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What is an Intelligent Transportation System (ITS)?

The European Telecommunication Standards Institute states that intelligent transport systems are those which include telematics and all types of communication between vehicles, in vehicles and between vehicles and fixed locations. Moreover, ITS is used not only in road transport but also in rail, water, and air transport. The Integrated System is quite capable enough of consuming a large amount of data and hence producing useful information to the traveler. This efficient information guides the traveler to reach their destination in the most optimized way.

Why a large amount of data is important?

We hear more often that people are getting fastly connected than ever because of accessibility to high-speed transportation and increased capability for sharing information, IoT in transportation is a fusion of this large amount of data which comes from multiple sources.

According to a study by McKinsey Global Institute recently it was stated that automotive industry will be the second largest producer of data in the forthcoming years and if we combine automotive with travel and logistics industry then the output for the same grow by an additional 30%.

How the data is fetched from sensors?

Sensors are highly important to get the raw data and if we consider the data of one sensor with another than we can get a deep understanding of the performance and behavior of the system. With the IoT in transportation, sensors and fetched data from them helps in detecting whether a wheel on the vehicle is slipping or not and accordingly alerts the driver or the concerned person to apply brakes. The data from these sensors help in the real-time analytics within the vehicle.

How this sensor system enables to take quick business decisions?

Data from various sources is collected and is integrated to reduce the uncertainty. Data collected from sensors is gathered and is analyzed further to have a deep insight such that it reduces the uncertainty for a particular situation. For example, the ambient air temperature being used alerts that the temperature is below freezing and the sensors used on the axle reports slippage then the situation come out to be that there is ice in that area. These alerts are sent by the cloud analytical system and (not sent manually) which warns the driver before entering a particular area.

Further, the cloud-based system sends this report to the operations center as well. This analysis report is further sent to nearby and surrounding systems which are outside the geo-fenced area. Alerts tell them about the detected ice region so that they can re-route if possible.  

 

The IoT and its myriad potential have changed the scenario of the Transportation industry. the system has enabled to make smarter and more informed decisions. The IoT technologies need to be embraced to cope up with the competition. As the industry was in continuous suffering from heavy operational costs, the implementation of Internet of Things has made safe and cost-efficient business operations. On leveraging IoT, the connectivity and capability for predictive analytics has streamlined the processes and hence has enhanced their bottom line.

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We are fast moving towards a future where cities will feature hundreds and thousands of smart connected objects, talking to each other, exchanging and producing meaningful data and insights, basically reshaping the urban landscape into intelligent and autonomous systems. Internet of Things will be at the heart of this technological transformation, as sensors and digital tags will find their way into various physical city infrastructure, monitoring traffic, weather, crime and even rat infestations! However, it’s not just hardware IoT and sensors that will provide city planners and authorities to gain more visibility into the working and management of a city. Smart connected products or ordinary consumer products tagged with digital ID’s and digital twins can open up new dimensions in how we imagine Smart Cities to function.

For the sake of painting a picture of the role of connected products within Smart Cities, let’s consider a pharmaceutical company supplying critical drugs to a city. Enabling every drug product at batch and serial item level to have a digital twin of its physical self will allow for exchange of product related data to happen between manufacturer, the supply chain, the city authorities, end consumers and the products themselves. Read on to see how the pharmaceutical industry could look like in the not so distant future.

Smart Logistics & Traceability: Digitally tagged consumer products such as medical products will paint a clearer picture of each item’s journey from the manufacturing facility to the hands of a customer, resulting in intelligent movement of products characterized by autonomy. Each time a product moves, whether it’s from the factory to a truck, or from the truck to a warehouse, its location and movement will be logged against its digital twin in real time with the help of a scanner, RFID reader, smartphone or other connected devices.

So, when situations arise where brands or smart city authorities become aware of substandard or defective products in circulation, the process of factoring on the production source for them and a faster and leaner product recall will become easier by tracing back to the relevant point in the product’s journey.

Smarter Production & Distribution Channels: Smart connected products will help in procuring the right amount in the right place at the right time. Complete visibility at all events of the supply chain will allow brands to better predict demand in respective locations in a city. Better predictive ability will help them to create seamless intelligent systems capable of efficiently managing production and distribution channels, ultimately leading to reduction of wastage by preventing accumulation of unused medicines.

In fact, brands will be able to predict demand on a much larger scale than before. They will anticipate when a particular medicine is supposed to run out at the city-level and trigger production cycles for the particular product.

Smarter response to Public Health Crises: With IoT powered smart products, the engagement and the monitoring does not stop at the customer level. Even after the product leaves the shelf, customers can input valuable data through the digital twins which can be mined into to tailor smarter responses to public health emergency situations.

For example, city authorities will be aware of exactly how many medical products are in inventories across the city by keeping track of their movement across every touchpoint in the supply chain. In situations where a contagious disease breaks out, public health officials will be instantly alerted by hospitals that are also hooked onto the network. By keeping track of the quantity and location of stocks of medicines dispersed across city, public health officials will always be prepared to tackle such high priority situations as they can more efficiently assess and redirect required medicines to appropriate locations.

Even smarter, cities of the future could be prepared for seasonal illnesses by predicting their onset based on algorithms derived from a mix of data from weather forecasts, hospital reports and product supply chains.

Smarter Citizens: Digital twins will give rise to smarter citizens, who will be capable of using smartphones to digitally interact with the packaging in order to obtain accurate information pertaining to authenticity, ingredients, color-coded expiry dates, instructions for use (IFU) etc. Not only will digital twins of medical products enforce transparency, but they will help in improving health literacy by weeding out counterfeits and providing easy-to-read and user-friendly formats to dispense IFUs.

Medical products empowered by IoT will also lay the foundations for a multiway communication channel between consumers, manufacturers, and city authorities, especially aiding researchers to collect and analyze feedbacks for clinical trials and development of new cures.

Smarter ways to tackle Counterfeits: Falsified medical products take the top spot in the fraudulent products market, being worth US$163 billion to $217 billion per year. Falsified, substandard and unlicensed medicines and medical devices pose a serious threat to public health. Counterfeit medicines are on the rise and no place remains untouched by them.

However, medical products with digital twins can have vast implications in fighting the war against falsified medical products. The sophisticated digital tags on these products can act as a unique identifier, at the same time providing a user-friendly way to verify their authenticity. Both retailers and consumers just need to authenticate the product using the digital tag which will allow it to confirm the product’s genuineness by running it against an online database.

Going one step further by taking advantage of a highly connected ecosystem, fraudulent products can instantly be reported by consumers directly to manufacturers and city authorities. City authorities can thus keep track of regions in the city reporting counterfeits and crack down on the sources for such illegal operations.

The goal of smart cities is to create intelligent urban spaces and infrastructures to improve the lives of their citizens. But the first step towards this goal is to set up digital twins for products to bring them onto the Internet of Things platform. For these automated and intelligent systems would be impossible without various products generating and transmitting data about themselves. At this point, we have barely scratched the surface with IoT’s potential to create smarter cities, and smart connected products will lead the way in laying the foundation for the cities of the future.

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As we covered in the past, Gartner is out with their predictions for IoT. This time for the year's 2018-2023. The announcement was made at the Gartner Symposium/ITxpo 2018 in Barcelona, Spain. 

Nick Jones, research vice president at Gartner said, “The IoT will continue to deliver new opportunities for digital business innovation for the next decade, many of which will be enabled by new or improved technologies. CIOs who master innovative IoT trends have the opportunity to lead digital innovation in their business.”

And CIOs if you're not paying attention, get on it. Gartner says you need skills and partners to support IoT. Come 2023 the average CIO will be responsible for more than three times as many endpoints as this year.

Gartner shortlisted the 10 most strategic IoT technologies and trends that will enable new revenue streams and business models, as well as new experiences and relationships:

Trend No. 1: Artificial Intelligence (AI)

Gartner forecasts that 14.2 billion connected things will be in use in 2019, and that the total will reach 25 billion by 2021, producing immense volume of data. “Data is the fuel that powers the IoT and the organization’s ability to derive meaning from it will define their long term success,” said Mr. Jones. “AI will be applied to a wide range of IoT information, including video, still images, speech, network traffic activity and sensor data.”

The technology landscape for AI is complex and will remain so through 2023, with many IT vendors investing heavily in AI, variants of AI coexisting, and new AI-based tolls and services emerging. Despite this complexity, it will be possible to achieve good results with AI in a wide range of IoT situations. As a result, CIOs must build an organization with the tools and skills to exploit AI in their IoT strategy.

Trend No. 2: Social, Legal and Ethical IoT

As the IoT matures and becomes more widely deployed, a wide range of social, legal and ethical issues will grow in importance. These include ownership of data and the deductions made from it; algorithmic bias; privacy; and compliance with regulations such as the General Data Protection Regulation.

“Successful deployment of an IoT solution demands that it’s not just technically effective but also socially acceptable,” said Mr. Jones. “CIOs must, therefore, educate themselves and their staff in this area, and consider forming groups, such as ethics councils, to review corporate strategy. CIOs should also consider having key algorithms and AI systems reviewed by external consultancies to identify potential bias.”

Trend No. 3: Infonomics and Data Broking

Last year’s Gartner survey of IoT projects showed 35 percent of respondents were selling or planning to sell data collected by their products and services. The theory of infonomics takes this monetization of data further by seeing it as a strategic business asset to be recorded in the company accounts. By 2023, the buying and selling of IoT data will become an essential part of many IoT systems. CIOs must educate their organizations on the risks and opportunities related to data broking in order to set the IT policies required in this area and to advise other parts of the organization.

Trend No. 4: The Shift from Intelligent Edge to Intelligent Mesh

The shift from centralized and cloud to edge architectures is well under way in the IoT space. However, this is not the end point because the neat set of layers associated with edge architecture will evolve to a more unstructured architecture comprising of a wide range of “things” and services connected in a dynamic mesh. These mesh architectures will enable more flexible, intelligent and responsive IoT systems — although often at the cost of additional complexities. CIOs must prepare for mesh architectures’ impact on IT infrastructure, skills and sourcing.

Trend No. 5: IoT Governance

As the IoT continues to expand, the need for a governance framework that ensures appropriate behavior in the creation, storage, use and deletion of information related to IoT projects will become increasingly important. Governance ranges from simple technical tasks such as device audits and firmware updates to more complex issues such as the control of devices and the usage of the information they generate. CIOs must take on the role of educating their organizations on governance issues and in some cases invest in staff and technologies to tackle governance.

Trend No. 6: Sensor Innovation

The sensor market will evolve continuously through 2023. New sensors will enable a wider range of situations and events to be detected, current sensors will fall in price to become more affordable or will be packaged in new ways to support new applications, and new algorithms will emerge to deduce more information from current sensor technologies. CIOs should ensure their teams are monitoring sensor innovations to identify those that might assist new opportunities and business innovation.

Trend No. 7: Trusted Hardware and Operating System

Gartner surveys invariably show that security is the most significant area of technical concern for organizations deploying IoT systems. This is because organizations often don’t have control over the source and nature of the software and hardware being utilised in IoT initiatives. “However, by 2023, we expect to see the deployment of hardware and software combinations that together create more trustworthy and secure IoT systems,” said Mr. Jones. “We advise CIOs to collaborate with chief information security officers to ensure the right staff are involved in reviewing any decisions that involve purchasing IoT devices and embedded operating systems.”

Trend 8: Novel IoT User Experiences

The IoT user experience (UX) covers a wide range of technologies and design techniques. It will be driven by four factors: new sensors, new algorithms, new experience architectures and context, and socially aware experiences. With an increasing number of interactions occurring with things that don’t have screens and keyboards, organizations’ UX designers will be required to use new technologies and adopt new perspectives if they want to create a superior UX that reduces friction, locks in users, and encourages usage and retention.

Trend No. 9: Silicon Chip Innovation

“Currently, most IoT endpoint devices use conventional processor chips, with low-power ARM architectures being particularly popular. However, traditional instruction sets and memory architectures aren’t well-suited to all the tasks that endpoints need to perform,” said Mr. Jones. “For example, the performance of deep neural networks (DNNs) is often limited by memory bandwidth, rather than processing power.”

By 2023, it’s expected that new special-purpose chips will reduce the power consumption required to run a DNN, enabling new edge architectures and embedded DNN functions in low-power IoT endpoints. This will support new capabilities such as data analytics integrated with sensors, and speech recognition included in low cost battery-powered devices. CIOs are advised to take note of this trend as silicon chips enabling functions such as embedded AI will in turn enable organizations to create highly innovative products and services.

Trend No. 10: New Wireless Networking Technologies for IoT

IoT networking involves balancing a set of competing requirements, such as endpoint cost, power consumption, bandwidth, latency, connection density, operating cost, quality of service, and range. No single networking technology optimizes all of these and new IoT networking technologies will provide CIOs with additional choice and flexibility. In particular they should explore 5G, the forthcoming generation of low earth orbit satellites, and backscatter networks.

Gartner clients can learn more in the report “Top Strategic IoT Trends and Technologies Through 2023.”

Photo credit: Jim Templeton Cross www.templeton-cross.com, Gartner Symposium/ITxpo Barcelona 2011

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Internet of Things is the talk of the town over in construction, manufacturing, healthcare, transportation and home automation. But we are yet to fully tap into the potential of IoT driven solutions to trigger disruption in and deliver value to the consumer retail industry.

Enabling smart attributes and inter-connectivity to store assets can have a plethora of exciting applications: engaging customer experiences, leaner and more efficient store operations, products and services as well as opening up of new streams for revenue generation. According to Zebra Technologies, 7 out of 10 retail brands will be investing in IoT technology by 2021 and a few have already begun rolling out IoT powered smart stores and services. With more and more retailers looking to reimagine every aspect of their supply chain with technology, let us look at some future possibilities for IoT in the retail industry :

1. Creating Experiences with Lighting

Lighting devices are an ubiquitous presence inside any retail store and connected smart lighting can do more than save energy. Emerging technology is exploring avenues to utilize connected and automated smart lighting for retail displays to create superior customer experiences and indoor positioning, expanding the horizon for an experiential store.

Retail giant Carrefour partnered with Philips to install LEDs in one of their hypermarkets in Lille, France. Enabled with Visible Light Communication (VLC) technology, these LEDs emit a code which is readable by any camera on a smartphone, connecting the smartphone to a digital experience provided by the store. Customers can then locate items on their shopping list using the indoor positioning activated by the LEDs, experiencing an in-store navigation system.

2. Smart Packaging and Digital Labeling

Under the constant pressures of demands for more consumer transparency and capricious regulations, brands and retailers are running out of space on the physical packaging of products to put relevant information. IoT will play a major role in the future of the labeling and packaging industry as brands turn to technology to solve challenges related to packaging.

QLIKTAG Software is providing solutions using their IoT platform to enable all products to have a globally unique identifier “QLIKTAG” and hence a digital twin, allowing “dumb” products to have a presence in and participate in the Internet through smart devices. These digital tags, consisting of barcodes, QR codes and Data matrix codes, pave the way for a vast variety of digital interactions like better stock and inventory management throughout the supply chain, product traceability all through its lifecycle, consumer transparency in multiple languages, product authentication, consumer feedback, insight and analytics as well as better consumer engagement experiences. Brands also save on costs incurred in reprinting and repackaging as these digital tags allow real time edits and updates of label content remotely.

3. Smarter Inventory Management Solutions

The future of retail will see increased integration of technology into brick and mortar stores and a more connected ecosystem giving rise to sophisticated experiences for both customers and retailers. IoT will enable the development of smarter inventory management solutions that will be capable of detecting and solving out-of-stock situations on its own.

WiseShelf is converting shelves in retail stores into smart shelves to address the issue of shelf out-of-stock incidents. Equipped with light sensors, the shelves can detect when an item is removed from the shelf and send alerts to the management application through WiFi when it assesses low levels of stock. Apart from leading to more efficient restocking operations and inventory management, these smart shelves are also freeing up employees to engage in more customer interactions. They are also providing key data and analytics on popularity of products, enabling better design of store layout in accordance to foot traffic.

4. Automated Events Of Supply Chain

Plenty of countries are plagued by an ageing population and rising labor costs and retailers as a result are turning to digital solutions to reinvent supply chains. Panasonic in partnership with Trial Company Inc. conducted a demonstration experiment for an automated self-checkout system with RFID tagged shopping baskets and products. The smart shopping baskets are capable of calculating the total cost and the number of items in the basket due to the RFID tags, generating your bill when you place it on the checkout counter. Not only does it allow automated billing, but on being placed on the self checkout counters, the bottom of the basket can open up releasing all contents into a bag, which the customer can collect and leave.

5. Facilitating Omni-channel Retailing

In order to consolidate online shopping practices with in-store ones, retailers are looking to ingrain technology into physical stores for a seamless customer experience. Ralph Lauren launched interactive fitting rooms in its flagship store in Manhattan, furnished with RFID tagged interactive mirrors. Powered by retail technology platform Oak Labs, the mirrors automatically detect and display the clothing items brought into the room along with available sizes, colors and recommendations for a complete look. Enriching the entire digital experience, customers also have the option to call an associate on the floor to the fitting room, to bring more items to try out for example.

6. Reducing Food Wastage and Spoilage

IoT could have vast implications in reducing global food wastage and spoilage, especially at the retail level. Wasteless, a startup from Israel, has successfully implemented IoT enabled digital pricing labels in an international Spanish retail store in an effort to reduce food waste. Using data regarding expiry dates encoded into the barcodes or RFID tags on labels, Wasteless’s platform enables a dynamic pricing system with the cost of the product dependent on its freshness, becoming cheaper as it nears its expiry date. The platform has led to reduction of waste by 33%, better inventory management and monitoring of products in terms of their expiry dates to reduce out-of-stock incidents as well as improved sales by allowing customers a more dynamic pricing range to shop from.

7. Food Traceability and Quality Control

The entire food supply chain will see a transformation as IoT enabled sensors and smart devices will become more common to track and optimize each supply chain event. With more demands for fresher food products and sustainable sourcing, these sensors will be able to collect and transmit relevant information like location, temperature etc to all supply chain stakeholders in real time. Consumers buying at retail stores can scan digital tags like QR codes, Data matrix codes or RFID tags on packaging to get assurance about the quality and provenance of the food product.

Zest Labs is working to improve real time visibility for farm to shelf at all levels of the supply chain. Their unique ZIPR code (Zest Intelligent Pallet Routing) enables real time tracking and monitoring of the actual freshness of each pallet of food product, using a combination of wireless IoT sensors and cloud based predictive analytics and machine learning. The result is in supply chain managers being able to make better decisions about sending a particular pallet across a certain distance based on its freshness, thus preventing food spoilage in-transit.

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Top challenges towards IoT Adoption

The discussion around IoT has been around since last 5 years. Take those 2020 projections to the table, and see how many have quoted them as the moment of truth for IoT. But those projections and data remained as it is in 2018 with mass adoption yet to become a reality.

As an IoT practitioner what amazes me is why only a handful enterprises and products have made it so far.

The undeniable truth about IoT is that it definitely brings huge competitive advantage to the table for consumer products and enterprises. While scaling IoT and building LPWAN networks that had more than 100,000 nodes we say more than just connectivity, we saw revolution, we saw how fast we could reduce “revenue leakage”, we saw how fast we could bring “disruption” to the table and we definitely saw business models that were never seen before.

The moment of Truth for me in IoT was when I built an LPWAN network that was more efficient than a six sigma process - That’s how powerful IoT really is!

But, let’s get to the point, what I really said before are the results. There are so many steps to actually reach there. When I take lessons from the software world, there’s something called “First mover’s advantage”, well that changes when it comes to IoT.

First movers fail, that too miserably. First movers for the most part are someone that fail and create a path for others.

So, what is it that makes adopting IoT so difficult?

Unfamiliar territory for business buyers

How many enterprises do you see that still run on managed data centers and legacy apps? Why do you think they aren’t adopting amazing next generation cloud technologies? Now put IoT at the center stage and try to understand this. At minimum there are three technologies that need closer inspect for any IoT implementation in enterprises: Cloud, hardware and wireless technologies.

Cloud has evolved and maturity is good enough to make sure that the adoption remains streamlined. But even with a mature technology, IoT use case challenges all forms of our existing cloud connectivity models.

As far as hardware is concerned, the last 30 years of manufacturing and other core engineering industries have been working, we have seen mass adoption of close sourced hardware in form of PLM and other technologies. With such a heavy industrial adoption, closed source tech has associated heavy costs even with minor pivots. This created agility issues that are difficult for industries to look through as they can’t simply throw away their entire infrastructure.

Open source tech has been influencing and driving IoT since last 3 years. But majority of our core engineering industries have largely been unaware of how open source tech works. There are potential pitfalls and immense opportunities with open source tech that software world has been extensively leveraging. But with hardwares, everything has a cost or license associated with it. With this closed nature of hardware industry, it gets difficult to customize off-the-shelf products to match with specific process or innovate on top it.

IoT security

Nobody, absolutely nobody has figured this out yet. No matter what you implement can be broken down, or at least blocked to create service disruption. Internal processes being disrupted is one thing, but what’s even more damaging is your IoT product/service compromising customer’s privacy.

It was until 2015, when we shifted IoT communication security from 64 bit encryption to 128 and then to 256 bit encryption. OWASP has established some best practices and awareness around IoT security, but as I said before this has been a largely the biggest factor preventing IoT adoption in enterprises as well as consumer facing products.  

Extremely painstaking product development

Lack of understanding of how IoT technologies work, how IoT product development should be done, and how the costs of product development should be controlled, etc makes IoT product development a discouraging step for many enterprises.

The technology fragmentation and lack of standardization further increases the pain that C-suites have to go through when they implement IoT programs.

For example, we were in middle of implementing a mesh network based out of Zigbee back in 2015, when we meet folks from Bluetooth SIG and nRF that hinted us towards an upcoming mesh network on nRF’s stack.

We saw the subsequent release in 2016, with an extremely easy to manage and govern stack. Point being that these technologies are evolving faster than ever, while the opportunity window seems to be shrinking as the technologies mature as well. So, it really boils down to risk vs opportunities and tons of fragmentation and vagueness ahead of it.

Legacy equipments

The way we have been building connectivity in the last 2 decades was dependent upon wireless, and built for a local-on-premise infrastructure. That had its benefits before Industry 4.0, but now things have changed.

“If you can connect it, you can improve it”

But we never built anything for low power and wireless connectivity. What we built was supposed to talk over RS232s, USB serial or PLCs.

I have met enterprises that have invested more than $20M and can’t replace even 10% of their existing investments in the next 2 years. They are stuck, stuck with legacy equipments that somehow needs to be connected.

If you have a programming experience like I do, you would straightaway think that simply by passing a serial command over USB or RS232/487 interfaces you should receive this information. But things aren’t this easy. The plant that I spoke about had 67 different devices, all have been implemented with Fortran, Cobol, Visual Basic, etc with each equipement being uniquely built. Most of the documentation was lost and the enterprise rarely knew what commands did what. It took us a month to figure that out for their behalf.

Even though we figured out all communication protocols, it was another puzzle to get through how the legacy programs actually worked. Remember, we were doing all of this without documentation. We even had the original programmers sat across the table and look at these programs. Even they couldn’t recall what they did at that time. Glad that code documentation and architectural documentation are a real thing these days.

Lack of off-the-shelf solutions to match for processes

Not each and every C-suite out there is looking towards custom hardware product development as a potential solution. With that being said, off-the-shelf solutions aren’t customizable enough to match their requirements. Not without inducing significant risks to the entire operation the C-suite is concerned with.

Take Beacon’s for example, ready made off-the-shelf beacons look good at the start, but if you are really looking to induce the battery life, implement a different routing algorithm and add more number of states, you will find them to fall short in a lot of ways.

Lack of subject matter expertise

I would personally rate this as one of the major concerns that industry executives have when they work with external or internal teams on IoT programs.

There’s a lack of understanding and product empathy in general when it comes to IoT. How many IoT vendors have you came across that talk about building automation systems and can get into specifics of HVAC?

If that wasn’t enough, the real revenue leakage in an industry comes from understanding real processes, not just blindly enabling tracking. Lack of real subject matter expertise is what prevents IoT adoption.

Well, these are some of the largest challenges that IoT faces before it goes mainstream. If you have any questions, or if you have any suggestions, feel free to drop a comment.

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What's IoT Trends 2019

The fourth edition of the Internet of Things Solutions World Congress (IoTSWC), which took place in Barcelona earlier this month, signaled an increasing interest in the technology, with the number of attendees jumping by 25 percent year over year, to 16,250. The range of topics discussed shows that IoT is being embraced by companies in every sector, and that the technology has now passed from the development phase to the implementation of practical solutions whose results are increasingly evident.

The 200 speeches and panels were divided into thematic areas (manufacturing, healthcare, connected transport, energy and utilities, buildings and infrastructures and open industry). Along with two related events, AI & Cognitive Systems Forum and Blockchain Solutions World, these included -- at the insistence of Richard Soley, Executive Director of the Industrial IoT Consortium -- presentations of concrete use cases. The Industrial IoT Consortium was co-organizer of the event together with Fira Barcelona.

Bringing order to the Babel of protocols

Although natural selection -- perhaps facilitated by the future evolution of 5G networks -- is likely to reduce the number, too many standards and communication protocols for the Internet of Things will continue for a long time. The "translation" of the signals and their integration into information flows will therefore continue to represent an opportunity for system integrators and companies operating in this sector. Although frameworks and platforms are emerging to manage and standardize the different peripheral systems (the Foundation's open source EdgeX Foundry proposal deserves attention), they do not exist yet and there will be no "plug and play" solutions for  IoT for a while.

Artificial intelligence to give value to data

Artificial intelligence is the fundamental ingredient needed to make sense of the vast amount of data collected these days, and increase its value for business. The easiest way to implement it is to resort to the API services of cloud operators such as  Amazon, Google, Microsoft and IBM. The risk of using standard solutions accessible to all is that they reduce the competitive advantage of the enterprises that use them, since they can be easily implemented by competitors. Creating a proprietary IA platform, however, will not be possible for  everyone.

Edge computing to overcome the limits of the cloud

The cloud, meanwhile, is showing its limits: Fast and constant connectivity is not always possible, especially in the case of connected vehicles or installations in remote areas; latency between sending data, processing and response is not always compatible with certain applications; and storage costs are are high even for data that is not necessarily indispensable.

There is therefore a growing tendency to relocate part of the storage and processing of data to the periphery of the network, close to sensors and connected objects. This so-called "edge computing" will be increasingly important and increasingly intelligent, thanks to chips optimized for machine learning and solutions able to bring "on premises" the AI algorithms of the "usual suspects", such as Amazon Greengrass, Google Cloud IoT Edge (still in alpha version) or Microsoft Azure IoT Edge.

Digital twins pass from objects to production flows

The creation of a digital twin, which thanks to data collected by sensors can provide a realistic virtual representation of products and systems, will be increasingly applied to entire production processes, allowing not only the monitoring of entire plants, but also predicting what will happen when a new model is out into production, or some variables change. This, according to proponents of the technology, will lead to greater efficiency, faster time-to-market and fewer glitches and non-compliance issues.

 

 

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Despite the industry being in a state of post-revolution following the onset and adoption of machinery, there is a batch of technologies that already define the state of technological innovation in agriculture.

 

The agricultural sector is in the middle of the data-driven transformation. Farmers and commodity traders are heading towards technological innovation in agriculture, adopting data analytics and smart farming technologies. Facing a crucial period in their history, agricultural businesses are tasked with combating the issues that will change not only their working methods but the world as we know it.

The agribusiness issues at hand

One of the greatest pain points associated with agriculture is the ability to predict the events that will achieve a given result.

Conditions play even less in the favour of farms positioned within markets that face rising production costs. The global population reaching 9.6 billion people by 2050, up from around 7 billion at present, according to forecasts from the United Nations, combined with the spread of economic prosperity are adding great pressure to the market. The UN suggests the doubling of crop production by 2050 as a countermeasure to this growth.

Some farmers simply cannot increase their land in order to grow more crops. As a result, there is a case for technology to make better use of the space available.

 

How IoT and predictive analytics can solve agriculture’s pressing problems

To become more efficient, agricultural businesses need data and plenty of it. This opens the door for technological innovation, as the size of these businesses and their plots of land prevent any kind of manual surveying.

Already we are seeing an active use of IoT devices to analyse the status of crops, capturing real-time data with sensors. For instance, with soil sensors, farmers can detect any irregular conditions such as high acidity and efficiently tackle these issues to improve their yield.

The data gathered from sensors allows to apply advanced analytics and get the insight that aid decisions around harvesting, while machine learning can transform the figures into solid predictions. Using advanced analytics, agricultural businesses can forecast yields, foresee unexpected weather conditions, predict market demand and mitigate risks, as well as better plan their capacity.

Agricultural drone is also among the key components of smart farming today. Tasked with the surveying of crop and livestock conditions from up high, their use of time lapsing within onboard cameras is helping farmers identify problems in areas like irrigation, which would otherwise go undetected.

Other members of the drone family allow for the spraying of crops at a greater accuracy than a tractor. As an added benefit, this also seeks to reduce the risk of human exposure to harmful chemicals. Back to ground level, there is potential for other robots to help out with manual duties like planting, ploughing and meat production.

The end goal in this case? A more efficient, more effective farm.

 

Conclusion

To spell things out: population growth could mean that every agricultural business will have to increase their levels of productivity over the next 30 years. That said, a review of the tech on today’s market suggests even the most specific problems can be matched with smart agribusiness solutions.

In the era of smart agriculture, IoT and predictive analytics are powering more efficient operations around the world. Combining IoT with analytics, agribusinesses get accurate predictions for crops and market conditions, allowing to increase their yields and profits. Smart application of technologies can facilitate warehouse and inventory management, help plan and execute seasonal works with the automated flow of data from the fields and agro-research labs.

Get in touch to discuss where the IoT can help futureproof your own agricultural business.

 

Originally published at eleks.com

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A Broad View of the Impact of Artificial Intelligence on Remanufacturing 

The advancement and utilization of Artificial Intelligence (AI) is poised to make a similar impact in the 4th Industrial Revolution we are currently experiencing as Henry Ford’s assembly line did over 100 years ago.  A convergence of machine learning algorithms, big data analytics, and connectivity between machines due to Internet of Things (IoT) capabilities are impacting and reshaping industry and business around the globe.  Here is a broad overview of some of the contexts within remanufacturing these advances are rapidly being applied.

 

Design for Remanufacturing

Barriers for remanufacturing can always be traced back to the initial product design stage.  If products were better designed to accomplish the goals of the remanufacturing process, massive improvements and efficiencies can be accomplished.  The adoption of ubiquitous information and communication technologies (ICTs) thanks to elements of advanced AI as described above continue to blur the lines between virtual environments and the real world to create more sophisticated cyber-physical production systems (CPPSs).

 

Advanced Remanufacturing Processes

Artificial intelligence technologies are exponentially expanding computing power and connectivity which results in greater volumes of data that can be analyzed in a more robust manner than ever before.  This will allow remanufacturers to think big and push the envelope to develop more ambitious goals and objectives for their programs.  Lack of data or advanced robotics capabilities will no longer be impediments for remanufacturers to successfully process a higher percentage of product components and materials.

 

Robotics in Remanufacturing

Robots have already proven their capabilities in remanufacturing under certain conditions with relatively small and simple batches of components that usually involve some significant human oversight.  Advances in AI are moving the needle in identifying and creating new patterns in the way humans and machines interact.  This application of emerging technology shows significant promise to expand the capabilities of robotics in remanufacturing to tackle progressively more complex scenarios with less and less human interaction with greater efficiency.

 

Critical Failure Prediction

In industrial manufacturing settings, there is continuous pressure to improve efficiency, increase productivity, and reduce costs.  IoT connectivity and other elements of AI are being brought to bear in this environment to improve predictive maintenance and avoid machine failure during critical phases of production.  These same benefits of monitoring automated equipment on the front-end of the manufacturing process can also deliver the same benefits to the remanufacturing setting as well.  Not only can unexpected downtime be eliminated, but the ability to plan and schedule preventive maintenance more proactively and efficiently can occur as well.

 

Inventory Forecasting

One of the most significant challenges all remanufacturers face is predicting how much demand there will be for returned products with the flow of returned items coming into the remanufacturing process.  Of course, the quality of the materials being returned can make a significant difference as well.  AI technologies can greatly improve upon existing forecasting models that attempt to predict product returns.  Elements of Big Data and Machine Language Learning can leverage and up-date real-time data on sales, product usage, and warranty activity and more accurately predict product life expectancy and the rate and timing of returns into the remanufacturing process.  

 

Resilient Remanufacturing Networks (ReRuN)

Sustainability is the objective of remanufacturing in a world that has shifted from a linear model where products used to end up in a landfill once they are no longer functioning for their intended use.  As a society, we continue to grow more aware of the finite nature of our natural resources that has led companies to produce products according to a circular model whereas many components of an item are reused as many times as is practical.  

 

As stated in the points above, AI and other emerging technologies are already making significant improvements in all phases of the product life-cycle that occur prior to remanufacturing.  By embracing a ReRuN mindset that is calculated as early as the product concept/design phase, remanufacturing outcomes are positioned for greater outcomes due to improved forecasting in all elements of the remanufacturing process.

 

Closed-Loop Supply Chain Management

There can be no true resiliency for remanufacturing unless a complete closed-loop supply chain management strategy is employed.  In-depth studies on remanufacturing are just now starting to take place and raise awareness of the opportunities to be leveraged during the remanufacturing process to impact economic and environmental sustainability.  The advances in AI and all emerging technologies will help put remanufacturing on equal footing with all other phases of product life cycle.  Because this emphasis on remanufacturing is just starting to expand and receive attention, it also holds the most potential for impacting the entire product lifecycle.

 

The Future is Now

In the news, every day we continue to see advancements in the development of products and processes that seem to be right out of science fiction movies and shows of the 1960’s and 1970’s.  From flying cars to putting a colony of people on Mars, humankind is entering a bold new era where we now have the technology to execute just about anything we can imagine.  This coupled with an increased global awareness of our finite resources and need to be good stewards of our planet, will continue to bring greater emphasis and attention to remanufacturing in all phases of the product cycle.  AI and other emerging technologies are finally catching up and giving industry the tools to create this new reality.

 

Joseph Zulick is a writer and manager at MRO Electric and Supply. MRO Electric and Supply maintains a comprehensive stock of FANUC CNC and FANUC Robotics parts which are used in several industries including but not limited to engineering, manufacturing, packaging, and plant automation.

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Why not to put everything on the Cloud?

The amount of load and data generated on the cloud is also increasing because of increasing applications and systems moving to cloud, making it difficult to perform analytics and extract important insights. To deal with this challenge, enterprises are leveraging edge analytics. Read on to find how edge analytics accelerates cloud analytics.

Mobile devices, wearables, cameras and many other connected devices or better-called “devices on edge” in different organizations and enterprises, generate a huge amount of decentralized data. Moving this data to the cloud to derive various insights and perform further analytics on them seems a very good option, but there is also a huge dependency that adds to the headache if we have everything on the cloud.

Why not to put everything on the cloud

  • Continuous consolidation and synchronizing of data on the cloud can drain resources.
  • Maintaining a consistent connection to the cloud gets difficult.
  • Costs attached to data transfer and data storage on cloud grow significantly over time.
  • Delay-induced due to data transfer and processing on cloud put a restriction in providing near real-time analysis.

 

So can edge analytics replace cloud analytics completely?

Honestly, edge analytics is not here to replace cloud analytics completely, but it is here to complement cloud analytics by driving near real-time analytics as it is close to the data source. Let us see how edge analytics empower cloud analytics.

According to the market research firm IDC, around 45 percent of data will be stored, managed, analyzed and kept right where it was produced, at the edge. So, organizations with 100% total cost of operations on the cloud can leverage edge computing to reduce it to 60%.

  • Cloud operations cost can be reduced by using a distributed edge computing architecture, where edge devices together process a critical operation, which a cloud device cannot process on its own, thereby reducing cloud dependency.
  • A combined edge-to-cloud architecture is critical for any industrial success. For this, experts need to differentiate and define the real-time analytics to be run at various levels, including edge sensor, infrastructure machine, gateway, controller within on premise appliances and racks or in the cloud.
  • We are seeing a tremendous growth in sensor technology. By combining the innovations of sensor technology with the reducing hardware costs, we can establish an edge-to-cloud paradigm. Sensors with processing units can help take critical actions in an inconsistent cloud environment and can later synchronize with the cloud. The required architecture can vary as per industry.

So overall, a well-defined edge-to-cloud architecture as per domain and data would be accelerating cloud computing.

RELATED BLOG  IoT Gateways – Drivers for Fog Computing

How edge analytics work for all industries

Edge analytics benefit organizations where data insights are needed at the edge. Manufacturing, retail, smart cities, energy, utilities, transportation, and logistics segments are leading the way in deploying edge analytics.

Let us look at sectors that can benefit from edge computing & analytics:

1) Retail:

Brick-and-mortar stores are rich with edge devices such as cameras, beacons, sensors, Wi-Fi networks etc. They are looking for competitive advantages that can help them beat eCommerce businesses, and real-time edge analytics can provide them just that. With edge analytics, sales data, images, coupons used, traffic patterns, and videos are created to provide unprecedented insights into consumer behavior. They have perfect infrastructure and devices to explore edge analytics. Moreover, the mobile devices of customers and data generated by store apps, make this number swell more.

Real-time insights are of prime importance since retail stores need to know their customers’ needs immediately when they enter the store to keep them in store. A recommendation or an offer coming after the customer has left store can be of no use. Identifying customers’ behavior data is something that requires heavy processing power on the cloud. Leveraging some processing at the edge like tracking items viewed, picked, and bought can be a good idea. Other than that, metadata can be sent to the cloud lake to get recommendations, offers, etc., keeping the entire process near to real-time. A distributed edge computing architecture can boost this up further.

RELATED BLOG

FAQs on IoT Gateway

2) Manufacturing:

Manufacturing is an industry that requires analytics and computing at the edge. Take an example, an average offshore oil rig has nearly 30,000 sensors. They measure gas emission, pressure, temperature, etc., continuously. Connecting these to cloud lake and deriving analysis will be too costly and time-consuming. A majority of this data is actually not required for analytics; hardly 1-3% of data is used for analysis after cleaning the data. It can bring tremendous advantageous if these edge devices knew what analysis needs to be performed and what data needs to be sent to the cloud, thus saving ample bandwidth. Embedding computing capability in the form of complex event processing (CPE), edge devices can filter out noisy data and collect only information that is deemed useful. In the absence of cloud, the distributed edge computing can process this data for analysis, take critical actions, and can later notify the cloud about the updates.

Another example is of a smart production line. We know that in a production line, each process is time bound. Every action has to be taken in line with production processes. Hence, it becomes important to derive analysis at the edge. Pointing out manufacturing defects or anomalies, badly printed stickers, packaging, etc., in real-time can be achieved using edge analytics.

3) Healthcare:

Healthcare is another domain where we are seeing a huge surge in the number of connected devices. In the near future, a hospital room on an average will have 15 to 20 medical devices, a majority of which will be networked.  A large hospital can have as many as 85,000 connected medical and IoT devices, putting a massive strain on the cloud network. Edge computing and analytics can reduce this burden to a great extent. Here again, real-time analytics will carry more importance than delayed analytics. For example, a clinician’s mobile device is the edge between the patient who is the data source and the cloud. A clinician treating a patient with a tablet will be able to enter patient data into the analytics platform at the edge where it is processed and displayed in near real-time. Patients no longer need to wait for analytics results, which may reduce their number of visits.

In addition, edge computing in healthcare offers another concept called collaborative edge. In a collaborative edge, geographically dispersed data can be fused by creating virtual shared views. This shared data is exposed to the users through some pre-defined interfaces, which edge devices can directly consume.

RELATED BLOG

How to Enable IoT Security from Edge to Cloud?

To sum this up, with edge computing practitioners and patients can get the best response times from the data that is generated and collected by healthcare facilities. As the healthcare sector is using more and more medical devices that are connected to a common network, edge computing is about to become a standard in health IT infrastructure.

 

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Given my Telco background, it was logical that back in 2014, I published some of my first articles in my  IoT Blog about the topic “IoT Connectivity . I described how the optimist predictions of analysts and companies like Cisco or Ericsson, made the Machine to Machine (M2M) an attractive market to invest.

The fact that “Tata Communications have acquired mobility and Internet of Things specialist Teleena is a clear indication of the phenomenal growth rate in the global IoT connectivity market. “By 2021, enterprises’ spending on mobility alone is set to surpass USD 1.7 trillion,” said Anthony Bartolo, Chief Product Officer, Tata Communications.  I hope to see Tata Communications/Teleena in the next Gartner´s Magic Quadrant for M2M Managed Services Worldwide.

There are still people who doubt that connectivity is a key component in the M2M/IoT Value Chain. Please remember without connectivity simply there is not IoT.

Obviously during these years many of my projects have been associated with IoT connectivity. From the analysis of M2M/IoT Service Providers to the conceptual design of end-to-end solutions where connectivity selection was a key component. One of the most interesting projects was the analysis that I made for the Telefonica project "IoT in a box". Without forget projects to compare LPWAN technologies, End to End Security, Identification of Uses cases for 5G. Sometimes also I had to sell IoT connectivity.

In the last years in the IoT connectivity market I have seen:

  • Consolidation of the market like “KORE buys Wyless” or “Sierra Wireless, Inc. Completes Acquisition of Numerex Corp.
  • The appearance of companies like 1NCE, the first dedicated Tier 1, Narrowband IoT MVNO providing fast, secure and reliable network connectivity for low data B2B applications offering a set of optimized product features – such as an IoT flat rate and the first of its kind 'BUY ONCE'​ lifetime fee
  • The still not bloody battle between LPWAN operators (SigFox, LoRA network operators, NB-IOT, LTE-M)
  • Telco Vendors, Operators and Analysts talking about the promise of 5G
  • New Wifi and Lifi IoT use cases
  • IoT Security breaches
  • Operators focus on key industries and use cases
  • The partnership M2M/IOT Service Providers ecosystem evolution
  • Agreements among M2M, MNO and Satellite operators.
  • The lack of standards in the Smart Home connectivity
  • The expectation for solve the real time connectivity challenges in Industry 4.0 and Edge Computing –
  • Time Sensitive Networking Industry 4.0 use cases and test bed by IIC members
  • ….

But in my opinion, enterprises still are confused and delaying their decisions to adopt IoT / IIOT because they need good advice about the right IoT connectivity not just the cheapest prices but easy integration or better customer support.

I want to remember again that I can help you in the selection of the right M2M /IoT Service Provider for your enterprise business requirements as a strategic decision.

IoT Connectivity - the ugly Duckling of IoT Network Operators

Telecoms operators’ more focused approach to bolstering their IoT businesses appears rooted in refining the technology inherent in their connectivity networks. And no wonder, The powerful GSMA has been helping Mobile Operators to define their role in IoT. At first sight, the best way for large telecoms operators generate value from the IoT might appear to be by providing connectivity via their networks. Additionally, they could leverage their vast experience in customer engagement, customer premise equipment (CPE) support and their robust, proven back-office systems by offering their OSS and BSS platforms externally to IoT users, using their OSS to provide users with a turnkey platform to manage their equipment proactively in real time, and their BSS to support the related billing requirements. In fact Global telcos set sights on IoT for growth in 2018.

Nevertheless, Analysys Mason, highlighted “Telcos have been working with the broader ecosystem, including developers, cloud players and hardware vendors this past year – all of which “should set the market up for an active 2018”.

Although many people think that IoT connectivity  is or will become a commodity with little value for customers and along with the hardware will form the ugly ducklings of the value chain, IoT Network Operators should strive to demonstrate that IoT connectivity is vital for the global adoption of the IoT and seek to increase the income derived from its connectivity services with aspect like security and the contextual data value that their networks transport.

IoT Data is the new Oil also for IoT Network Operators

If connectivity seems doomed to play the role of ugly duckling, the data on the other hand see how its value increases and increases with each new technology.

How many times have we seen a presentation with the title "Data is the new Oil”? Even taught by me

Many Telcos are in process of Digital Transformation. The want to compete with the Google, Apple, Facebook, and Amazon (GAFA) and avoid same situation lived with these Over the Top (OTT) vendors.  IoT is giving them an opportunity to monetize the IoT data and convert their networks in pipelines of value.

IoT data is a new source of revenue without forget that will also produce incremental profit through operational productivity and efficiency.

The new stream of data coming from the physical world and the billions connected things are mostly transported by the IoT Network Operator´s networks and once these data is captured, the IoT Network Operators can monitor everything and feed their AI systems. Is then, when finally, IoT Network Operators can make a lot of money of IoT contextual data and aggregated data.

Can you imagine the opportunities leveraged by the connection of millions of devices and intelligent things over your IoT network? A vast amount of useful data generated by smart containers, smart home appliances, smart cities, connected cars, smart healthcare devices, or wearables, which for many businesses is an extremely valuable commercial tool. IoT Network Operators possess the capability of performing real-time data analytics on readily available data to determine product performance, improve customer experience and forecast network capacity, all of all which IoT-ready businesses could benefit from.

Key Takeaway

IoT connectivity is still at the core of all IoT Network Operators / M2M Service Providers. But some of them are implementing different strategies to capture more business of the IoT value chain. The idea of IoT connectivity will become a commodity with not added value is influencing the decision to invest in new IoT enabled networks (5G, LTE-M, NB-IoT).

It’s clear that there are some strong opportunities for IoT Network Operators / M2M Service Providers looking to capture the full potential of IoT, and it’s time that they open up their services to support companies from all sectors who are looking to employ IoT connectivity but also machine data intelligence as part of their business models in this IoT driven digital transformation.

Telcos offering IoT connectivity should look to monetise data and offer businesses unique insights that could potentially open doors to new revenue streams or even improve operational efficiencies. 

If IoT business is about data and assets, Telcos need to shift from technology and connectivity to business value and creation of valued services.

 

Thanks in advance for your Likes and your Shares.

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One of the more significant elements of AI and IoT is the rise in automation technology, with the use of robots in particular having risen substantially between 2016 and 2017. According to Statista, the worldwide shipment of robots has risen from 294,000 to 387,000 - a huge number in a relatively small period of time.

This rise in industrial robotics has led to an increased interest in the use of robots in our everyday lives as well. While this technology is being designed to make our lives easier, how do people feel about the rise of robotics?

The capability of robots in the near future

From a long-term perspective, as technology develops there is a very high ceiling on the tasks that robots can realistically perform. At present, however, robotics are mostly used in an industrial setting, particularly for manufacturing.

As this technology continues to develop, in the near future, many experts believe that robotics will be more capable at assisting with everyday tasks domestically and at work - particularly household chores and other manual tasks.

This increased capability of robots has led to a dividing of opinion amongst the public, so how do people feel about robots, and what tasks are people most comfortable with a robot performing?

What the public thinks of robots around the home

In a study of people’s opinions towards the increase of robotics, there was a mixed attitude towards this technological development. While 34% of those surveyed believed that robots would make everyday life better, 32% disagreed, believing that robots would instead disrupt their lives.

A considerable element of this opinion seemed to be rooted in a fear of job loss, with 22% of those surveyed suggesting this concern. There was also a far greater percentage of those who claimed they would trust a human over a robot (54%) than vice versa (14%), suggesting there is a generalised mistrust of robotics amongst the general public.

The study also shows that the women surveyed were more concerned about robots than men, with 34% of female respondents worried about the influence of robots, compared to 29% of men. Additionally, only 32% of women believed that robots would benefit everyday life, compared to a far greater 46% of men.


There was also a higher level of acceptance and trust towards robotics amongst the younger respondents in the survey, compared to the older interviewees, who displayed a bigger fear of robots than their younger contemporaries.

Perhaps because of this divide in opinion, there were very few tasks that a large number of respondents claimed they’d be happy to let a robot perform. Only household chores received a large percentage in favour, with 40% of respondents saying they would be happy to use robots around the house.

These numbers suggest that while researchers and business innovators are confident that robots will have a big part to play in the future, the general public is yet to be convinced of their role in our everyday lives.

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In 2016, the Industrial Internet Consortium gained agreement upon an understanding of the term “trustworthiness” and its effect on design and operation of an industrial system. At the core of that understanding was a definition of trustworthiness and the designation of five characteristics that define trustworthiness.

As defined by the IIC in its recently released Industrial Internet of Things Vocabulary v2.1 document: “Trustworthiness is the degree of confidence one has that the system performs as expected. Characteristics include safety, security, privacy, reliability and resilience in the face of environmental disturbances, human errors, system faults and attacks.”

Let’s take a deeper look at the 5 foundational characteristics at the core of trustworthiness:

  • Safety ensures that a system operates without causing unacceptable risk of physical injury or damage to the health of people. This protection of humans is focused either directly or indirectly, as the result of damage to property or to the environment.
  • Security protects a system from unintended or unauthorized access, change or destruction while Information Technology (IT) security ensures availability, integrity and confidentiality (AIC model) of data at rest, in motion or in use.
  • Reliability describes the ability of a system or component to perform its required functions under stated conditions for a specified period of time.
  • Resilience describes the ability of a system or component to prevent or at least reduce any serious impact of a disruption while maintaining an acceptable level of service.
  • Privacy protects the right of individuals to control or influence what information related to them may be collected and stored and by whom and to whom that information may be disclosed.

Achieving trustworthiness in industrial IoT systems requires recognition that a complex IoT system is comprised of subsystems and the integral components of the subsystems. The trustworthiness of the overall system depends upon the trustworthiness of each of the subsystems and each of the components, how they are integrated, and how they interact with each other. Trustworthiness must be pervasive in IoT systems, which means there must be trustworthiness by design and a means to achieve assurance that the trustworthiness aspects have been addressed properly. Permeation of trust is the flow of trust within a system from its overall usage down to its smallest components and requires trustworthiness of all aspects of the system. Trustworthiness requires ongoing effort over time as systems and circumstances change.

As such, the IIC Trustworthiness Task Group, in close cooperation with the IIC Security Working Group, is tasked to frequently enhance and redefine the definition and role of trustworthiness in industrial systems as the IIoT continues to evolve. Ultimately, their goal is to moves system designers from traditional safety thought processes into a new paradigm for system design that takes into consideration all 5 of the trustworthiness characteristics and their interactions within the system.  

You can read more about trustworthiness and its relationship with industrial systems and the convergence of IT/OT in the Fall 2018 issue of ICC’s Journal of Innovation.

By Marcellus Buchheit, Co-founder of Wibu-Systems AG and President and CEO of Wibu-Systems USA

This blog originally appeared as a Wibu-Systems Blog

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Implementing Smart City leveraging IoT and connected technology helps promote economic development, improve infrastructure and environment, enhance transportation systems and optimize costs of managing public assets.

To cope with increasing population, hyper-urbanization, globalization as well as to ensure economic and environmental stability, cities are now focusing on becoming smart cities. The smart city is a concept of utilizing technologies and connected data sensors to enhance and become powerful in terms of infrastructure and city operations. This includes monitoring and managing of public assets, transportation systems, citizens, power plants, water supplies, information systems, civil bodies, and other community services.

Connected technologies and IoT solutions for smart cities play important roles in transforming cities into smart cities. Implementing smart city with IoT and connected technology helps enhance the quality, performance, and interactivity of urban services, optimize resources and reduce costs.

Let’s see the various components of smart city and their impact in the IoT era:

  1. Smart Infrastructure

The global market for smart urban infrastructure in smart cities, include advanced connected streets, smart parking, smart lighting, and other transportation innovations. Here’s how they work:

  • Smart Lighting: With smart lighting, city authorities can keep real-time tracking of lighting to ensure optimized illumination and deliver demand-based lighting in different zones. Smart lighting also helps in daylight harvesting and save energy by dimming out sectors with no occupancies For e.g. parking lots can be dimmed during work hours and when a car is entering, it will be detected and appropriate sectors can be illuminated, while others can be kept at diffused setting.
  • Connected Streets: Connected and smart streets are capable of acquiring data and delivering information and services to and from millions of devices, which includes information about traffic, road blockages, roadworks, etc. This helps in efficient management of resources and people to enhance public transportation and the urban landscape.
  • Smart Parking Management: Smart parking management system can be used to find the vacant location for a vehicle at different public places. Smart Parking’s In-Ground Vehicle Detection Sensors are core technologies, playing a key part in the Smart Parking solution that is revolutionizing how drivers in the malls and city centers can find an available parking space. Wireless sensors are embedded into parking spaces, transmitting data on the timing and duration of the space used via local signal processors into a central parking management application. Smart Parking reduces congestion, decreases vehicle emissions, lowers enforcement costs and cuts driver stress. For effective deployment of smart parking technologies, each device needs to have a reliable connectivity with the cloud servers.
  • Connected Charging Stations: Smart infrastructure also includes implementing charging stations in parking systems, city fleets, shopping malls and buildings, airports, and bus stations across the city. Electronic vehicle (EV) charging platforms can be integrated with IoT to streamline the operations of EV charging and addresses the impact of the power grid.
  1. Smart Buildings & Properties

Smart buildings utilize different systems to ensure safety and security of buildings, maintenance of assets and overall health of the surrounding.

  • Safety & Security Systems: These include implementing remote monitoring, biometrics, IP surveillance cameras, and wireless alarms to reduce unauthorized access to buildings and chances of thefts. It also includes utilizing Perimeter Access Control to stop access to restricted areas of the property and detect people in non-authorized areas.
  • Smart Garden & Sprinkler System: Smart sprinkler system synced with connected technologies and cloud can be used to water plants with the assurance that plants get the right amount of water. Smart garden devices can also perform tasks such as measuring soil moisture and levels of fertilizer, helping the city authorities to save on water bill (smart sprinkler devices use weather reports and automatically adjust their schedule to stay off when it rains), and keep the grass from overgrowing in the convenient way (robot lawnmowers).
  • Smart Heating & Ventilation: Smart heating and ventilation systems monitor various parameters such as temperature, pressure, vibration, humidity of the buildings and properties such as movie theatres, and historical monuments. Wireless sensor network deployment is the key to ensuring appropriate heating and ventilation. These sensors also collect data to optimize the HVAC systems, improving their efficiency and performance in the buildings.
  1. Smart Industrial Environment

Industrial environments present unique opportunities for developing applications associated with the Internet of things and connected technologies which can be utilized in the following areas:

  • Forest Fire Detection: Helps in monitoring of combustion gases and preemptive fire conditions to define alert zones.
  • Air/Noise Pollution: Helps in controlling of CO2 emissions of factories, pollution emitted by cars and toxic gases generated on farms.
  • Snow Level Monitoring: Helps in identifying the real-time condition of ski tracks, allowing security corporations for avalanche prevention.
  • Landslide and Avalanche Avoidance: Helps in monitoring of soil moisture, earth density, as well as vibrations to identify dangerous patterns in land conditions.
  • Earthquake Early Detection: Helps in detecting the chances of tremors by utilizing distributed controls at specific places of tremors.
  • Liquid Presence: Helps in detecting the presence of liquid in data centers, building grounds, and warehouses to prevent breakdowns and corrosion
  • Radiation Levels: Helps in distributed measurement of radiation levels in nuclear power stations surroundings to generate leakage alerts
  • Explosive and Hazardous Gases: Helps in detecting gas levels and leakages in chemical factories, industrial environments, and inside mines
  1. Smart City Services

Smart city services include services for public safety and emergencies.  Below are the key areas where IoT and connected technologies can help:

  • Smart Kiosk: Smart kiosks play an important role in providing different city services to the public such as Wi-Fi services, 24×7 IP surveillance cameras and analytics, Digital signage for advertisement and public announcements. In some cases, free video calling and free mobile charging station, as well as environmental sensor integration can also be implemented. Smart kiosks also provide information about restaurants, retail stores, and events in the immediate area. It can also provide mapping for visitors and can sync with smartphones to give additional data as needed.
  • Monitoring of Risky Areas: Sensors (cameras, street lights) and actuators for real-time monitoring can be implemented in risky areas or areas prone to accidents. Upon detecting any crime, or mishap, these sensors can alert the citizens to avoid such areas temporarily.
  • Public Security: IoT sensors can be installed at public organizations and houses to protect citizens and provide real-time information to fire and police departments when it detects a theft.
  • Fire/Explosion Management: Smart fire sensors can detect and automatically take actions based on the level of severity, such as detecting false alarms, informing firefighters and ambulance, blocking off nearby streets/buildings on the requirement, helping people to evacuate, and coordinating rescue drones and robots.
  • Automatic Health-Care Dispatch: Smart healthcare devices can be implemented at public places to provide 24/7 health care for patients like dispensing medicines and drugs to patients. These devices can also be used to call an ambulance to pick up the patients in cases of emergencies.
  1. Smart Energy Management

Here’s how cities can implement smart energy management:

  • Smart Grid: Smart grids are digitally monitored, self-healing energy systems that deliver electricity or gas from generation sources. Smart grid solutions can be across industrial, residential as well as in transmission and distribution projects. Various IoT solutions like gateways can be used to achieve energy conservation at both the transmission level and consumer level. For e.g., gateways can provide a broader view of energy distribution patterns to utility companies with high connectivity and real-time analytics. Also, it develops a Demand-Response mechanism for the utility providers to optimize energy distribution based on the consumption patterns.
  • Smart Meters: Smart meters can be used in residential and industrial metering sectors for electricity and gas meters where there is a need to identify the real-time information on energy usage. Consumers and utilities with smart meters can monitor their energy consumption. Moreover, energy analytics, reports, and public dashboards can be also accessed over the internet using mobile applications integrated with these smart meters.
  1. Smart Water Management

IoT and connected devices enable smart water management in the following ways:

  • Potable Water Monitoring: Monitors the quality of tap water in the cities.
  • Chemical Leakage: Identifies leakages and wastes of factories in rivers.
  • Swimming Pool Remote Measurement: Controls the swimming pool conditions remotely.
  • Pollution Levels in the Sea: Controls the occurrence of leakages and wastes in the sea.
  • Water Outflows: Detects of liquid presence outside tanks and pressure variations along pipes.
  • River Floods: Monitors water level variations in rivers, dams, and reservoirs.
  1. Smart Waste Management

Smart solutions for tracking wastes help municipalities and waste service managers the ability to optimize wastes, reduce operational costs, and better address the environmental issues associated with an inefficient waste collection.

Implementation of a smart city comes with enormous opportunities to transform the lives of people and improve the overall city infrastructure and operations. Smart sensor networks, Internet of Things (IoT) and connected technologies are the key solutions for smart city implementation.

 

 Photo by Arturo Castaneyra on Unsplash

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