A Semidefinite Programming Approach to Source Localization in Wireless Sensor Networks

Graph invariants for unique localizability in cooperative localization of wireless sensor networks: Rigidity index and redundancy index

This chapter describes one of the major challenges in technology advancement of Wireless Sensor Networks (WSNs), i.e., Localization in WSNs. In recent years, sensor node localization is an emerging research area in WSNs. The sensor data become useless, if we do not know the location of the reporting node. Coordinates determination of the sensor node is a challenging problem and it is referred as localization problem. The nodes which has unknown coordinates is termed as target Nodes. Various localization methods can be utilized to find out the location of sensor nodes, those coordinates are not known in a system/network. Efficient WSN localization can be treated as multi-dimensional optimization problem which can be addressed through population based stochastic techniques, which involves the minimization of a function of differences between Euclidean and measured distance between sensor nodes. In this chapter various connectivity, range information and mobility based localization algorithms have been discussed. For optimizing the results of these algorithms, various computational intelligence (CI) based optimizing algorithms like Particle Swarm Optimization, Biogeography Based Optimization, Firefly Algorithm and Genetic Algorithm have been discussed. A choice between these algorithm is influenced by the localization accuracy expected to be and convergence rate.

This paper addresses the problem of joint multi-source localization and environment perception in wireless sensor networks (WSNs) based on received signal strength (RSS). Contrary to the traditional RSS approaches which assume known number of sources and known parameter of environments, we propose to estimate the sources with unknown number and the unknown environmental parameter simultaneously. By assuming that the sources are sparse in the sensing field, a non-convex l1 regularized least squares problem is formulated. This non-convex problem is then decomposed to two simple subproblems, one for multi-source localization and another for environment perception. A joint multi-source localization and environment perception algorithm is proposed, with extensive simulations to validate its effectiveness.

In this paper, we investigate source localization for wireless sensor networks based on received signal strength. We first formulate the localization problem as the intersection computation of a group of sensing rings, and then convert this non-convex problem into two weighted convex optimization problems. We next propose a unified distributed alternating projection algorithm to solve the resulting weighted optimization problems, where sensor nodes can communicate only locally with their neighbors over a time-varying jointly-connected topology. We also show that sensor nodes' estimates can achieve consensus on a possible minimizer. Both theoretical analysis and some comparative simulations reveal that the proposed approach has good estimation performance in both the consistent and inconsistent cases.

SummaryIndoor localization in wireless sensor networks is a challenging task. Static localization and moving target monitoring are addressed using ensemble learning method and received signal strength indicator. The suggested strategy combines several regression trees to have better performance. This solution has been experimentally evaluated using real measurements in an office room. The performance results have been analyzed through a comparison with learning‐based localization algorithms currently available in the literature. The analysis shows that the adopted solution is simple in term of computation, accurate and robust to environmental variation.

Wireless sensor networks have important military and civilian applications where determining the geographical location of a specific event is crucial. High-accuracy event localization is a challenging task that depends on the type of event to track. Hence, this work proposes a general approach for achieving event localization in wireless sensor networks, keeping independence from the kind of signals emitted by such events. The proposed approach estimates the distance between an event and a sensor node according to the intensity of the signal perceived by that sensor. In addition, lateration and fingerprinting techniques are implemented to determine the location of particular events. In order to evaluate our proposal, acoustic event localization is implemented in a real wireless sensor network based on the Arduino hardware. Results from this specific implementation show an event localization error of approximately 0.2 m inside a 2×2 m area.

There has been a growing interest in the applications of wireless sensor networks in unattended environments. In such applications, sensor nodes are usually deployed randomly in an area of interest. Knowledge of accurate node location is essential in order to correlate the gathered data to the origin of the sensed phenomena and assure the relevance of the reported information. In this paper, we present an efficient anchor-free protocol for localization in wireless sensor networks. Each node discovers its neighbors that are within its transmission range and estimates their ranges. Our algorithm fuses local range measurements in order to form a network wide unified coordinate systems while minimizing the overhead incurred at the deployed sensors. Scalability is achieved through grouping sensors into clusters. Simulation results show that the proposed protocol achieves precise localization of sensors and maintains consistent error margins.

In this paper, we design a new distributed angle estimation method for localization in wireless sensor networks (WSNs) under multipath propagation environment. We employ a two-antenna anchor that can emit two linear chirp waves simultaneously, and propose to estimate the angle of departure (AOD) of the emitted waves at each receiving node via frequency measurement of the local received signal strength indication (RSSI) signal. An improved estimation method is further proposed where multiple parallel arrays are adopted to provide the space diversity. The proposed methods rely only on radio transceivers and do not require frequency synchronization or precise time synchronization between the transceivers. More importantly, the angle is estimated at each sensor in a completely distributed manner. The performance analysis is derived and simulations are presented to corroborate the proposed studies.

The paper describes a concept of software tool for development of nodes localization in large wireless sensor network. The basic module is the event driven simulator that enables loading position of wireless network nodes, description of the behavior of nodes, description of the environment and modeling of wave propagation in defined space. Node localization and deployment problem in wireless sensor network are supported by implemented algorithms.

In wireless sensor networks (WSNs), localization is one of the most important topics because the location information is typically useful for many applications.The primary data used in a localization process include the locations of anchor nodes and the distances between neighboring nodes.However, these data may contain outliers that deviate from their true values.The existence of the outliers might make the estimated positions not accurate.Thus, it is important to detect and handle outliers in order to achieve high localization accuracy.In this paper, we survey the existing outlier detection techniques for localization in wireless sensor networks.We provide taxonomy for classifying outlier detection techniques in WSNs localization based on different features.In addition, we present comparisons of these techniques.Finally, we discuss the future research directions in this area.

In wireless sensor networks, sensor node localization is an important topic because sensor nodes are randomly scattered in the region of interest and they get connected into the network on their own. Localization information is needed in routing of data, reducing energy consumption and in location dependent data acquisition. We propose a localization scheme plus a medium access protocol that: (a) reduces power consumption; (b) does not require high power beacons; (c) provides better localization accuracy; and (d) helps in reducing collisions. The proposed localization scheme is based on received signal strength by the three masters. Moreover, it is a distributed algorithm.

Intelligent fusion of information derived from received signal strength and inertial measurements for indoor wireless localization

This paper is concerned with localization in wireless sensor networks (WSNs). We propose a novel localization algorithm based beamforming. The performed localization algorithm is adopted for a typical communication model in WSNs involving two clusters of sensors at both transmitter and receiver sides of the communication link. The set of collaborative sensor nodes located at the transmit cluster could be assimilated to a distributed Multiple Input Multiple Output (MIMO) system [1], [2], [3]. We tackle the task of estimating the position of randomly distributed sensor nodes and we present a novel algorithm based on the beamforming approach [4], [5] and the received signal strength (SS) measurement. The correct operation of the proposed algorithm has been proved by simulation. The collected results for the sampling deviation of the estimator and the estimator variance are envisaged in order to evaluate the accuracy of the proposed algorithm.

Target localization and tracking problems in Wireless Sensor Networks (WSNs) have received considerable attention recently, driven by the need to achieve high localization accuracy, with the minimum cost possible. A wide range of proposed approaches regarding the localization area have emerged, however most of them suffer from either requiring an extra sensor, high power consumption, inaccessible indoors, or offer high localization error. This paper presents a research and development of a hybrid WSN tracking system using the Received Signal Strength Indicator (RSSI) and the inertial system. The proposed system is an efficient indoors, where it offers reasonable localization accuracy (0.1 - 0.7) meters, and achieves low power consumption. A number of real experiments have been conducted to test the efficiency of the proposed system using XBee modules.

In recent years node localization in wireless sensor networks (WSNs) has attracted much attention due to the increase of usage and applications of WSNs. Many algorithms and techniques for locating sensor nodes have been proposed in the literature. A recent algorithm, which is based on a given set of pairwise distance estimates among nodes and the target, generates a map of node locations. This algorithm, known as FastMap, uses projections onto orthogonal hyperplanes to find the coordinates successively. In this paper, we analytically study the performance of the FastMap algorithm for 2D positioning. Moreover a modified version of FastMap with better performance is proposed and analyzed. The analysis shows that the optimum anchor placement should be at the edge of the network.