Article 1

Smart Cities and Quality of Life: Urbanization Underscores the Value of Internet of Things Applications

Chung-Sheng Li

More people live in cities today than at any time in human history, and the urbanization of the global population appears set to continue as the 21st century unfolds.

The multi-faceted pressures of urbanization will force cities to develop efficiencies and strategies in order to remain viable. Smarter ways of doing everything better, with fewer resources, will be required. So will the ability to forecast disruptive events such as natural disasters and their effect on urban dwellers and the infrastructure that supports them.

 


Article 2

The Hardware Enablers for the Internet of Things - Part I

Timothy Lee

The premise of the Internet of Things (IoT) is that this new technology trend will connect billions of devices using the internet starting around 2020, with ecosystems that will address wearables, smart home, automotive, smart cities, the workspace and industrial applications. The IoT system consists of three domains: Sensors, Connectivity and Applications. Most attention for IoT has been focused on the applications for the home (consumer), transport (mobility), health (body), buildings (infrastructure), factory (industrial) and cities (utilities, security). What is missing from much of the discussion are the underlying hardware and sensor technologies that enables the IoT applications, intelligence and links to the 'cloud'.

 


Article 3

Towards a Practical Architecture for Internet of Things: An India-centric View

Prasant Misra, Yogesh Simmhan and Jay Warrior

The current, widespread thinking on the Internet of Things (IoT) makes several (arguably misplaced) assumptions. IoT architectures are often a repackaging of existing ideas or a clean-slate, costly design. Some of these design assumptions include hundreds of tightly coupled devices; costly devices ($5 - $500) customized for an application; exclusive use of well-structured, always-on communication networks (e.g., IPv6); centralized data collection, analysis and control in the cloud; and a single vendor who owns the vertical application: platform, cloud services, data and ecosystem. Examples of these vertically integrated silos include smart power grids, supervisory control and data acquisition (SCADA) systems, and personal monitoring devices like FitBit.

 


Article 4

RELYonIT: Dependability for the Internet of Things

Carlo Alberto Boano, Kay Römer and Thiemo Voigt

The Internet of Things will represent the backbone of modern society and will embrace a system of wireless networks delivering end users a plethora of attractive services and applications. The latter will rely heavily on the dependable operation of embedded wireless sensors and actuators despite their low-priced hardware, battery limitations, and resource constraints. Wireless systems employed, for example, to build smart cities, smart grids, and smart healthcare applications, represent a long-term investment and are therefore required to reliably convey information about the state of things and places, as well as to remain available for a prolonged time.

 

 

This Month's Contributors

Chung-Sheng Li is currently the director of the Commercial Systems Department. He has been with IBM T.J. Watson Research Center since May 1990.
Read More >>

Timothy Lee is the 2015 IEEE Microwave Theory and Techniques Society (MTT-S) President.
Read More >>

Prasant Misra is senior member, technical staff at the Robert Bosch Centre for Cyber Physical Systems in the Indian Institute of Science, Bangalore.
Read More >>

Yogesh Simmhan is an Assistant Professor at the Supercomputer Education and Research Centre at the Indian Institute of Science, Bangalore.
Read More >>

Jay Warrior has over 20 years of experience creating new, high technology based, business opportunities for Agilent Technologies, Hewlett-Packard, Fisher-Rosemount, and Honeywell in the US and in Asia.
Read More >>

Carlo Alberto Boano (IEEE member since 2009) has been a research assistant at the Institute for Technical Informatics of TU Graz, Austria since September 2013.
Read More >>

Kay Römer is currently a Professor and head of the Institute for Technical Informatics at TU Graz.
Read More >>

Thiemo Voigt leads the Networked Embedded Systems Group at SICS Swedish ICT, formerly Swedish Institute of Computer Science.
Read More >>

 

Contributions Welcomed
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Would you like more information? Have any questions? Please contact:

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

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stuartsharrock@ieee.org

 

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.

RELYonIT: Dependability for the Internet of Things

Carlo Alberto Boano, Kay Römer and Thiemo Voigt
January 13, 2015

 

The Internet of Things will represent the backbone of modern society and will embrace a system of wireless networks delivering end users a plethora of attractive services and applications. The latter will rely heavily on the dependable operation of embedded wireless sensors and actuators despite their low-priced hardware, battery limitations, and resource constraints. Wireless systems employed, for example, to build smart cities, smart grids, and smart healthcare applications, represent a long-term investment and are therefore required to reliably convey information about the state of things and places, as well as to remain available for a prolonged time.

Failure in delivering the sensed values in a reliable and timely fashion or in minimizing battery depletion may result in high costs, insufficient user satisfaction, and physical damage to people or things. As developing such systems is challenging, we see few dependable IoT applications today.

Most of these wireless systems have very limited computation capabilities and their transmitting range is often restricted by their limited energy budget. Therefore, it is fundamental that they can form unsupervised networks that autonomously adapt to environmental changes in an energy-efficient fashion. This is however very challenging to attain, as the surrounding environment often affects the performance of networked sensors or actuators and their capability to meet application-specific dependability requirements.

Radio interference from surrounding wireless devices and electrical appliances may impair packet reception, reduce throughput, lead to high latencies, and cause retransmissions that may cause earlier battery depletion. The mobility of nodes in dynamic environments can affect the forwarding of information in multi-hop networks and cause long delays and network partitions. Even temperature variations can cause, among others, loss of synchronization and degradation of the wireless link quality, affecting the reliability and availability of networked embedded systems. Figure 1 shows that the normal on-board temperature fluctuations occurring during a day can indeed transform a perfect link (100% packet reception rate) between two wireless sensors into an almost useless one.

Figure 1

Figure 1: A high temperature has a strong negative effect on the packet reception rate

This vulnerability to the surrounding environment affects the design of IoT applications, and the vast majority of the latter are still non-critical, i.e., they are not able to give any dependability guarantee. How can developers create smart city solutions if parking spot occupancy and pollution concentration sensors are not operating as expected during the hottest times of the day or in the presence of radio interference in dense urban environments?

Solutions require proper test bed infrastructures

To solve this problem, there is a strong need to get a better understanding of the performance of networked embedded devices as a function of the environment. In order to obtain such a deep understanding and to come up with dependable solutions, we have extended existing test bed infrastructures to enable the repeatable playback of pre-recorded environmental conditions. In particular, we have designed low-cost extensions for wireless sensor network test beds that allow to (i) study the impact of temperature with only little hardware overhead (TempLab), and (ii) to create realistic and repeatable interference patterns without hardware overhead (JamLab). These test bed extensions allow us to rerun experiments under identical environmental conditions and hence play a crucial role for the investigation of network performance.

TempLab (see Figure 2) can accurately reproduce temperature traces recorded in outdoor environments with an average error of only 0.1°C, and preliminary experiments using TempLab have revealed several limitations of state-of-the-art communication protocols. For example, high temperature can drastically change the topology of a network and lead to network partitions, reduce significantly the performance of MAC protocols, as well as increase the processing delay in the network. JamLab provides simple models to emulate the interference patterns generated by typical appliances, as well as a playback capability to regenerate recorded interference patterns. Through a simple software upload, JamLab selects automatically the portion of the test bed nodes that reproduce radio interference and allows us to study how protocols can cope with and adapt to the existing interference autonomously.

We used these test bed facilities to devise accurate models capturing the impact of the environment on IoT hardware and protocols, and to develop new protocols that can be automatically configured to meet application-specific dependability requirements. In particular, some of our communication protocols anticipate and correct faults introduced by environmental conditions [1,2] and significantly help in increasing (i) the reliability of low-power wireless communications and (ii) the availability of the network.

More dependability, more business

These solutions have been developed in the context of the EU-funded RELYonIT project [3] and have been shown to significantly increase the dependability of IoT solutions to radio interference and temperature fluctuations. Industrial partners in the consortium used these technologies to develop a new line of dependable real-world applications involving different types of sensors and actuators. These applications include highly demanding monitoring scenarios such as smart parking systems and energy-efficient buildings. We expect that our solutions will pave the way for a broader range of dependable IoT applications in critical domains such as smart cities, smart grids, and smart healthcare.

Figure 2

Figure 2: Heating lamp on top of a wireless sensor node in our extended TempLab test bed

 

References

[1] C.A. Boano, M.A. Zúñiga, K. Römer, and T. Voigt. JAG: Reliable and Predictable Wireless Agreement under External Radio Interference. In Proc. of the 33rd Real-Time Systems Symposium (RTSS). December 2012

[2] C.A. Boano, K. Römer, and Nicolas Tsiftes. Mitigating the Adverse Effects of Temperature on Low-Power Wireless Protocols. In Proc. of the 11th Conference on Mobile Ad hoc and Sensor Systems (MASS). October 2014

[3] RELYonIT: Research by Experimentation for Dependability on the Internet of Things, http://www.relyonit.eu, November 2014

 


 

Carlo Alberto BoanoCarlo Alberto Boano (IEEE member since 2009) has been a research assistant at the Institute for Technical Informatics of TU Graz, Austria since September 2013. Previously, he was affiliated with the University of Lübeck, Germany and the Swedish Institute of Computer Science, Sweden. He received a doctoral degree with distinction from TU Graz in 2014, with a thesis on dependable wireless sensor networks. Dr Boano's research interests include the design and implementation of dependable networked embedded systems, with emphasis on energy-efficient and reliable low-power wireless communications.

 

Kay RömerKay Römer is currently a Professor and head of the Institute for Technical Informatics at TU Graz. Before he held positions of Professor at the University of Luebeck in Germany, and senior researcher at ETH Zurich in Switzerland. Prof Römer received his doctoral degree from ETH Zürich in 2005 with a thesis on wireless sensor networks. His research interests encompass design, implementation, and evaluation of networked embedded systems, with current focus on increasing their dependability, efficiency, and scalability.

 

Thiemo VoigtThiemo Voigt leads the Networked Embedded Systems Group at SICS Swedish ICT, formerly Swedish Institute of Computer Science. He is also a Professor at Uppsala University from where he received his PhD. His research interests include networking, systems and security issues in wireless networks and the Internet of Things. He is a member of IEEE.

 

 

Smart Cities and Quality of Life: Urbanization Underscores the Value of Internet of Things Applications

Chung-Sheng Li
January 13, 2015

 

More people live in cities today than at any time in human history, and the urbanization of the global population appears set to continue as the 21st century unfolds.

The multi-faceted pressures of urbanization will force cities to develop efficiencies and strategies in order to remain viable. Smarter ways of doing everything better, with fewer resources, will be required. So will the ability to forecast disruptive events such as natural disasters and their effect on urban dwellers and the infrastructure that supports them.

Cities that that succeed in improving their sustainability and adaptability in the face of disruptive events will attract people and enterprises that improve the local economy and create a desirable quality of life. “Smart cities” is the new tag for this goal.

How can the “smart cities” vision be made real?

Enter: Internet of Things (IoT)

Set aside, for a moment, the notion of IoT as a network of innumerable devices that 'talk' to each other, perhaps in the service of energy efficiency or machine-to-machine functional coordination. Certainly a day may come when every object has an Internet address and definable attributes that aid interconnectivity with other objects for various purposes.

For this article, however, let’s reimagine IoT as a set of applications that are the outcome of behavioral models that reflect the real world and anticipate its behavior. These applications will be made possible by a proliferation of sensors, data and timely data analysis that takes place either centrally or closer to the system’s edge. Some smart, IoT-enabled systems may become fully automated and run in the background to support urban infrastructure, for instance, while other applications will be designed to provide insights to people making everyday decisions on how they interact with their environment.

The infrastructure and daily processes of urban life may well benefit from such IoT-related applications to support and improve economic growth, public safety and security, transportation, buildings, work, food, healthcare and arts and culture – in short, all the elements of a vibrant, sustainable city.

Thus, let’s consider at a high level how IoT might aid urban infrastructure-related functions and provide insight into how to manage infrastructure and mitigate damage in the event of a natural disaster or other similarly disruptive event.

[For more technical detail on likely IoT components, processes, behaviors, applications and challenges, readers can replay an IEEE IoT webinar held Oct. 29, “Orchestrating a Smarter Planet in the World of IoT.”]

The urban foundation

One of the obvious challenges to urban centers is ensuring that the fundamental needs of city-dwellers are met. Water, food, power, transportation and healthcare must be consistently available to all. Moreover, these services – and many others – are linked by complex inter-dependencies. Optimizing each system must be accompanied by the optimization of an inter-dependent system of systems, both for ordinary blue sky days as well as under the duress of disruptive events.

Given the vast, often subterranean nature of a city’s infrastructure, the dynamics and demands of a so-called blue sky day are challenging enough. Simply maintaining operations in an optimal manner is a challenge, as anyone who reads a major metropolitan newspaper will attest. Power to traffic signals can be lost without warning. Water mains suddenly break. Natural gas lines may spring a leak. Construction mishaps can block streets.

IoT comes into play by integrating the widest possible, relevant data sets and models to identify normal operating metrics, asset conditions and vulnerabilities, as well as providing notice of conditions that indicate incipient failures.

Apply this IoT-supported methodology to power, water, natural gas or other critical supply lines such as food, transportation, medical care – you name it – and one can see how system optimization and proactively addressing predictable failures in infrastructure would aid efficient, cost-effective operations that underpin a city’s quality of life.

Fortunately, asset management techniques are shifting from time-based to condition-based assessments that will aid optimal operations. In the past, an infrastructure component designed for 10 years of service might be prioritized for replacement – or monitored for failure – after 10 years. In condition-based or context-based monitoring, sensors record various parameters so that asset managers can understand current conditions and forecast impending failures. “Smart grid” and “smart water” are two areas where IoT-like applications are being used today.

It’s difficult to imagine, let along articulate, the many permutations of IoT-related outcomes possible in the interdependent world of urban infrastructure. But sit down with a traffic engineer sometime and ask what’s involved in programming a city’s traffic lights. It’s complicated. IoT-related applications may offer the best tools for managing these complexities.

Disaster preparedness and response

Natural disasters will always occur on a dynamic planet, but their destructive impacts on infrastructure and people – particularly those concentrated in urban centers – can be mitigated. IoT-related systems can provide practical insights into how a storm will behave and, coupled with knowledge of infrastructure vulnerabilities, how a storm may impact essential services. With data-driven insights into probable impacts, improved preparation can lessen the blow. Even an advantage measured in seconds could save lives in an evacuation.

If maintaining infrastructure in an urban environment presents a challenge, imagine the disruptions caused when a hurricane hits a coastal city and roads are washed away, a water main breaks, a gas line leaks or an underground power substation is flooded. Suddenly, access to a dangerous gas leak is blocked. A loss of power leads a hospital to evacuate its patients. Flooding contaminates drinking water.

New York City experienced all of these impacts simultaneously when it was struck by Hurricane Sandy in 2012. With more extreme weather forecast for the decades ahead, and sea level expected to rise as the planet warms, coastal cities will need to actively manage such risks. Recent, deadly and destructive tsunamis in Southeast Asia underscore this point. Inland cities will face their own set of challenges.

It’s not difficult to imagine IoT-related models, based on real-world data, providing an advantage in preparing for extreme weather events. For instance, power utilities typically call in out-of-state crews and marshal their resources in the face of an impending storm. If they were able to pre-position crews inside areas likely to be hardest hit, power restoration might be swifter. Or utility officials might pre-emptively take a section of the grid down to help isolate expected damage, as New York City’s Consolidated Edison did in Hurricane Sandy.

Obviously, the sensors and data and communication networks that will enable sustainability and resiliency will themselves have to be strategically designed, located and maintained so that IoT applications continue to deliver useful insights during the very emergencies they’re intended to mitigate. Smart buildings, for instance, might have motion- and/or life-detecting sensors that could aid evacuations or guide incoming public safety personnel to trouble spots or safe havens.

Thus, IoT might markedly improve familiar responses or it might yield new insights and inform entirely new ways of optimizing infrastructure and mitigate damage to a city’s foundations.

Conclusion

Of course, the implementation of IoT applications to our cities will require coordination and cooperation among many different entities in the public and private sectors, so inevitably it will take root differently in different societies.

In the United States the market may be the driver as cities compete to attract the most productive people and businesses. New York City may find it necessary to apply IoT-related applications to guard against catastrophic sea surges and their impact on infrastructure. In China, in contrast, a top-down centrally planned and run city and economy could rival a free market approach for effectiveness. Some cities will tackle across-the-board challenges while others might attempt to address a particularly vexing issue, such as Beijing’s air pollution challenge.

By the same token, mid-sized cities may not have acute or chronic issues, but instead must meet prosaic challenges to their very existence, as urban centers compete for high-quality workers and cutting-edge businesses. Bigger isn’t necessarily better in the quest for urban adaptability. Perhaps Peoria, Illinois, will manage to compete with Chicago because it possesses less entrenched public bureaucracies and more community-minded private enterprises.

Whatever the approach, the goal will be similar. When a city is approached as a gigantic system, its various moving parts can be optimized for efficiencies and its dwellers can be aided in adapting their lifestyles to a changing environment. A generic IoT approach will use sensors, data and analytical models to produce insights into how the world behaves and, more importantly, how it will behave, to enable people and systems to be more adaptable.

Though such an approach could work anywhere, the urban environment and its inherent challenges and mounting population pressures offer a clear opportunity and compelling use case for IoT-related innovations.

 


 

Chung-Sheng LiChung-Sheng Li is currently the director of the Commercial Systems Department. He has been with IBM T.J. Watson Research Center since May 1990.

His research interests include cloud computing, security and compliance, digital library and multimedia databases, knowledge discovery and data mining, and data center networking. He has authored or coauthored more than 130 journal and conference papers and received the best paper award from IEEE Transactions on Multimedia in 2003. He is both a member of IBM Academy of Technology Leadership Team and a Fellow of the IEEE.

 

 

The Hardware Enablers for the Internet of Things - Part I

Timothy Lee
January 13, 2015

 

The premise of the Internet of Things (IoT) is that this new technology trend will connect billions of devices using the internet starting around 2020, with ecosystems that will address wearables, smart home, automotive, smart cities, the workspace and industrial applications. The IoT system consists of three domains: Sensors, Connectivity and Applications. Most attention for IoT has been focused on the applications for the home (consumer), transport (mobility), health (body), buildings (infrastructure), factory (industrial) and cities (utilities, security). What is missing from much of the discussion are the underlying hardware and sensor technologies that enables the IoT applications, intelligence and links to the 'cloud'.

In Part I of this article, the relationship between hardware and applications is discussed as well as the challenge hardware engineers face in meeting the IoT requirements.

What we must remember is that it is the underlying hardware that enables the sensing functions that bring the 'smartness' to the devices and the wireless transmission of data between devices and the backend intelligence that act on the acquired sensor data (temperature, pressure, speed, acceleration, GPS location, heartbeat rate, respiration rate, energy consumption, etc.) to bring about a response that optimizes productivity, conservation, and better quality of life. There are many challenges that the hardware community must meet to invent new devices and technologies that must be smaller, lighter, more efficient and lower cost than ever before if the Internet of Things revolution is to happen with all the heightened expectations.

Entering the domain of microwave engineering

Information from the transmitter node travels down from its application layer (OSI Layer 7) down to the PHY layer (OSI layer 1) where the data bit stream is translated into physical variations in voltages and current waveforms, which are sent across the physical transmission medium to the receiver node. At the receiver node, the modulated signals are converted back into a digital bit stream and relayed back up its layer stack to the consumer’s application layer. The medium can be air, copper, optical fiber cable or any other materials, which can carry 'waves'.

In the RF Front-End, microwave low-noise amplifiers (LNAs) and power amplifiers (PAs) are used to receive/send the modulated RF carrier signals from one node to another by propagating the radio wave through an antenna. Just behind the RF section, the signals are up- and down-converted from baseband using mixers. Distances traveled can be as short as 1 meter or as far as 35,800 km (the distance from ground to a geostationary satellite.)

Common wireless communications systems that operate in the microwave frequency range include IEEE 802.11 (WiFi), Bluetooth, NFC, cellular and satellite communications at C-, Ku- and Ka-bands. Communications equipment used to be bulky, stationary and plugged into the wall for power. The application layer demands for high data-rate, low latencies, error-free transmission, long battery life and high capacities of multi-user scenarios have led to the current state-of-the-art in mobile handsets.

A new paradigm for communications hardware

The size, weight, power and cost (SWaP-C) demands for the IoT ecosystems will force the creation of a new paradigm for the hardware. These metrics must be improved by factors of 10 to 100 in order to make IoT realizable. If today’s hardware costs $10 a piece for a 100 million device market, then the same function may have to be well under $1 to address a 20 to 50 billion device market. Most electrical devices today have ready access to prime power to energize their circuits. In the IoT world, there are many use cases where connecting the device to the wall outlet or changing / charging the battery is a showstopper. Therefore, improved power efficiency, smart power management, energy harvesting and wireless power transmission will all need to be investigated and made viable for IoT applications. In today’s hardware, milliwatt dissipation may be sufficient. In the IoT world of 2020, microwatts or even nanowatt power dissipation will be required. In many sensor applications, the IoT device must operate at a very low duty cycle; waking up for milliseconds to perform its function, transmit its data payload and then go back to sleep.

The good news is that the advanced silicon CMOS technologies being developed today in the world's leading foundries feature sizes ranging from 32nm down to 10nm. The design of the next generation of low-power RF transceivers, mixed-signal ADCs/DACs and micro-controllers will not be easy nor is first-pass design success assured. Even more challenging will be the design and fabrication of packaging, inter-connects and PWB to meet the same IoT metrics. EDA CAD tools must also evolve to design, simulate and lay out the highly integrated microsystems and IoT System-on-a-Chip (SOCs) realizations. There has been much discussion about the end of Moore’s Law as we approach 10nm geometries, but this may be overstated.

Other important IC technologies that will need attention include power converters, micro-controllers such as Raspberry Pi, ARM, Arduino and Intel’s Edison platform. Many traditional hardware vendors such as Freescale, Intel, TI, Broadcom, Qualcomm, STMicroelectronics and Samsung are all actively promoting their own IoT hardware ecosystems for the marketplace. Many consortia are being formed.

For further readings on the development of advanced semiconductor technologies for IoT, please read the International Technology Roadmap for Semiconductors (ITRS) found here.

In Part II, the state of sensor technologies for IoT will be discussed. It is the sensors that provide situational awareness and inputs to make our IoT intelligent'. It is also interesting to note that sensor technology does not follow Moore’s Law and is often described as “More than Moore’s Law”.

 


 

Timothy LeeTimothy Lee is the 2015 IEEE Microwave Theory and Techniques Society (MTT-S) President. Currently he is a Boeing Technical Fellow, working in Boeing Research and Technology in Southern California. He has over 35 years' experience in the design of MMICs, microwave components and sub-systems for electronic systems. His interests in IEEE MTT-S include the continued excellence of technical journals and conferences for the microwave engineering communities worldwide and the development of microwave/wireless solutions to benefit humanitarian needs.

 

 

Comments

2015-01-13 @ 9:33 PM by Grefford, John

Thanks for the succinct overview...great contribution!

2015-01-15 @ 7:04 AM by Wade, Phillip

I enjoyed reading your article. As a student taking a series of embedded microprocessor courses your article offers a lot of insight.

2015-01-24 @ 6:02 PM by Sung, Liang-Hsin

Thanks for the summary of this technology. A great article indeed. 

2015-02-04 @ 6:19 PM by Kolias, Nicholas

Great summary --- looking forward to part II...

2015-06-17 @ 5:17 PM by Poddar, Ajay

Dear Tim

Very good technical report, I must thank you for your effort and time towards taking this initiative for the benefit of IEEE members.

Ajay Poddar

 

Towards a Practical Architecture for Internet of Things: An India-centric View

Prasant Misra, Yogesh Simmhan and Jay Warrior
January 13, 2015

 

The current, widespread thinking on the Internet of Things (IoT) makes several (arguably misplaced) assumptions. IoT architectures are often a repackaging of existing ideas or a clean-slate, costly design. Some of these design assumptions include hundreds of tightly coupled devices; costly devices ($5 − $500) customized for an application; exclusive use of well-structured, always-on communication networks (e.g., IPv6); centralized data collection, analysis and control in the cloud; and a single vendor who owns the vertical application: platform, cloud services, data and ecosystem. Examples of these vertically integrated silos include smart power grids, supervisory control and data acquisition (SCADA) systems, and personal monitoring devices like FitBit.

However, an effective architecture for IoT, particularly for an emerging nation like India [1] with limited technology penetration at the national scale, should be based on: (1) tangible technology advances in the present, (2) practical application scenarios of social and entrepreneurial value, and (3) ubiquitous capabilities that make the realization of IoT affordable and sustainable. This is especially critical as India embarks on an ambitious program to upgrade 100 existing and new cities into Smart Cities. In this context, a rethink of the above assumptions suggests:

  • Thousands of loosely connected devices in the immediate vicinity, and millions more further out;
  • Ultra-cheap devices ($0.01 − $3.00) combined with existing generic, in-person devices like smartphones;
  • A mix of ad hoc P2P, 2G/3G/4G, IPv6 and WiFi-based networks having intermittent connectivity;
  • Data collection and personalized analytics that seamlessly span edge devices and the cloud, with control over data sharing and ownership while encouraging Open Data;
  • A vendor and domain-neutral open ecosystem using internet/web standards, allowing reuse of devices and data.

Here, we propose ten design paradigms to achieve a sus­tainable and practical IoT architecture for India.

1) Human-centric rather than thing-centric design

Current IoT architectures are device or network oriented. How­ever, the key value proposition of IoT is from the interaction of 'Things' with humans and society; and the benefits gained for humans who are part of, affected by and influence the network. Technologies, services and decision making must create an IoT experience that deeply engages with people. This may be mundane, like providing optimal traffic routing in a smart city; or essential, like offering personalized health suggestions for patients. As a result, we favor devices, networks, data, and analytics that are in close proximity to humans and are widely prevalent, e.g., smart and feature phones, Bluetooth/WiFi/ 2G/3G /4G wireless interfaces, wearables/body area networks.

2) Span virtual and physical worlds

Much of the IoT conversation is about the physical infras­tructure and its optimization. Bringing in humans and social elements (with their virtual online avatars such as social networks and virtual agents) helps span the digital and physical worlds, and also integrate across humans and infrastructure. Capturing proximity and interactions between humans and 'Things' (H2H, H2M, M2M), both in the physical and virtual worlds, is necessary for actionable intelligence.

3) Big-little data

Analytics performed on information from diverse sources within the IoT architecture helps with data-driven decision making. There are two classes of such data: (1) transient sensor and personal data collected continuously from hu­mans/physical devices, i.e., 'Little' data; and (2) persistent knowledge-bases and archives that span domains and available in central repositories/clouds, i.e., 'Big' data. Meaningful analytics requires both 'Big' and 'Little' data to be combined, and often in real time.

4) Analytics from the edge to the cloud

Related to Big-Little data is performing distributed analytics and decision making. The current model of pushing all data to a central cloud for analytics will not scale, is inefficient, and raises privacy concerns. Given the enhanced capabilities of edge devices like smartphones coupled with intermittent network connections, decisions on whether a subset of the Big data and decision-analytic should be pushed to the phone, or the Little data and analytic aggregated in the cloud have to be automated. These are informed by the device capability, privacy needs, energy and network costs, and application QoS.

5) Bring the network to the sensor

As tens of thousands of cheap IoT devices proliferate, they will be constrained in energy and communication capabilities. Rather than rely on massive deployment of custom sensor networks and new standards, there is value in piggybacking on existing, widely adopted standards and reusing symbiotic infrastructure. For example using smartphones as P2P data mules for last mile connectivity to sensors, combined with highly functional gateways and clouds for coordination, suggests an asymmetric architecture.

6) How 'low' can you go?

Technology penetration has not been uniform across countries, regions, or, for that matter, industries. This disparity is a reflection of the differences in infrastructure, cost of access, telecom networks and services, and policies among different economies. Hence, the cost and technology behind the sensing, device, networking and analytic solutions for the IoT should be affordable and scale to billions of users. This requires reuse of commodity hardware and sensors, and existing infrastructure in novel ways rather than custom solutions with cutting-edge capabilities, or canned solutions developed for advanced economies. The cost-to-benefit trade-offs become critical.

7) Whose data is it anyway?

The intersection of devices, communication, data and humans within IoT offers interesting incentives and business models. A key success of the WWW is the ability for businesses to monetize users’ data (e.g., Google Ads using user’s web data pays for free search and email services). With IoT, devices are going to be even closer to humans and blend into our environment. Ensuring transparency in data ownership, sharing, and usage are important. Further, there is scope for data brokering that encourages open data sharing by users with business in return for clear rewards, be they monetary, peer recognition, or for the greater good.

8) When is 'good enough' enough?

IoT is naturally a diverse ecosystem with unreliability and uncertainties as: (1) cheap sensors mean questionable data quality, (2) humans are fickle to model, (3) physical systems are complex, (4) distributed 'Things' and intermittent com­munication are a given, and (5) data privacy puts bounds on its availability. As a result, analytics and decision making have to be probabilistic; and the system and application has to be conscious of what is 'good enough' and not fail in the absence of perfect behavior.

9) Context determines the action

Given the uncertainties of the system and humans being central entities, much of the decision making within the IoT infrastructure and applications has to be contextual. Context binds people and things to a common scope, and hence, will ease mining of relevant information. There has to be semantic knowledge that captures system and social behavior, some specified while others are learned using models. Intelligent agents will often act on the behalf of humans. They may be aware of personal preferences (e.g., Apple’s Siri, Microsoft’s Cortana, and Google Now), and these will interact with digital agents of service providers, utilities and vendors. Semantic context will have to complement web standards for structural syntax to allow such M2M interaction to be effective.

10) Business canvas

If the IoT is to yield successful business models, we first need to recognize that IoT is not a new product or market. What IoT brings is an additional set of technologies, lower power, more computation and storage, cheaper devices, better wireless connectivity, much more granular control and observation capabilities. What it enables is scaling in both directions – up and down, and the ability to look at ourselves and the world in an unprecedented degree of detail.

IoT business models fall into two broad categories: (1) hori­zontals, concerned with enabling components and technology; and (2) verticals, which integrate these technologies to supply an end user with a value proposition. The first set of horizontal business models is the development of specific sensors and actuators that enable the generation of new or more cost effective observations. The second model is the deployment horizontal, a business model that addresses the needs of building out to scale of the data gathering, data storage and data curation and data brokering needs of IoT based systems. The third horizontal business model addresses the needs for a portfolio of analytical techniques to convert the data gathered into actionable information. While the first two have been the focus of IoT’s precursor technologies, IoT’s scale is driving active development across the board.

Verticals will pull solutions and services across these horizon­tals to deliver final end customer value. The emphasis here will be on the necessary domain and system integration expertise and the ability to build the necessary collaborations across customers and suppliers. Likely verticals in developing countries will be based on condition monitoring needs for infrastructure, especially in energy and water. While healthcare also seems to be a popular topic, the most likely focus will be on device- based models.

Conclusion

In this article, our attempt has been to identify the core problems in the existing IoT ecosystem, and propose design paradigms that will make the realization of IoT afford­able and sustainable. For developing nations such as India, with their unique set of infrastructure challenges, the solutions needed are also different. Through such innovative solutions informed by our design proposals, developing nations can potentially leapfrog into more advanced IoT outcomes, just as the 2G and 3G telecom revolution in India skipped past pervasive landline telephones.

 

References

[1] Internet of Things (IoT): how to tap the $12 billion market in India. http://goo.gl/m6ui7X.

 


 

Prasant MisraPrasant Misra is senior member, technical staff at the Robert Bosch Centre for Cyber Physical Systems in the Indian Institute of Science, Bangalore. He performs research in low-power sensing, wireless communication, and energy-efficient computing with a focus on system design and implementation within the general framework of Cyber Physical Systems and Internet of Things. He received his Ph.D. in Computer Science and Engineering from the University of New South Wales, Sydney. Currently, he serves as an associate technical editor of IEEE Communication Magazine, and is a member of IEEE and ACM.

 

Yogesh SimmhanYogesh Simmhan is an Assistant Professor at the Supercomputer Education and Research Centre at the Indian Institute of Science, Bangalore. Previously, he was a Research Assistant Professor of Electrical Engineering and Associate Director of the Center for Energy Informatics at the University of Southern California, Los Angeles. His research explores abstractions, algorithms and applications on distributed data and computing systems, to advance fundamental knowledge and to offer a practitioner's insight, on building scalable systems for Big Data applications. He has a Ph.D. in Computer Science from Indiana University and is a Senior Member of IEEE and ACM.

 

Jay WarriorJay Warrior has over 20 years of experience creating new, high technology based, business opportunities for Agilent Technologies, Hewlett-Packard, Fisher-Rosemount, and Honeywell in the US and in Asia. His expertise covers the whole innovation cycle from market opportunity identification through strategy, business model and technology development, to building and managing execution teams for the new business. A senior member of IEEE, he has a long association with the development of IEEE standards for industrial automation and sensors.

 

 

Comments

2015-03-03 @ 6:10 AM by Gil, Reynaldo

I agree with your principles. The IoT will become a reality when the capital costs are significantly reduced per node while achieving high levels of autonomy.  The centralized cloud infrastructure cannot support sensor data analysis in a sustainable manner for what is largely a decentralized, localized physical world.