A. Farhat, M. A. Issa, A. Eldosouky, M. Ibnkahla, J. Jaskolka and A. Matrawy, “IoT-Enabled e-Health Systems: Navigating Security Challenges and Strategic Recommendations,” 2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD), Monastir, Tunisia, 2025, pp. 1-6, doi: 10.1109/SSD64182.2025.10990002.

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The rapid integration of intelligence and automation into future Internet of Things (IoT) systems, empowered by Intent-based Networking (IBN) and Network Slicing (NS) technologies, is transforming the way novel services are envisioned and delivered. The automation capabilities of IBN depend significantly on key facilitators, including data management and resource management. A robust data management methodology is essential for leveraging large-scale data, encompassing service-specific and network-specific data, enabling IBN systems to extract insights and facilitate real-time decision-making. Another critical enabler involves deploying intent-based mechanisms within an NS system that translate and ensure user intents by mapping them to precise Management and Orchestration (MO) commands. Nevertheless, data management in IoT systems faces significant security and operational challenges due to the diverse range of services and technologies involved. Furthermore, intent-based resource management demands intelligent proactive, and adaptive MO mechanisms that can fulfill a wide range of intent requirements. Existing surveys within the field have focused on technology-specific advancements, often overlooking these challenges. In response, this paper defines Intelligent Intent-Based Network Slicing (I-IBNS) systems exemplifying the integration of intelligent IBN and NS for the MO of IoT systems. Furthermore, the paper surveys I-IBNS systems, focusing on two critical domains: resource management and data management. The resource management segment examines recent developments in IBN mechanisms within an NS system. Meanwhile, the second segment explores data management complexities within IoT networks. Moreover, the paper envisions the roles of intent, NS, and the IoT ecosystem, thereby laying the foundation for future research directions.

D. H. Hussein and M. Ibnkahla, “Towards Intelligent Intent-based Network Slicing for IoT Systems: Enabling Technologies, Challenges, and Vision,” in IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2025.3570052.

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The recent Internet of Things (IoT) adoption has revolutionized various applications while introducing significant security and privacy challenges. Traditional security solutions are unsuitable for IoT systems due to their dynamicity, heterogeneity, and resource constraints. Trust-based solutions are emerging as promising alternatives due to their ability to track the dynamic behavior in IoT systems. However, existing trust management schemes are implemented at the device level, raising several challenges, including device modification that compromises certification and scalability, increased network overhead, and higher device resource utilization. To address these challenges, this paper proposes a novel trust management scheme that shifts its implementation to a higher layer in the IoT system, specifically to the IoT access layer (e.g., gateway). The proposed scheme establishes trust based on typical device interactions with the gateway without requiring additional information from the device. It relies on objective attributes spanning communication, security, and advanced dimensions to compute the trust value of an IoT device. Additionally, an Artificial Neural Network (ANN) is integrated to determine if the device acts maliciously or behaves normally. Simulation results demonstrate a notable improvement in the detection rate, primarily due to incorporating the proposed ANN, compared to the threshold-based approaches in the literature. Overall, the improvements highlight the significant advantage of the proposed scheme’s robustness.

A. Farhat, A. Eldosouky, M. Ibnkahla and A. Matrawy, “Interaction-aware Trust Management Scheme for IoT Systems With Machine Learning-Based Attack Detection,” in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3539646

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The integration of Internet of Things (loT)-based solutions in various applications introduced several challenges in security and privacy. Due to the nature of loT systems, traditional security solutions are not suitable for solving these challenges. Researchers introduced trust management as a viable solution due to its ability to track the dynamic behavior of loT devices. Compared to traditional security solutions, trust does not require an extensive amount of resources. Several loT trust solutions rely on distributed models that increase network overhead and consume additional energy. To this end, this work proposes a trust management scheme for loT systems that can be implemented at the access layer of loT systems. The proposed scheme establishes trust for loT devices through device-system interaction and communication attributes without requiring any additional information or modifications to the device. The trust value is computed using the trust attributes over a specific window size of interactions and using a forget factor. Simulation results show the ability of the proposed scheme to track the behavior of loT devices. Results also show that the proposed scheme maintains high performance in detecting persistent attacks compared to existing schemes from the literature, while improving the detection rate of ON-OFF attacks by 15%.

Farhat, A. Eldosouky, M. Ibnkahla and A. Matrawy, “IoT Trust Establishment Through System Level Interactions and Communication Attributes,” GLOBECOM 2023 – 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 5919-5924, doi: 10.1109/GLOBECOM54140.2023.10436821.

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Network operators are faced with a strategic puzzle on how to balance limited resource availability, dynamic IoT traffic requirements, and dynamic IoT device behavior in an end-to-end communication paradigm. This work formally defines the IoT-aware Virtual Network Function (VNF) placement problem. We consider realistic IoT traffic statistics from a smart-city PoC to evaluate an indicative set of placement algorithms with different objective functions under static and dynamic traffic scenarios, to study their impact on the overall performance.

Y. Rafique, A. Leivadeas, and M. Ibnkahla, “An IoT-Aware VNF Placement Proof of Concept in a Hybrid Edge-Cloud Smart City Environment,” in 2022 IEEE Wireless Communications and Networking Conference (WCNC), Apr. 2022, pp. 1395–1400. doi: 10.1109/WCNC51071.2022.9772004.

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Although the market valuation and adoption of IoT in various sectors is in an uptrend, the actual deployment is lagging when compared to the industrial forecasted data. The main reason behind this drawback is the lifespan of IoT devices due to their limited battery capacities. A solution to the problem is to deploy energy harvesting units (e.g., solar to replenish the batteries). However, due to the time varying availability of both irradiance and temperature and their effect on the power output, it is essential to predict both variables. To this end, we propose in this paper a prediction system that does not consume a lot of energy and that can be deployed on low computational nodes. This model consists of a Seasonal Auto Regressive Integrated Moving Average (SARIMA) with a Kalman filtering (KF) component. We build this model using an actual dataset for Ottawa, Ontario, Canada. We then demonstrate its effectiveness by presenting the results for randomly selected days in the Winter Season. In this context, we show that the SARIMA-KF outperforms the SARIMA in all scenarios with an average error reduction of 59.3%.

M. Azzam, Z. Bouida, and M. Ibnkahla, “Irradiance and Temperature Forecasting for Energy Harvesting Units in IoT Sensors using SARIMA-KF,” in 2022 IEEE Wireless Communications and Networking Conference (WCNC), Apr. 2022, pp. 1701–1706. doi: 10.1109/WCNC51071.2022.9771763.

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The wide adoption of large-scale Internet of Things (IoT) systems has led to an unprecedented increase in backhaul traffic congestion, making it critical to optimize traffic management at the network edge. In IoT systems, the backhaul network is supported by various backhauling technologies that have different characteristics. Also, the characteristics of the backhaul links can be sometimes time-varying and have an unknown state, due to external factors such as having the resources shared with other systems. It is the responsibility of the edge devices to be able to forward IoT traffic through the unknown-state backhaul network by selecting the suitable backhaul link for each collected data flow. To the best of our knowledge, this type of backhaul selection problem is not addressed in the literature. Therefore, there is a crucial need to develop intelligent approaches enabling edge devices to learn how to deal with unknown-state (partially observable) components of the backhaul network, which is the primary goal of this paper. We propose an edge-based backhaul selection technique for improving traffic delivery by exploiting multi-objective feedback on delivery performance. The proposed approach relies on the Advantage-Actor-Critic Deep Reinforcement Learning (DRL) methods. Moreover, to improve the DRL training performance in large-scale deployments of distributed IoT systems, Federated Learning (FL) is applied enabling multiple edge devices to collaborate in training a shared backhaul selection policy. The proposed Federated DRL (F-DRL) approach is able to solve the backhaul selection problem as verified and demonstrated through extensive simulations.

A. Jarwan and M. Ibnkahla, “Edge-Based Federated Deep Reinforcement Learning for IoT Traffic Management,” IEEE Internet of Things Journal, pp. 1–1, 2022, doi: 10.1109/JIOT.2022.3174469.

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In cluster-based Information-Centric Wireless Sensor Networks (ICWSNs), mobile sensor nodes are grouped into clusters in rounds. In each cluster, a Cluster Head (CH) is selected, which collects, aggregates, and forwards locally sensed data to a sink node. CHs further store a copy of data for the round period to act as cache nodes and deliver data to mobile users upon requests. Nevertheless, clustering and securing mobile ICWSNs is challenging. This is because, in addition to sensor nodes’ and users’ mobility, sensor nodes are often resource-constrained. Therefore, clustering and security resource allocation in mobile ICWSNs should be carefully re-designed to ensure efficient ICWSN operation, data security, and timely data access to mobile users. This paper proposes a Node Embedding with Security Resource Allocation (NESRA) clustering algorithm for mobile ICWSNs in rounds. NESRA allocates security resources to sensor nodes based on the location, mobility, and energy resources available in the first step. An optimization problem is formulated to select CHs that maximizes network coverage and minimizes data delivery delay to mobile users in the second step. In the third step, NESRA utilizes network representation learning that embeds sensor nodes’ location, mobility, and expected energy expenditure features into a 2D space to form well-separated clusters of sensing nodes. Compared to existing works, NESRA achieves lower energy consumption, nodes’ death rate, and latency, and allows higher throughput and cache nodes’ utilization with stable data security. Still, NESRA has some challenges to overcome in high-mobility networks.

A. Gharib and M. Ibnkahla, “Node Embedding for Security-Aware Clustering of Mobile Information-Centric Sensor Networks,” in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2022.3152183.

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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.

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The ĐÓ°ÉÔ­´´-Cisco Internet of Things (IoT) testbed is being built at ĐÓ°ÉÔ­´´ 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 ĐÓ°ÉÔ­´´ 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, “ĐÓ°ÉÔ­´´-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.

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