Efficient selfish node detection using SVM in IoT‐MANET environment

• Published in: Transactions on emerging telecommunications technologies Vol. 34; no. 12
• Main Authors: Ghosh, Subhankar, Banerjee, Anuradha, Sufian, Abu, Gupta, Sachin Kumar, Alsamhi, S. H., Saif, Abdu
• Format: Journal Article
• Language: English
• Published: 01.12.2023
• DOI: 10.1002/ett.4858
• ISSN: 2161-3915

The communication between nodes in the Internet of Things‐based mobile ad‐hoc networks environment is typically dependent on their selfless attitude. Whenever a node needs to send a message to another node in the network, it needs the help of one or more nodes to act as a router(s) that will bridge the gap between the source and the destination. All these selfless forwarding's require energy, bandwidth, and other resources. Therefore, some routers often raise a link breakage attack to save their resources. They keep silent after receiving a message; neither they acknowledge it nor they forward it. These selfish nodes must be identified and carefully avoided while choosing routes; otherwise, numerous messages will have to be resent, including control messages like route‐request. The identification depends on the past behavior of the node as well as velocity, the direction of movement, current geographical location and so forth. This article presents a support vector machine‐based node classification method that checks whether nodes are intentionally issuing a link breakage attack or it is really out of the radio range of its predecessor. The simulation results of the proposed method demonstrate that the proposed technique can correctly detect most of the selfish activities in the network and that also in much lesser time. It also enhances the packet delivery ratio and reduces delay and energy consumption. This article presents a support vector machine‐based node classification method that checks whether nodes are intentionally issuing a link breakage attack or it is really out of the radio range of its predecessor. The simulation results of the proposed method demonstrate that the proposed technique can correctly detect most of the selfish activities in the network and that also in much lesser time.