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Localization in wireless sensor network is attracting many researchers because of its significance in many real-time applications to identify the origin of events. Many applications such as battlefield, environment monitoring, habitat monitoring, and forest fire detection, etc. wherein origin of event occurrence is very important. Hence, researchers has proposed different algorithms for location identification of the sensor nodes in wireless sensor network, utilizing simulations or by creating the real-time scenarios. This paper presents the comprehensive review of localization algorithms based on soft computing techniques. This paper also presents the Particle Swarm Optimization (PSO) based clustering to find the location of the sensor nodes using the combination of Received Signal Strength Indicator (RSSI) and the Angle of Arrival (AoA). This combination of RSSI and AoA makes the clustering algorithm more efficient to achieve better accuracy in location estimation with less computational complexity. The lifetime of the sensor network which is mainly dependent on the energy dissipation of the sensor nodes is also considered in the proposed approach. The energy dissipation in the proposed approach can be reduced by adopting the density control strategy during cluster formation and reducing the communication among the sensor nodes and hence increasing the lifetime of the network. [ABSTRACT FROM AUTHOR]

Detecting pollutants and/or odorants is a challenge in environmental monitoring. Wireless sensor network can sense and gather data pertaining to different environmental parameters. However, data collected by wireless sensor network has no significance if the location of data is unknown. Thus, localization is important in wireless sensor network used in environmental monitoring. Range-free localization like DV-Hop is preferred in many applications due to its simplicity and low cost. However, nodes in wireless sensor network are vulnerable to many attacks. Without proper measure the localization may be affected once nodes are compromised. A location-aware application may fail with such faulty localization. This paper gives a simple but secured localization for environmental monitoring on the top of DV-Hop algorithm utilizing fuzzy logic. Results obtained from simulation demand that the proposed algorithm can prevent attacks in range-free localization. [ABSTRACT FROM AUTHOR]

Localization of sensor nodes is one of the major challenge in wireless sensor networks. It helps in identifying the geographically area where an event occurred. Most of the cases it is one time process wherein nodes are localized once in network life time. However, in real time scenario nodes may get dislocated within the network due to land slide or any event occurrence. In such events, localization of these sensor nodes is required. In this paper, we have proposed a new framework of localization for displaced sensor nodes. The proposed framework consider DV-Hop localization methods using particle swarm optimization (PSO). Radio irregularity model is also considered to show the applicability of proposed algorithm in anisotropic network. Simulation results show that our proposed localization framework effectively minimizes the elapsed time, localization error and energy consumption as compared to traditional framework of PSO DV-Hop. [ABSTRACT FROM AUTHOR]

Localization is an essential requirement in the increasing prevalence of wireless sensor network (WSN) applications. Reducing the computational complexity, communication overhead in WSN localization is of paramount importance in order to prolong the lifetime of the energy-limited sensor nodes and improve localization performance. This paper proposes an effective Cuckoo Search (CS) algorithm for node localization. Based on the modification of step size, this approach enables the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability for avoiding local convergence. Further, the approach restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. Extensive experiments were conducted to study the effects of parameters like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. In addition, a comparative study was conducted to realize the same localization task using the same network deployment. Experimental results prove that the proposed CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and Particle Swarm Optimization (PSO) algorithm. [ABSTRACT FROM AUTHOR]

Adding a Tew shortcuts to a wireless sensor network can construct a small-world network, which not only reduces the average energy expenditure per sensor node, but also the non-uniformity energy expenditure across the nodes. Localization still is a fundamental and essential issue lor the small-world networks. This paper presents a localization algorithm of wireless sensor networks under a shortcut constraint. By constraining the lengths, quantity and directions of the shortcuts, the inconsistency between the small-world network and localization precision can be solved. Analysis, simulation and experimental results demonstrate that the proposed algorithm can decrease the working nodes, guarantee the sparseness of the network topology and improve the estimation accuracy of the node locations. [ABSTRACT FROM AUTHOR]

In this paper, a new model utilizing all the information derived from connectivity-based sensor network localization is introduced. The connectivity information between any pair of nodes is modeled as convex and non-convex constraints. The localization problem is solved by searching for a solution that would satisfy all the constraints established in the problem. A two-objective evolutionary algorithm called Pareto Archived Evolution Strategy (PAES) is used to solve the localization problem. The solution can reach the most suitable configuration of the unknown nodes because the information on both convex and non-convex constraints related to connectivity has been utilized. From simulation results, a relationship between the communication range and accuracy is obtained. Furthermore, a two-level range connectivity-based sensor network localization method is proposed to enrich the connectivity information. The two-level range/indication of connectivity between each pair of nodes would indicate three levels of connectivity: strong, weak, or nil. A comparison on accuracy between the one-level and two-level ranges of connectivity is carried out by simulation using six different topological networks all containing 100 nodes. Simulation results have shown that better solution can be obtained by using two-level range connectivity compared with the usual one-level range connectivity-based localization. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In wireless sensor networks the Nodes are mobility enabled, since the communication among nodes fully depend on the distance between nodes and time taken to transmit the data packets. Any routing protocols suggesting the shortest path based route formation is the best concept for fast and safe transmission and it improves the QoS of the WSN in terms of speed and accuracy. Without locating the nodes and the accurate distance, the shortest path finding is inefficient. In this paper a novel Localization approach is introduced to localize the nodes accurately with the help of Beacon Nodes in the network. The simulation results produced better performance than the existing systems. [ABSTRACT FROM AUTHOR]

In operating and managing wireless sensor networks (WSNs) and their applications, the high accuracy of localization and the low operating costs are considered the substantial and key issues. The literature is rich in algorithms for localized WSN devices in hostile and unreachable outdoor environment. Majority of the literature considered mobile anchor as one of the solutions in locating sensor nodes. In this situation, the critical issue is the trajectory planning. All algorithms supposed that the mobile anchor should travel following the shortest path to determine the positions of sensor nodes with minimum localization error. A localization algorithm, which is called efficient localization algorithm based path planning for mobile anchors (ELPMA), is proposed. ELPMA is based on a one-mobile anchor moving in adjustable circular trajectory to scan the target area. It considers that the received signal strength indicator is the ranging function to determine the distance between mobile anchor and sensor nodes. ELPMA supposed the mobile anchor starts the motion from the center of the target area. The travelling paths are planned in advance by ELPMA based on the distance measurements between the mobile anchor and the sensor nodes. Simulation results demonstrate that ELPMA has better performance compared to other algorithms based on static path. This performance was evident in the localization accuracy and trajectory planning. [ABSTRACT FROM AUTHOR]

Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today's communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given. [ABSTRACT FROM AUTHOR]

Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO. [ABSTRACT FROM AUTHOR]

Knowledge of positions of sensor nodes in Wireless Sensor Networks (WSNs) will make possible many applications such as asset monitoring, object tracking and routing. In WSNs, the errors may happen in the measurement of distances and angles between pairs of nodes in WSN and these errors will be propagated to different nodes, the estimation of positions of sensor nodes can be difficult and have huge errors. In this paper, we will propose localization algorithm based on both distance and angle to landmark. So, we introduce a method of incident angle to landmark and the algorithm to exchange physical data such as distances and incident angles and update the position of a node by utilizing multiple landmarks and multiple paths to landmarks. [ABSTRACT FROM AUTHOR]

The applications of wireless senor networks (WSN) vary in diversified field, at different geographic locations. Location aware computing is the key for the success of such applications. Henceforth, there is a need of efficiently estimating the location of individual WSN nodes deployed in the remote geographic locations. Manually estimating the locations of these densely deployed nodes is impossible, therefore the WSN nodes must be able to localize themselves collecting local information from its neighboring nodes, which is called as the localization technique. The information used for localization is generally distance and bearing information obtained from the ranging techniques which are not accurate and are prone to error. Therefore there is a need for techniques which can cope up with this perturbed distance information. Rigid graphs have the property of sustaining various kind of deformations due to translation, rotation and reflection. Hence, it is more fruitful using the concepts of rigid graphs, for estimating accurate location coordinates from error prone distance measurements. In this paper we will scrutinize the sound theoretical background which defines the need of rigid graph based localization and different localization techniques, with associated algorithms which uses the concepts of rigid graphs. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have gained global attention of both, the research community and various application users. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Localization algorithms have become an essential requirement to enhance the effectiveness of WSNs demonstrating relative estimation of sensor node position of anchor nodes with their absolute coordinates. We have done a comprehensive performance evaluation of some feedforward artificial neural networks (FFANNs) training algorithms for developing efficient localization framework in WSNs. The proposed work utilizes the received signal strength observed by anchor nodes by means of some multi-path propagation effects. This paper aims for best training algorithm output while comparing results of different training algorithms. The FFANNs is designed with 3-dimensional inputs and one hidden layer with variable neurons and two outputs. For hidden layer tansigmoid transfer function while for output layer linear transfer function is used. The best training algorithm of FFANNs based model can provide better position accuracy and precision for the future applications. We have analysed and proposed the usage of training algorithms that improves the accuracy and precision of localization algorithms. The simulation results demonstrate the effectiveness and huge potential in optimizing hardware for localization module in sensor nodes. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) protocols and applications assume the knowledge of geographic location of nodes. In emerging WSN applications, localization is a recent area of research. Requirement of its applications and availability of resources need feasible localization algorithm with lower cost and higher accuracy. Locations of nodes can be estimated by a number of localization algorithms, which inevitably may introduce various types of errors in their estimations. Range-based and Range-free localization are the mainly two types of localization algorithms. In this paper, we locate unknown nodes by proposing a new algorithm named TOA-based advanced DV-hop (TOADV) that incorporates the advantages of the two types of localization algorithms. The proposed algorithm TOADV reduces the localization error and increases location accuracy without requiring additional hardware and computational costs. TOADV has lesser correction factor in the distance between anchor and the unknown node compared with existing algorithms. Simulation results show that the performance of our proposed algorithm is superior to the existing algorithms in all considered scenarios. [ABSTRACT FROM AUTHOR]

Node localization is essential to wireless sensor networks (WSN) and its applications. In this paper, we propose a particle swarm optimization (PSO) based localization algorithm (PLA) for WSNs with one or more mobile anchors. In PLA, each mobile anchor broadcasts beacons periodically, and sensor nodes locate themselves upon the receipt of multiple such messages. PLA does not require anchors to move along an optimized or a pre-determined path. This property makes it suitable for WSN applications in which data-collection and network management are undertaken by mobile data sinks with known locations. To the best of our knowledge, this is the first time that PSO is used in range-free localization in a WSN with mobile anchors. We further derive the upper bound on the localization error using Centroid method and PLA. Simulation results show that PLA can achieve high performance in various scenarios. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In a Wireless Sensor Network (WSN), system resources, such as the total node energy, need to be utilized effectively while executing the task of interest. Since a number of sensor nodes may be needed in various type of applications, it is also important to develop each sensor node in a cost effective manner. The purpose of this paper is to develop a custom made, low power, cost-effective wireless sensor node based on a MSP430G2553 microprocessor and nRf24l01+ wireless transceiver module. Then, using these nodes, we form a simple WSN testbed, and locate the position of a light emitting source. [ABSTRACT FROM AUTHOR]

Fast and accurate localization of randomly deployed nodes is required by many applications in wireless sensor networks (WSNs). However, mobile nodes localization in WSNs is more difficult than static nodes localization since the nodes mobility brings more data. In this paper, we propose a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the Monte Carlo Localization Boxed (MCB) algorithm to improve the localization accuracy. This algorithm utilizes Received Signal Strength Indication (RSSI) ranging technique to build a sample box and adds a preset error coefficient in sampling and filtering phase to increase the success rate of sampling and accuracy of valid samples. Moreover, simplified Particle Swarm Optimization (sPSO) algorithm is introduced to generate new samples and avoid constantly repeated sampling and filtering process. Simulation results denote that our proposed RMCB algorithm can reduce the location error by 24%, 14% and 14% on average compared to MCB, Range-based Monte Carlo Localization (RMCL) and RSSI Motion Prediction MCB (RMMCB) algorithm respectively and are suitable for high precision required positioning scenes. [ABSTRACT FROM AUTHOR]

Node localization is one of the essential requirements to most applications of wireless sensor networks. This paper proposes a clustered localization approach for WSNs based on Second Order Cone Programming (SOCP). The cluster solves the SOCP problem as a global minimization to get positions of the cluster sensor nodes. To enhance localization accuracy, a cluster level refinement step is implemented using Gauss-Newton optimization. The initial position for the Gauss-Newton optimization is the position drawn from the preprocessor SOCP. The proposed approach scales well for large networks and provides a considerable reduction in computation time while yielding good localization accuracy. [ABSTRACT FROM AUTHOR]

Abstract: In wireless sensor networks (WSNs), irregularly deployed nodes can significantly degrade the performance of the localization system. In this paper, we propose a novel localization scheme for the irregularly deployed WSNs. The basic approach is to control the transmission of location messages by using the fuzzy c-means (FCM) clustering algorithm. Next, each node selects its localization method according to the node density. Simulation studies show that the proposed approach can enhance the localization accuracy, while reducing the retransmission messages in the irregularly deployed WSNs. [Copyright &y& Elsevier]

This paper presents an investigation of energy demands during localization of wireless nodes in ad-hoc networks. We focus on the method based on the received signal strength (RSS) to estimate the distances between the nodes. To deal with the uncertainty of this technique, statistical methods are used. It implies more measurement samples to be taken and consequently more energy to be spent. Therefore, we investigate the accuracy of localization and the consumed energy in the relation to the number of measurement samples. The experimental measurements were conducted with IRIS sensor motes and their results related to the proposed energy model. The results show that the expended energy is not related linearly to the localization error . First, improvement of the accuracy rises fast with more measurement samples. Then, adding more samples, the accuracy increase is moderate, which means that the marginal energy cost of the additional improvement is higher. [ABSTRACT FROM AUTHOR]

Many applications of wireless sensor networks (WSN) require information about the geographic location of each sensor node. Devices that form WSN are expected to be remotely deployed in large numbers in a sensing field, and to self-organize to perform sensing and acting task. The goal of localization is to assign geographic coordinates to each device with unknown position in the deployment area. Recently, the popular strategy is to apply optimization algorithms to solve the localization problem. In this paper, we address issues associated with the application of heuristic techniques to accurate localization of nodes in a WSN system. We survey and discuss the location systems based on simulated annealing, genetic algorithms and evolutionary strategies. Finally, we describe and evaluate our methods that combine trilateration and heuristic optimization. [ABSTRACT FROM AUTHOR]

Abstract: Location information is critical essential and indispensable for many applications of wireless sensor networks. Beacon-based localization schemes are more precise than beacon-free ones, but the beacon nodes are more expensive than ordinary sensor nodes. Recently, there has been much focus on mobile-beacon based localization, for they can lower the cost while keeping the localization precision. This paper surveys the current state of the art for mobilebeacon based localization algorithms. We give the taxonomy of these schemes including range-base vs. range-free, single beacon vs. multi-beacons, omni-directional vs. directional antenna. Moreover, we also give a review of path planning methods which are critical to localization algorithm with mobile beacon, and we classify them into static and real-time schemes. We briefly introduce the steps of each localization and path planning algorithm, which serves as an introduction and survey for researchers interested in the area of localization of wireless sensor networks. [Copyright &y& Elsevier]

Localization algorithms are one of the most important protocols that may be used in many different fields of wireless sensor networks. The border problem is a considerable challenge for localization algorithms. In this regard, the sensor nodes that are placed on the boundary of a deployment area have a larger localization error as compared to the other sensor nodes. In this article, a new criterion is proposed for measuring the impact of the border problem on the performance of localization algorithms in isotropic networks. The performance of some range-free localization algorithms is studied by simulation. The results show that, contrary to LSVM and NN algorithms, the impacts of the border problem are reduced by a reduction in the dimensions of the deployment environment in the DV-hop algorithm. Besides, the effect of the border problem on the performance of the localization algorithms can be reduced by an increase in the number of anchor nodes. On the other hand, the number of sensor nodes does not have a significant impact on reducing the effect of the border problem in localization algorithms. Finally, a solution is proposed to reduce the negative impact of this issue on the performance of localization methods. [ABSTRACT FROM AUTHOR]

Node localization plays an important role in many current and envisioned WSN applications. Most of existing algorithms are not applicable for networks in a concave area. To address this problem, a novel greedy optimization localization algorithm (GOLA) is proposed. The key of GOLA is the design of neighborhood function which only uses distances between neighbor nodes to generate a new set of estimated locations from an old one, two correction operations are used to alleviate the flip ambiguity. Furthermore, a method of initial solution generation is given. Both rangebased and range-free localization can use GOLA to estimate locations of nodes if distances between neighbor nodes are measured by hardware or estimated by distance estimation algorithms. Simulation results indicate that GOLA can achieve more accurate and reliable localization results in a concave area. [ABSTRACT FROM AUTHOR]

Location is one of the most essential contexts for manyWireless Sensor Network (WSN) applications. Despite its potentiality in mobile beacon assisted localization, Unmanned Aerial Vehicle (UAV) still suffers from considerable performance degradation due to RF irregularity in 3D space and the long distance environment. In this study, we design and implement an aerial beacon assisted localization system, named ABAL, which is based on the RSS maxima generated by straight line movement of a common omnidirectional antenna with a very narrow radiation angle in vertical plane. ABAL takes advantage of the narrow vertical radiation angle of antenna to enhance the recognizability of RSS maxima, and calculates sensor location using the perpendicular relationship between the line from sensor node to RSS maxima and the straight line, tolerating RF irregularity and enabling effective localization in long distance environment. Instead of simulation, we developed a prototype system and conducted comprehensive experiments in real flights. The evaluation results show that an impressive improvement of 30%~150% over RSS ranging based approach can be achieved with the minimum mean absolute error (1.8 m) close to the error of high-quality single point GPS (1.5 m). [ABSTRACT FROM AUTHOR]

Considering energy consumption, hardware requirements, and the need of high localization accuracy, we proposed a power efficient range-free localization algorithm for wireless sensor networks. In the proposed algorithm, anchor node communicates to unknown nodes only one time by which anchor nodes inform about their coordinates to unknown nodes. By calculating hop-size of anchor nodes at unknown nodes one complete communication between anchor node and unknown node is eliminated which drastically reduce the energy consumption of nodes. Further, unknown node refines estimated hop-size for better estimation of distance from the anchor nodes. Moreover, using average hop-size of anchor nodes, unknown node calculates distance from all anchor nodes. To reduce error propagation, involved in solving for location of unknown node, a new procedure is adopted. Further, unknown node upgrades its location by exploiting the obtained information in solving the system of equations. In mathematical analysis we prove that proposed algorithm has lesser propagation error than distance vector-hop (DV-Hop) and other considered improved DV-Hop algorithms. Simulation experiments show that our proposed algorithm has better localization performance, and is more computationally efficient than DV-Hop and other compared improved DV-Hop algorithms. [ABSTRACT FROM AUTHOR]