Localization Accuracy Improvement in WSNs using Trilateration Techniques

This paper presents a particle swarm optimization (PSO)‐based approach to enhance node localization accuracy in wireless sensor networks (WSNs). Traditional range‐based methods such as RSSI suffer from high positioning errors, while conventional DV‐Hop algorithms rely on assumptions that often fail under real‐world conditions. To overcome these limitations, the proposed method integrates a bounding box technique to constrain the initial search space, along with anticipatory and refinement strategies to address flip ambiguity. Additionally, inter‐node distance information, including distances between unknown nodes, is leveraged to further improve localization accuracy. Simulation results demonstrate that the proposed PSO‐based approach significantly reduces localization error, enhances accuracy and coverage, and lowers variance compared to standard DV‐Hop and Improved DV‐Hop (IDV‐Hop) algorithms. These improvements contribute to more accurate, reliable, and computationally efficient localization in WSNs.

Localization is a significant consideration in a Wireless Sensor Network (WSN), as it is essential to correlate sensor information with the geographic location it originates from. The DV-Hop algorithm, being a range-free, distributed algorithm, is an attractive option in this regard, due to its simplicity. In this paper, we present two algorithms to improve the localization accuracy of the DV-Hop algorithm. The Hop Size derived through a distributed fashion by the DV-Hop algorithm is optimized using the knowledge of the position of the anchor nodes. The locations of the unknown nodes are then estimated using the optimum hop size. We examine the performance of the proposed algorithms through simulations in environments with varying node numbers, anchor ratios and radio range. The results show that the proposed algorithms have improved performance in localization accuracy and stability compared to the original DV-Hop algorithm.

Wireless sensor networks are an emerging technology deployed in many fields. Knowing the positions of sensor nodes is critical for many applications to perform their tasks. Hence, accurate node localisation in wireless sensor networks is an essential issue. The node localisation problem can be formulated as a multidimensional optimisation problem. In this study, the authors propose a node localisation scheme based on a sine–cosine algorithm. The proposed scheme addresses the flip ambiguity problem, which can badly affect the localisation accuracy of the entire network. The efficacy of the proposed scheme is evaluated through intensive experimentation under different scenarios. The obtained results show the superiority of the proposed scheme against other optimisation algorithms in terms of the localisation accuracy, the number of localised nodes, and the computation time.

In wireless sensor networks (WSN) high-accuracy localization is crucial for both of WNS management and many other numerous location-based applications. Only a subset of nodes in a WSN is deployed as anchor nodes with their locations a priori known to localize unknown sensor nodes. The accuracy of the estimated position depends on the number of anchor nodes. Obviously, increasing the number or ratio of anchors will undoubtedly increase the localization accuracy. However, it severely constrains the flexibility of WSN deployment while impacting costs and energy. This paper aims to drastically reduce anchor number or ratio of anchor in WSN deployment and ensures a good trade-off for localization accuracy. Hence, this work presents an approach to decrease the number of anchor nodes without compromising localization accuracy. Assuming a random string WSN topology, the results in terms of anchor rates and localization accuracy are presented and show significant reduction in anchor deployment rates from 32% to 2%.

On the interaction between localization and location verification for wireless sensor networks

This chapter describes one of the major challenges in technology advancement of Wireless Sensor Networks (WSNs), i.e., Localization in WSNs. In recent years, sensor node localization is an emerging research area in WSNs. The sensor data become useless, if we do not know the location of the reporting node. Coordinates determination of the sensor node is a challenging problem and it is referred as localization problem. The nodes which has unknown coordinates is termed as target Nodes. Various localization methods can be utilized to find out the location of sensor nodes, those coordinates are not known in a system/network. Efficient WSN localization can be treated as multi-dimensional optimization problem which can be addressed through population based stochastic techniques, which involves the minimization of a function of differences between Euclidean and measured distance between sensor nodes. In this chapter various connectivity, range information and mobility based localization algorithms have been discussed. For optimizing the results of these algorithms, various computational intelligence (CI) based optimizing algorithms like Particle Swarm Optimization, Biogeography Based Optimization, Firefly Algorithm and Genetic Algorithm have been discussed. A choice between these algorithm is influenced by the localization accuracy expected to be and convergence rate.

In wireless sensor networks, the connectivity-based localization protocols are widely studied due to low cost and no requirement for special hardware. Many connectivity-based algorithms rely on distance estimation between nodes according to their hop count, which often yields large errors in anisotropic sensor network. In this paper, we propose a virtual potential field algorithm, in which the estimated positions of unknown nodes are iteratively adjusted by eliminating the inconsistency to the connectivity constraint. Unlike current connectivity-based algorithms, VPF effectively exploits the connectivity constraint information, regardless of distance estimation between nodes, thus achieving high localization accuracy in both isotropic and anisotropic sensor networks. Simulation results show that VPF improves the localization accuracy by an average of 47% compared with MDS in isotropic network, and 42% compared with PDM in anisotropic network. As a refinement procedure, the average improvement factor of VPF is 56% and 50%, based on MDS and PDM respectively.

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

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

Based on the requirements of low energy consumption and high bandwidth efficiency, a primary area of study is how to improve the estimation accuracy and tracking performance for target localization in wireless sensor networks. In this paper, we propose a weighted sign algorithm (WSA) for positioning and tracking in wireless sensor networks. In terms of the reliability of distance estimation, the location estimation of a target is formulated as a weighted least squares (WLS) problem, which is solved based on the WSA in an iterative and decentralized manner. We also propose a cooperative message-passing algorithm for inter-sensor information exchange, adaptively picking the participating sensor nodes, and making the paths for cyclic routing in the network. On each routing path, a target's location estimate is updated on each cycle and continues among the participating sensor nodes that surround the target. To ensure that the WSA-based scheme has a convergence behavior, a convergence analysis is given. Computer simulation results demonstrate that, as compared with previous methods, our proposed scheme has a faster convergence rate, better location accuracy, and improved tracking performance.

We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.

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

Localization with low cost and high accuracy is of utmost importance for most applications in wireless sensor networks (WSNs). Most existing range-free schemes have limitations in one or more areas, such as low accuracy, low scalability and threat from the malicious nodes. Secure sensor localization in WSNs has received considerable attention with the rise of Internet of things. In this paper, we propose an efficient and secure range-free localization scheme based on the outlier elimination and the vector refinement process. An outlier elimination method is introduced to the localization process by filtering inaccurate beacon nodes in the presence of the malicious beacon nodes, which can prevent the location attacks and improve the localization accuracy. A vector refinement feedback process is introduced to further improve the localization accuracy. Parameters that affect the localization accuracy are discussed. Simulation and experiment are carried out to evaluate the performance of the proposed outlier elimination vector refinement DV-hop with the presence of the malicious beacon nodes. All the results show that the localization accuracy is improved significantly compared to the DV-hop algorithm and the vector refinement DV-hop algorithm, for both of the cases of with and without the malicious beacon nodes.

The localization process in Wireless Sensor Networks (WSNs) is carried out for finding the exact position of the sensor node. WSN are applied in various number of applications like monitoring, humidity, hospitality and so on. Hundreds to thousands of nodes in the sensor sense the data to the base station. Thousands of nodes in the WSN makes the installing of Global processing System (GPS). It is more costly and the GPS not provide the exact location in the indoor environment. In spite of dense network, manual configuration is not possible in the indoor environment. This cause a raise in problem to identify the exact position without GPS or any manual configuration methods. This paper presents the methods to overcome the above issues in WSN localization, by path planning, metaheuristic and deep learning are the techniques having high scopes in the industries, mining etc. Many times sensing of data are restricted by accuracy, energy, power and efficiency in the nodes. This paper discuss the WSN localization process classifications and the methodologies in localization. Localization issues are minimized by various techniques like ToA, TDoA and RSSI. The types of localization, its applications are also discussed.

In wireless sensor networks (WSNs), localization is one of the most important topics because the location information is typically useful for many applications.The primary data used in a localization process include the locations of anchor nodes and the distances between neighboring nodes.However, these data may contain outliers that deviate from their true values.The existence of the outliers might make the estimated positions not accurate.Thus, it is important to detect and handle outliers in order to achieve high localization accuracy.In this paper, we survey the existing outlier detection techniques for localization in wireless sensor networks.We provide taxonomy for classifying outlier detection techniques in WSNs localization based on different features.In addition, we present comparisons of these techniques.Finally, we discuss the future research directions in this area.