2021 Publications Archives - Sensor Systems and The Internet of Things

IEEE Internet of Things Journal Publication: Information-Oriented Traffic Management for Energy-Efficient and Loss-Resilient IoT Systems

mohannadabuissa — Wed, 26 Jan 2022 05:30:55 +0000

Abdallah Jarwan, Ayman Sabbah, and Mohamed Ibnkahla

Internet-of-Things (IoT) systems are driven by the massive data generation at the sensing layer. Most of data management protocols in the sensing layer target optimizing different Quality-of-Service (QoS) metrics such as data rate, packet loss, and delay, while taking the limitations of Wireless Sensing Networks (WSNs) into consideration. However, it is also critical to consider improving the quality of collected data, especially when the WSNs are congested by delay-sensitive data packets, leading to high packet loss. While Value-of-Information (VoI), which represents data freshness and time-relevance, is commonly used to define data value, it does not provide a notion of data recoverability and tolerance-to-loss. In this paper, the main contribution is to develop data management schemes to cope with inevitable data loss by identifying the data portion with a higher value to the overlaying applications. We also develop a novel metric that is referred to as Information-Content (IC), quantifying the amount of information in data. The IC is defined such that data holding information of low-probable events have higher IC than data holding information of high-probable events. In this context, the VoI and IC are exploited in developing information-oriented traffic forwarding and reduction schemes to ensure that all dropped packets are more accurately recoverable through a traffic recovery scheme, and therefore the running applications are not disrupted. Through extensive Monte-Carlo simulations, we show that the proposed information-oriented data management improves the performance in terms of data congestion, lifetime, packet loss, delay, and data recovery accuracy.

A. Jarwan, A. Sabbah and M. Ibnkahla, “Information-Oriented Traffic Management for Energy-Efficient and Loss-Resilient IoT Systems,” in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3132925.

For more details:

2021 IEEE Globecom Workshops (GC Wkshps): �Ӱ�ԭ��-Cisco IoT Testbed: Architecture, Features, and Applications

mohannadabuissa — Tue, 25 Jan 2022 21:20:43 +0000

Zied Bouida, Ismael AlShiab, Yousef Rafique, Abdallah Jarwan, Manishkumar Moorjmalani, Sajib Kumar Kuri, and Mohamed Ibnkahla

The �Ӱ�ԭ��-Cisco Internet of Things (IoT) testbed is being built at �Ӱ�ԭ�� University’s IoT Lab. Hosting diverse applications, the testbed adopts a layered approach composed of four layers: Sensing, Edge, Fog, and Cloud layers. The IoT testbed is distributed over multiple sites at �Ӱ�ԭ�� University’s Campus and the City of Ottawa. In this paper, we first present the architecture and the features of the testbed. Then, we give an overview of the applications running on the testbed and their underlying techniques and protocols. In particular, we highlight the use of Edge computing and data analytics in selected applications. We also present the testbed’s deployment challenges and the proposed solutions.

Z. Bouida, I. AlShiab, Y. Rafique, A. Jarwan, M. Moorjmalani, S. K. Kuri, and M. Ibnkahla, “�Ӱ�ԭ��-Cisco IoT Testbed: Architecture, Features, and Applications,” Proc. 2021 GlobeCom Workshop on Experimental wireless platforms and testbeds for computing, communication and networking research, pp. 1-6, Madrid, Spain, Dec 2021.

For more details:

ICC 2021-IEEE International Conference on Communications Publication: Virtual Sensing Networks and Dynamic RPL-Based Routing for IoT Sensing Services

mohannadabuissa — Wed, 15 Sep 2021 15:39:47 +0000

AlShiab, Ismael, Aris Leivadeas, and Mohamed Ibnkahla

Abstract:

IoT applications are quickly evolving in scope and objectives while their focus is being shifted toward supporting dynamic users’ requirements. IoT users initiate applications and expect quick and reliable deployment without worrying about the underlying complexities of the required sensing and routing resources. On the other hand, IoT sensing nodes, sinks, and gateways are heterogeneous, have limited resources, and require significant cost and installation time. Sensing network-level virtualization through virtual Sensing Networks (VSNs) could play an important role in enabling the formation of virtual groups that link the needed IoT sensing and routing resources. These VSNs can be initiated on-demand with the goal to satisfy different IoT applications’ requirements. In this context, we present a joint algorithm for IoT Sensing Resource Allocation with Dynamic Resource-Based Routing (SRADRR). The SRADRR algorithm builds on the current distinguished empowerment of sensing networks using recent standards like RPL and 6LowPAN. The proposed algorithm suggests employing the RPL standard concepts to create DODAG routing trees that dynamically adapt according to the available sensing resources and the requirements of the running and arriving applications. Our results and implementation of the SRADRR reveal promising enhancements in the overall applications deployment rate.

AlShiab, Ismael, Aris Leivadeas, and Mohamed Ibnkahla. “Virtual Sensing Networks and Dynamic RPL-Based Routing for IoT Sensing Services.” ICC 2021-IEEE International Conference on Communications. IEEE, 2021.

ICC 2021 – IEEE International Conference on Communications: Security Aware Cluster Head Selection with Coverage and Energy Optimization in WSNs for IoT

mohannadabuissa — Fri, 06 Aug 2021 20:54:39 +0000

Anastassia Gharib and Mohamed Ibnkahla

Nodes in wireless Internet of Things (IoT) sensor networks are heterogeneous in nature. This heterogeneity can come from energy and security resources available at the node level. Besides, these resources are usually limited. Efficient cluster head (CH) selection in rounds is the key to preserving energy resources of sensor nodes. However, energy and security resources are contradictory to one another. Therefore, it is challenging to ensure CH selection with appropriate security resources without decreasing energy efficiency. Coverage and energy optimization subject to a required security level can form a solution to the aforementioned trade-off. This paper proposes a security level aware CH selection algorithm in wireless sensor networks for IoT. The proposed method considers energy and security level updates for nodes and coverage provided by associated CHs. The proposed method performs CH selection in rounds and in a centralized parallel processing way, making it applicable to the IoT scenario. The proposed algorithm is compared to existing traditional and emerging CH selection algorithms that apply security mechanisms in terms of energy and security efficiencies.

A. Gharib and M. Ibnkahla, “Security Aware Cluster Head Selection with Coverage and Energy Optimization in WSNs for IoT,” IEEE International Conference on Communications (ICC), June 2021, pp. 1-6.

For more details:

IEEE Internet of Things Magazine: Distributed Spectrum Sensing for IoT Networks: Architecture, Challenges, and Learning

mohannadabuissa — Tue, 06 Apr 2021 20:33:59 +0000

Anastassia Gharib, Waleed Ejaz, and Mohamed Ibnkahla

Spectrum sensing is believed to be a prominent solution to spectrum scarcity caused by the presence of a large number of devices, particularly in Internet of Things (IoT) applications. Providing spectrum access to all of these devices is one of the paramount issues for I systems. Nevertheless, IoT poses several challenges for spectrum sensing that have yet to be overcome. Conventional spectrum sensing techniques have to be carefully modified to be applied to sophisticated and scalable IoT systems. In this paper, an analysis of spectrum sensing for IoT and its possible architecture configurations are presented. We provide an extensive list of challenges associated with spectrum sensing for IoT systems. Focus is given to distributed learning approaches known as incremental, consensus, and diffusion learning in the context of IoT. We further present a case study on cooperative spectrum sensing for IoT systems, where we propose an optimized distributed solution based on diffusion learning. Finally, simulation results demonstrate that the proposed solution improves detection performance and aggregate secondary IoT network throughput, and can minimize hardware complexity for secondary IoT users.

A. Gharib, W. Ejaz and M. Ibnkahla, “Distributed Spectrum Sensing for IoT Networks: Architecture, Challenges, and Learning,” IEEE Internet of Things Magazine, vol. 4, no. 2, pp. 66-73, June 2021.

For more details: