only at the past assumes the patterns, problems and anomalies observed in the past will happen in the future; this works well enough for financial reporting, many forecasting tasks and generally when the world is stable, repeatable and under control.

Streaming IoT data provides the raw material needed to think differently: to analyze and act on emerging conditions instead of assuming conditions, trends and relationships will remain the same. Here’s how self-service streaming analytics combined with IoT data can change how you think about the game that is your digital business.

Source: Shukhrat Umarov, Pexels.

The dawn of a new analytics era

Years ago, self-service BI tools put the power to analyze data into the hands of domain experts. By reducing the dependency on IT, these tools ushered in a new era of insight-driven business. Suddenly, if you could use a computer, you could use analytics tools such as Tableau, Qlikview, or Spotfire on your own without waiting weeks or months for IT.

Self-service visual exploration fueled dramatic growth in the BI market, thanks to the productivity gains from making opaque business processes, transactions and data fully transparent to domain experts and process stakeholders. For the first time, they could finally see what had been previously hidden.

A new, similar era of disruption is now upon us, because recent innovations in self-service streaming analytics empower business users to see IoT data like they have never seen before. These technologies are streaming BI and data science in real time.

Streaming BI

Streaming BI is like an algorithmic canary in the IoT data coalmine. It provides business users continuous, live insight into what’s happening now by connecting directly to streaming IoT data.

From a business analyst point of view, streaming BI works like a conventional BI tool, but instead of connecting it to historical data, you connect it directly to information in motion: IoT data flowing through messaging systems like Kafka, MQTT, OSI PI or OPC UA, an increasingly popular IoT protocol on Microsoft’s Azure platform.

The magic happens after you create a BI dashboard. Streaming BI remembers the queries needed for each BI visualization and continuously monitors data streams for changes in the query result. The moment the result changes, visualizations update.

Users can create alerts that use rules or data science algorithms to monitor IoT sensor data. When the incoming data shows changes in defined alert conditions, the dashboard changes and alerts are delivered according to rules the business user creates.

For example, you can ask an algorithm to display all trains on a map, highlight in red trains that are predicted to be more than 1 minute late, send me a text message if that prediction becomes greater than 5 minutes late. This visualization constantly updates without re-asking the question. Even if you’re not sitting at your desk, you’ll be notified when something happens that makes or increases the chances a train will be unusually late.

This form of BI completely changes the interaction model between the analyst and data. Insights are live algorithms pushed to the analyst directly. Analysts can just set it and forget it. The public can be alerted that a train is arriving late while they are still driving to the station. Better yet, the problem can be fixed before it happens, and passengers will never know there might have been a problem in the first place.

Now, imagine instead of a train running late, sales are not materializing or a storm is coming that might affect safety. Anticipating such outcomes before they materialize and in real time while you can still do something about it is critical.

Streaming data science powers streaming BI

The second recent innovation in IoT analytics is streaming data science.

The AI field of adaptive learning is the AI equivalent of how humans learn: instead of training models only on what has already happened, adaptive learning can train models on changing data streams by injecting Python, Tensorflow or Java data science algorithms into streaming data for continuous evaluation.

Streaming data science presents new opportunities for adaptive learning. For example, in high-tech manufacturing, a nearly infinite number of different failure modes can occur. To avoid such failures, machine learning models applied to IoT data can help identify patterns associated with quality problems as they emerge and as quickly as possible. When never before seen root causes — such as machines or manufacturing inputs — begin to affect product quality, staff can respond more quickly.

Adaptive learning with streaming data means continuous learning and calibration of models based on the newest data. Sometimes applying specialized algorithms to streaming data can simultaneously improve the prediction models and make the best predictions at the same time.

When you combine streaming data science and streaming BI, business analysts can gain continuous algorithmic insights. This helps democratize data science models by helping humans manage automated algorithmic insights.

Self-service streaming BI and streaming data science at work

These innovations open an entirely new market for BI: digital operations. Until now, BI was useless for digital operations because there’s no value in understanding how you should have acted yesterday to avoid problems or seize opportunities.

For example, smart city transportation analysts can now understand and act on congestion in real time by evaluating sensor data from vehicles, satellite imagery and weather forecast data with streaming BI. Analysts can receive notifications when data science models predict that problems are about to occur and decide how to reroute vehicles, notify drivers or alert the public to avoid problems before they happen.

Other examples of streaming BI and data science at work include:

  • Shipping operations can be alerted when a model predicts that a cargo ship will arrive earlier than expected and doesn’t have a place to dock. In shipping, wasted time is wasted money, and shipping operators can use real-time insights to reoptimize port operations and eliminate delays.
  • Insurance adjusters can continuously monitor weather patterns and models that predict areas where claims will be prevalent so that they can proactively alert customers and schedule adjusters to reduce financial loss and improve customer engagement.
  • High-tech manufacturing operations can monitor sensor readings from equipment, apply adaptive learning models and predict and fix problems before they impact production or quality.
  • Online marketers and web user experience designers can evaluate how specific offers are working, change layout and evaluate goal attainment on a commerce site.

Until recently, continuous insights like these were only possible through laborious custom programming. Now, self-service streaming BI and streaming data science puts real-time power in the hands of analysts.

Stop looking at the past and start looking toward the future

Today, nearly every business is stuck looking only at the past. Thanks to the plummeting cost of IoT sensors and innovations in streaming analytics, continuous insights can now be made available to any business user. It’s as easy as opening a spreadsheet.

The agility gained from self-service continuous insight allows subject matter experts to understand what’s happening now, predict problems before they occur and decide how to act while the game is still on.

A decade ago, self-service BI disrupted the analytics market; now, self-service streaming BI promises to democratize continuous, real-time insight. Old-school BI will always have its place, but with ubiquitous IoT data, business users can stop looking at what could have been, get in the digital game, act and win.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.


November 7, 2019  4:39 PM

How is IoT driving the future of connected cars?

Sanjeev Verma Profile: Sanjeev Verma
"Automotive industry", automotive, connected cars, Connected vehicles, Connectivity, Industrial IoT, iot, IoT device, IoT device management

With an expected compound annual growth rate of 14.8%, the market value of connected cars is estimated to reach around $220 billion by 2025, according to MarketsandMarkets research. This growth is quite astonishing, and various automakers are investing their resources to develop a series of connected and automated vehicles.

Development in the technology of IoT has brought in a radical transformation in the automotive industry. Its implementation has brought in several dynamic changes in vehicles in terms of safety, comfort and luxury. A person in a connected car is given the connection, entertainment and network that he experiences at his office, home or in an entertainment center. Embedded IoT sensors on the surface of the vehicles further prevent accidents and enforce safe and easy driving.

Connectivity’s influence on the automotive industry

Connectivity capabilities have played a major role in deciding the future of connected cars. The soon to come 5G era can be easily visualized in the present. Recently, Dongfeng Motor Corporations partnered with China Mobile Companies and Tencent Holdings to initiate the development of connected vehicles for 5G connection. Such developments are sure to empower vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connection that will additionally enhance the aptitude of connected cars.

Different applications of IoT automotive are emerging that hep fuel the growth of connected vehicles. Fitted with advanced technologies, they will offer a wide range of features and functions that will completely revolutionize the way we travel and interact with them. Here are 9 ground-breaking IoT applications that are driving the future of connected cars:

Autonomous vehicles. Even though the automobile sector is still working towards the development of completely autonomous vehicles, semi-autonomous cars have already been developed and used in some countries.

These vehicles can take partial control over driving to avoid accidents and park itself in vacant spots. Different car manufacturers are attempting to develop a fully autonomous vehicle that can use the capabilities of IoT to take full control over driving. However, their wide implementation will require more time as other technologies, such as vehicle-to-everything (V2X) and telematics, need to be developed properly.

Voice commands. Nowadays, everything can be operated using simple voice commands. Amazon’s Alexa and Google’s Voice Assistant are few examples of devices that have given our voices the power to operate our mobile devices, music systems and even coffee machines.

Various automobile manufacturers and individual vendors have developed in-car speech recognition systems that have enhanced the way in which people communicate with their vehicles. With simple voice commands, a driver can change the radio station, switch on the headlights and look for direction through GPS without even lifting a finger. This lets the driver focus on driving and reduce the chances of accidents.

Advanced maintenance features. A sudden breakdown of even a single component can overheat the whole engine and cause it to fail in unexpected situations. Even with proper inspection, it sometimes gets insanely difficult to detect malfunctions that cause breakdown. IoT-powered predictive maintenance is a logistically prudent and economical method that uses time-series data to identify parts that require maintenance. This time-saving method also reduces the cost associated with part change, and helps drivers avoid frustrating situations on roads.

V2V connectivity. V2V is an advanced application of IoT that has given a push to the automotive industry in creating intelligent and semi-autonomous vehicles. As the name implies, this feature allows vehicles on roads to communicate with each other.

The cars share information related to speed, road conditions and traffic congestion via connected networks. This prevents car accidents and helps drivers to easily glide through big traffic jams. V2V connection along with other frameworks of V2X will allow vehicles to connect with smart traffic signals and signs, along with conducting instant transactions at pumps and tolls.

In-vehicle infotainment. The growing demand for luxurious and smart vehicles has resulted in the development of in-vehicle infotainment (IVI) systems. Modern IVI systems consist of heads-up displays and integrated head units, powered by high-end GPU’s and connectivity modules.

These powerful computation capabilities display the complete real-time information of the vehicle and also allow car owners to connect their smartphones with it. Thus, a car owner using an IVI can make a call from his dashboard and play songs on his phone from the vehicle’s music system.

Brain-to-vehicle technology. Development in IoT technology has brought in several modifications in the medical and healthcare field as well. The amalgamation of medical science and the automobile industry has brought in the creation of brain-controlled vehicles.

With brain-to-vehicle technology, a device is used to measure the activity in the brain that is analyzed by the vehicle’s system to maneuver it. This system empowers a human to control several vehicle functions just by thinking about them. Even though this method of maneuvering cars is still underdeveloped, it opens multiple doors for impaired and specially-abled people to experience the thrill of driving a car. It can also lower the chances of accidents caused due to sudden lane change and related unsafe driving practices.

Telematics. Telematics is a perfect tool for people who are hyper concerned about their cars’ safety. Telematics refers to the long-range transmission of data. Sensor applications gather data about the vehicle and directly transfer it to the smartphone of a car owner. IoT telematics allows a person to confirm the location, safety, and condition of its car at all times. The real-time alarm system warns the owner about the forceful opening of car doors. This remote monitoring of vehicle is helpful for managers who monitor and control a large fleet.

Fleet management. A fleet refers to several trucks or other load-carrying vehicles. Fleet managers often carry the heavy burden of monitoring and controlling the traffic of its fleet. An IoT-powered fleet management system allows a manager to easily track the location of every vehicle in the fleet. With use of different weight and quantity monitoring sensors, a fleet operator can also confirm the safety of the cargo at all times.

Intelligent infrastructure. V2I is another important application of the V2X network. The network connects vehicles with nearby infrastructures, such as traffic signals, signboards, petrol pumps and toll booths. A V2I network smooths the flow of traffic and avoids traffic congestions at various places. It also facilitates quick money transfers at petrol pumps and toll booths automatically.

Final thoughts

Implementation of different technologies, such as IoT and artificial intelligence, has transformed cars into a moving computer. The market of connected cars is soon going to soar in terms of market cap and revolutionize the way we interact with our vehicles. The above applications are the evidence of the future that the technology of IoT will bring for the connected cars.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.


November 7, 2019  12:40 PM

IoT for small and medium-sized enterprises

Harnil Oza Harnil Oza Profile: Harnil Oza
Internet of Things, iot, IoT benefits, IoT data, IoT strategy, IoT verticals, Machine learning, SMEs

IoT is one of the most recurrent concepts in the planning of IT architectures today. IoT entails the connection of devices and data to each other via the internet. Contrary to what many may think, there are many advantages of IoT for small to medium-sized enterprise (SME). It is not a technology only for large companies.

IoT will facilitate the automation of processes, reduce operating expenses and improve customer satisfaction, which are the three basic pillars to optimize and make any business profitable.

IoT is especially attractive to the industrial sector, but it also has many B2B and B2C applications in other business sectors. The provision of sensors and the machine-to-machine interconnection generates information to start intelligent installations, such as factories, offices and shops, to know how the products are used or to optimize key processes such as the logistics chain.

Examples of IoT use in SMEs

Big data and IoT are deeply interconnected. IoT sensors allow collecting and constantly generating data. Data is optimally analyzed, to automate processes in business management and to make decisions based on real situations and behaviors to increase their profitability. Companies are already using IoT for the optimization of their business.

The sensors in strategic points of a company provide information about the clients in real-time, including the number of people present, the direction of their movement or if they have the company’s app developed by top app development companies. With this data, it is possible to propose individualized offers visualized in the devices.

The management of the warehouses can also be improved thanks to the IoT. The device in a bookshelf can vibrate at the moment when there are no more products in the warehouse.

Logistics companies can have information on each of the phases of a shipment as well as the resources involved in this interconnecting of different devices located in vehicles, warehouses and pallets. The data will give the key not only to improve customer satisfaction, but to save costs by detecting the most optimal routes and the most productive personnel.

The maintenance of facilities and machines is another area where you can see the benefits of the IoT. Imagine an air conditioner in an office that only activates if it detects that people are working. The precise location of sensors can also warn that something is not working well on a computer before breaking down.

What are the benefits of IoT?

It is a truism to point out the speed at which changes are taking place in the digital world. The change accelerates exponentially to the extent that before you learn about a tool, another technology is ready to displace it.

Technology is changing from static websites to mobile and social digital environments. Connecting to the internet and managing life from a smartphone or tablet was unthinkable less than a decade ago. Top app development companies are creating top-notch apps that were never thought about before.

It is in this scenario that the concept of IoT becomes increasingly relevant, a term used to define the integration in our daily lives of technological devices that connect everyday objects to the internet that provide data for its monitoring and control.

Although it is still an incipient technology on the horizon, there are already glimpses of the revolution coming alongside the development of IoT.

Little by little, many of the everyday objects that surround us will have an internet connection, including thermostats, refrigerators and cars. These items will be able to provide a huge amount of data, giving its manufacturers valuable information about its use and customer problems. This will allow companies to know their habits and even anticipate their demands, offering customized and predictive products and services. And this will create a change in the decision-making processes within the companies. Companies can contact top app development companies to get apps that will enable easy sharing of data.

Several sectors will benefit substantially from the development of IoT. For example, IoT will have a great impact on the management of infrastructures, including  monitoring and control of traffic lights, bridges or urban and rural roads; detection of changes in structural conditions and immediate response in emergencies.

Thanks to IoT, brands can know what the problems of their consumers are at the same time as them or even before. With apps developed by top app development companies, companies can now get sufficient information from their clients, a circumstance that will not only facilitate the creation of more personalized and predictive products and services, but will also modify the business processes and work structure. Many jobs will disappear, and others will arise due to the need to have experts.

The huge proliferation of mobile devices ensures a broad database of customers, new products and services and knowing what information needs to be linked. There are presently lots of information on the internet, thanks to apps by top app development companies. But, which sectors can benefit from these innovation opportunities?

Infrastructure: Monitoring and control of traffic lights, bridges or roads , detection of changes in structural conditions, immediate response in emergencies and improvement of quality.

Environment: Optimization of resources when it comes to preventing and improving efficiency. For example, establishing quality control of air or water, atmospheric conditions or soil.

Industry and mass production: Programming repair and maintenance activities, control and centralized process management, optimization of the production chain and rapid response to product demands.

Energy: Remote monitoring of energy consumption, intelligent storage, detection and action systems and optimization of energy consumption.

Medicine and health: In this case, apps created by top app development companies can be used to provide operational data in real-time, enable emergency notification systems and remote surveillance or monitor and ensure the general welfare of the elderly or chronically ill.

Logistics and Transportation: Monitoring of transport systems that include the vehicle, driver, and infrastructure, intelligent traffic control, parking choice, implementation of electronic toll collection systems, logistics and fleet management or road assistance and security.

Entertainment: Improvement and creation of sensors in mobile devices, virtual reality technologies or consoles with motion sensors that serve to improve the user experience.

Although it seems easy, IoT requires a tremendously complex structure, in addition to special attention to the security of all stored data. However, its advantages and opportunities make it worthwhile. They add efficiency to our life, and its application is not excessively expensive. It opens up new business opportunities and improves the existing work processes.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.


November 6, 2019  2:24 PM

The breach with the longest shelf-life: Healthcare

Gorav Arora Gorav Arora Profile: Gorav Arora
connected devices, eHealth, healthcare IoT, Internet of Things, IoMT, iot, medical IoT, patient data privacy, patient data security

The IoT landscape is rapidly evolving and these “things” are becoming countless. A few years ago, it would be hard to believe that medical devices would have network-connected capabilities that can track our health and monitor recovery progress and save lives of every age and demographic. This brings us to the internet of medical things (IoMT), a marketplace that covers a variety of applications, including patient monitoring, telemedicine and other devices that have internet connectivity. As we embrace the benefits IoMT brings, we must also ask essential questions about protecting patients: Where is their health data being stored, and is it safe?

Such devices can react in real time to relay critical information to the doctors, first responders and caregivers that are saving lives and improving health outcomes and patient experiences. However, according to the 2019 Thales Data Threat Report-Healthcare Edition, the healthcare industry experiences the highest rate of attack compared to any other industry studied.

Incredible health benefits, but also tech risks

It is clear that IoMT positively impacts healthcare providers and the lives of patients. Patients suffering from chronic diseases can avoid frequent visits to the doctor through remote patient monitoring. Every-day sick visits can turn into convenient video chats. Doctors can give their patients faster and more accurate diagnoses. Wearable devices can detect potential heart problems. While these examples demonstrate freedom, accessibility, and a painless experience for patients, IoMT technology surprisingly has the potential to cause some real “pain”.

Hospitals may assume patient data is being protected in the cloud, but according to this the study, 100% of healthcare organizations — more than any other industry sector — are collecting, storing and sharing sensitive data within digital transformation technologies while fewer than 38% are encrypting data within these environments.

One significant benefit of connected devices is the capability to collect and store a large volume of information, enabling doctors to access patient health data in real time and increasing the accuracy of diagnosis and spotting of trends. Unfortunately, data collection and storage can bring increased vulnerability around privacy and security. The range of possibilities for IoMT seems infinite, but to take advantage of them, the security of connected medical devices and related applications must be implemented thoughtfully to ensure data attacks and misuse are avoided.

While IoMT significantly improves healthcare, there are staggering numbers that indicate healthcare organizations are failing to implement good data security practices, putting themselves in danger of non-compliance and putting patients in danger of becoming victims of fraud. When sensitive patient data is compromised — intentionally or otherwise — medical records can be sold on the dark web for upwards of $1,000 per record, according to Experian. Unlike a credit card hack, where the bank can shut down the account and provide the consumer with a new credit card number, this healthcare data is out there for good – exhibiting a shelf-life longer than dried beans.

Where do we go from here?

As data breaches reach an epidemic level, Healthcare leaders do not need to choose whether or not to implement IoMT technologies within their business. Instead, they must be sure to check two things off their to-do list:

Partner with the right companies. Developers and the hospitals that implement these technologies must consider integrating key security features that protect the device and patient from encountering any malicious activity. Nowadays, every business is inclined to function as a technology company when it comes to implementing IoT and security. In a previous blog, we discovered that less than half, 48%, of companies could detect if any of their IoT devices have been breached. Breach detection and mitigation are especially crucial for the healthcare sector, because businesses must partner with the right security companies that can help ensure safe data storage, compliance and security protection features.

Meet security compliance regulations and educate patients. It is important for healthcare providers to not only confirm that their collection and use of data is HIPAA compliant but also ensure healthcare practitioners are explaining to patients the privacy issues and security risks that come along with IoMT devices. In addition, personal identifiable information is increasingly becoming a hot button for consumers at large. A prime example is California’s Consumer Privacy Act. Privacy will continue to be a focus for legislators over time, so it is imperative for healthcare organizations to understand regulatory mandates and compliance issues and how those impact their IoMT strategy.

The world of digital transformation is upon us, and our healthcare providers may need a shot in the arm to safeguard IoMT, because an apple a day won’t keep a data breach away.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.


November 5, 2019  1:14 PM

Using technology to track holiday shipments

Marc Pegulu Profile: Marc Pegulu
Internet of Things, iot, IoT application, IoT benefits, IoT data management, IoT devices, IoT hardware, IoT sensors, smart manufacturing, Smart sensors

With the holiday season right around the corner, the need for smart technology solutions is essential. The influx of products developed, managed, qualified and shipped between October and December each year is vast, and facilities manufacturers are always looking for ways to streamline efficiencies and drive down costs.

As such, it’s becoming increasingly important for companies to automate the tracking process within manufacturing facilities and plants, especially during peak shipment times such as the holiday season. Depending on the size of the organization, there may be hundreds of thousands of products being produced, which then are being packaged and shipped throughout the world.

This is a highly complex procedure that requires precision and accuracy across the entire manufacturing process. If an aspect of the process fails and a crate is mislabeled and moved to the incorrect shipping truck, a store might not receive an entire shipment of the desired product. Not to mention a double shipment of the same product to a different location. This mix-up not only causes challenges for the retailer, but it’s a costly mistake for the distribution plant as the product now needs to be reshipped via a rush order to its real destination.

Typically, this process and management falls to the warehouse workers who manually track assets throughout expansive facilities and follow goods while they are out for shipping. However, we’re increasingly witnessing organizations turning to technology and connectivity solutions for tracking, which is helping to ensure better productivity and cost savings.

For example, a France-based company, Ineo Sense, has created and implemented Clover-Core, a series of LoRa-enabled sensor products for smart asset tracking in manufacturing settings. By integrating wireless connectivity via the LoRaWAN protocol, product managers and engineers across a warehouse setting can effectively monitor the usage, functionality, current status and location of expensive manufacturing equipment and shipment assets in real time. That means staff can remotely monitor their global operations and assets produced from one central location.

The holiday season is a very challenging time for retailers with many companies relying heavily on the revenue generated in this short window of time to reach their year-end sales goals. That said, IoT devices can seamlessly be installed into an existing manufacturing plant to help mitigate potential error and streamline the process. All consumers want the ability to purchase their holiday gifts in time for the season’s close, and manufacturers can do their part by ensuring the product is delivered on time.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.


November 4, 2019  3:27 PM

The role of the car data marketplace for third-party mobility services

Kevin Valdek Profile: Kevin Valdek
"Automotive industry", commercial IoT, Internet of Things, iot, IoT application, IoT applications, IoT data, IoT strategy, Mobile applications

Developing applications and services for the connected car market can be a hard path to navigate. The car data marketplace is a platform that enables third-party mobility services to integrate with personalized car data, which then promotes these services to users whose driving and car-owning experiences can be dramatically improved by them.

Unlike mobile app developers who have marketplaces, such as Google Play and the Apple Store, and access to retrievable data from hand-held devices for their products, the connected car market is still finding its feet in terms of accessing personalized vehicle data with a customer’s consent, and promoting the available services to users.

The rise in third-party mobility services

When we talk about third-party mobility services, we are referring to applications that seek to assist or enhance the user’s experience of driving, riding in, charging or owning a vehicle. These applications might be built to work with a user’s phone or the cloud.

Carmakers themselves have created their own applications and services for their customers to use specifically within their own vehicles, but over the last five years we have seen a significant rise in mobility services designed and built by companies and developers who are not affiliated with carmakers. Essentially, these companies or individuals are external operators working independently with their own business plans and objectives and building their own user bases.

However, because of the nature of their services, these third parties require access to data from their users’ vehicles. Data such as a person’s driving style, vehicle model and location enables them to tailor the experience of using their service to that specific person. We can be sure that a tailored, personalized service is going to be far more valuable to a user than a generic one.

It’s for this reason that third-party mobility services need to make some sort of agreement with a carmaker. They must get access to the personalized data of the carmaker’s customers which they would otherwise not be privy to. Without this data, the applications they want to offer have significantly less value.

But how do third-party services work with multiple carmakers and car models simultaneously? This is where a car data marketplace comes in. Thanks to car data marketplaces, third parties no longer have to make separate agreements — or integrations — with multiple carmakers, but can instead offer their services to multiple users via a streamlined portal.

What is a car data marketplace?

A car data marketplace — also sometimes known as a vehicle data platform — works between the two parallel ecosystems of carmakers and developers and third-party operators. Essentially, the marketplace works as a data broker. A third-party service can request to connect to the vehicle data it needs to operate via the car data marketplace, which can check it for quality, relevance and security before verifying it to connect to vehicle data or suggesting improvements.

The car data marketplace can verify applications and services because it already has contracts in place with carmakers. In addition, the car data marketplace will have a marketplace API, which means third-party services only have to integrate once with this API to potentially connect with car data from multiple carmakers. Thanks to this API, third parties do not have to make different arrangements or technical integrations with numerous carmakers, or work with different hardware or software, saving significant amounts of time and potential complications.

Where do carmakers come in?

As we just mentioned, the carmakers will be working directly with the car data marketplace, not with the third-party services themselves. It will be the car data marketplace which negotiates individual contracts and costs with the carmakers.

All financial transactions go through the marketplace, which protects both sides and keeps everything transparent and fair. The marketplace is also where applications and services will be approved or rejected. If a service is approved, it will then connect to the car data. If the data does not connect, the team who built it will be offered advice on improving their offering to ensure future success.

The neutral server

But how do these things work from a technical perspective? For carmakers to share personalized car data from their customers with third-party mobility services, they need to use a neutral server.

The beauty of a neutral server is that it can enable third-party applications and services to have access to vehicle data without those third parties needing to come to their own unique agreement with a carmaker. It is an independent intermediary that is engaged by the OEMs, and not owned by them. These servers are neutral because they are not subsidiaries of the vehicle manufacturers, but instead wholly independent of them, both financially and operationally.

A neutral server enables more customer choice. Not only can car users work with a carmaker’s range of services, they can work with literally any other service provider they want. It frees up the movement of the third parties too, as they are no longer tied to one carmaker. With the neutral server, third parties they can access multiple carmakers with one simple integration.

The carmakers generally broker data to third parties within a specified and agreed scope, and the neutral server protects the direct visibility of third-party business models from carmakers. Another important feature of the neutral server is that it can provide compatibility between third-party services and carmakers that use different technology interfaces.

Certain companies provide API. For example, High Mobility provides a carmaker agnostic Auto API, which enables third parties to potentially work with any carmaker after a single integration, irrespective of the system it is using. This significantly

How do third party services connect to vehicle data?

The vehicle data market is still new, both for data providers and third parties. At this stage, what’s key is for third parties to figure out what kind of data they need from the vehicle. Is it personalized data that would be most valuable to their product? Or is anonymized data in bulk quantity for big data-related services that they are looking for? These third parties will also need to think about what their required data update rate is. Is once a day enough to inform their applications, or does it need a data update every five minutes to perform at its best?

Secondly, third parties need to know which providers are offering the specific vehicle data that they are looking for. In the current climate, different vehicle data platforms generally have different agreements with carmakers and several data product bundles or offerings for those looking to integrate data into their applications. It is worthwhile for third-party services to spend some time researching what these different product offerings consist of and their pricing in order to have a good understanding of which vehicle data marketplace is the right one for their product.

Another factor influencing whether an application or service may choose to access vehicle data via one car data marketplace over another is the type of integration tools which that vehicle data platform offers. These tools could be SDKs, a testing environment, vehicle emulators or tutorials. Some car data marketplaces may offer substantial technical support, while others may not. Depending on the needs and level of experience of the third-party service, these additional features will make some vehicle data platforms more attractive than others.

Finally, the pricing model used by the car data marketplace will affect how many developers and third-party services access data via that platform. Easy to understand and transparent pricing is likely more attractive to third parties who are wary of being caught out by unexpected or hidden costs. Small companies will also want to calculate their costs in advance and work out how these costs will grow as they increase the number of vehicles they connect to. A data marketplace that is transparent and clear about how its pricing works is likely to generate loyal and repeat customers.

When a third-party service has chosen which data marketplace it wishes to work with, it will then need to integrate with that marketplace’s standardized API. This standardized API will enable it to connect to multiple carmakers by seeking verification to connect to their customers’ vehicles. The integration of the marketplace’s API is likely to involve entering a data contract with that marketplace. Once that contract is signed, the third-party service should then be free to submit their applications to the many different carmakers who are also in an agreement with that marketplace.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.


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