Abstract: Network lifetime is the essential parameter of efficient wireless sensor network. This can be achieving by the using different localization and routing algorithm. In this paper, we propose the hybridization of Support Vector Machine (SVM) and Ant colony Optimization (ACO). SVM is the supervised learning model, which is used to trained the dataset points and classification them into two class that is dead nodes and alive nodes. Whereas ACO, select optimal or shortest path among all adjacent possible path from source node to destination node for data transmission. The proposed technique improves the network lifetime as well as detects failure nodes in wireless sensor network. The proposed work compared with Artificial Bee Colony (ABC) and Particle Swarm Optimization algorithm (PSO) at different nodes.
Keywords: WSN, ACO, SVM, wireless sensor network