Article 1

The Internet is failing society: New principles for the design of the Internet of things

David Langley and Frank Berkers

The opinions expressed in this article are solely those of the authors.

Make no mistake about it: the Internet, with its websites, social media and mobile apps, will enter the history books as a societal catastrophe. Historians will write about how hard-won civil rights, such as privacy, were suddenly abandoned without a fight. They will write about how digital apparatus had an increasingly damaging effect on the environment at the beginning of the 21st century, and about how the Internet’s primary use to stimulate hyper consumerism further increased the damage.

 


Article 2

Three Major Challenges Facing IoT

Ahmed Banafa

The Internet of Things (IoT) — a universe of connected things providing key physical data and further processing of that data in the cloud to deliver business insights— presents a huge opportunity for many players in all businesses and industries . Many companies are organizing themselves to focus on IoT and the connectivity of their future products and services. For the IoT industry to thrive there are three categories of challenges to overcome and this is true for any new trend in technology not only IoT: technology, business and society [1, 2, 3].

 


Article 3

IoT-Control of Dynamic Systems Using Cloud-Fog Machine Learning

Mehdi Roopaei

The concept of cloud is to centralize computing, storage and network management, due to the massive resources available. It provides elastic computation power and storage to support request of end user devices for resources. However, recently, a new movement in computation is proceeding with the task of clouds being gradually tending towards the network edges.

 


Article 4

EWSN Dependability Competition: Experiences and Lessons Learned

Carlo Alberto Boano, Markus Schuß and Kay Römer

Low-power wireless sensor networks are an important underlying infrastructure for the Internet of Things (IoT) and are increasingly used in application domains imposing strict dependability requirements on network performance, such as smart production, smart cities, or connected cars. These application domains are safety-critical: any failure in meeting application-specific requirements and in conveying information in a dependable (i.e., reliable, timely, and energy-efficient) manner may result in high costs and physical damage to people or things.

 


Article 5

WF-IoT News

The IEEE IoT Activities Board is seeking proposals for selection of the 5th and 6th IEEE WF-IoT Conferences (the IEEE World Forum on the Internet of Things) for 2019 and 2020. The deadline for consideration is July 1st, 2017. The WF-IoT is the premier IEEE conference on the Internet of Things and brings together a broad based program from Academia, Industry and Government. The 4th IEEE WF-IoT will be held Feb 5th-9th, 2018, in Singapore. We expect that the 5th WF-IoT in 2019 will be held in Europe and the 6th WF-IoT will be held in the Americas, both in the February-March time frames. To send inquiries and proposals, please use the contact form on our website.

 

 

This Month's Contributors

David J. Langley works at TNO, the largest applied research institute in the Netherlands, as senior scientist in the field of internet, innovation and strategy.
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Frank Berkers MSc also at works TNO Strategic Business Analysis dept. in The Hague as is senior scientist in the field of business modelling and strategy.
Read More >>

Ahmed Banafa has extensive experience in research, operations and management, with a focus on the IoT area.
Read More >>

Mehdi Roopaei is Research Scientist of Open Cloud Institute at the University of Texas, San Antonio (USA).
Read More >>

Carlo Alberto Boano (IEEE member since 2009) is an assistant professor at the Institute for Technical Informatics of Graz University of Technology, Austria.
Read More >>

Markus Schuß is a PhD student at the Institute for Technical Informatics of Graz University of Technology, Austria.
Read More >>

Kay Römer is professor at and director of the Institute for Technical Informatics at Graz University of Technology, Austria.
Read More >>

 

Contributions Welcomed
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Raffaele Giaffreda, Editor-in-Chief
raffaele.giaffreda@create-net.org

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About the IoT eNewsletter

The IEEE Internet of Things (IoT) eNewsletter is a bi-monthly online publication that features practical and timely technical information and forward-looking commentary on IoT developments and deployments around the world. Designed to bring clarity to global IoT-related activities and developments and foster greater understanding and collaboration between diverse stakeholders, the IEEE IoT eNewsletter provides a broad view by bringing together diverse experts, thought leaders, and decision-makers to exchange information and discuss IoT-related issues.

The Internet is failing society: New principles for the design of the Internet of things

David Langley and Frank Berkers
March 14, 2017

 

The opinions expressed in this article are solely those of the authors.

Make no mistake about it: the Internet, with its websites, social media and mobile apps, will enter the history books as a societal catastrophe. Historians will write about how hard-won civil rights, such as privacy, were suddenly abandoned without a fight. They will write about how digital apparatus had an increasingly damaging effect on the environment at the beginning of the 21st century, and about how the Internet’s primary use to stimulate hyper consumerism further increased the damage.

Yes, we enjoy all the fun features of Google, Facebook and WhatsApp. Tumblr is adorable! These free applications are simply the diversionary trinkets of the digital colonists. The real progress is in their innovations for ultra-targeted advertising: the impressive surveillance systems for making money out of our private data. Take the time to install Ghostery in your browser to see how much you are being watched without your knowledge or consent. Another disastrous effect of the social Internet is the ever-increasing impact on the environment. All those computers, chargers and data centers produce as much greenhouse gas as the entire airline industry. And our data usage is growing exponentially. Added to this, the personalized advertisements stimulate high-waste consumerism more than ever.
But most countries and regions do not even benefit from this digital revolution since the real value – both in terms of finance and knowledge – mainly flows to a few high-tech giants in the US. Together normal hard-working organizations pay billions to Amazon, Google and other dominant platforms, just to be able to go about their business. As we said, for most of society the Internet is a disaster.
And now we are at the beginning of a new digital revolution. Following the social Internet comes the Internet of things, whereby doors, street lights, cows and just about anything is being connected to the Internet via new wireless networks. This is creating an explosion of new products and new revenue models. But, unless the way we develop and implement our technology changes drastically, we will fall into the same trap as before. And the trouble has already started: your smart TV has a microphone that can eavesdrop on your conversations in your own living room!
The whole problem arose because most IT developers are preoccupied with what is technically possible. And not what we want as a society. With the Internet of things an awful lot is possible – much of which we will not want.It is time that we develop digital technology for the benefit of normal citizens and entrepreneurs. We need to break away from the current dominant model that enables a few huge companies to freely do what they want with our data and our environment. I advocate a new design for the Internet of things, based on two social principles. 

Basic Social Principle 1: the business model and its underlying technology follow human patterns of behavior, and not vice versa. This means that we – citizens and entrepreneurs – are able to determine which data we want to share with which specific stakeholders for which specific reasons. Do you want your Internet-enabled mattress to pass data about your private bedroom behavior to Google, Apple and Cisco? Without the new direction that we advocate, this nightmare scenario will quickly come to pass. The Internet of things must be designed differently. For example, we need new platform architecture and protocols which will prevent firms from freely accessing our data. Which will put people in control. Which will allow them to determine whether and with what firm they want to share which parts of their data, and what specific benefit that will give them. Such a real alternative to the current model can compete in the marketplace: ordinary people will opt for services based on this new model when they know exactly what they reveal and what they get in return.

Case description: precision farming in the Netherlands
How can firms own and control the data they generate? A step in this direction is illustrated by a Dutch Precision Farming project. In order to prepare for the emerging flux of real-time sensor data flows from various IoT applications in and around the farm, TNO and project partners designed and set up a data sharing platform (information broker) to stimulate the creation of new data driven innovations for precision farming.
But the farmers clearly voiced their concerns about the way their data would be managed: “We don’t want our data roaming about, and eventually be sold back to us!” So, instead of taking the approach of acquiring that data via the equipment providers, the initiative searched for an alternative solution and, together with TNO and other parties, developed a scalable data sharing platform that does not hold data itself, but acts as a registry. The data stays where it is. It is only passed on with explicit consent. Furthermore, for companies participating in this ecosystem, the initiative applies the principle that farmers cannot be charged twice for the same data. This bypasses the discussion on ownership of data.
Although there are still many hurdles to overcome, this example shows that ethical principles can be accommodated – by new technology and by changing organizational conduct.

Basic Social Principle 2: the business model and its underlying technology enhance environmental sustainability, rather than damage it. This means that the products that come online need to make use of the connectivity to optimize the efficient use of energy and raw materials. The social web has had limited success in this area: Via eBay old products are continually given a new lease of life. But overall, our use of fossil fuels and other raw materials is increasing all the time. According to a recent study in the leading journal Science, one in six species of animals and plants are at threat – through our actions – of being lost forever. A better design of the Internet of things can contribute to the solution by enabling producers and consumers to make optimum use of raw materials and energy: from the design and use of products, to the disposal and recycling of materials. We can achieve a circular economy, and we can market it as a commercially viable alternative.
It is possible for us to develop a wholly new Internet of things. One that forms the basis of a healthy digital society and, at the same time, one that is economically attractive for us all.


David J. Langley
 works at TNO, the largest applied research institute in the Netherlands, as senior scientist in the field of internet, innovation and strategy. He received his PhD in 2009 and is also Associate Research Fellow at the department of Innovation Management and Strategy at the University of Groningen. Since 2015 he is columnist for the Dutch Financial Times writing about the societal implications of internet technologies. He is acting chair of the European Alliance for IoT Innovation (AIOTI), Ecosystems working group. Working in the area of internet innovations since 1991, he has set up and led research projects in a wide variety of industries, including telecoms, energy, banking and the public sector. He is the inventor of an award-winning method to predict the adoption of innovative products and services at an early stage of their development. His scientific research has been published in leading journals, including Technological Forecasting and Social Change, Journal of Interactive Marketing and Journal of Product Innovation Management.

 


Frank Berkers MSc
 also at works TNO Strategic Business Analysis dept. in The Hague as is senior scientist in the field of business modelling and strategy. In context of innovation Frank analyses and helps to set up business ecosystems in several (cross-) domains (e.g. Cooperative Mobility, Agrofood, Smart Cities, Smart Industry and Internet of Things) as well as strategic consultancy projects with both business and government actors – in total over 450 project initiatives. Frank holds a master’s degree in Econometrics from Maastricht University and currently pursues a PhD at Eindhoven University of Technology in Information Systems. Before joining TNO, he has held positions at ABN AMRO as consumer intelligence analyst and at marketing research agency SKIM where he advised and set up preferred supplierships with McKinsey and Monitor on the field of computer aided choice experiments.

 

 

Three Major Challenges Facing IoT

Ahmed Banafa
March 14, 2017

 

The Internet of Things (IoT) — a universe of connected things providing key physical data and further processing of that data in the cloud to deliver business insights— presents a huge opportunity for many players in all businesses and industries . Many companies are organizing themselves to focus on IoT and the connectivity of their future products and services. For the IoT industry to thrive there are three categories of challenges to overcome and this is true for any new trend in technology not only IoT: technology, business and society [1, 2, 3].

 Technology

This part is covering all technologies needed to make IoT systems function smoothly as a standalone solution or part of existing systems and that’s not an easy mission, there are many technological challenges, including Security, Connectivity, Compatibility & Longevity, Standards and Intelligent Analysis & Actions [4].

Figure 1: Technological Challenges

 

Security: IoT has already turned into a serious security concern that has drawn the attention of prominent tech firms and government agencies across the world. The hacking of baby monitors, smart fridges, thermostats, drug infusion pumps, cameras and even the radio in your car are signifying a security nightmare being caused by the future of IoT. So many new nodes being added to networks and the internet will provide malicious actors with innumerable attack vectors and possibilities to carry out their evil deeds, especially since a considerable number of them suffer from security holes.

The more important shift in security will come from the fact that IoT will become more ingrained in our lives. Concerns will no longer be limited to the protection of sensitive information and assets. Our very lives and health can become the target of IoT hack attacks [1].

There are many reasons behind the state of insecurity in IoT. Some of it has to do with the industry being in its “gold rush” state, where every vendor is hastily seeking to dish out the next innovative connected gadget before competitors do. Under such circumstances, functionality becomes the main focus and security takes a back seat.

Connectivity: Connecting so many devices will be one of the biggest challenges of the future of IoT, and it will defy the very structure of current communication models and the underlying technologies [2]. At present we rely on the centralized, server/client paradigm to authenticate, authorize and connect different nodes in a network.

This model is sufficient for current IoT ecosystems, where tens, hundreds or even thousands of devices are involved. But when networks grow to join billions and hundreds of billions of devices, centralized systems will turn into a bottleneck. Such systems will require huge investments and spending in maintaining cloud servers that can handle such large amounts of information exchange, and entire systems can go down if the server becomes unavailable.

The future of IoT will very much have to depend on decentralizing IoT networks. Part of it can become possible by moving some of the tasks to the edge, such as using fog computing models where smart devices such as IoT hubs take charge of mission-critical operations and cloud servers take on data gathering and analytical responsibilities [5].

Other solutions involve the use of peer-to-peer communications, where devices identify and authenticate each other directly and exchange information without the involvement of a broker. Networks will be created in meshes with no single point of failure. This model will have its own set of challenges, especially from a security perspective, but these challenges can be met with some of the emerging IoT technologies such as Blockchain [6].

Compatibility and Longevity: IoT is growing in many different directions, with many different technologies competing to become the standard. This will cause difficulties and require the deployment of extra hardware and software when connecting devices.

Other compatibility issues stem from non-unified cloud services, lack of standardized M2M protocols and diversities in firmware and operating systems among IoT devices.

Some of these technologies will eventually become obsolete in the next few years, effectively rendering the devices implementing them useless. This is especially important, since in contrast to generic computing devices which have a lifespan of a few years, IoT appliances (such as smart fridges or TVs) tend to remain in service for much longer, and should be able to function even if their manufacturer goes out of service.

Standards: Technology standards which include network protocols, communication protocols, and data-aggregation standards, are the sum of all activities of handling, processing and storing the data collected from the sensors [3]. This aggregation increases the value of data by increasing, the scale, scope, and frequency of data available for analysis.

 Challenges facing the adoptions of standards within IoT

  • Standard for handling unstructured data: Structured data are stored in relational databases and queried through SQL for example. Unstructured data are stored in different types of NoSQL databases without a standard querying approach.
  • Technical skills to leverage newer aggregation tools: Companies that are keen on leveraging big-data tools often face a shortage of talent to plan, execute, and maintain systems.

Intelligent Analysis & Actions: The last stage in IoT implementation is extracting insights from data for analysis, where analysis is driven by cognitive technologies and the accompanying models that facilitate the use of cognitive technologies.

 Factors driving adoption intelligent analytics within the IoT

  • Artificial intelligence models can be improved with large data sets that are more readily available than ever before, thanks to the lower storage
  • Growth in crowdsourcing and open- source analytics software: Cloud-based crowdsourcing services are leading to new algorithms and improvements in existing ones at an unprecedented rate.
  • Real-time data processing and analysis: Analytics tools such as complex event processing (CEP) enable processing and analysis of data on a real-time or a near real-time basis, driving timely decision making and action

Challenges facing the adoptions of intelligent analytics within IoT

  • Inaccurate analysis due to flaws in the data and/or model: A lack of data or presence of outliers may lead to false positives or false negatives, thus exposing various algorithmic limitations
  • Legacy systems’ ability to analyze unstructured data: Legacy systems are well suited to handle structured data; unfortunately, most IoT/business interactions generate unstructured data
  • Legacy systems’ ability to manage real- time data: Traditional analytics software generally works on batch-oriented processing, wherein all the data are loaded in a batch and then analyzed

The second phase of this stage is intelligent actions which can be expressed as M2M and M2H interfaces for example with all the advancement in UI and UX technologies.

Factors driving adoption of intelligent actions within the IoT

  • Lower machine prices
  • Improved machine functionality
  • Machines “influencing” human actions through behavioral-science rationale
  • Deep Learning tools

Challenges facing the adoption of intelligent actions within IoT

  • Machines’ actions in unpredictable situations
  • Information security and privacy
  • Machine interoperability
  • Mean-reverting human behaviors
  • Slow adoption of new technologies

Business

The bottom line is a big motivation for starting, investing in, and operating any business, without a sound and solid business model for IoT we will have another bubble , this model must satisfy all the requirements for all kinds of e-commerce; vertical markets, horizontal markets, and consumer markets. But this category is always a victim of regulatory and legal scrutiny.

End-to-end solution providers operating in vertical industries and delivering services using cloud analytics will be the most successful at monetizing a large portion of the value in IoT. While many IoT applications may attract modest revenue, some can attract more. For little burden on the existing communication infrastructure, operators have the potential to open up a significant source of new revenue using IoT technologies.

IoT can be divided into 3 categories, based on usage and clients base:

  • Consumer IoT includes the connected devices such as smart cars, phones, watches, laptops, connected appliances, and entertainment systems.
  • Commercial IoT includes things like inventory controls, device trackers, and connected medical devices.
  • Industrial IoT covers such things as connected electric meters, waste water systems, flow gauges, pipeline monitors, manufacturing robots, and other types of connected industrial devices and systems.
Figure 2: Categories of IoT

Clearly, it is important to understand the value chain and business model for the IoT applications for each category of IoT.

Society
Understanding IoT from the customers and regulators prospective is not an easy task for the following reasons:

  • Customer demands and requirements change constantly.
  • New uses for devices—as well as new devices—sprout and grows at breakneck speeds.
  • Inventing and reintegrating must-have features and capabilities are expensive and take time and resources.
  • The uses for Internet of Things technology are expanding and changing—often in uncharted waters.
  • Consumer Confidence: Each of these problems could put a dent in consumers' desire to purchase connected products, which would prevent the IoT from fulfilling its true potential.
  • Lack of understanding or education by consumers of best practices for IoT devices security to help in improving privacy, for example change default passwords of IoT devices.

Privacy
The IoT creates unique challenges to privacy, many that go beyond the data privacy issues that currently exist. Much of this stems from integrating devices into our environments without us consciously using them.

This is becoming more prevalent in consumer devices, such as tracking devices for phones and cars as well as smart televisions. In terms of the latter, voice recognition or vision features are being integrated that can continuously listen to conversations or watch for activity and selectively transmit that data to a cloud service for processing, which sometimes includes a third party. The collection of this information exposes legal and regulatory challenges facing data protection and privacy law.

In addition, many IoT scenarios involve device deployments and data collection activities with multinational or global scope that cross social and cultural boundaries. What will that mean for the development of a broadly applicable privacy protection model for the IoT?

In order to realize the opportunities of the IoT, strategies will need to be developed to respect individual privacy choices across a broad spectrum of expectations, while still fostering innovation in new technologies and services.

Regulatory Standards
Regulatory standards for data markets are missing especially for data brokers; they are companies that sell data collected from various sources. Even though data appear to be the currency of the IoT, there is a lack of transparency about; who gets access to data and how those data are used to develop products or services and sold to advertisers and third parties. There is a need for clear guidelines on the retention, use, and security of the data including metadata (the data that describe other data).

References

  1. http://www.microwavejournal.com/articles/27690-addressing-the-challenges-facing-iot-adoption
  2. https://blog.apnic.net/2015/10/20/5-challenges-of-the-internet-of-things/
  3. https://www.sitepoint.com/4-major-technical-challenges-facing-iot-developers/
  4. https://www.linkedin.com/pulse/iot-implementation-challenges-ahmed-banafa?trk=mp-author-card
  5. https://www.linkedin.com/pulse/why-iot-needs-fog-computing-ahmed-banafa?trk=mp-author-card
  6. http://iot.ieee.org/newsletter/january-2017/iot-and-blockchain-convergence-benefits-and-challenges.html


Ahmed Banafa
has extensive experience in research, operations and management, with a focus on the IoT area. He is a reviewer and a technical contributor for the publication of several technical books. He served as a faculty member at several well-known universities and colleges, including the University of California, Berkeley; California State University-East Bay; San Jose State University; and University of Massachusetts. He is the recipient of several awards, including Distinguished Tenured Staff Award of 2013, Instructor of the year for 2013, 2014, and Certificate of Honor for Instructor from the City and County of San Francisco. He was named as number one tech voice to follow by LinkedIn in 2016.

https://www.linkedin.com/in/ahmedbanafa
@BanafaAhmed

 

IoT-Control of Dynamic Systems Using Cloud-Fog Machine Learning

Mehdi Roopaei
March 14, 2017

 

The concept of cloud is to centralize computing, storage and network management, due to the massive resources available. It provides elastic computation power and storage to support request of end user devices for resources. However, recently, a new movement in computation is proceeding with the task of clouds being gradually tending towards the network edges.

Fog networking is a promising approach to cope with the ever-increasing computational demands requested from ever-growing internet-connected devices. Fog computing attempts to provide the cloud capabilities as close as possible to the things that make and perform on the information created in the field. In another word, all the required processing does not need to send back-end to the clouds and portion of the workloads will handle on the fog as near-edge nodes between front-edge devices in the field and the back-end cloud servers.

Dynamic systems as edge devices can be controlled by the computing power and storage space distributed at the network edges. However, limitation in computational recourses at edge servers caused to cloud controllers still being involved and support the extensive dynamic systems control data analysis. Latency is the critical issue for communication of controller at edge and cloud servers. Therefore, control of dynamic systems at edge needs to design edge-controller to pursue fusion strategy from both disciplines of computing network and wireless communications, as shown in Figure 1.


Figure 1: IoT-Control at Edge and Cloud

 

In dynamic systems, information from Internet of Things (IoT) across multiple smart sensors is collected for decision-making and control. Control at cloud has inherent challenge in real-time situation due to latency caused by congestion. Latency is one of the important problem for stability in control. Moreover, internet suddenly makes large latency and delays with stable remote control of the dynamic systems. Edge control servers perform data processing from the dynamic systems in extremely low latency in mobile fog computing, however computational nodes at edge are limited in compare with cloud control servers and they should have accurate interaction in explicit time. The controller at edge can provide the stability of the dynamic system against the network fluctuations and the internal control signals automatically switch between edge and cloud to prevent instability. The cloud controller supports extensive range of dynamic system control data and using large number of computational resources. The IoT-control systems need an appropriated network architecture to handle end-to-end delay in autonomous systems at edge by providing access network structure and internet for fast communication of various devices from edge to cloud servers.

Benefits of IoT-Control
IoT-Control provides several benefits in handling of dynamic systems when it is collaborating with traditional controllers. Smart IoT devices include of sensors and infrastructures could determine local information aside of dynamic systems and accordingly predict the behavior of objects and situation. There are many external disturbances appear in trajectory of a dynamic systems that affect the performance of the traditional controller designed for this situations. Robust and adaptive control strategies have solutions for this issue however, due to complexity in behavior of disturbances and uncertainties the performance of a dynamic system might be failed even in the presence of accurate robust and adaptive controllers. Therefore, edge-controllers supported with advanced data analytics algorithms and cloud-fog computational environment could help dynamic systems to understand their own local information and provide appropriated control command to get rid of disturbances, as shown in Figure 2.

 

Figure 2: IoT-Control Cycle

 

Feedback control strategy provides the information of update situation of dynamic systems. This control methodology will understand how establish a command signal based on information assigned for desired trajectories. In most situation behavior of the disturbances is so fast and the feedback signals released from states of dynamic system could not understand and recognize their behavior. Therefore, the dynamic system responses in wrong direction and inaccurate time. Cloud-Fog machine learning understand the object and situation around dynamic systems using deep feature extraction of local information. Multi source of information, analysis, match and fuse at cloud and fog servers and the results send to the controller. The IoT-controller attempts to combine the traditional signals with the resulted from cloud-fog machine learning. The introduced IoT-Controller has the ability to predict the environment and aware the dynamic system to send compatible control signal to tackle fast and complicated behavioral of external disturbances.

The IoT-Control structure depicted in Figure 3 shows different layers that should be designed to control of dynamic systems at edge. The feature extraction layer consists of an advanced machine learning platform with huge number of trained models on different kind of signals to discover the hidden features existed in the states of the system and those captured form IoT devices.

 


Figure 3: IoT-Control: structural layers

 

IoT-Control Application
Autonomous cars are complex dynamic systems which needs IoT-Control. They should have smart environment using vehicle-to-vehicle and vehicle-to-infrastructure communication. In this process, local information of under-control vehicle is collected and sent to machine learning cloud-fog to extract deep features of the environment. Then, IoT-Control makes decision based on the traditional control signals and the analyzed information received from cloud fog machine learning, as shown in Figure 4.

 

Figure 4: IoT-Control of Autonomous Cars

 

IoT-Control could help dynamic systems to analyze local information collected from their around smart environment. Cloud-fog Machine learning will predict and recognize the behavior, situation and objects in real time and make the deep extracted information available for IoT-Controller to make accurate decision. IoT-Controller has potential to automatically reconfigure own structure from one control methodology to another one based on the situation and objects understands and recognized form cloud fog machine learning. Finally, with the accurate information provided by real time data analytics algorithms, control of dynamic systems has a chance to predict their environments not only from their state variables but also from multiple smart sensors, infrastructures with real time processing in cloud fog machine learning 


Mehdi Roopaei
is Research Scientist of Open Cloud Institute at the University of Texas, San Antonio (USA). His research interests include intelligent control systems, artificial intelligence, image processing, vision detection/recognition systems, and deep learning. Roopaei holds a PhD in Artificial Intelligence from Shiraz University, Iran. He is senior member of IEEE, AIAA and ISA. Contact him at mehdi.roopaei@utsa.edu.

 

 

 

EWSN Dependability Competition: Experiences and Lessons Learned

Carlo Alberto Boano, Markus Schuß and Kay Römer
March 14, 2017

 

Low-power wireless sensor networks are an important underlying infrastructure for the Internet of Things (IoT) and are increasingly used in application domains imposing strict dependability requirements on network performance, such as smart production, smart cities, or connected cars. These application domains are safety-critical: any failure in meeting application-specific requirements and in conveying information in a dependable (i.e., reliable, timely, and energy-efficient) manner may result in high costs and physical damage to people or things.

A key challenge in achieving dependable low-power wireless IoT communications is the increasing congestion of the freely available ISM bands. Radio interference from surrounding wireless devices and electrical appliances often impairs packet reception, reduces throughput, leads to high latencies, and causes retransmissions that may accelerate the depletion of batteries.

For more than a decade, academia and industry have proposed a large number of protocols and RF mitigation techniques to increase the dependability of low-power wireless IoT communications in the presence of radio interference. However, these solutions have rarely been benchmarked under the exact same settings and with a focus on the end-to-end performance. As a result, the research community is still disputing on which communication technique is the most dependable for a given application scenario (e.g., stateful routing vs. stateless flooding).

To shed light in this domain, we have started a competition to quantitatively compare the dependability of state-of-the-art wireless sensing systems in environments rich with radio interference. The first two editions of the competition, co-located with the International Conference on Embedded Wireless Systems and Networks (EWSN), have focused on a scenario emulating the operation of a wireless sensor network monitoring discrete events [1].

 

Benchmarking end-to-end system performance under repeatable conditions
The scenario employed in the competition is sketched in Figure 1. A sensor node is placed in proximity of a light source and monitors its brightness. As soon as a sudden variation in the lighting condition is detected, this sensor node immediately reports this information over a multi-hop wireless network to a central unit. The competing solutions are evaluated based on the end-to-end latency (i.e., the delay between the event is generated and received by a sink node), on reliability (i.e., on the number of events correctly reported to the sink node), and on energy consumption (i.e., the sum of the energy consumed by all nodes in the network). Controlled radio interference is generated in the competition area using JamLab [2], and the same patterns are repeated for each contestant.

 

Figure 1: The competition scenario emulates an industrial control application in which a single sensor observes and reports events over a multi-hop wireless network to a central unit.

 

The competition scenario differs from existing protocol comparisons found in the literature in three ways. First, all competing solutions are tested on the same hardware and on the exact same settings. Second, the evaluation focuses on end-to-end metrics and on whether the application goal is fulfilled, as opposed to protocol-specific low-level metrics such as the number of parent switches or the path length in hops, which do not allow drawing any conclusion on the end-to-end dependability of a system. Third, to increase fairness and realism, we allow the developers of the competing solutions to interact with the benchmarking infrastructure and to optimize the protocol parameters for the specific application scenario at hand. As tiny differences in the parametrization may lead to quite different results (traditional comparisons rely on the protocols’ default settings), and as the spirit of the competition spurs all contestants into pushing the performance of their solution to the edge, we can understand how suitable state-of-the-art IoT protocols are for the application scenario at hand.

To create our benchmark scenario with minimal costs (i.e., to accurately profile the power consumption of a device, measure end-to-end latency at sub-microsecond accuracy, and detect the occurrence of specific events), we have designed D-Cube [1], a low-cost tool built on top of a Raspberry Pi 3, as shown in Figure 2.  D-Cube measures the desired dependability metrics and graphically depicts their evolution in real-time using InfluxDB and Grafana, hence allowing the contestants to monitor the performance of their protocols in real-time and to parametrize them accordingly [1].

 

 Figure 2: D-Cube, the open-source profiling tool used to benchmark the protocols. Further info can be found in [1].

 

New-generation protocols are reliable and timely
The results of both the EWSN 2016 competition that took place in Graz, Austria (http://www.iti.tugraz.at/EWSN2016/cms/index.php?id=8), as well as the EWSN 2017 edition held in Uppsala, Sweden (http://www.ewsn2017.org/dependability-competition.html) have shown that new-generation IoT protocols can satisfy the strict dependability requirements of safety-critical systems monitoring discrete events. The top solutions could successfully capture and deliver events even in the presence of high interference, while pushing the end-to-end delay well below 100 milliseconds in networks with a diameter of up to seven hops. Key difference between the two editions was the density of the nodes in the network: in the first edition, the network was very dense, with more than 45 nodes deployed in an area of 150 m2. In the second edition, instead, the network was very sparse, and between three and seven hops were required to reliably convey information from the sensing to the sink node.


Flooding with hopping is the reference solution for scenarios with single nodes reporting events
In both dense and sparse network settings, all top solutions were the ones employing frequency diversity, showing that the shift towards frequency hopping protocols such as IEEE 802.15.4e, BLE, and WirelessHART is a necessity, especially in safety-critical settings. 

Remarkably, almost all top solutions are based on Glossy [3] and carry out network flooding over multiple channels. Flooding-based solutions have achieved the best performance in both dense and sparse settings, as well as shown that stateless flooding is superior to stateful routing in scenarios in which single nodes need to quickly report discrete events towards a central unit. The energy efficiency of flooding approaches was the highest across all solutions and superior to standard routing approaches. This hints that flooding can guarantee low latencies without incurring a significantly higher energy expenditure, an argument that has long been debated in the research community. Flooding-based solutions are also gaining an increasing interest from the industrial community, as shown by the fact that several of the competing solutions based on flooding were produced by companies such as ABB, Airbus, Infineon, and Toshiba.

Implications for existing IoT applications
While IoT applications have vastly different requirements, we could demonstrate that for settings in which a discrete event has to be transmitted reliably to a central station, stateless flooding with channel hopping is clearly the best solution. There is quite a number of IoT applications that match this description, and include home automation systems making use of smoke detectors or intrusion detection sensors, body worn sensors used for elderly monitoring or health-care applications, as well as simple industrial monitoring systems.

What about multiple sources of traffic?
We plan to improve upon the competition over the next years to evaluate solutions for even more challenging tasks and scenarios. As we have now covered both dense and sparse networks in which discrete events need to be quickly reported to a central unit, we plan to introduce several sources of traffic as well as feedback loop between nodes. We are also considering the addition of boundaries on the consumed energy per node in order to emulate battery depletion, or on the end-to-end latency in order to evaluate the determinism of the competing solutions.
 

References

[1] M. Schuß, C.A. Boano, M. Weber, and K. Römer. A Competition to Push the Dependability of Low-Power Wireless Protocols to the Edge. In Proceedings of the 14th International Conference on Embedded Wireless Systems and Networks (EWSN). Uppsala, Sweden. February 2017.

[2] C.A. Boano, T. Voigt, C. Noda, K. Römer, and M.A. Zúñiga. JamLab: Augmenting Sensornet Testbeds with Realistic and Controlled Interference Generation. In Proceedings of the 10th International Conference on Information Processing in Sensor Networks (IPSN). Chicago, IL, USA. April 2011.

[3] F. Ferrari, M. Zimmerling, L. Thiele, and O. Saukh. Efficient network flooding and time synchronization with Glossy. In Proceedings of the 10th International Conference on Information Processing in Sensor Networks (IPSN). Chicago, IL, USA. April 2011.

 


Carlo Alberto Boano
(IEEE member since 2009) is an assistant professor at the Institute for Technical Informatics of Graz University of Technology, Austria. He received a doctoral degree sub-auspiciis praesidentis from Graz University of Technology in 2014 with a thesis on dependable wireless sensor networks, and holds a double Master degree from Politecnico di Torino, Italy, and KTH Stockholm, Sweden. His research interests encompass the design of dependable networked embedded systems, with emphasis on the energy-efficiency and reliability of low-power wireless communications, as well as on the robustness of networking protocols against environmental influences.

 


Markus Schuß
is a PhD student at the Institute for Technical Informatics of Graz University of Technology, Austria. He received his M.Sc degree in Information and Computer Engineering from Graz University of Technology in 2016. As part of his Master’s thesis, he developed a simulation engine based on SystemC-AMS for systems modelled in UML within the semiconductor and automotive sector. His research interests encompass the development of testbeds and benchmarking infrastructures, as well as the evaluation of the robustness and energy-efficiency of wireless communication protocols used in the Internet of Things and in industrial automation.

 


Kay Römer
is professor at and director of the Institute for Technical Informatics at Graz University of Technology, Austria. Before he held positions of Professor at the University of Lübeck in Germany, and senior researcher at ETH Zürich in Switzerland. Prof. Römer obtained his Doctorate in computer science from ETH Zürich in 2005 with a thesis on wireless sensor networks. His research interests encompass wireless networking, fundamental services, operating systems, programming models, dependability, and deployment methodology of networked embedded systems, in particular Internet of Things, Cyber-Physical Systems, and sensor networks.