Accurate and Energy Efficient Mobile Object Tracking in Wireless Sensor Network

Abstract

newline The Wireless Sensor Network (WSN) has ample applications in urban and militant industries,primarily for object tracking in a restricted field such as surveillance system, in-houseperson tracking, vehicle position tracking, intruder detection, supply chain management.The military services and applications require surveillance in large acres of areas. Many military forces have to be deployed and remain active to safeguard the citizens and enhance the nation s security. It lacks a proper mechanism to alert the military officers in surveillance areas due to rugged terrains. WSN is one of the advanced technologies that newlineeffectively combine modern electronics and communication technology. The auto configurable sensor nodes are deployed in an area under observation to track an enemy vehicle.As soon as an enemy vehicle enters the sensing area, the sensors keep track of the vehicle and send its real-time location to the nearest base camp. The other applications are seismic activity monitoring, earthquake detection, and disaster relief operations. newlineThe design of an energy-efficient target tracking scheme for resource-constrained wireless sensor networks is a challenging task. The target is non-corporative, and it tries to remain untracked while transverse. The current research state shows a clear scope for developing algorithms that can work, accompanying both energy efficiency and accuracy. The prediction-based algorithms can save network energy by carefully selecting suitable nodes for continuous target tracking. However, the conventional prediction algorithms are confined to fixed motion models and generally fail in accelerated target movements. The neural networks can learn any nonlinearity between input and output as they are model-free estimators. To design a neural network-based prediction algorithm for resource-constrained tiny sensor nodes that is accurate enough still is lightweight, i.e., comprising less hardware to reduce the computational burden, is a challenging task.