Energy Constraints of Localization Techniques in Wireless Sensor Networks (WSN): A Survey <s> Distributed localization <s> In this paper, we develop an energy-efficient distributed estimation method that can be used in applications such as the estimation of a diffusive source and the localization and tracking of an acoustic target in wireless sensor networks. We first propose a statistical measurement model in which we separate the linear and nonlinear parameters. This modeling strategy reduce the processing complexity. We then study the distributed implementation of the Gauss-Newton method in the maximum likelihood estimation. After that we propose a fully distributed estimation approach based on an incremental realization of the Gauss-Newton method. We derive three modifications of the basic algorithm to improve the distributed processing performance while still considering the energy restriction. We implement the idea of information-driven collaborative signal processing and provide a sensor-node scheduling scheme in which the Cramer-Rao bound (CRB) is used as the performance and information utility measure to select the next sensor node. Numerical examples are used to study the performance of the distributed estimation, and we show that of the methods considered here, the proposed multiple iteration Kalman filtering method has the most advantages for wireless sensor networks. <s> BIB001 </s> Energy Constraints of Localization Techniques in Wireless Sensor Networks (WSN): A Survey <s> Distributed localization <s> The goal of the sensor network localization problem is to determine positions of all sensor nodes in a network given certain pairwise noisy distance measurements and some anchor node positions. This paper describes a distributed localization algorithm based on second-order cone programming relaxation. We show that the sensor nodes can estimate their positions based on local information. Unlike previous approaches, we also consider the effect of inaccurate anchor positions. In the presence of anchor position errors, the localization is performed in three steps. First, the sensor nodes estimate their positions using information from their neighbors. In the second step, the anchors refine their positions using relative distance information exchanged with their neighbors and finally, the sensors refine their position estimates. We demonstrate the convergence of the algorithm numerically. Simulation study, for both uniform and irregular network topologies, illustrates the robustness of the algorithm to anchor position and distance estimation errors, and the performance gains achievable in terms of localization accuracy, problem size reduction and computational efficiency. <s> BIB002 </s> Energy Constraints of Localization Techniques in Wireless Sensor Networks (WSN): A Survey <s> Distributed localization <s> The localization of sensor nodes is a fundamental problem in sensor networks and can be implemented using powerful and expensive beacons. Beacons, the fewer the better, can acquire their position knowledge either from GPS devices or by virtue of being manually placed. In this paper, we propose a distributed method to localization of sensor nodes using a single moving beacon, where sensor nodes compute their position estimate based on the range-free technique. Two parameters are critical to the location accuracy of sensor nodes: the radio transmission range of the beacon and how often the beacon broadcasts its position. Theoretical analysis shows that these two parameters determine the upper bound of the estimation error when the traverse route of the beacon is a straight line. We extend the position estimate when the traverse route of the beacon is randomly chosen in a real-world situation, where the radio irregularity might cause a node to miss some crucial coordinate information from the beacon. We further point out that the movement pattern of the beacon plays a pivotal role in the localization task for sensors. To minimize estimation errors, sensor nodes can carry out a variety of algorithms in accordance with the movement of the beacon. Simulation results compare variants of the distributed method in a variety of testing environments. Real experiments show that the proposed method is feasible and can estimate the location of sensor nodes accurately, given a single moving beacon. <s> BIB003
A distributed localization algorithm decentralizes computation to most sensor nodes in the network. Each sensor node is configured to estimate its location with or without reference to some anchor nodes in the network. This algorithm usually employs inter node communication for estimating the location of the sensor nodes. Inter node communication consumes less energy as compared to the energy consumption in the case of centralized algorithms. It is usually hard to implement but has high flexibility and computational efficiency for large scale deployed sensor networks. Distributed localization is a research area that is still largely unexplored. In this class of algorithms there is no notion of a hub center that collects information from other sensors. Instead, sensors exchange information to localize the source in a distributed manner. A distributed implementation based on second order cone programming (SOCP) was proposed in BIB002 , where both inaccuracies in anchor positions and distance measurements are considered to refine the position estimation of the algorithm. BIB003 also proposed using a single moving beacon in a distributed manner for sensor nodes to compute their position estimates based on the range-free technique. Energy efficiency is a major concern in distributed algorithm implementation, in an effort to address this, BIB001 proposed an energy-efficient distributed parameter estimation method for applications in wireless sensor networks. The estimation approach is based on an incremental realization of GaussNewton method.