Search

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]

Wireless Sensor Networks are a collection of nodes which contain tiny devices having low power and work with minimal cost. In such a network, the functioning of these nodes plays a very important role to sense data and also forward the sensed data to the target observer. For segregation of nodes in effective locations, there is a need to localize these nodes by combining location information with the sensed data for tracking and monitoring of malicious nodes, goods tracking etc. The various approaches towards localization may be classified as range-free or range-based, both having their own pros and cons. This paper presents a discussion on different localization approaches, followed by simulation results of localization using lognormal shadowing model for distance estimation and trilateration for location computation. The results depict the individual impacts of node density and area of sensed region on the localization error. It is observed that with a fixed node count, the errors in location estimates increase with the increase in node density. It is also depicted that the localization error increases towards the boundary of a network. Since the approach discussed in the paper uses loss in received power for distance computation, there is no cost overhead for additional hardware involved in the implementation. Thus the approach used is cost effective. [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]

Wireless sensor networks (WSNs) have become popular for sensing areas-of-interest and performing assigned tasks based on information on the location of sensor devices. Localization in WSNs is aimed at designating distinct geographical information to the inordinate nodes within a search area. Biologically inspired algorithms are being applied extensively in WSN localization to determine inordinate nodes more precisely while consuming minimal computation time. An optimization algorithm belonging to the metaheuristic class and named penguin search optimization (PeSOA) is presented in this paper. It utilizes the hunting approaches in a collaborative manner to determine the inordinate nodes within an area of interest. Subsequently, the proposed algorithm is compared with four popular algorithms, namely particle swarm optimization (PSO), binary particle swarm optimization (BPSO), bat algorithm (BA), and cuckoo search algorithm (CS). The comparison is based on two performance metrics: localization accuracy and computation time to determine inordinate nodes. The results obtained from the simulation illustrate that PeSOA outperforms the other algorithms, achieving an accuracy higher than 30%. In terms of computation time to determine inordinate nodes, the proposed algorithm requires 28% less time (on average) than the other algorithms do. [ABSTRACT FROM AUTHOR]

Network localization and navigation (NLN) is a promising paradigm, in which mobile nodes exploit spatiotemporal cooperation, to provide reliable location information for a diverse range of wireless applications. This paper presents a theoretical foundation of NLN, including a mathematical formulation for NLN, an introduction of equivalent Fisher information analysis, and determination of the fundamental limits of localization accuracy. Key ingredients such as spatiotemporal cooperation, array signal processing, and map exploitation are then studied. We also develop a geometric interpretation to provide insights into the essence of NLN for network design. Finally, the paper highlights the connection between the theoretical foundation and algorithm development for NLN, guiding the design and operation of practical localization systems. [ABSTRACT FROM AUTHOR]

Both localization and synchronization of mobile nodes are important for wireless networks. In this paper, we propose new methods for network localization and synchronization (NLS) using full-duplex radios through only two frames of transmission. Specifically, all nodes simultaneously transmit their signature signals in the first frame, while receiving others’ signals via full-duplex radios. In the second frame, nodes transmit either scrambled versions of their received signals in the first frame or a digital packet of the channel parameter estimates of the received signals. We develop distributed algorithms to estimate the arrival times of different components in the received signals. These arrival times are then used to determine the local network geometry and clock offsets. The Cramér-Rao lower bounds for internode distances and clock offsets are derived, and the former can be translated into error bounds of the node positions. Compared with conventional frequency division duplex or time-division duplex, we demonstrate the high efficiency of NLS using full-duplex radios, revealing its potential beyond data communications in future wireless networks. [ABSTRACT FROM AUTHOR]

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network. [ABSTRACT FROM AUTHOR]

Localization in wireless sensor networks (WSNs) is required to examine the coordinates of the sensor nodes deployed in the sensing field. It is the process that determines the location of the target nodes relative to the location of deployed anchor nodes. The anchor nodes positions are known as the nodes that have GPS unit incorporated with them. All sensor nodes are generally not configured with GPS as it is not suitable for indoor environments and/or underwater areas. A network becomes more expensive and utilizes more energy if all nodes are equipped with GPS that is a major drawback of WSNs. Various localization schemes have been proposed in literature, while most research proposals deal with the study of 2D applications. However, in the 3D applications, the area under observation may have a complexity in the sensing environment. An optimized algorithm is required for the determination of node location in 3D environment. In this paper, we propose an adaptive flower pollination algorithm (AFPA) with enhanced exploration and exploitation capabilities of conventional FPA for the localization of sensor nodes in WSN. To test the performance of AFPA, benchmark functions (CEC 2019) are used to compare it with other meta-heuristics. The results show that proposed AFPA outperforms in terms of convergence speed and provides better results for most of the benchmark functions. Also, the proposed AFPA is tested on WSN Localization problem, it provides least localization error in comparison to other techniques applied in 3D WSN environments. [ABSTRACT FROM AUTHOR]

Randomly deployed sensor nodes can form a wireless sensor network (WSN) in order to track changes in a certain physical quantity. These sensors can exchange the measurement data with themselves and with an outer interface. The measured data may be a location specific. Therefore, the localization in WSNs becomes a necessity. This localization can be carried out by using a lot of measurement models. These models include; time of arrival (TOA), time difference of arrival (TDOA), direction of arrival (DOA), and received signal strength. TOA and TDOA based localization can offer a very good accuracy than the other types. In this paper, the cooperative TOA localization with virtual anchors selection is proposed in order to increase the accuracy of the localization procedure. The proposed cooperative TOA procedure is explained and mathematically analyzed based on linear least square algorithm. Simulation results show that the proposed cooperative TOA can reduce the mean square positioning error. Furthermore, it is more tolerable to noise and errors than the ordinary TOA. In addition, it can provide a 3 dB performance over the ordinary TOA method. [ABSTRACT FROM AUTHOR]

To localize the wireless sensor networks nodes, only the hop-based information (i.e., hops’ number, average hop size, and so on) has been, so far, exploited by range-free techniques, with poor-accuracy, however. In this paper, we show that localization accuracy may greatly benefit from joint exploitation, at no cost, of the information already provided by the forwarding nodes (i.e., relays) between each anchor (i.e., position aware) and sensor nodes pair. As such, we develop a novel range-free localization algorithm, derive its average location estimation error (LEE) in closed-form, and compare it in LEE performance with the best representative algorithms in the literature. We show that the proposed algorithm outperforms them in accuracy. In contrast to the latter, we further prove that it is able to achieve an LEE average and variance of about 0 when the number of sensors is large enough, thereby achieving an unprecedented accuracy performance among range-free techniques. [ABSTRACT FROM PUBLISHER]

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]

Localization has emerged as an important and critical component of research in Wireless Sensor Networks (WSNs). WSN is a network of numerous sensors distributed across broad areas of the world to conduct numerous activities, including sensing the data and transferring it to various devices. Most applications, like animal tracking, object monitoring, and innumerable resources put in the interior as well as outdoor locations, need to identify the position of the occurring incident. The primary objective of localization is to identify the locality of sensor nodes installed in a network so that the location of a particular event can be traced. Different optimization approaches are observed in the work for solving the localization challenge in WSN and assigning the apt positions to undiscovered sensor nodes. This research employs the approach of localizing sensor nodes in a 2D platform utilizing an exclusive static anchor node and virtual anchors to detect dynamic target nodes by projecting these six virtual anchors hexagonally at different orientations and then optimizing the estimated target node co-ordinates employing Whale Optimization-based Naked Mole Rat Algorithm (WONMRA). Moreover, the effectiveness of a variety of optimization strategies employed for localization is compared to the WONMRA strategy concerning localization error and the number of nodes being localized, and it has been investigated that the average error in localization is 0.1999 according to WONMRA and is less than all other optimization techniques. [ABSTRACT FROM AUTHOR]

Node localization is critical for accessing diverse nodes that provide services in remote places. Single-anchor localization techniques suffer co-linearity, performing poorly. The reliable multiple anchor node selection method is computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. Node localization in wireless sensor networks (WSNs) is challenging due to the number and placement of anchors, as well as their communication capabilities. These senor nodes possess limited energy resources, which is a big concern in localization. In addition to convention optimization in WSNs, researchers have employed nature-inspired algorithms to localize unknown nodes in WSN. However, these methods take longer, require lots of processing power, and have higher localization error, with a greater number of beacon nodes and sensitivity to parameter selection affecting localization. This research employed a nature-inspired crow search algorithm (an improvement over other nature-inspired algorithms) for selecting the suitable number of anchor nodes from the population, reducing errors in localizing unknown nodes. Additionally, the weighted centroid method was proposed for identifying the exact location of an unknown node. This made the crow search weighted centroid localization (CS-WCL) algorithm a more trustworthy and efficient method for node localization in WSNs, with reduced average localization error (ALE) and energy consumption. CS-WCL outperformed WCL and distance vector (DV)-Hop, with a reduced ALE of 15% (from 32%) and varying communication radii from 20 m to 45 m. Also, the ALE against scalability was validated for CS-WCL against WCL and DV-Hop for a varying number of beacon nodes (from 3 to 2), reducing ALE to 2.59% (from 28.75%). Lastly, CS-WCL resulted in reduced energy consumption (from 120 mJ to 45 mJ) for varying network nodes from 30 to 300 against WCL and DV-Hop. Thus, CS-WCL outperformed other nature-inspired algorithms in node localization. These have been validated using MATLAB 2022b. [ABSTRACT FROM AUTHOR]

Localization is one of the most important challenges of wireless sensor networks because the location information is typically used in other domains such as coverage, deployment, routing, and target tracking. There exist some localization algorithms that facilitate the sensor nodes to locate itself using the mobile anchor node position. Some crucial attempts have been made in the past for optimizing the mobile anchor node trajectory with good accuracy. This paper presents a novel path planning scheme, D-connect, which ensures the localization of all the sensor nodes with minimum trajectory length. The performance of the proposed scheme is evaluated through a series of simulations. Experimental results reveal that the shortest path for traversing the whole area can be traced with the minimum localization error using this method. It also shows that D-connect outperforms the existing methods in terms of the anchor node trajectory length as well as the localization error. [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]

This paper addresses the problem of localization in sensor networks where, initially, a certain number of sensors are aware of their positions (either by using GPS or by being hand-placed) and are referred to as anchors. Our goal is to localize all sensors with high accuracy, while using a limited number of anchors. Sensors can be equipped with different technologies for signal and angle measurements. These measures can be altered by some errors because of the network environment that induces position inaccuracies. In this paper, we propose a family (AT-Family) of three new distributed localization techniques in wireless sensor networks: free-measurement (AT-Free) where sensors have no capability of measure, signal-measurement (AT-Dist) where sensors can calculate distances, and angle-measurement (AT-Angle) where sensors can calculate angles. These methods determine the position of each sensor while indicating the accuracy of its position. They have two important properties: first, a sensor node can deduce if its estimated position is close to its real position and contribute to the positioning of others nodes; second, a sensor can eliminate wrong information received about its position. This last property allows to manage measure errors that are the main drawback of measure-based methods such as AT-Dist and AT-Angle techniques. By varying the density and the error rate, simulations show that the three proposed techniques achieve good performances in term of high accuracy of localized nodes and less energy consuming while assuming presence of measure errors and considering low number of anchors. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents an ultra-low-power, low-voltage sensor node for wireless sensor networks. The node scavenges RF energy out of the environment, resulting in a limited available power budget and causing an unstable supply voltage. Hence, accurate and extensive power management is needed to achieve proper functionality. The fully integrated, autonomous system is described, including the scavenging circuitry with integrated antenna, the power detection and power control circuits, the on-chip clock reference, the UWB transmitter and the digital control circuitry. The wireless sensor node is implemented in $$0.13 \,\upmu \hbox {m}$$ CMOS technology. The only external components are a storage capacitor and a UWB transmit antenna. The system consumes only $$113\, \upmu \hbox {W}$$ during burst mode, while only 8 nW is consumed during the scavenging operation, enabling an efficiency of 5.35 pJ/bit which is significantly better than current state-of-the-art UWB tags. Due to the use of impulse-radio UWB, also cm-accurate localization of the tag can be achieved. [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]

This paper proposes a novel node localization method in sparse underwater wireless sensor networks (UWSNs) where the locations of only a small number of anchor nodes are available. The proposed method estimates the Euclidean distances from anchor nodes to multi-hop sensor nodes with the help of angle of arrival (AoA) measurements in the local coordinate systems of the routing nodes. A new distance estimation method is proposed for sensor nodes with greater-than-two-hops to an anchor node by accurately estimating the rotation matrix between the routing nodes. By forwarding distances hop-by-hop through the network, the sensor nodes are able to obtain distance estimates to more than four or five anchor nodes. Then the location of the sensor node is solved by a weighted Least Squares method. This paper develops a practical table of weights for different number of hops and AoA estimation errors. Simulation results show that the proposed localization method outperforms the existing multi-hop localization algorithms, such as DV-hop, DV-distance, Euclidean, and Cosine-law methods, in terms of distance estimation and location estimation, even if the AoA measurement error is large. Meanwhile, the proposed method maintains almost the same high localization coverage as the existing methods. [ABSTRACT FROM AUTHOR]

Reliable and accurate position information is of great importance for many mass-market and emerging applications. Network localization and navigation (NLN) is a promising paradigm to provide such information ubiquitously, where a network of nodes is used to aid in localizing its members. This paper explores various network operation strategies, which play an essential role in NLN as they determine the network lifetime and localization accuracy. Efficient network operation requires several functionalities, including node prioritization, node activation, and node deployment. The roles of these functionalities are described and different techniques for implementing respective functionalities via algorithmic modules are introduced. Some important concepts such as cooperative operation, robustness guarantee, and distributed design in the development of the network operation strategies are also introduced. Finally, numerical results are provided to demonstrate the localization performance improvement attributed to the optimized network operation strategies. [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]

In this paper, optimal power allocation strategies are investigated for jammer nodes in a wireless localization system. Building upon the concept of the restricted Bayesian approach, a generalized optimization strategy, called the restricted scheme, is proposed for power allocation of jammer nodes, and its theoretical properties are characterized. In the restricted scheme, the aim is to maximize the average Cramer-Rao lower bound (CRLB) of target nodes while keeping their minimum CRLB above a predefined level in the presence of average (total) and peak power constraints. It is proved that the average CRLB achieved by the restricted scheme is a strictly decreasing and concave function of the constraint on the minimum CRLB level. A closed-form solution is obtained for the restricted scheme when the tradeoff parameter and the total power limit are below certain thresholds. In addition, it is shown that the optimal solution of the restricted scheme corresponds to the use of at most NT jammer nodes, where NT is the number of target nodes, and that the optimal solution of the minimum CRLB maximization scheme is determined by at most NJ target nodes, where NJ is the number of jammer nodes. Extensions of the restricted scheme and an alternative scheme that aims to maximize the number of disabled target nodes (whose CRLBs are above a preset level) are considered, and the corresponding optimal strategies for jammer power allocation are identified. Numerical examples are provided to verify the theoretical derivations for various network configurations. [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]

In recent years, there has been considerable interest in robust range-based Wireless Sensor Network (WSN) localization due to the increasing importance of accurately locating sensors in various WSN applications. However, achieving precise localization is often hampered by the presence of outliers or underestimations in range measurements, particularly when employing the RSS technique. To tackle these issues, we introduce a Two-Step Localization (namely, the SelMin approach). In the initial phase, the approach utilizes Second-Order Cone Programming (SOCP) to minimize distance discrepancies. It does this by comparing a reference Euclidean Distance Matrix (EDM) with a weighted one derived from imprecise distances between sensor nodes. In the subsequent phase, a heuristic method is employed to identify a specific number of imprecise distances, referred to as outliers, that will be disregarded in the first phase, and this two-phase process continues iteratively. The experimental results demonstrate that the SelMin strategy performs better than the DSCL method when evaluated using the Root Mean Square Error (RMSE) metric. This superior performance is maintained even in challenging conditions, such as when there are many outliers (i.e, around 30 % ) in the network. This indicates that SelMin is a reliable and robust choice for these environments. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), many applications require sensor nodes to obtain their locations. Now, the main idea in most existing localization algorithms has been that a mobile anchor node (e.g., global positioning system-equipped nodes) broadcasts its coordinates to help other unknown nodes to localize themselves while moving according to a specified trajectory. This method not only reduces the cost of WSNs but also gets high localization accuracy. In this case, a basic problem is that the path planning of the mobile anchor node should move along the trajectory to minimize the localization error and to localize the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) in WSNs. LMAT algorithm uses a mobile anchor node to move according to trilateration trajectory in deployment area and broadcasts its current position periodically. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms. Copyright © 2011 John Wiley & Sons, Ltd. [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]

Existing localization approaches are divided into two groups: 1) range based and 2) range free. The range-free schemes often suffer from poor accuracy and low scalability, whereas the range-based localization approaches heavily depend on extra hardware capabilities or on the absolute received signal strength indicator (RSSI) values, which is far from practical. In this paper, we propose a mobile-assisted localization scheme called Perpendicular Intersection (PI), setting up a delicate tradeoff between range-free and range-based approaches. Instead of directly mapping RSSI values into physical distances, by contrasting RSSI values from the mobile beacon to a sensor node, PI utilizes the geometric relationship of a perpendicular intersection to compute node positions. We have implemented the prototype of PI with 100 TelosB motes and evaluated PI in both indoor and outdoor environments. Through comprehensive experiments, we show that PI achieves high accuracy, significantly outperforming the existing range-based and the mobile-assisted localization schemes. [ABSTRACT FROM PUBLISHER]

This paper addresses the localization problem in wireless sensor networks using signal strength. We use a kernel function to measure the similarities between sensor nodes. The kernel matrix can be naturally defined in terms of the signal strength matrix. We show that the relative locations of sensor nodes can be obtained by solving a dimension reduction problem. To capture the structure of the whole network, we use the kernel spectral regression (KSR) method to estimate the relative locations of the sensor nodes. Given sufficient anchor nodes, the relative locations can be aligned to global locations. The key benefits of adopting KSR are that it allows us to define a graph to optimally preserve the topological structure of the sensor network, and a kernel function can capture the nonlinear relationship in the signal space. Simulation results show that we can achieve small average location error with a small number of anchors. We also compare our method with several related methods, and the results show that KSR is more efficient than the others in our simulated sensor networks. Copyright © 2009 John Wiley & Sons, Ltd. [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]

The current restricted area based range free localization schemes use various geometric shapes such as triangle, rectangle, and circle to determine the residence area of the sensor node. Then this residence area is used to localize the sensor node. However, the existing work requires higher density of anchor deployment to gain the acceptable accuracy of position estimation. In this paper, we overcome the density dependent accuracy of localization by proposing a new localization scheme called geometric anchor beacon (GAB). The proposed scheme is based on analytical geometry of an arc. In the proposed scheme, the $$Cramers \ rule$$ is used, where the intersecting point of two perpendicular bisectors of the chords is taken as the estimated position of the sensor node. Simulation results show that, GAB shows less estimation error than any other range-free localization schemes such as CAB, Centroid, Convex, and APIT. [ABSTRACT FROM AUTHOR]

This paper proposes a progressive Isomap algorithm for node localization. The algorithm is an extension of centralized Isomap and is capable of effectively localizing new nodes progressively, without neglecting preceding computational results. In the initial startup phase, sensor nodes within two hop range of three non-collinear anchor nodes are localized using range ( $$\epsilon$$ )-Isomap. Afterwards, in the progressive phase, the remaining nodes are localized in a sequential manner, using recently localized nodes. The localization process initiated at different nodes in the network give rise to the overlapping substructures of localized nodes. The overlapped substructures are stitched to form a single coordinate system. Finally, Helmert Transformation is used to obtain the global coordinates of the nodes. The effects of varying various parameters on the accuracy and scalability of the proposed algorithm are studied through the simulation. Results indicate that the proposed algorithm has good positioning accuracy and noise-robustness. [ABSTRACT FROM AUTHOR]

In many applications of wireless sensor network, the position of the sensor node is useful to identify the actuating response of the environment. The main idea of the proposed localization scheme is similar with most of the existing localization schemes, where a mobile beacon with global positioning system broadcast its current location coordinate periodically. The received information of the coordinates help other unknown nodes to localize themselves. In this paper, we proposed a localization scheme using mobile beacon points based on analytical geometry. Sensor node initially choose two distant beacon points, in-order to minimize its residence area. Later using the residence area, sensor node approximate the radius and half length of the chord with reference to one of the distant beacon point. Then the radius and half length of the chord are used to estimate the sagitta of an arc. Later, sensor node estimate its position using radius, half length of the chord, and sagitta of an arc. Simulation result shows the performance evaluation of our proposed scheme on various trajectories of mobile beacon such as CIRCLE, SPIRAL, S-CURVE, and HILBERT. [ABSTRACT FROM AUTHOR]

Node localization is one of the key technologies in wireless sensor networks, which provides technical support for many location-based applications. In the process of node localization, due to the limitation of cost and energy consumption of wireless sensor networks, mobile anchor nodes more suitable for localization as a way to reduce costs. However, to the best of our knowledge, there has been no comprehensive and in-depth study dedicated to the problem of assisted localization of mobile anchor nodes for multidimensional wireless sensor networks in the last decade, as well as there are no latest research reports reflecting the latest progress in this field. In this paper, we focus on a detailed study of the mobile anchor node assisted localization problem for multidimensional wireless sensor networks. Firstly, a model for mobile anchor node assisted localization is described. Then static path planning and dynamic path planning methods for mobile anchor nodes in 2D and 3D environments are introduced, respectively, and compare them in terms of localization environments, number of mobile anchor nodes, mobile model, area full coverage, energy consumption of anchor nodes, localization ratio, localization error and path length. Finally, problems of the existing methods in this field and the possible future research directions are discussed. Intended to provide reference for researchers in this field and enable readers to gain a deeper understanding of Multi-Dimensional Path Planning Method for mobile anchor node Localization in wireless sensor networks (MDPPML), as well as research trends and future research directions in this field. [ABSTRACT FROM AUTHOR]

One of the main parameters in wireless sensor networks (WSNs) is the design of energy-efficient protocols. And accuracy is another central goal of localization. Since sensor nodes run on battery power, any WSN application and accurate localization needs to be energy-efficient. In this paper, the accuracy of localization is increased by accurate measurement of the distance between the mobile sensors. Limit error in multiple-input multiple-output (MIMO) has been calculated by CRB method. Virtual MIMO (VMIMO) technique can obtain better localization precision and the localization is energy-efficient. Optimum selection of the number of the transceiver nodes is obtained by the lowest possible energy consumption, the existent localization error, and speed of nodes. Mathematical relation between energy consumption and localization of mobile nodes is presented and then verified by simulation. VMIMO decreases power of transmitters and this in turn will result in decreasing destructive effects of electromagnetic sensitivity (EMS) on body. Furthermore, optimized localization parameters will increase the efficiency of the system and network lifetime. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are increasingly being used in remote environment monitoring, security surveillance, military applications, and health monitoring systems among many other applications. Designing efficient localization techniques have been a major obstacle towards the deployment of WSN for these applications. In this paper, we present a novel lightweight iterative positioning (LIP) algorithm for next generation of wireless sensor networks, where we propose to resolve the localization problem through the following two phases: (1) initial position estimation and (2) iterative refinement. In the initial position estimation phase, instead of flooding the network with beacon messages, we propose to limit the propagation of the messages by using a random time-to-live for the majority of the beacon nodes. In the second phase of the algorithm, the nodes select random waiting periods for correcting their position estimates based on the information received from neighbouring nodes. We propose the use of Weighted Moving Average when the nodes have received multiple position corrections from a neighbouring node in order to emphasize the corrections with a high confidence. In addition, in the refinement phase, the algorithm employs low duty-cycling for the nodes that have low confidence in their position estimates, with the goal of reducing their impact on localization of neighbouring nodes and preserving their energy. Our simulation results indicate that LIP is not only scalable, but it is also capable of providing localization accuracy comparable to the Robust Positioning Algorithm, while significantly reducing the number of messages exchanged, and achieving energy savings. [ABSTRACT FROM AUTHOR]

Area-based localization algorithms use only the position of some reference nodes, called anchors, to estimate the residence area of the remaining nodes. Existing algorithms use a triangle, a ring or a circle as the geometric shape that defines the node’s residence area. However, existing algorithms suffer from two major problems: (1) in some cases, they might make wrong decisions about a node presence inside a given area, or (2) they require high anchor density to achieve a low location estimation error and high ratio of localizable nodes. In this paper, we overcome these shortcomings by introducing a new approach for determining the node’s residence area that is geometrically shaped as a half-symmetric lens. A novel half symmetric lens based localization algorithm (HSL) is proposed. HSL yields smaller residence areas, and consequently, better location accuracy than contemporary schemes. HSL further employs Voronoi diagram in order to boost the percentage of localizable nodes. The performance of HSL is validated through mathematical analysis, extensive simulations experiments and prototype implementation. The validation results confirm that HSL achieves better location accuracy and higher ratio of localizable nodes compared to competing algorithms. [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]

Localization is the key supporting technology for wireless sensor networks. Security and accuracy are the premise of the localization application. Real-world applications of wireless sensor networks are often subject to a variety of adverse circumstances interference and the localization performance is seriously affected. In this paper, we focus on the problem of node localization with malicious beacon nodes and solve it based on a repeated game method. Trust evaluation method is introduced to distinguish malicious beacon nodes. A repeated game framework is established and the strategies and payoffs are analyzed. Both malicious and normal nodes are driven by profits to a cooperation state and the whole localization performance is improved. Extensive simulation results show normal nodes can obtain higher location precision based on the repeated game. Malicious beacon nodes prefer to take the long-term cooperation attitude. The total losses to the network are less than the total profits caused by malicious nodes with game control. [ABSTRACT FROM AUTHOR]

Sensor node localization with an acceptable accuracy is one of the most important challenges for location-aware applications of Wireless Sensor Networks (WSNs). The Received Signal Strength (RSS)-based localization techniques are very attractive because of their simplicity and low cost. However, those methods suffer from evident estimation errors. In our previous work, we developed fuzzy set-based localization method, called Fuzzy Ring Overlapping Range Free (FRORF) localization. In this paper we further extend the concept and propose an improved FRORF localization method, IFRORF for short, which uses two-layer fuzzy processing. IFRORF is an area-based localization approach that partitions the localization space into overlapping rings modeled as fuzzy sets with ambiguous boundaries, and then generates fuzzy set of regions by intersecting rings from different fuzzy ring sets. IFRORF estimates the location based on degrees of sensor node membership in regions in the fuzzy set of regions that are additionally tuned using the second layer of fuzzy processing. Simulations demonstrate that IFRORF method outperforms localization accuracy of FRORF. [ABSTRACT FROM AUTHOR]

Analysis of errors in computing locations of nodes in a wireless sensor network (WSN) is an important task. In this paper, we analyse the error of localization of sensors in a WSN. We analyse the effect of errors introduced in the location of sensors and the measurement of their distances to anchors. During our analysis, we focus on the error and normalized error. We analyse how the error and normalized error vary with errors in locations and transmission ranges of anchors. The localization error is normalized with respect to either coordinates of a node or transmission range of nodes in the network. [ABSTRACT FROM AUTHOR]

The information available to connectivity-based positioning algorithms is the radio range of sensor devices and the position estimates of neighbors and neighbors of neighbors. This information creates special graph theoretic structures which impose new constraints on the positions of sensor devices. The new constraints sometimes lead to a feasible set of positions with disconnected regions. These properties can be used to reduce the set of feasible positions for a node. In this paper, a new fully distributed positioning algorithm, called Orbit, which exploits these properties is presented for mobile sensor networks. The algorithm uses additional constraints and trims disconnected regions. These new constraints are generated through cooperation between neighbors. The performance of Orbit is examined for many communication and mobility models, including a probabilistic communication model generated from radio experiments. Computer simulation experiments demonstrate that Orbit outperforms a recently proposed positioning algorithm in terms of positional accuracy under different models with a wide range of parameter values. Orbit is implemented on resource limited sensor devices. This implementation demonstrates the feasibility of the algorithm for sensor devices. The algorithm is tested on deployments of the sensor devices in a field and the results are comparable to those from the simulation experiments. [ABSTRACT FROM AUTHOR]

In this paper we propose the WNMCL algorithm that allows iteratively locate unknown wireless nodes. The algorithm requires only one mobile node, which collects RSS signal from other nodes with unknown position and approximates their distance by path-loss model. These data are used for the probability occurrence correction of the nodes in the space modelled by Monte Carlo. The WNMCL algorithm was validated using simulations and the results are presented in this paper. [ABSTRACT FROM AUTHOR]

The earth quake is the disaster that is handled in this paper. The location of the earth quake can be finding by using the various localization process available in the WSN. This chapter firstly discusses the localization techniques then the proposed technique that is used to get the accurate position of the earth quake. Node localization refers to determine the accurate and precise physical location of each node in the network. To measure significant data we need location information of the sensor nodes. Node localization is required to answers questions about the network coverage, report queries about sensors and origin of event occurs. The proposed system uses the KALMAN filter to get the exact location of the earth quake. [ABSTRACT FROM AUTHOR]

Devices that form a wireless sensor network (WSN) system are usually remotely deployed in large numbers in a sensing field. WSNs have enabled numerous applications, in which location awareness is usually required. Therefore, numerous localization systems are provided to assign geographic coordinates to each node in a network. In this paper, we describe and evaluate an integrated software framework WSNLS (Wireless Sensor Network Localization System) that provides tools for network nodes localization and the environment for tuning and testing various localization schemes. Simulation experiments can be performed on parallel and multi-core computers or computer clusters. The main component of the WSNLS framework is the library of solvers for calculating the geographic coordinates of nodes in a network. Our original solution implemented in WSNLS is the localization system that combines simple geometry of triangles and stochastic optimization to determine the position of nodes with unknown location in the sensing field. We describe and discuss the performance of our system due to the accuracy of location estimation and computation time. Numerical results presented in the paper confirm that our hybrid scheme gives accurate location estimates of network nodes in sensible computing time, and the WSNLS framework can be successfully used for efficient tuning and verification of different localization techniques. [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]

Localization of nodes in a sensor network is essential for the following two reasons: ( i) to know the location of a node reporting the occurrence of an event, and ( ii) to initiate a prompt action whenever necessary. Different localization techniques have been proposed in the literature. Most of these techniques use three location aware nodes for localization of an unknown node. Moreover, the localization techniques also differ from environment to environment. In this paper, we proposed a localization technique for grid environment. Sensor nodes are deployed in a grid pattern and localization is achieved using a single location aware or anchor node. We have identified three types of node in the proposed scheme: ( i) Anchor node, ( ii) Unknown node and ( iii) Special node. First, the special nodes are localized with respect to the anchor node, then the unknown nodes are localized using trilateration mechanism. We have compared the proposed scheme with an existing localization algorithm for grid deployment called Multiduolateration. The parameters considered for localization are localization time and localization error. It is observed that localization time and error in the proposed scheme is lower than that of Multiduolateration. [ABSTRACT FROM AUTHOR]