Research And Implementation On Node Localization In Wireless Sensor Networks

WSN (wireless sensor networks) consists of a large number of sensor nodes with limited energy, limited computing capability and limited memory. And WSN combines the logical information world and the ture physical world seamlessly, which will completely change the interaction between human being and nature.Nowadays, WSN has been widely used in military, environment monitoring, medical treatment and nursing, intelligent home application, traffic controlling, etc.Node localization is regarded as a fundamental middleware service in wireless sensor networks. Location information can be exploited within the network protocol stack at all levels from improved physical layer communication to routing and on to the application level where positions are needed to meaningfully interpret any physical measurements that the sensors take. Node localization faces many challenges, such as self-configuration, dynamic topology change, surrounding environment influence and stringent constraints in computation, communication and energy resources. In this work, we aim to achieve robust and high-accuracy localization algorithm jointly by lightweight computation. The main contributions in this paper are listed as below.(1) We present a novel approach to wireless localization using label propagation based on semi-supervised learning. Our aim is to reduce the effort of collecting labeled data in the offline training phrase, which are expensive to obtain. This learning algorithm combines labeled and unlabeled data in learning process to fully realize a global consistency assumption:similar data should have similar labels, which has intimate connections with random walks to propagate label through the dataset along high density areas defined by unlabeled data. We test our algorithm in 802.11 wireless LAN environments, and demonstrate the advantage of our approach in both accuracy and its ability to utilize a much smaller set of labeled training data.(2) Most localization algorithms focuse on static wireless sensor networks without taking mobility of sensor nodes into account. Aiming at the drawbacks, an efficient approach for localization in mobile sensor networks using QR factorization is proposed. This algorithm first uses QR factorization to solve the linear equations for every beacon, and then mobile blind sensors update the results received from their nearest beacon without computing QR factorization again to estimate the locations, which reduce the mobile blind sensors' cost of computation in a large extent. Also the estimation lower bound (the Cramer-Rao lower bound) is analyzed for the location error characters in wireless sensor networks. The experimental results show this algorithm can efficiently reduce the cost of computation and perform well in locating the mobile blind sensors.(3) Design and Implement the wireless sensor networks localization system based RSSI (Received Signal Strength Indicator) in WiFi environments. In this localization system, the location tag receives RSSI from several access points and sends them to the localization server. After receiving the RSSI, the localization server locates the tag using some localization algorithm and then show the location result. Also this system can provide location based service for the third party applications in the form of web service.