Theoretic analysis of unique localization for wireless sensor networks

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.

We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.

The localization problem is to determine an assignment of coordinates to nodes in a wireless ad-hoc or sensor network that is consistent with measured pairwise node distances. Most previously proposed solutions to this problem assume that the nodes can obtain pairwise distances to other nearby nodes using some ranging technology. However, for a variety of reasons that include obstructions and lack of reliable omnidirectional ranging, this distance information is hard to obtain in practice. Even when pairwise distances between nearby nodes are known, there may not be enough information to solve the problem uniquely. This paper describes MAL, a mobile-assisted localization method which employs a mobile user to assist in measuring distances between node pairs until these distance constraints form a globally rigid'* structure that guarantees a unique localization. We derive the required constraints on the mobile's movement and the minimum number of measurements it must collect; these constraints depend on the number of nodes visible to the mobile in a given region. We show how to guide the mobile's movement to gather a sufficient number of distance samples for node localization. We use simulations and measurements from an indoor deployment using the Cricket location system to investigate the performance of MAL, finding in real-world experiments that MAL's median pairwise distance error is less than 1.5% of the true node distance.

In this paper, we investigate the joint time synchronization and localization in wireless sensor networks. Specially, based on time-of-arrival (TOA), we consider a squared-range-based least squares formulation problem and propose a generalized trust region subproblem (GTRS) algorithm based joint time synchronization and localization, which can guarantee an optimal solution to the joint time synchronization and localization problem. Sufficient experiments results show that the estimation accuracy of the proposed algorithm outperforms the traditionalunconstraint linear least squares (ULLS) and nearly coincides with the Cramer-Rao lower bound (CRLB).

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.

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.

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.

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.

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 consider the range-based sensor network localization with inaccurate anchor position information. First, a novel optimization algorithm named sequential greedy optimization (SGO) algorithm is proposed, and then two distributed localization algorithms are obtained: the first is obtained by applying the SGO algorithm to a convex formulation of the localization problem, named CSGLA; while the second is obtained by applying the SGO algorithm to a nonconvex formulation of the localization problem, named NCSGLA. The CSGLA must converge globally while the NCSGLA may converge locally. Both algorithms are partially asynchronous and can be implemented in a distributed fashion in networks. We demonstrate the localization performance via simulations. Simulation results show that, 1) the CSGLA algorithm works faster than the synchronous algorithm [16] with the same localization accuracy; 2) with a reasonably good initialization, the NCSGLA can work much better than the CSGLA.

<span class="fontstyle0">The problem of localization in wireless sensor networks has received considerable attention from researchers over the past decades. Several methods and algorithms have been proposed to solve this problem. The effectiveness of these algorithms depends on the accuracy of the estimated positions and the information required to calculate the coordinates. In this paper, we propose to evaluate four of the most commonly used localization methods in sensor networks. Our study considers a mathematical description of the studied methods in order to evaluate their complexity, and then a practical implementation on the simulation tool Cooja. We evaluate the performance of the studied methods as a function of the number of deployed sensor nodes and their degree of mobility in terms of several performance metrics. The objective is to reveal the most suitable localization method for a particular case of deployment. Improvement proposals are also provided to improve the most relevant localization method for the investigated study.</span> <br /><br />

Recent works show that the Hilbert curve path for mobile-beacon-based localization in sensor networks has better accuracy than other approaches, such as randomized and circular paths. In this paper, the modified Hilbert curve path is introduced, and its theoretical lower bound error, compared with the basic Hilbert curve path, has been shown using Cramér–Rao lower bound analysis. It is shown that the proposed approach has a more uniform localization accuracy across all the nodes in the environment than the basic Hilbert approach. The simulation results show that this approach, combined with inter-node communication, provides better and close to uniform accuracy compared with previously proposed approaches.

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.

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

Wireless Sensor Network (WSN) localisation is an essential requirement in the increasing prevalence of WSN applications. It is an important part of the Internet of Things (IOT) and has become a hot research area. Distance Vector-Hop algorithm (DV-Hop), a range-free algorithm, is widely deployed to solve the localisation problem in WSN. However, the results of the estimation precision are usually not satisfactory. In order to improve the WSN positioning accuracy, in this paper, we propose a new coupling algorithm based on Bacterial Foraging Algorithm (BFA) and Glow-worm Swarm Optimisation (GSO) (BFO-GSO). The algorithm has good convergence speed, local search ability of BFO and global convergence of GSO. The optimisation performance is verified by CEC2013 benchmarks in those designs against the original algorithm. Furthermore, Wilcoxon's rank-sum non-parametric statistical test and Friedman test are carried out to judge whether the results of the proposed algorithm differ from those of the other algorithms in a statistically significant way. The numerical results prove that it is able to significantly outperform others on majority of the benchmark functions. Finally, the proposed algorithm is also combined into the DV-Hop algorithm to improve the WSN positioning accuracy. Experimental results show that our improved algorithm achieves better performance when compared with other DV-Hop algorithms.