Optimal distributed detection and estimation in static and mobile wirelesssensor networks

Energy allocation;Correlated random walk;BLUE estimator;Distributed estimation;Distributed detection;Sensor networks

Sun, Xusheng ; Electrical and Computer Engineering

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Energy allocation;    Correlated random walk;    BLUE estimator;    Distributed estimation;    Distributed detection;    Sensor networks;   

Others  :  https://smartech.gatech.edu/bitstream/1853/44825/1/xusheng_sun_201208_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

This dissertation develops optimal algorithms for distributed detection and estimationin static and mobile sensor networks. In distributed detection or estimation scenariosin clustered wireless sensor networks, sensor motes observe their local environment,make decisions or quantize these observations into local estimates of finite length, andsend/relay them to a Cluster-Head (CH). For event detection tasks that are subject toboth measurement errors and communication errors, we develop an algorithm thatcombines a Maximum a Posteriori (MAP) approach for local and global decisions withlow-complexity channel codes and processing algorithms. For event estimation tasks thatare subject to measurement errors, quantization errors and communication errors, wedevelop an algorithm that uses dithered quantization and channel compensation to ensurethat each mote's local estimate received by the CH is unbiased and then lets the CH fusethese estimates into a global one using a Best Linear Unbiased Estimator (BLUE). We thendetermine both the minimum energy required for the network to produce an estimatewith a prescribed error variance and show how this energy must be allocated amongst themotes in the network.In mobile wireless sensor networks, the mobility model governing each node will affect thedetection accuracy at the CH and the energy consumption to achieve this level of accuracy.Correlated Random Walks (CRWs) have been proposed as mobility models thataccounts for time dependency, geographical restrictions and nonzero drift. Hence, thesolution to the continuous-time, 1-D, finite state space CRW is provided and its statisticalbehavior is studied both analytically and numerically. The impact of the motion of sensoron the network's performance is also studied.