An improved genetic algorithm for wireless sensor networks localization

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.

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.

This paper is focused on the localization problems of scattered nodes in the traditional wireless sensor network. A node localization method based on particle swarm optimization aided least square support vector regression is proposed. Firstly, the nearest neighbor clustering algorithm is used to determine the data type, where the nodes closest to the unknown node are employed as the clustering center. Then, a distributed structure based on the meshing of wireless sensor network is presented. Finally, the particle swarm optimization aided least square support vector machine algorithm is proposed for the wireless sensor network localization. Numerical experiments show that the proposed method is accurate and effective in wireless sensor network localization problems.

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.

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.

This paper studies the problem of mobile target localization in wireless sensor networks and proposes a mobile target localization algorithm based on dynamic ring structure. Firstly, a multiple rings structure is constructed in the network. To obtain the optimal division, a Genetic Algorithm based on elitist preservation strategy is used to set the ring width. Then, a binary coding algorithm is proposed to assign binary code for each ring and determine the target appearing area using binary code sequence. Finally, a comprehensive set of simulations are presented. We conclude that the proposed localization algorithm yields excellent performance.

A fundamental problem in wireless sensor networks is localization -- the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile. MSL and MSL* are range-free algorithms -- they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors. We present simulation results to demonstrate that MSL and MSL* outperform existing algorithms in terms of localization error in very different mobility conditions. MSL* outperforms MSL in most scenarios, but incurs a higher communication cost. MSL outperforms MSL* when there is significant irregularity in the radio range. We also point out some problems with a well known lower bound for the error in any range-free localization algorithm in static sensor networks.

Sensor node localization is a crucial aspect of many location-related applications that utilize wireless sensor networks. Among the many studies in the literature, multidimensional scaling-based localization techniques have been proven to be efficient, obtaining high accuracy with lower information requirements. However, when applied to large-scale wireless sensor networks with coverage holes, which are common in many scenarios, such as underground mines, the transmission path can become deviated, degrading the localization performance of this type of connectivity-based technique. Furthermore, in such complex wireless environments, non-line-of-sight reference objects, the presence of obstacles and signal fluctuations change the communication range and make it difficult to obtain an accurate position. In this article, we present a anchor-free localization scheme for large-scale wireless sensor networks called the ranging and multidimensional scaling–based localization scheme. We use ranging and non-line-of-sight error mitigation techniques to estimate accurate distances between each node pair and attempt to find inflection nodes using a novel flooding protocol to correct transmission paths that have become deviated by a coverage hole. Moreover, we replace the singular value decomposition with an iterative maximum gradient descent method to reduce the computational complexity. The results of the simulations and experiments show that our scheme performs well on wireless sensor networks with different coverage holes and is robust to varying network densities.

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.

VLSI floorplanning design automation based on soft computing is discussed. A novel approach based on the fusion of genetic algorithms and fuzzy inference for the automation of VLSI floorplanning design is proposed. The fuzzy rules are used to infer the initial position of the on-chip blocks based on analysis of the accumulated knowledge of the expert design engineer. Only the dominant combinations of place and block are inferred. Blocks deemed suitable candidates for placement at the center, relative to the four-corners of the chip, are inferred. These inferences are then reflected in the initial population of the genetic algorithms. The rest of the block placement phase is entrusted to the genetic algorithms. Experimental software to implement the proposed approach was developed. This was in turn used to perform computer-based experiments. The results of the experiments showed a level and quality of placement close to that of the expert design engineer.

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

A new method is developed for finite element (FE) domain decomposition. This method employs a hybrid graph-genetic algorithm for graph partitioning and correspondingly bisects finite element (FE) meshes.A weighted incidence graph is first constructed for the FE mesh, denoted by G0. A coarsening process is then performed using heavy-edge matching. A sequence of such operations is employed in n steps, which leads to the formation of Gn with a size suitable for genetic algorithm applications.Hereafter, Gn is bisected using conventional genetic algorithm. The shortest route tree algorithm is used for the formation of the initial population in genetic algorithm. Then an uncoarsening process is performed and the results are transferred to the graph Gn-1. The initial population for genetic algorithm on Gn-1 is constructed from the results of Gn. This process is repeated until G0 is obtained in the uncoarsening operation. Employing the properties of G1, the graph G0 is bisected by the genetic algorithm.

Patch and stitch is a well emerging technique for localization in wireless sensor networks. However, due to the presence of measurement error ordinary patch stitch may suffer from flip error. Particularly, during incremental stitching, flip error propagates from one phase to another causing avalanche error propagation. In this paper, we propose an improved patch stitch algorithm which uses a beacon degree based core map selection and minimizes the possibility of flip occurrence by using a robust triangle based stitching. Simulation results show that the improved patch stitch achieves higher accuracy as compared to the ordinary patch stitch algorithm.

Wireless sensor networks are widely adopted inmany location-sensitive applications including disastermanagement, environmental monitoring, militaryapplications where the precise estimation of each nodeposition is inevitably important when the absolute positionsof a relatively small portion as anchor nodes of theunderlying network were predetermined. Intrinsically,localization is an unconstrained optimization problem basedon various distance/path measures. Most of the existinglocalization methods focus on using different heuristic-basedor mathematical techniques to increase the precision inposition estimation. However, there were recent studiesshowing that nature-inspired algorithms like the ant-basedor genetic algorithms can effectively solve many complexoptimization problems. In this paper, we propose to adaptan evolutionary approach, namely a micro-geneticalgorithm, as a post-optimizer into some existing localizationmethods such as the Ad-hoc Positioning System (APS) tofurther improve the accuracy of their position estimation.Obviously, our proposed MGA is highly adaptable andeasily integrated into other localization methods.Furthermore, the remarkable improvements attained byour proposed MGA on both isotropic and anisotropictopologies of our simulation tests prompt for severalinteresting directions for further investigation.