Article 1

Edge Intelligence for Connected In-home Healthcare: Challenges and Visions

Yuchen Zhao, Hamed Haddadi, and Payam Barnaghi

Over the coming decades, we will face an increasing number of challenges in the public health sector. Longer life expectancy brings a large aging population that requires more daily healthcare than before. Population living with long-term conditions such as dementia, which decrease their performance of daily activities, need not only extra medical resources but also social resources for their healthcare. Global-scale pandemics such as coronavirus disease 2019 (COVID-19) can cause unprecedented challenges that overwhelm the healthcare system that we currently have.

 


Article 2

Virtualized Gateways for Scalable Multi-access Networks

Saptarshi Hazra, Thiemo Voigt, and Chenguang Lu

Billions of devices are expected to be connected to the Internet through low-power wireless access technologies. Different wireless access technologies satisfy different service requirements in terms of coverage, data rate, responsiveness, etc. Upcoming applications like smart cities and the smart industry require combinations of these wireless access technologies to fulfill various service requirements.

 


Article 3IoT, Healthcare, and Medicine: Future Perspectives

Euclides Lourenço Chuma

We can say that the integration of IoT with Healthcare started with smartwatches monitoring some signals and storing this data in the cloud. With 5G and WiFi 6 networks accelerating the internet connection speeds, and at the same time, the Artificial Intelligence offering better analytics tools, sensors technological improvements more companies in the market are launching new IoT products, including healthcare IoT products.

 


Article 4Teaching Users New IoT Tricks: A Model-driven Cyber Range for IoT Security Training

Michalis Smyrlis, George Spanoudakis, and Konstantinos Fysarakis

As IoT ecosystems become mainstream, malicious actors routinely launch impactful attacks that affect both organizations and individuals - e.g., see the Mirai IoT botnet and its variants, and the intensification of these attacks in the COVID-19 era - ultimately corroding our trust in IoT applications and services.

 

 

EVENTS & ANNOUNCEMENTS


Article 5

IEEE Internet of Things Initiative - Upcoming Events

IEEE 7th World Forum on Internet of Things - 2021
14 June-31 July 2021 // New Orleans, Louisiana, USA / Hybrid Event
Sign up to be a Technical Paper Reviewer.
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IEEE International Conference on Omni-layer Intelligent Systems 2021 (IEEE COINS 2021)
23-25 August 2021 // Barcelona, Spain

IEEE COINS includes a multi-disciplinary program from technical research papers, to panels, workshops, and tutorials on the latest technology developments and innovations. IEEE COINS will address all important aspects of the IoT ecosystem from smart things to the circuit and system, design automation, Edge-Fog-Cloud computing, big data, machine learning, artificial intelligence, blockchain, security, and smart products/services as well as business models. IEEE COINS solicits papers and proposals accompanying submissions for presentations in the Vertical and Topical Tracks. Please visit the website for more information.


Article 5

IEEE Internet of Things Magazine

Internet of Things Magazine logoThe IEEE Internet of Things Magazine solicits high quality articles that: a) describe in depth and/or breadth the state-of-the-art multi-disciplinary IoT-centric research and deployments, b) present groundbreaking novel practical contributions and insights into emerging IoT hot topics and futuristic applications, c) develop/share best practices, vision and lessons learned on integrated IoT environments, and d) establish guiding principles for the advancement of IoT-centered research as well as for the technical, operational and business successes.

Become an author - Submit an article today!
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This Month's Contributors

Yuchen Zhao is a Research Associate in the Dyson School of Design Engineering at Imperial College London.
Read More >>

Hamed Haddadi is a Reader in Human-Centred Systems and the Director of Postgraduate Studies at the Dyson School of Design Engineering at The Faculty of Engineering, Imperial College London.
Read More >>

Payam Barnaghi is Chair in Machine Intelligence Applied to Medicine in the Department of Brain Sciences at Imperial College London.
Read More >>

Saptarshi Hazra is currently working as a researcher at RISE, Sweden.
Read More >>

Thiemo Voigt received a Ph.D. degree from Uppsala University, Sweden, in 2002.
Read More >>

Chenguang Lu received an M.Sc. degree in digital communication and a Ph.D. degree in wireless communication from the Chalmers University of Technology, Sweden, in 2005, and Aalborg University, Denmark, in 2008.
Read More >>

Euclides Lourenço Chuma earned a degree in Mathematics (2003) from the University of Campinas (UNICAMP), a graduate degree in network and telecommunications Systems (2015) at INATEL, an M.Sc. in electrical engineering (2017) at UNICAMP, and a Ph.D. in electrical engineering (2019) at UNICAMP, SP-Brazil.
Read More >>

Michalis Smyrlis (B.Sc., Ph.D. in progress) is a Senior Software Security Engineer at SPHYNX TECHNOLOGY SOLUTIONS AG.
Read More >>

Konstantinos Fysarakis (B.Sc. Applied Mathematics, M.Sc. Information Security, Ph.D. Electronic & Computer Engineering – Embedded Systems Security) is the Chief Technology Officer of SPHYNX ANALYTICS LIMITED.
Read More >>

George Spanoudakis (B.Sc., M.Sc., Ph.D. Computer Science) is the chairman of the management board of SPHYNX TECHNOLOGY SOLUTIONS AG.
Read More >>

 

Contributions Welcomed
Click Here for Author's Guidelines >>

 

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

Raffaele Giaffreda, Editor-in-Chief
rgiaffreda@fbk.eu

Massimo Vecchio, Managing Editor
massimo.vecchio@uniecampus.it

 

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.

Edge Intelligence for Connected In-home Healthcare: Challenges and Visions

Yuchen Zhao, Hamed Haddadi, and Payam Barnaghi
March 17, 2021

 

Over the coming decades, we will face an increasing number of challenges in the public health sector. Longer life expectancy brings a large aging population that requires more daily healthcare than before. Population living with long-term conditions such as dementia, which decrease their performance of daily activities, need not only extra medical resources but also social resources for their healthcare. Global-scale pandemics such as coronavirus disease 2019 (COVID-19) can cause unprecedented challenges that overwhelm the healthcare system that we currently have.

Technologies such as the Internet of Things (IoT) provide new opportunities to help meet the soaring demands in public healthcare. Morden commercial IoT devices can collect a variety of sensory data, ranging from simple ambient data to complex physiological data. By deploying millions of these IoT devices at people's homes and running machine learning (ML) algorithms on these data at the edge of the network, we can closely monitor people's health status and provide both short-term and long-term in-home healthcare services.

In-home Healthcare Powered by IoT and ML

Deploying IoT devices at home can help us collect different types of sensory data that indicate changes in patterns of people's daily activities associated with their health and well-being. For instance, passive infrared (PIR) sensors can monitor the presence and movement within the home. Smart plugs can record the way individuals use different appliances. Wearable devices can measure main vital signs such as body temperature and heart rate. All this environmental and physiological information can be used to analyze patterns of activities and analyze healthcare-related incidents with predictive models and adaptive algorithms. For example, training deep-learning models on time-series data [1] can extract patterns from the data and predict the potential risks. This will provide a new approach for proactive and predictive models of in-home care and health monitoring. Figure 1 depicts an intelligent healthcare system that runs ML algorithms both on sensory data on edge devices and on processed data on a Cloud server.

Figure 1: An intelligent healthcare system running ML algorithms both at the edge and on the Cloud.

Figure 1: An intelligent healthcare system running ML algorithms both at the edge and on the Cloud.

What Are the Challenges?

IoT systems in real-world deployment have heterogeneity at different layers of the network and system. For example, IoT devices made by different manufacturers may not use the same protocol or interface. The same types of devices may not use the same data format or sampling frequency, which causes issues when integrating them into the system. The set of deployed devices may vary from one household to another due to many factors such as budgets, demands, and layouts, which increases the complexity of designing and implementing analytical algorithms. These issues are from different system levels and exist during different stages in the system's lifecycle. Thus, different solutions, including standardization and deploying edge processing solutions are needed.

Another major challenge is the privacy issues of collecting data from people's homes and processing these data, especially when the data contain sensitive and personal information. Communicating the data to a Cloud server to train and use ML models leads to risks of exposing this information to untrusted parties. To enable privacy-aware processing, edge intelligence, and local processing is a preferred choice for applications that require a higher level of privacy and more control on the data. However, providing edge intelligence on an isolated node in healthcare applications will limit analytical models from accessing and learning from wider and population data in many use-case scenarios. There are several approaches to address this issue. One approach is to use federated learning (FL) [2], which allows different clients to jointly train a model without releasing their raw data and use the trained model locally.

Apart from technical challenges, people's ability to interact with IoT devices may also cause issues when running the system. Using and managing IoT devices with limited interfaces can be challenging. Therefore, it is likely that data quality may vary. Issues such as missing data or missing labels may happen regularly in the system. A data analysis system for such applications requires the analytical models to be robust to process data with varying qualities and missing values.

What Are the Opportunities?

The growing capability of sensors is one major factor that brings new opportunities. Although current commercial IoT devices mainly focus on ambient and physiological data, devices that can sense more complex data are being developed and available to deploy. For example, high-definition activity recognition with radio signals [3] can detect human activities through walls and may replace PIR sensors in the future. Medical checks and electroencephalogram (EEG) measurements are becoming available at home on low-cost IoT devices. These devices will broaden the definition of “sensory data” and enable several new healthcare services and applications.

The computational power of IoT edge units also keeps growing. Consequently, we can conduct complex machine learning tasks on IoT sensory data at the edge. These tasks use data with higher dimensionalities and bit rates, such as live videos, which can provide more accurate and fine-grained healthcare monitoring. With machine learning frameworks customized for IoT edge units, it is possible to conduct real-time monitoring and healthcare-related emergency detection.

With privacy-preserving data analytics running on a local node, a system can routinely extract different digital biomarkers from the raw data stored at the edge and use the biomarkers in different applications such as vital sign measurements and analysis, which traditionally can only be done in clinical settings.

Conclusions

In-home healthcare using IoT technologies and ML algorithms will play an important role in reshaping healthcare systems shortly. It can greatly improve the quality of life for people living with long-term conditions and provide continuous health and activity monitoring. Edge intelligence provides an opportunity to create more privacy-aware and efficient solutions for analyzing sensitive and personal information and potentially giving more control to the end-users on their data. However, to achieve this, we not only need robust technical solutions, and we also need participation from other related sectors, including clinicians, social and community services, user groups, to develop end-to-end solutions that are clinically safe and socially acceptable. At the Care Research & Technology (CR&T) center[1] of UK Dementia Research Institute (UKDRI), scientists, engineers, and doctors work together to utilize the cutting-edge technologies in artificial intelligence, engineering, robotics, and sleep science to build new healthcare systems [4] [5] [6] that will create dementia-friendly healthy homes.

[1] https://ukdri.ac.uk/centres/care-research-technology

References

  1. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
  2. H. B. McMahan, E. Moore, D. Ramage, S. Hampson and A. Blaise Aguera y, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017.
  3. M. Zhao, T. Li, M. Abu Alsheikh, Y. Tian, H. Zhao, A. Torralba and D. Katabi, “Through-Wall Human Pose Estimation Using Radio Signals,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
  4. S. Enshaeifar, P. Barnaghi, S. Skillman, A. Markides, T. Elsaleh, S. T. Acton, R. Nilforooshan and H. Rostill, “The Internet of Things for Dementia Care,” IEEE Internet Computing, vol. 22, no. 1, pp. 8-17, 2018.
  5. S. Enshaeifar, A. Zoha, A. Markides, S. Skillman, S. T. Acton, T. Elsaleh, M. Hassanpour, A. Ahrabian, M. Kenny, S. Klein, H. Rostill, R. Nilforooshan and P. Barnaghi, “Health Management and Pattern Analysis of Daily Living Activities of People with Dementia Using In-home Sensors and Machine Learning Techniques,” PLOS ONE, vol. 13, no. 5, 2018.
  6. Y. Zhao, H. Haddadi, S. Skillman, S. Enshaeifar and P. Barnaghi, “Privacy-Preserving Activity and Health Monitoring on Databox,” in Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking, Heraklion, Greece, 2020.

 

Yuchen ZhaoYuchen Zhao is a Research Associate in the Dyson School of Design Engineering at Imperial College London. He is also with the Care Research and Technology Centre at the UK Dementia Research Institute. He obtained an MSc and BEng both in Information Security from Wuhan University and Huazhong University of Science and Technology, respectively. He studied for Ph.D. in Computer Science at the University of St Andrews. His research revolves around privacy protection and includes topics such as usable privacy, user experience in Human-Data Interaction, and privacy metrics. Currently, his research focuses on privacy-preserving data analytics and federated machine learning on distributed IoT platforms.

 

Hamed HaddadiHamed Haddadi is a Reader in Human-Centred Systems and the Director of Postgraduate Studies at the Dyson School of Design Engineering at The Faculty of Engineering, Imperial College London. He leads the Systems and Algorithms Laboratory and is an Academic Fellow of the Data Science Institute. He is also a Visiting Professor at Brave Software where he works on developing privacy-preserving analytics protocols. He is interested in User-Centred Systems, IoT, Applied Machine Learning, and Data Security & Privacy. He enjoys designing and building systems that enable better use of our digital footprint while respecting users' privacy. He has spent time working and collaborating with Intel Research, Microsoft Research, AT&T Research, Telefonica, and Sony Europe. When not in the lab, he prefers to be on a ski slope or in a kayak.

 

Payam BarnaghiPayam Barnaghi is Chair in Machine Intelligence Applied to Medicine in the Department of Brain Sciences at Imperial College London. He is Deputy Director and Group Lead in the Care Research and Technology Centre at the UK Dementia Research Institute. He is an associate editor of the IEEE Transactions on Big Data and vice-chair of the IEEE SIG on Big Data Intelligent Networking. His main research goal is to develop AI and machine learning solutions for healthcare and create affordable and scalable digital systems that can be applied across a range of health conditions. He works on machine learning, the Internet of Things (IoT), semantic computing, adaptive algorithms, and computational neuroscience to solve problems and develop new technologies for future healthcare systems.

 

 

Virtualized Gateways for Scalable Multi-access Networks

Saptarshi Hazra, Thiemo Voigt, and Chenguang Lu
March 17, 2021

 

Billions of devices are expected to be connected to the Internet through low-power wireless access technologies. Different wireless access technologies satisfy different service requirements in terms of coverage, data rate, responsiveness, etc. Upcoming applications like smart cities and the smart industry require combinations of these wireless access technologies to fulfill various service requirements.

But the lack of convergent access results in overprovisioning of network infrastructure and the need to deal with multiple network management utilities while convergent access inherently increases the capacity and availability of the network. We have previously introduced our edge-based virtualized gateway architecture, VGATE [1, 2], for providing convergent access for multiple wireless access technologies. In this article, we refine our earlier design to increase scalability and enable the integration of multiple multi-access gateways.

Motivation

Smart cities and smart industries need to integrate IoT devices over geographically large areas, with a variety of wireless access technologies. From a networking point of view, we would need to manage multiple heterogeneous networks across hundreds of gateways. This diversity and scale make it difficult to develop tightly integrated services. To alleviate this issue, we need unified data and control flows for distributing and managing services.

We present an architecture based on VGATE that integrates control and data flow across multiple wireless access technologies. Furthermore, we address the problem of the dominant resource consumer, usually the physical layer (PHY) that dictates demand-based scaling decisions. We redesign the network stack as a set of loosely-coupled microservices instead of a single monolithic stack. This opens up opportunities for integration between gateways and wireless access technologies at multiple layers.

Scalable Virtualized Gateway Architecture

Figure 1 shows a logical view of our refined architecture. We have a three-layer design of cloud, edge, and gateway.  The cloud runs the IoT applications that perform data processing and generate service-level control policies for the management of the network. In Figure 1, we show a building monitoring application as an example IoT application. It monitors the power and water usage through the light (blue) and water (green) IoT sensors. Next, we have the geo-distributed edge which hosts the different components of the network stack for the gateway. Finally, we have the gateway, a flexible software radio responsible for maintaining radio connections to and from the IoT devices. The network stack consists of three main microservices: Transport, Network, and PHY+DataLink performing their corresponding functionality as in the network stack. This microservices-based architecture allows us to integrate at mainly two levels: network-level integration and transport-level integration.

Figure 1: Scalable Virtualized Gateway Architecture.

Figure 1: Scalable Virtualized Gateway Architecture.

 

Network-level Integration

Traditional devices across multiple gateways and wireless access technologies are considered to be part of different networks. This view of a network of networks limits the scope of network-level optimization to local networks only. With network-level integration, devices of multiple wireless technologies across multiple gateways can be logically integrated into a single network. This provides an overall view of all the devices allowing for more flexible network-level optimization such as load-balancing across multiple gateways and wireless access technologies. For example, if a device is reachable across multiple gateways or multiple access technologies, then having the global view helps to rearrange the network dynamically to select the link that maximizes the quality of service. Similarly, for closed-loop control systems, the interaction of heterogeneous devices in the same network facilitates the design of device-to-device data flow. This kind of flow reduces feedback loop delay as the data does not need to be propagated to the cloud-based IoT application. One major limitation is that the addressing, routing, and security methods used in network layers across different wireless access technologies, such as LoRa, Bluetooth Low Energy (BLE), IEEE 802.15.4 are different. However, there have already been major strides towards the adoption of IPv6 and RPL as the standard addressing and routing method in LoRa [3], BLE [4], and IEEE 802.15.4 [5]. Such efforts make this type of integration much more feasible.

Transport-level Integration

The transport layer facilitates the multiplexing of multiple services to the same network. Tightly integrated network stacks limit the search for the best path to the device across multiple networks. Integrating multiple networks at the transport layer would enable load-balancing, traffic shifting, and aggregation across multiple networks similar to multi-path TCP [6]. For example, if a device is reachable across multiple networks, then transport-level integration allows for optimizing the data path to ensure service level requirements are met.

Scalability and Flexibility

The approach of resource scaling on the entire stack typically leads to over-provisioning of resources because of the differences in the traffic load processed by each layer.

The dominant resource consumer would dictate the scaling decision-making. Our microservice-based design allows us to implement demand-based scaling per layer. Instances supporting different requirements for each layer can be used instead of instantiating the whole network stack. For example, if the service changes the requirement for the network layer, we can create a new network layer instance while still using the previous instances of the other layers.

Services can easily be defined as a chain of microservices with requirements specified for each layer. This enables service developers to update parts of the network that are useful to them, while still maintaining compatibility with the other layers. Hence, our approach reduces the development and deployment time of the services.

We are currently implementing this framework for integration across multi gateways and exploring the design of the orchestrator for management of this framework in the context of the EU H2020 project 5G-DIVE.

References

  1. Hazra, Saptarshi, et al. "Handling Inherent Delays in Virtual IoT Gateways." 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2019.
  2. Hazra, Saptarshi, et al. "Multi-Radio Access Technology IoT Gateway." (2020).
  3. Sartori, Benjamin, et al. "Enabling RPL multihop communications based on LoRa." 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, 2017.
  4. Nieminen, et al. “RFC 7688: IPv6 over BLUETOOTH(R) Low Energy” Internet Engineering Task Force (2015).
  5. Montenegro, et al. “RFC 4944: Transmission of IPv6 Packets over IEEE 802.15.4 Networks “Internet Engineering Task Force (2007).
  6. Ford, Alan, et al. "RFC 6824: TCP extensions for multipath operation with multiple addresses." Internet Engineering Task Force (2013).

 

Saptarshi HazraSaptarshi Hazra is currently working as a researcher at RISE, Sweden. His current research focuses on the development of flexible and scalable platforms for the Internet of Things networks.  He received Masters in Embedded Systems from TU Berlin and KTH Royal Institute of Technology in 2018.

 

Thiemo VoigtThiemo Voigt received a Ph.D. degree from Uppsala University, Sweden, in 2002. He is a Professor of computer science at the Department of Information Technology, Uppsala University. He also leads the Networked Embedded Systems group at RISE Computer Science. His current research focuses on system software for embedded networked devices and the Internet of Things. He is the author or co-author of more than 190 reviewed publications and his work has been cited more than 16200 times and received awards at multiple conferences.

 

Chenguang LuChenguang Lu received an M.Sc. degree in digital communication and a Ph.D. degree in wireless communication from the Chalmers University of Technology, Sweden, in 2005, and Aalborg University, Denmark, in 2008. He is currently a Master Researcher at Ericsson Research, Sweden. His current research focuses on efficient fronthaul interface design for 5G and 6G. He has co-authored over 30 publications in peer-reviewed conferences and journals and has filed over 70 patent applications.

 

 

IoT, Healthcare, and Medicine: Future Perspectives

Euclides Lourenço Chuma
March 17, 2021

 

We can say that the integration of IoT with Healthcare started with smartwatches monitoring some signals and storing this data in the cloud. With 5G and WiFi 6 networks accelerating the internet connection speeds, and at the same time, the Artificial Intelligence offering better analytics tools, sensors technological improvements more companies in the market are launching new IoT products, including healthcare IoT products.

The link between IoT with Health and Medicine sectors is so strong that subcategories have emerged: IoMT (Internet of Medical Things) that encompass an array of internet-capable medical devices that are in constant communication with each other or with the cloud; IoHT (Internet of Healthcare Things) that is the digital transformation of the healthcare industry. This article presents some of the new IoT for healthcare and medicine that are emerging with the arrival of the new 5G and WiFi 6 fast networks and new types of sensors.

Connectivity, IoT, Healthcare, and Medicine

The introduction of 5G mobile networks and Wi-Fi 6 networks (that is the 802.11ax standard approved by the IEEE in February 2021) will enable much faster communications and less latency, that is, with less data delay time. This faster communication will enable IoT devices efficient communication between devices, doctors, and patients. For example, we can have ambulances connected to hospitals, that is, the equipment inside of the ambulance is transmitting information to doctors who are waiting for patients in the hospitals and are already monitoring such patients, thus getting ready for faster patient care [1]. Remote surgery will become a reality because low latency and high speeds of the new internet networks that will provide high-resolution videos in real-time so that the doctor can, through robotics, perform surgeries on patients in distant locations [2].

Figure 1: Connected ambulances and Remote surgery.

Figure 1: Connected ambulances and Remote surgery.

 

IoT and Healthcare are the basis of Body Sensor Networks (BSN), which proposes the development of miniaturized wireless sensing systems for continuous capturing of physiological signals and information and storing in the cloud. The BSN will require an omnipresent communication with the cloud that will be possible with 5G networks using the concepts of Femtocell, Picocell Microcell, and Macrocell [3].

Figure 2: 5G base station types.

Figure 2: 5G base station types.

 

Sensors for Healthcare and Medicine

The new generation of radar sensors based on mmWave frequencies above 60 GHz and are sensitive enough to sense even a heartbeat [4] or used to detect human gestures like elderly fall-detection [5]. These radar sensors manufactured by companies like Infineon [6] can replace traditional cameras in many situations protecting people’s privacy during its operation and their ability to operate efficiently in environments where there are light and sound interferences as well as adverse atmospheres. Therefore, the mmWave radar sensors can be used in healthcare monitoring situations. Soon, ultra-compact spectrum sensors such as the Hamamatsu [7] and Ibsen Photonics [8] ones may be integrated into smartphones and will open up a range of possibilities for healthcare, ranging from the identification of contaminated or spoiled foods to the identification of diseases in the human body. These spectrum sensors will bring a great revolution to society.

Healthcare Smart Environment and IoT

Wearables, such as fitness trackers, heartbeat, blood glucose monitors, and other connected medical devices, have already achieved people very quickly. However, all of these wearables are in contact with the people being monitored.

The future for health and medical technologies is to monitor people without any physical contact and autonomously, being invisible to the people being monitored. The people will receive a report with their health situation and preventive actions to improve their health without using any wearable device.

Companies and startups like Imec [9], bitsensing, and Invisible Monitor [10] are already developing systems for monitoring vital signs without physical contact that will soon be available on the market and all solutions presented are based on the Internet of Things and Artificial Intelligence.

Challenges and Opportunities

For this ubiquitous monitoring future to be possible, millions or billions of connected smart sensors are needed, and IoT is the answer to turn this future possible. However, it is important to remember that all of these sensors produce large amounts of information that need to be stored and analyzed using Artificial Intelligence so that all data collected on the IoT has relevance to people and society.

References

  1. O. Udawant, et al., "Smart ambulance system using IoT", International Conference on Big Data, IoT and Data Science (BID), 2017
  2. H. Su, et al., "Internet of Things (IoT)-based Collaborative Control of a Redundant Manipulator for Teleoperated Minimally Invasive Surgeries", IEEE International Conference on Robotics and Automation (ICRA), 2020
  3. Qorvo, Small Cell Networks and the Evolution of 5G (Part 1), https://www.qorvo.com/design-hub/blog/small-cell-networks-and-the-evolution-of-5g, (accessed Mar. 15, 2021).
  4. S. Dong, et al., “Doppler Cardiogram: A Remote Detection of Human Heart Activities”, IEEE Transactions on Microwave Theory and Techniques, 2020, v.68, i.3
  5. E. L. Chuma, et al., "Internet of Things (IoT) Privacy–Protected, Fall-Detection System for the Elderly Using the Radar Sensors and Deep Learning", IEEE International Smart Cities Conference (ISC2), 2020
  6. Infineon, Radar sensors for IoT, https://www.infineon.com/cms/en/product/sensor/radar-sensors/radar-sensors-for-iot/, (accessed Mar. 15, 2021).
  7. Hamamatsu, MEMS-FPI spectrum sensors, https://www.hamamatsu.com/us/en/product/optical-sensors/spectrometers/mems-fpi-spectrum-sensor/index.html, (accessed Mar. 15, 2021).
  8. Ibsen Photonics, PEBBLE Ultra-Compact OEM Spectrometers, https://ibsen.com/products/oem-spectrometers/pebble-spectrometers/, (accessed Mar.1 5, 2021).
  9. Imec, Technology for vital sign monitoring devices, https://www.imec-int.com/en/connected-health-solutions/vital-sign-monitoring, (accessed Mar. 15, 2021).
  10. Invisible Monitor, Contactless sensors to health monitoring, http://invisiblemonitor.com/site/about/, (accessed Mar. 15, 2021).

 

Euclides Lourenco ChumaEuclides Lourenço Chuma earned a degree in Mathematics (2003) from the University of Campinas (UNICAMP), a graduate degree in network and telecommunications Systems (2015) at INATEL, an M.Sc. in electrical engineering (2017) at UNICAMP, and a Ph.D. in electrical engineering (2019) at UNICAMP, SP-Brazil. His research interests are microwave, millimeter-wave, photonics, bioengineering, sensors, wireless power transfer, and telecommunications.

 

 

Comments

2021-03-23 @ 4:50 AM by Shekhawat, Rajveer

Hello Dr. Chuma, you have nicely captured shape of things to come and the central role IoT is going to play. We have to, however, note that all the sensor data can not be pumped up to the cloud and thus we need to evolve edge analytics methods which can be deployed right near the sensor, ths avoiding huge amount of data to be uploaded, improve response time, and maintain privacy of sensitive health data. Light-weight machine learning and encryption algorithms is the need of hour, nothwithstanding the rapid rise in computing power  of edge hardware.

Teaching Users New IoT Tricks: A Model-driven Cyber Range for IoT Security Training

Michalis Smyrlis, George Spanoudakis, and Konstantinos Fysarakis
March 17, 2021

 

As IoT ecosystems become mainstream, malicious actors routinely launch impactful attacks that affect both organizations and individuals - e.g., see the Mirai IoT botnet and its variants, and the intensification of these attacks in the COVID-19 era - ultimately corroding our trust in IoT applications and services.

While a barrier in tackling these issues is the existence of heterogeneous IoT applications and devices with inherent security flaws [1][2], the situation is further exacerbated by the lack of cybersecurity awareness and training. Users are typically not informed of the relevant risks and how to minimize them, nor are they trained to promptly identify and react to cyber-attacks (e.g., IoT botnets [3], and COVID-19 -focused ones [4])). Instead, users act as enablers for the various threat actors to deploy attacks successfully, and this is true both for enterprise [5] (e.g., Industrial IoT) as well as consumer [6] (e.g., smart home) environments.

In this landscape, cyber-security training is becoming increasingly pertinent as an effective way of mitigating IoT security risks. The need for more skilled cybersecurity professionals and well-trained individuals (e.g., employees, smart homeowners), regardless of their security expertise, is becoming pressing. Nevertheless, to be effective, cybersecurity training should be tailored to the different environments and trainee types, while gained knowledge should be validated to provide evidence of said effectiveness, enabling the adoption of overall security and privacy-aware behavior. To accomplish that, modern training strategies are not only limited to learning software and hardware skills but also include training to understand actual cybersecurity threats, along with resistance-training techniques. However, training should also be adjustable to fit the ever-changing needs of the targeted domains, user behaviors, and the evolution of the threat landscape, to ensure it remains relevant [7].

To address the above requirements, a model-driven IoT Cyber Range approach has been conceived, centered Cyber Threat and Training Preparation (CTTP) Models and associated Training Programmes (CTTP Programmes), and is currently being validated in the EU-funded H2020 THREAT-ARREST project[1] [8]. The delivery of Cyber Range Training Programmes is based on these CTTP models which define the structure and automate the development of the training programs by determining the number of different aspects, such as (a) the assets of a cyber-system, their relations, and the threats covered by the CTTP Programme; (b) the ways these assets will be emulated and simulated; (c) the trainee evaluation, based on their actions and level of expertise, and; (d) the preparedness and effectiveness level that the trainees are expected to achieve on the specific training program. The benefit of having a model for every different aspect of a Training Programme is the direct mapping it provides with the actual cyber system and the automated (model-driven) specification of the training environment that it allows. Furthermore, adaptations to the models can be introduced to facilitate the delivery of training programs that follow current training needs and do not become obsolete. As of today, such a model-driven approach that incorporates emulation, simulation, serious gaming, and visualization techniques, aiming at preparing individuals with different roles and levels of expertise to defend cyber systems against known and new cyber-attacks, does not exist.

The CTTP Models

At the core of the model-driven approach to Cyber Range training, is the development of the CTTP Models:

  1. the Cyber System Asset model; specifies the assets of the cyber system that the training pertains to, their relations, and the relevant threats
  2. an Emulation sub-model; specifies automated generation and interconnection of emulated cyber system components, to be dynamically parsed by virtual infrastructure management solutions (e.g., OpenStack[2], Kubernetes[3])
  3. Simulation sub-model; specifies information for the simulation of different layers in the cyber systems implementation stack, to be dynamically parsed by simulators (e.g., NS-3[4])
  4. Serious Game sub-model; includes information needed to create a Serious Game environment
  5. Data Fabrication sub-model; includes information used for the creation of synthetic events
  6. Training Delivery Parameter model; an orchestrator of the aforementioned models which includes critical information for the instantiation of a new Training session.

Figure 1 provides a view of the model specification Graphical User Interface at the heart of the Cyber Range platform. A Training Programme can only be valid if it contains one Training Delivery Parameter model and at least one of the Cyber System Asset, Emulation, Simulation, Gamification, and Data Fabrication models. The process of Training Programme specification consists of three main phases, namely: (i) the analysis and creation of the Cyber System Asset Model; (ii) the Creation of Training Programme and, finally; (iii) the initiation of it. Figure 2 presents the Training Programme preparation process in detail.

Figure 1: Part of the model specification graphical user interface.Figure 1: Part of the model specification graphical user interface.

 Figure 2: Model-driven IoT Cyber Range approach.

Figure 2: Model-driven IoT Cyber Range approach.

 

The Training Programmes: Indicative IoT Smart Home Environment Scenario

Let us consider a training program that aims to train IoT device consumers with no security knowledge on how to respond to abnormal behavior and take immediate actions to mitigate the risk.

At first, the user is presented with the scenario background: “As the owner of a smart plug, the plug’s web-based application allows you to monitor its power consumption and/or on/off behavior. It also provides alerts through the system if abnormal behavior is detected. An intruder has gained access to your smart plug and executed a malicious application that stopped the smart plug from reporting its power consumption and turned a switch on and off at random time points. You noticed, when viewing the energy data graphs through the web application, that abnormal behavior was detected, and you are asked to bring the device back to its expected behavior.”

Figure 3: IoT-enabled Smart Home training scenario.

Figure 3: IoT-enabled Smart Home training scenario.

 

The scenario is implemented using various emulated and simulated components, comprising a smart home (see Figure 3), involving Emulation, Simulation, Gamification, and Training tools. To achieve this, a smart device is simulated within the Simulation Tool. Energy readings from this device are gathered by an emulated edge gateway and pushed to the emulated private cloud broker.

The progression of this simple Training Programme would be as follows:

  • The trainee is informed about the security concerns surrounding smart devices and, upon installation of the edge device, receives an incident response and abnormal behavior guideline.
  • He/she then checks the energy consumption graphs in the homeowner dashboard which displays an abnormal pattern in the smart plug power consumption graph caused by the smart plug not reporting power consumption.
  • As instructed in the Guideline, he/she needs to power cycle the smart plug by turning it off for 20 seconds then back on. He/she checks the graphs presented in the web application but observes that the abnormal behavior is still there (i.e., no power consumption is presented).
  • The trainee then moves to the second step of the guideline and resets the device itself.
  • Finally, the trainee checks the graphs, and observers that both the smart plug started reporting its power consumption and the connected device was not reporting abnormal behavior.

The automated evaluation of the trainee is performed via the simulation tool, which periodically checks if the trainee has made the proper remediating actions for the deployed case. An evaluation report must also be fulfilled in the training tool, where the trainee must complete information related to the type of issue encountered and remediating actions taken.

A card game can also be made available through a Gamification tool [9], to raise awareness around IoT smart device security. Furthermore, Training Programmes of different difficulty levels are also available, such as training for (a) the secure configuration of an IoT system (such as firewall policy of a gateway), (b) the identification of a botnet attack, and (c) a digital investigation analysis on an IoT cloud broker.

Concluding Remarks

Adopting a model-driven approach to Cyber Range training requires some effort and introduces increased complexity to create and parse the Models. Nevertheless, this enables the use of an evidence-based approach to Cyber Range training, and the provision of programs that are mapped to the actual cyber system and its security posture, thus targeting the most pertinent threats in the context of the specific environment. With the core functionality of the CTTP Cyber Range tested and validated through the THREAT-ARREST project, the current focus is on developing adaptation mechanisms allowing CTTP Models to follow changes to the cyber systems and the IoT threat landscape while checking the completeness and consistency of the entire specification of CTTP Models and Programmes in the context of these changes.

References

  1. OWASP Internet of Things Project, https://wiki.owasp.org/index.php/OWASP_Internet_of_Things_Project, (accessed Mar. 15, 2021).
  2. Palo Alto Networks, “2020 Unit 42 IoT Threat Report”, Mar. 2020. https://unit42.paloaltonetworks.com/iot-threat-report-2020/ (accessed Mar. 15, 2021).
  3. H. Griffioen and C. Doerr, “Examining Mirai’s Battle over the Internet of Things,” in Proceedings of the ACM Conference on Computer and Communications Security, 2020.
  4. B. Acohido, “Pushing back against IoT attacks intensified by Covid-19”, Avast, Nov. 2020. https://blog.avast.com/iot-attacks-intensified-by-covid-19-avast (accessed Mar. 15, 2021).
  5. IoT security awareness – why it is still a concern for organizations, i-SCOOP. https://www.i-scoop.eu/internet-of-things-guide/iot-security-awareness/ (accessed Mar. 15, 2021).
  6. M. Sharbaf, “Cybersecurity Awareness in IoT Threats”, IEEE Computer Society, 2020. https://www.computer.org/publications/tech-news/events/cybersecurity-month-2020/awareness-iot-threats (accessed Mar. 15, 2021).
  7. Somarakis, M. Smyrlis, K. Fysarakis, and G. Spanoudakis, “Model-Driven Cyber Range Training: A Cyber Security Assurance Perspective,” in Computer Security, 2019 pp. 172–184.
  8. M. Smyrlis, K. Fysarakis, G. Spanoudakis, and G. Hatzivasilis, “Cyber Range Training Programme Specification Through Cyber Threat and Training Preparation Models,” in International Workshop on Model-Driven Simulation and Training Environments for Cybersecurity, 2020 pp. 22–37.
  9. S. Pape, L. Goeke, A. Quintanar, K. and Beckers, “Conceptualization of a CyberSecurity Awareness Quiz” in International Workshop on Model-Driven Simulation and Training Environments for Cybersecurity, 2020 pp. 61-76.

[1] https://www.threat-arrest.eu/

[2] https://www.openstack.org/

[3] https://kubernetes.io/

[4] https://www.nsnam.org/


 

Michalis SmyrlisMichalis Smyrlis (B.Sc., Ph.D. in progress) is a Senior Software Security Engineer at SPHYNX TECHNOLOGY SOLUTIONS AG. His interests are in software security, privacy, cyber insurance, and big data. He has expertise in the development of security solutions for platforms supporting big data analytics and has worked in multiple H2020 EU projects, including THREAT ARREST, C4IIoT, SEMIoTICS, SPIDER, TOREADOR, and EVOTION. He is also doing a Ph.D. as an external part-time student at City, University of London. His research, as part of his Ph.D., is on cybersecurity risk assessment for cyber systems based on continuous and hybrid assurance assessment schemes.

 

Konstantinos FysarakisKonstantinos Fysarakis (B.Sc. Applied Mathematics, M.Sc. Information Security, Ph.D. Electronic & Computer Engineering – Embedded Systems Security) is the Chief Technology Officer of SPHYNX ANALYTICS LIMITED. His interests revolve around the security, privacy, dependability, and sustainability challenges that arise with the integration of smart ecosystems and next-generation networking infrastructures into various vertical domains and our everyday lives, having authored over 50 peer-reviewed journal and conference publications (over 800 citations, H-index 15), while also serving as a reviewer and chair at various academic venues about his research interests.

 

George SpanoudakisGeorge Spanoudakis (B.Sc., M.Sc., Ph.D. Computer Science) is the chairman of the management board of SPHYNX TECHNOLOGY SOLUTIONS AG. His research interests are in software systems security, software engineering, and biomedical computing, having published extensively in these areas (over 175 peer-reviewed publications, over 4500 citations, H-index 34). He has more than 20 years of expertise in managing R&D projects, receiving over  €120m of R&D funding from national funding bodies, the EU, and the industry, being the principal investigator of more than 30 FP6, FP7, and H2020 projects at Sphynx and before it at City, University of London.