A new range-free localisation in wireless sensor networks using support vector machine

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.

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.

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 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.

The problem of localization in wireless sensor networks where nodes do not use ranging hardware, remains a challenging problem, when considering the required location accuracy, energy expenditure and the duration of the localization phase. In this paper we propose a framework, called StarDust, for wireless sensor network localization based on passive optical components. In the StarDust framework, sensor nodes are equipped with optical retro-reflectors. An aerial device projects light towards the deployed sensor network, and records an image of the reflected light. An image processing algorithmis developed for obtaining the locations of sensor nodes. For matching a node ID to a location we propose a constraint-based label relaxation algorithm. We propose and develop localization techniques based on four types of constraints: node color, neighbor information, deployment time for a node and deployment location for a node. We evaluate the performance of a localization system based on our framework by localizing a network of 26 sensor nodes deployed in a 120-60ft2 area. The localization accuracy ranges from 2 ft to 5 ft while the localization time ranges from 10 milliseconds to 2 minutes.

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.

Development of location estimation algorithms with improvement in location precision with lower cost, less energy consumption and less hardware support has become more important for many applications in Wireless Sensor Networks (WSNs). This paper addresses the problem of secure location determination, known as secure localization in WSNs using voting based technique which gives a search region in presence of anchor nodes. From the obtained search region trilateration is applied to know the position of sensor nodes. To avoid the involvement of sensor nodes in further location estimation process, bilateration is applied. Experimental analysis shows that the maximum number of nodes can be localized and accurate location of a node can be determined efficiently with low estimation error. To avoid the attacks and involvement of malicious nodes in the localization process, we implement an improved authentication and security algorithm. Using few location reference points in the localization process reduces the communication cost. The proposed scheme also provides very good localization accuracy.

Many sensor network applications are tightly coupled with the geometric environment where the sensor nodes are deployed. The topological skeleton extraction for the topology has shown great impact on the performance of such services as location, routing, and path planning in wireless sensor networks. Nonetheless, current studies focus on using skeleton extraction for various applications in wireless sensor networks. How to achieve a better skeleton extraction has not been thoroughly investigated. There are studies on skeleton extraction from the computer vision community; their centralized algorithms for continuous space, however, are not immediately applicable for the discrete and distributed wireless sensor networks. In this paper, we present a novel Connectivity-bAsed Skeleton Extraction (CASE) algorithm to compute skeleton graph that is robust to noise, and accurate in preservation of the original topology. In addition, CASE is distributed as no centralized operation is required, and is scalable as both its time complexity and its message complexity are linearly proportional to the network size. The skeleton graph is extracted by partitioning the boundary of the sensor network to identify the skeleton points, then generating the skeleton arcs, connecting these arcs, and finally refining the coarse skeleton graph. We believe that CASE has broad applications and present a skeleton-assisted segmentation algorithm as an example. Our evaluation shows that CASE is able to extract a well-connected skeleton graph in the presence of significant noise and shape variations, and outperforms the state-of-the-art algorithms.

Range-free localization schemes are much promising in wireless sensor networks because they can estimate the location of a sensor node without power-consuming and expensive devices for measuring distance or angle information. Among range-free schemes, multi-hop schemes can localize sensor nodes with a few beacon nodes through the multi-hop connection between nodes. Since the localization accuracy depends largely on the communication range in multi-hop schemes, the communication range should be determined considering the relationship between the communication range and the localization accuracy. In this paper, we propose the selection method of the communication range for the DV-Hop scheme, a typical multi-hop scheme. The DV-Hop scheme utilizing the selection method of the communication range leads to better position determination than some existent range-free multi-hop schemes.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Meta-heuristic-based guidance model for node localization in wireless sensor networks

As an information acquisition and processing method, wireless sensor network is also an important component of the Internet of Things. As a key core technology of wireless sensor networks, localization technology has become an important direction in the current and future research of wireless sensor networks, which is also a key issue related to the real application of wireless sensor networks and the Internet of Things. The research of localization algorithm based on intelligent computing technology is paid more attention. In this paper, a novel intelligent computing method — grey wolf optimization was used to localization in wireless sensor network and proposed a novel localization algorithm. The validity and practicability of the proposed algorithm were verified by simulation experiments. The convergence performance and localization result was discussed and compared by the classical traditional intelligent computing methods—particle swarm optimization algorithm. Moreover, the localization performance under different anchor node proportion and different communication radius were analyzesed in this paper. The simulation results show that the proposed algorithm has higher localization accuracy, and it needs fewer anchor nodes and smaller communication radius to achieve the same accuracy, thus saving cost.

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.

Localization is one of the most important research subjects in terms of the wireless sensor networks (WSNs), because most of the information measured and distributed by the sensors are useful when sensors locations are well known. In this paper, a range free localization algorithm for sensor positioning is proposed. It is based on sensor distributed network model, coverage radius, hop count between each sensor and anchor, and improvement in the minimum mean square error, which computes coefficients for distance estimation between sensors and anchors. In the proposed method, the best coefficient for each hop count is computed with offline processing and Monte Carlo method. Then, these coefficients are stored in each sensor database and they are used for localization in the practical environment. Unlike some existent positioning methods, in this algorithm, it is not required to compute distance estimation coefficient by sensors. In the proposed method, it is assumed that all sensors use the omnidirectional antenna for their usual information transmission. High precision in geographical coordinate determining, less traffic load, and especially good performance in the homogeneous and non-homogeneous environment are the most important features of this algorithm. Simulation results show that the proposed algorithm has a good position determination and reduced traffic load for WSNs, as compared with some existent positioning schemes. Indeed, the proposed method improves localization precision and reduces traffic load simultaneously.

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.