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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]

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 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]

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]

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]

Highlights • The proposed method alternately calculates the position and times of the source node that is more computationally efficient. • The convergence behavior of the proposed method is further theoretically proved. • First-order perturbation analysis shows that the performance of the proposed method can approximately reach the CRLB. • Compared with some previous methods, the proposed method requires fewer anchor nodes and less communication cost. Abstract This paper efficiently solves the problem of joint localization and synchronization in wireless sensor networks by using two-way message exchange mechanism. Employing an affine model of unsynchronized local clocks, we first establish the famous Maximum Likelihood (ML) estimator for joint localization and synchronization, which derives the best consistent solution. Unfortunately, achieving directly the ML estimator is usually difficult because the ML cost functions are highly non-linear and non-convex. In order to effectively obtain the ML estimator, a novel bi-iterative algorithm is proposed by alternately searching the sensor node location and clock parameters (clock skew and clock offset). We prove that the proposed algorithm is convergent, and analyze that its estimation accuracy for the sensor node locations and clock parameters can approximately reach the Cramer–Rao Lower Bound (CRLB) under the mild noise condition. Compared with some previous methods, the proposed bi-iterative algorithm is more computationally efficient, and takes fewer anchor nodes and less communication overhead. Simulation results verify theoretical analysis and show that the proposed algorithm has the better performance than the several existing algorithms. [ABSTRACT FROM AUTHOR]

Abstract Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensed data/events. In most of the applications of wireless sensor networks, the event information without its location information has no significance. The well known traditional Distance-Vector Hop (DV Hop) algorithm and weighted centroid DV Hop based algorithms can be easily implemented in real wireless sensor networks with low cost and no additional hardware requirement, but it has poor localization accuracy and high power consumption. In order to avoid these limitations, a weighted centroid DV-Hop algorithm is proposed in this paper. The proposed algorithm uses weights that consider the influence of different factors such as number of anchors, communication radius, and nearest anchor to determine location of unknown node. Simulation results and theoretical analysis prove that proposed algorithm outperforms the traditional DV-Hop algorithm in terms of localization error and power consumption. [ABSTRACT FROM AUTHOR]

Localization is the method of estimating the location of a wireless node using measured inputs such as distances from nodes with known locations. When the measured distances from anchor nodes are used for localization, compromised nodes that are involved in the process can give false information that produces inaccurate location estimates. This paper proposes a Generalized Likelihood Ratio (GLRT) based approach to find the compromised nodes that deliberately give false information. After detecting such malicious nodes, the measurements given by them are eliminated from the localization computation to improve the location estimate. The proposed method works for Gaussian range measurement errors, which is considered more realistic, as compared to a method available in literature that works only for uniformly distributed range errors. Extensive simulations were carried out to assess the performance of the algorithm under various conditions. The proposed method was found to give better localization accuracy as compared to previous methods available in the literature which address the same problem. Simulations also showed that the algorithm performs well even when some of the assumptions used in the algorithm do not hold true. [ABSTRACT FROM AUTHOR]

Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique. [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]

Localization is one of the key aspects of wireless sensor networks (WSNs) that has attracted significant research interest. A wide variety of proposed approaches regarding the research topic has recently emerged; however, the majority of the existing approaches are limited by at least one of the following restrictions: inaccuracy, high cost, fast energy depletion, inappropriate indoor performance, or the requirement of an additional positioning hardware. In this paper, we present the research and development of a hybrid range-free WSN localization system, using the hop-count and received signal strength (RSS) methods. The proposed system is reliable and efficient indoors in terms of localization accuracy, cost and power consumption. Reference and target nodes have been designed and implemented, while real experiments have been carried out to assess the proposed system's efficiency. [ABSTRACT FROM AUTHOR]

In recent years, localization turns out to be a crucial aspect in the realm of wireless sensor networks (WSNs) sparking a lot of research interest among researchers. It is the procedure of discovering the locality of target nodes concerning the installed anchor nodes whose placements are well known as they have a GPS component integrated into them. But as GPS is incompatible with indoor and/or aquatic situations, all sensor nodes are often not set up with it. If all the nodes are fitted with GPS, a network becomes too expensive and uses extra energy, which is a key disadvantage of WSNs. In the literature, various localization strategies have been presented; however, the majority of research ideas focus on 2D applications. In 3D implementations, however, the region under consideration in the sensing environment may be complicated. The determination of node placement in a 3D environment necessitates an optimal algorithm. In this research, we proposed a repulsion-based improved grey wolf optimizer (R-GWO) for the sensor nodes localization that outperforms the traditional GWO in terms of exploration and exploitation abilities. The suggested R-GWO has been evaluated on the WSN Localization problem and has shown to have the lowest localization error when contrasted to the other strategies used in 3D environments. [ABSTRACT FROM AUTHOR]

Local Positioning Systems are collecting high research interest over the last few years. Its accurate application in high-demanded difficult scenarios has revealed its stability and robustness for autonomous navigation. In this paper, we develop a new sensor deployment methodology to guarantee the system availability in case of a sensor failure of a five-node Time Difference of Arrival (TDOA) localization method. We solve the ambiguity of two possible solutions in the four-sensor TDOA problem in each combination of four nodes of the system by maximizing the distance between the two possible solutions in every target possible location. In addition, we perform a Genetic Algorithm Optimization in order to find an optimized node location with a trade-off between the system behavior under failure and its normal operating condition by means of the Cramer Rao Lower Bound derivation in each possible target location. Results show that the optimization considering sensor failure enhances the average values of the convergence region size and the location accuracy by 31% and 22%, respectively, in case of some malfunction sensors regarding to the non-failure optimization, only suffering a reduction in accuracy of less than 5% under normal operating conditions. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have a wonderful potential to interconnect with the physical world and collect data. Data estimation, long lifespan, deployment, routing, task scheduling, safety, and localization are the primary performance difficulties for WSNs. WSNs are made up of sensor nodes set up with minimal battery power to monitor and reveal the occurrences in the sensor field. Detecting the location is a difficult task, but it is a crucial characteristic in many WSN applications. Locating all of the sensor nodes efficiently to obtain the precise location of an occurrence is a critical challenge. Surveillance, animal monitoring, tracking of moving objects, and forest fire detection are just a few of the applications that demand precise location determination. To cope with localization challenges in WSNs, there is a variety of localization algorithms accessible in the literature. The goal of this research is to use various optimization strategies to solve the localization problem. In this work, a modified learning enthusiasm-based teaching–learning-based optimization (mLebTLBO) algorithm is used to cope with a 2D localization problem applying the notion of an exclusive anchor node and movable target nodes. A modified LebTLBO algorithm seeks to increase overall efficiency by assessing the exploration and exploitation abilities. The computational results reveal that this technique outperforms others with respect to localization errors in a 2D environment of WSN. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) represent an interesting technology for designing early warning systems for landslides as they can ensure real-time and continuous monitoring. Through accurate localization techniques, changes in the position of installed nodes can be detected even during the early stage of field instability. This is through an accurate detection of nodes position changes independently from systematic deviations resulting from outdoor environmental conditions. In this study, we propose an accurate measurement system for distance measurement between wireless sensor nodes based on an ultra-wideband (UWB) localization method. In particular, distance measurements at different real weather conditions were performed to identify the impact of weather changes on distance measurement deviations. A prototype for a landslide warning system has been developed realizing a localization accuracy of 98%. [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]

Location estimation in wireless sensor networks (WSNs) has received tremendous attention in recent times. Improved technology and efficient algorithms systematically empower WSNs with precise location identification. However, while algorithms are efficient in improving the location estimation error, the factor of the network lifetime has not been researched thoroughly. In addition, algorithms are not optimized in balancing the load among nodes, which reduces the overall network lifetime. In this paper, we have proposed an algorithm that balances the load of computation for location estimation among the anchor nodes. We have used vector-based swarm optimization on the connected dominating set (CDS), consisting of anchor nodes for that purpose. In this algorithm, major tasks are performed by the base station with a minimum number of messages exchanged by anchor nodes and unknown nodes. The simulation results showed that the proposed algorithm significantly improves the network lifetime and reduces the location estimation error. Furthermore, the proposed optimized CDS is capable of providing a global optimum solution with a minimum number of iterations. [ABSTRACT FROM AUTHOR]