Department of Electrical and Electronics Engineering
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Item RPL*: An Explainable AI-based routing protocol for Internet of Mobile Things(Elsevier, 2024-10) Shenoy, Meetha V.The Internet of Mobile Things (IoMT) is an emerging paradigm of Internet of Things (IoT) with special focus on enabling mobility to the ‘things’. Several IoMT applications such as group of robots or drones performing collaborative search and rescue operation, identification of mines, warehouse management, goods delivery, etc can be considered as examples of IoMT systems. In the applications mentioned above, the nodes may send the information in a multi-hop manner to the root or coordinator node which may be static or mobile. While the Routing Protocol for Low Power and Lossy Networks (RPL) is extensively utilized in static IoT networks, it encounters significant limitations in handling mobility and providing resilience against routing attacks in mobile IoT networks. In this work, we propose a modified RPL, RPL* which is robust to handling mobility in nodes and is resilient towards routing attacks. In RPL*, any deviation from the normal behaviors of the network are identified as anomalies using an unsupervised Explainable Artificial Intelligence (XAI) strategy. In RPL*, we propose a novel mobility detection mechanism that will identify the mobility in the network in an energy efficient manner without incurring additional communication overhead. To maintain the connectivity with parent node, we propose a novel proactive connectivity management mechanism in RPL* which will ensure a smooth transition from one parent to another if required, thus avoiding the network partitioning due to mobility. The performance analysis of the system has demonstrated an improvement in packet delivery ratio of the mobile nodes by 40% due to the proposed RPL* when compared to RPL. Also, the proposed XAI strategy provided an F1-score of over 95% for the detection of sink hole and black hole attacks in the tested IoMT network scenarios. It was observed that RPL* improves the performance of the IoMT network when compared to RPL. However it may be noted that the mechanisms introduced to support mobility does not lead to a drop in PDR or increase in control packet overhead for static networks. Hence, RPL* can be considered as an alternative to RPL for IoT as well as IoMT networks.Item HFedDI: A novel privacy preserving horizontal federated learning based scheme for IoT device identification(IEEE, 2023-05) Shenoy, Meetha V.As the number of IoT devices that are getting connected to the Internet is increasing, there is a need to automatically detect the IoT devices connected to the network for efficient network resource management and planning, identification of security attacks and anomalies in the network. Centralized machine learning techniques for device identification lead to the use of excessive communication bandwidth and can at times lead to privacy issues as data has to be transported to the central location. In this paper, we propose HFedDI- a novel horizontal learning based federated learning scheme for IoT device identification. The proposed federated learning technique is scalable as an edge device that does device identification can identify the devices which were never connected to it previously due to the achieved generalization accuracy as a result of model updates from other edge devices. Our work has indicated significant performance improvement in device identification across IID scenarios, and non-IID (specifically tested for data and label distribution skew) scenarios when tested on three publically available datasets. The proposed device identification technique is privacy preserving and is promising as the existing work in literature which utilized federated learning for other applications has indicated very poor results under non-IID label skew scenarios.