An Energy Efficient Technique for Object Tracking in Wireless Sensor Networks

A wireless sensor network (WSN) consists of spatially dispersed and dedicated sensor for controlling and monitoring the physical conditions of the environment. Object-Tracking Sensor Network (OTSN) based applications are widely referred as the most interesting applications of WSNs. OTSN, is a kind of WSN, is used to track the objects in a monitored area and to report their location of objects to base station, it has many real-life applications such as wild life monitoring, security applications for buildings and international border monitoring for illegal crossings. The issues which have to be resolved in the wireless sensor networks are high energy consumption and low packet delivery rate. When compared with other Wireless Sensor Network applications OTSN provides better energy consumption. In the existing system a prediction-based Tracking Technique using Sequential Patterns (PTSPs) it provides minimum energy consumption by object tracking sensor networks and also it maintains the acceptable missing rates. The experimental result of OTSN clearly shows that, it provides more efficiency in energy consumption when compared with other basic tracking techniques (Scheduled monitoring and Continuous monitoring). But it has one drawback is prediction based tracking technique using sequential pattern deals with the object tracking even when the sensor node routing fails. It has two stages, Energy Calculation and Route finding is used to increase the network life time, Markov Decision Process or learning techniques is proposed to be predict the future location of moving objects by mathematical calculations and also to reduce the object missing rate.