SENSING COVERAGE AND RESOURCE ALLOCATION ALGORITHMS IN SENSOR NETWORKS

Abstract

Sensor network has emerged as one of the hottest research areas recently. It has a wide range of applications; namely, security sensing in military defense systems, environment monitoring, manufacturing surveillance, human healthcare monitoring, etc. In this project, we investigate two scenarios of deploying a sensor network. In the first scenario, we study the usage of acoustic sensors for enemy target detection and tracking. Sensor coverage areas can be overlapped. A sensor needs to exchange information about its battery life and direction of detected target to its nearby neighbors. The goal is to achieve maximal coverage area and highly accurate tracking while optimizing the battery usage of the sensors. In the second scenario, we investigate target tracking with optical sensors. Each optical sensor has a surveillance cone which can be fully rotated around the sensor center point. A sensor can only tell the angle of approaching target but not the exact distance. After detecting a target, the optical sensor alarms its neighbors and co-operates with them to do the tracking. The Stansfield algorithm is used to combine one or more directional information from sensors to form positional information in terms of the coordinates of best point estimate and ellipse error. After verifying the scenarios with software simulation, we implemented the algorithms on sensor network hardware using Crossbow Cricket, MicaZ sensor mote kit and Canon communication cameras.