Anisotropic Wireless Sensor Networks Research Articles

Fuzzy Distance Jaya Algorithm based Node Localization in Anisotropic Wireless Sensor Networks

Fuzzy Distance Jaya Algorithm based Node Localization in Anisotropic Wireless Sensor Networks

Localization algorithm for anisotropic wireless sensor networks based on the adaptive chaotic slime mold algorithm

Localization algorithm for anisotropic wireless sensor networks based on the adaptive chaotic slime mold algorithm

Range-Free Localization Using Extreme Learning Machine and Ring-Shaped Salp Swarm Algorithm in Anisotropic Networks

Node localization is one of the basic requirements in various Internet of Things applications. Among a wide range of localization schemes, the range-free localization algorithm is promising as a cost-effective technique. However, the localization accuracy of this technique is susceptible to various anisotropy factors such as the existence of holes, non-uniform node distribution, and dynamic radio propagation pattern. To this end, an accurate range-free localization model using extreme learning machine and ring-shaped salp swarm algorithm is proposed for anisotropic wireless sensor networks. First, the integer hop count between two adjacent nodes is quantized as a real number according to the Jaccard coefficient of their shared neighbor nodes. Second, exploiting the strong generalization and fast learning speed of extreme learning machine, a distance mapping model based on the modified real hop count is developed for solving anisotropic signal attenuation. Third, the coordinate calculation of normal nodes is formulated as a minimum problem by taking into account the weighted squared error of estimated distance, and the bounding box method is utilized to initialize the possible location boundary area of normal nodes. Finally, the salp swarm algorithm based on the ring-shaped topology is designed to compute the coordinates of normal nodes. Extensive simulations on several network topologies are conducted with the effect of multiple anisotropic factors. Experimental results show that the proposed algorithm is superior to other developed ones not only in localization accuracy but also in robustness against network anisotropy.

A localization algorithm using reliable anchor pair selection and Jaya algorithm for wireless sensor networks

In wireless sensor networks (WSNs) and large-scale IoT applications, node localization is a challenging process to identify the location of the target or unknown nodes for accurate information transmission between sensor nodes. Due to their ease of hardware implementation and suitability for large-scale WSNs, range-free localization techniques have been shown in previous studies. The existing range-free localization algorithms did not consider the anisotropy factors typically seen in WSNs, leading to poor positioning accuracy. We proposed a range-free localization solution that combines the benefits of geometric constraint and hop progress-based approaches to address this issue. Each unknown node categorizes the anchor node pairs into one of three proposed categories, and the discriminating conditions are designed using the geometric information provided by the combination of the anchor node pairs and unknown nodes. A node localization algorithm is proposed to determine the position of target nodes or unknown nodes and to reduce the effect of anisotropic factors in isotropic, O-shaped, and S-shaped anisotropic WSNs using the parameter-less Jaya algorithm (JA) and range-free method of reliable anchor pair (RAP) selection approach. In the case of anisotropic WSNs (AWSNs), finding the location of target nodes is more complicated. The presented work is compared with the existing node localization methods, including Distance Vector (DV)-maxHop, Particle Swarm Optimization (PSO), and Quantized Salp Swarm Algorithm (QSSA) based localization algorithms. The proposed approach provides improved localization accuracy compared to the existing node localization methods regarding the number of anchor nodes and node density. The proposed algorithm also looks at how the degree of irregularity and computation time affect the performance.

Optimized Range-Free Localization Scheme Using Autonomous Groups Particles Swarm Optimization for Anisotropic Wireless Sensor Networks

Location information is a required concern in localization-based service application in the field of wireless sensor networks (WSNs). Distance Vector-Hop (DV-Hop) algorithm as the most typical range-free localization scheme is much fitter for large-scaled WSNs. Its localization performance is good in even distributed networks. However, it demonstrated extremely poor accuracy under anisotropic networks. It is an urgent problem that need to be addressed. Accordingly, an optimized DV-Hop localization algorithm is put forward in this study with considering several anisotropic factors. Accumulated hop size error and collinearity are two main reason that led to low accuracy and poor stability. Hence, hop size error of anchors is reduced by introducing distance gap based on anchors. Besides, weighted least square method is adopted to replace the least square method to against anisotropic factors caused by irregular radio patterns. Moreover, an Autonomous Groups Particles Swarm Optimization (AGPSO) is employed to further optimize the obtained coordinate in the first round. It developed a novel method to determine localization coverage. The localization coverage is also added to be one evaluation metric in our study, which makes up for the lack of this evaluation indicator in most of the studies. Simulation results display good localization accuracy and strong stability under anisotropic networks. In addition, it also concluded that metaheuristic optimization algorithm and weighted least square method are more suitable to conquer anisotropic factor. It briefly points out a new direction for the future research work in the localization area under anisotropic networks.

Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

Internet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT. This is a network of sensor nodes that is used in a vast range of applications. WSN is formed by the random deployment of sensor nodes in various fields of interest. The practical fields of deployment can be 2D or 3D, isotropic or anisotropic depending on the application. The localization algorithms must provide accurate localization irrespective of the type of field. In this paper, we have reported a localization algorithm called Range Reduction Based Localization (RRBL). This algorithm utilizes the properties of hop-based and centroid methods to improve the localization accuracy in various types of fields. In this algorithm, the location unknown nodes identify the close-by neighboring nodes within a predefined threshold and localize themselves by identifying and reducing the probable range of existence from these neighboring nodes. The nodes which do not have enough neighbors are localized using the least squares method. The algorithm is tested in various irregular and heterogeneous conditions. The results are compared with a few state-of-the-art hop-based and centroid-based localization techniques. RRBL has shown an improvement in localization accuracy of 28% at 10% reference node ratio and 26% at 20% reference node ratio when compared with other localization algorithms.

Range-free localization using Reliable Anchor Pair Selection and Quantum-behaved Salp Swarm Algorithm for anisotropic Wireless Sensor Networks

Localization is one of the essential problems in the Internet of Things (IoT) and other wireless sensor applications. Most traditional range-free localization algorithms ignore the anisotropy factors, which are frequently observed in Wireless Sensor Networks (WSNs) and result in low positioning precision. To mitigate the impact of anisotropy on localization, we propose an accurate localization approach based on Reliable Anchor Pair Selection (RAPS) and Quantum-behaved Salp Swarm Algorithm (QSSA) for anisotropic networks. First, the proposed algorithm uses hop count threshold to limit the number of message transmissions between nodes, which assists to decrease communication overhead. Next, based on the geometric constraints, the selected reliable anchor pairs are divided into two types, namely, super anchor pairs and suboptimal ones. Then, we design different distance estimation equations for the reliable anchor pairs to reduce ranging error. Finally, the QSSA is introduced to calculate the coordinates of regular nodes, which tends to lower the impact of anisotropy factors and improve location accuracy. Extensive simulations show that the proposed algorithm outperforms state-of-the-art algorithms in terms of accuracy and robustness against network anisotropy.

Comment on “Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks”

Comment on “Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks”

Range-free localization using expected hop progress in anisotropic wireless sensor networks

Accurate localization of nodes is one of the key issues of wireless sensor network (WSN). A localization algorithm using expected hop progress (LAEP) has been successfully applied in isotropic wireless sensor networks. However, range-free LAEP cannot be directly used for anisotropic WSNs because anisotropic problems limit the applicability of multi-hop localization. In order to solve the problem, an improved localization algorithm is proposed to reduce the localization error. In this paper, we adapt the expected hop progress to anisotropic WSNs by considering both hop count computation and anchor selection. Then, particle swarm optimization algorithm is introduced to improve the positioning accuracy. The experimental results demonstrate that our algorithm has better higher precision than do state-of-the-art algorithms. Even for isotropic WSNs, our algorithm always outperforms its counterparts.

Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks

The original DV-hop algorithm performs pretty well in isotropic Wireless Sensor Networks in which nodes distribute uniformly. However, the localization accuracy degrades severely in anisotropic networks caused by uneven nodal distribution or irregularity of deployment region. In this paper, we propose a novel DV-hop algorithm based on Locally Weighted Linear Regression (LWLR-DV-hop), in which kernel method is adopted to improve the localization accuracy by raising the weight of neighboring anchor nodes. In the simulation section, algorithms are evaluated within two deployments and three topologies: the regular and random deployments, the L-shaped, O-shaped and X-shaped topologies. As performance metrics, the Average Localization Error and the Cumulative Distribution Function are used. The results of simulation and experiment reveal that LWLR-DV-hop performs better than original DV-Hop in anisotropic networks of different topologies, in which localization accuracy is improved by about 40% on average.

3D localization of moving target nodes using single anchor node in anisotropic wireless sensor networks

In this paper, novel 3D node localization algorithms using applications of Computational Intelligence (CI) for moving target nodes are attained using single reference node (anchor node) in an anisotropic network. Target nodes are randomly deployed at beneath and middle layers. Whereas single anchor node is deployed at top layer. Recent applications of Computational Intelligence (CI), i.e., Particle Swarm Optimization (PSO), H-Best Particle Swarm Optimization (HPSO), Biogeography Based Optimization (BBO) and Firefly Algorithm (FA) are used respectively to estimate the optimum location of moving target nodes. In this paper, each target node has heterogeneous properties (due to different battery backup statuses). Degree of Irregularity (DOI) of 0.01 is considered for radiation pattern. In heterogeneous network, the geometric distance between two nodes is not proportional to their hop count. Once a moving target falls under the range of a deployed anchor node, three virtual anchor nodes (minimum 4 anchor nodes are required for 3D positions) in surrounding of anchor and respective moving target node are projected by using umbrella projection form to find the 3D position. The proposed algorithms are designed for rescue operations in highly hostile environment.

A novel heuristic algorithm for node localization in anisotropic wireless sensor networks with holes

The node localization is a crucial technology that affects practicality, accuracy and effectiveness of the wireless sensor networks (WSNs). Sensor nodes are often deployed non-uniformly in anisotropic WSNs with holes in various applications such as monitoring area terrain. The existence of holes will invariably affect the Euclidean distances between nodes and result in low accuracy of node localization. In this paper, a Heuristic Multidimensional Scaling (HMDS) algorithm is proposed to improve accuracy of node localization in anisotropic WSNs with holes. By exploring the virtual node and constructing the shortest paths between nodes, the Euclidean distances between nodes are obtained via employing the heuristic approach such that they can be used to calculate more accurate locations of the nodes. The HMDS algorithm greatly reduces the communication complexity and computational complexity compared with the MDS-MAP algorithm. Simulation results demonstrate that the HMDS algorithm requires fewer anchors to obtain the node locations. The HMDS algorithm is suitable for four different topologies, including the semi-C-shape topology, the O-shape topology, the multiple O-shape topology and the concave-shape topology and is exceedingly accurate and efficient comparing with state-of-the-art methods in anisotropic WSNs with holes.

Accurate Nodes Localization in Anisotropic Wireless Sensor Networks

An accurate localization algorithm tailored for anisotropic wireless sensors networks (WSNs) is proposed in this paper. Using the proposed algorithm, each regular or position-unaware node estimates its distances only to reliable anchors or position-aware nodes. The latter are properly chosen following a new reliable anchor selection strategy that ensures an accurate distance estimation making thereby our localization algorithm more precise. It is shown that the proposed algorithm is implementable in both 2-dimensional (2D) and 3-dimensional (3D) scenarios. A power saving mechanism aiming to enhance the WSN lifetime is also envisaged in this paper. It is proven that the proposed algorithm could easily incorporate such a mechanism. Simulations show that our algorithm, whether combined or not with the power saving mechanism, consistently outperforms the best representative localization algorithms currently available in the literature in terms of accuracy, even with the presence of nonuniform node distribution or radiation irregularities.

Range-free 3D node localization in anisotropic wireless sensor networks

In this paper, we propose two computationally efficient ‘range-free’ 3D node localization schemes using the application of hybrid-particle swarm optimization (HPSO) and biogeography based optimization (BBO). It is considered that nodes are deployed with constraints over three layer boundaries, in an anisotropic environment. The anchor nodes are randomly distributed over the top layer only and target nodes distributed over the middle and bottom layers. Radio irregularity factor, i.e., an anisotropic property of propagation media and heterogenous properties of the devices are considered. To overcome the non-linearity between received signal strength (RSS) and distance, edge weights between each target node and neighboring anchor nodes have been considered to compute the location of the target node. These edge weights are modeled using fuzzy logic system (FLS) to reduce the computational complexity. The edge weights are further optimized by HPSO and BBO separately to minimize the location error. Both the proposed applications of the two algorithms are compared with the earlier proposed range-free algorithms in literature, i.e., the simple centroid method and weighted centroid method. The results of our proposed applications of the two algorithms are better as compared to centroid and weighted centroid methods in terms of error and scalability.

A locality-based range-free localization algorithm for anisotropic wireless sensor networks

Location discovery is one of the fundamental services in Internet of things built with wireless sensor networks (WSNs). In this paper, we propose AnSuper, a novel range-free localization algorithm that provides accurate location estimates in anisotropic WSNs. In AnSuper, every anchor first selects a set of friendly anchors to which its distance estimates can be accurately obtained. These anchors are then broadcasted to neighboring common nodes, with which they can obtain accurate distance estimates to the anchors by using a novel locality-based algorithm and consequently calculate their positions. We discuss how to suppress error accumulation in the localization process. AnSuper can handle multiple anisotropy factors, including uneven nodal distribution and irregularity of the deployment region. Simulation results show that, compared with state-of-the-art solutions, AnSuper improves localization accuracy by more then 30 %.

Multihop range-free localization with approximate shortest path in anisotropic wireless sensor networks

AbstractThis paper presents a multihop range-free localization algorithm that tolerates network anisotropy with a small number of anchors. A detoured path detection is proposed to detect if the shortest path between nodes is detoured from their direct path by measuring the deviation in the hop count between the direct and shortest paths. A novel distance estimation method is introduced to approximate the shortest path based on the path deviation and to estimate their distance by taking into account the extent of the detour of the approximate shortest path. Compared to other range-free algorithms, the proposed algorithm requires fewer anchors while achieving higher localization accuracy in anisotropic networks. We demonstrated its superiority over existing range-free localization algorithms through extensive computer simulations.

Detour Path Angular Information Based Range-Free Localization in Wireless Sensor Network

Range-free localization algorithm continues to be an important and challenging research topic in anisotropic Wireless Sensor Networks (WSNs). Designing range-free localization algorithms without considering obstacles or holes inside the network area does not reflect the real world conditions. In this paper, we have proposed Detour Path Angular Information (DPAI) based sensor localization algorithm to accurately estimate the distance between an anchor node and a sensor node. We utilized the Euclidean distance and transmission path distance among anchor nodes to calculate the angle of the transmission path between them one by one. Then the estimated hop distance is adjusted by the angle between the anchor pairs. Based on the angle of the detoured path (which is the key factor for accuracy), our algorithm determines whether the path is straight or detoured by anisotropic factors. Our proposed algorithm does not require any global knowledge of network topology to tolerate the network anisotropy nor require high sensor node density for satisfactory localization accuracy. Extensive simulations are performed and the results are observed to be in good agreement with the theoretical analysis. DPAI achieved average sensor localization accuracy better than 0.3r in isotropic network and 0.35r in anisotropic network when the sensor density is above 8.

Holes Detection in Anisotropic Sensornets: Topological Methods

Wireless sensor networks (WSNs) are tightly linked with the practical environment in which the sensors are deployed. Sensor positioning is a pivotal part of main location-dependent applications that utilize sensornets. The global topology of the network is important to both sensor network applications and the implementation of networking functionalities. This paper studies the topology discovery with an emphasis on boundary recognition in a sensor network. A large mass of sensor nodes are supposed to scatter in a geometric region, with nearby nodes communicating with each other directly. This paper is thus designed to detect the holes in the topological architecture of sensornets only by connectivity information. Existent edges determination methods hold the high costs as assumptions. Without the help of a large amount of uniformly deployed seed nodes, those schemes fail in anisotropic WSNs with possible holes. To address this issue, we propose a solution, named PPA based on Poincare-Perelman Theorem, to judge whether there are holes in WSNs-monitored areas. Our solution can properly detect holes on the topological surfaces and connect them into meaningful boundary cycles. The judging method has also been rigorously proved to be appropriate for continuous geometric domains as well as discrete domains. Extensive simulations have been shown that the algorithm even enables networks with low density to produce good results.

Multihop Range-Free Localization in Anisotropic Wireless Sensor Networks: A Pattern-Driven Scheme

This paper focuses on multihop range-free localization in anisotropic wireless sensor networks. In anisotropic networks, geometric distance between a pair of sensor nodes is not always proportional to their hop count distance, which undermines the assumption of many existing range-free localization algorithms. To tolerate network anisotropy, we propose a pattern-driven localization scheme, which is inspired by the observation that in an anisotropic network the hop count field propagated from an anchor exhibits multiple patterns, under the interference of multiple anisotropic factors. Our localization scheme therefore for different patterns adopts different anchor-sensor distance estimation algorithms. The average anchor-sensor distance estimation accuracy of our scheme, as demonstrated by both theoretical analysis and extensive simulations, is improved to be better than 0.4r when the average sensor density is above eight, and the sensor localization accuracy thus is approximately better than 0.5r. This localization accuracy can satisfy the needs of many location-dependent protocols and applications, including geographical routing and tracking. Compared with previous localization algorithms that declares to tolerate network anisotropy, our localization scheme excels in 1) higher accuracy stemming from its ability to tolerate multiple anisotropic factors, including the existence of obstacles, sparse and nonuniform sensor distribution, irregular radio propagation pattern, and anisotropic terrain condition, 2) localization accuracy guaranteed by theoretical analysis, rather than merely by simulations, and 3) a distributed solution with less communication overhead and enhanced robustness to different network topologies.

Rendered Path: Range-Free Localization in Anisotropic Sensor Networks With Holes

Sensor positioning is a crucial part of many location-dependent applications that utilize wireless sensor networks (WSNs). Current localization approaches can be divided into two groups: range-based and range-free. Due to the high costs and critical assumptions, the range-based schemes are often impractical for WSNs. The existing range-free schemes, on the other hand, suffer from poor accuracy and low scalability. Without the help of a large number of uniformly deployed seed nodes, those schemes fail in anisotropic WSNs with possible holes. To address this issue, we propose the rendered path (REP) protocol. To the best of our knowledge, REP is the only range-free protocol for locating sensors with constant number of seeds in anisotropic sensor networks.