A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale UWSN.One of the major issues in such a network is the localization of underwater nodes.Localization is required for tracking objects and detecting the target.It is also considered tagging of data where sensed contents are not found of any use without localization.This is useless for application until the position of sensed content is confirmed.This article’s major goal is to review and analyze underwater node localization to solve the localization issues in UWSN.The present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes underwater.Also,a detailed subdivision of these localization schemes is given.Further,these localization schemes are compared from different perspectives.The detailed analysis of these schemes in terms of certain performance metrics has been discussed in this paper.At the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

无线传感器网络基于测距的节点定位算法综述OverviewoftheNodeLocalizationAlgorithmBasedonRangingofWirelessSensorNetworks

基于测距的定位方法对测量的距离信息运用几何知识求解未知节点的位置,常用在定位精度较高的领域,可在误差、能耗、受环境因素影响等方面进行优化。本文对基于测距的无线传感器网络节点定位算法进行详细地分析和比较。

A Mobile Localization Strategy for Wireless Sensor Network in NLOS Conditions

The problem of mobile localization for wireless sensor network has attracted considerable attention in recent years. The localization accuracy will drastically grade in non-line of sight(NLOS) conditions. In this paper, we propose a mobile localization strategy based on Kalman filter. The key technologies for the proposed method are the NLOS identification and mitigation. The proposed method does not need the prior knowledge of the NLOS error and it is independent of the physical measurement ways. Simulation results show that the proposed method owns the higher localization accuracy when compared with other methods.

基于IRF-PF算法的WLAN室内定位方法

针对室内复杂环境下无线局域网接收信号强度的时变波动性,提出一种基于改进随机森林算法并融合粒子滤波的室内定位方法。首先,通过自适应滑动窗口选取接收信号强度较为平滑的信号接入点作为定位指纹特征建立决策树,从而减少特征空间的噪声;然后,利用bagging抽样后的袋外数据赋予决策树不同的投票权重等级,增大决策树类别间判别差异性,优化随机森林算法在目标位置与接收信号强度间弱映射关联性场景下定位精度低的问题,依据改进的随机森林算法建立位置坐标与接收信号强度间的映射模型,计算定位点的位置;最后,为了提高定位准确性与稳定性,将依据改进随机森林算法计算出的位置作为粒子滤波的观测量,利用粒子滤波对目标位置进行估计和预测。在Zenodo数据集上的实验结果表明:提出的算法在定位误差2 m范围内精度达到75.8%,在4 m范围内精度已经高达94.8%;相比于传统随机森林算法,能够将2 m内的定位精度提高11.5%;与K近邻、岭回归和贝叶斯等室内定位算法相比,定位精度和算法实时性也得到明显提升。

Broadcasting with Controlled Redundancy and Improved Localization in Wireless Sensor Networks

Wireless sensor networks （WSNs） consist of sensor nodes that broadcast a message within a network. Efficient broadcasting is a key requirement in sensor networks and has been a focal point of research over the last few years. There are many challenging tasks in the network, including redundancy control and sensor node localization that mainly depend on broadcasting. In this paper, we propose a broadcasting algorithm to control redundancy and improve localization （BACRIL） in WSNs. The proposed algorithm incorporates the benefits of the gossip protocol for optimizing message broadcasting within the network. Simulation results show a controlled level of redundancy, which is up to 57.6% if the number of sensor nodes deployed in a 500 m×500 m area are increased from 50 to 500.

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate users.Moreover,the security design of the wireless structure is vulnerable to versatile attacks.For example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false messages.Althoughmany security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world applications.This paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks effectively.The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack.The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in theWLAN.The proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related work.The experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

NBP-based localization algorithm for wireless sensor networks in NLOS environments

To mitigate the impacts of non-line-of-sight（NLOS） errors on location accuracy, a non-parametric belief propagation（NBP）-based localization algorithm in the NLOS environment for wireless sensor networks is proposed.According to the amount of prior information known about the probabilities and distribution parameters of the NLOS error distribution, three different cases of the maximum a posterior（MAP） localization problems are introduced. The first case is the idealized case, i. e., the range measurements in the NLOS conditions and the corresponding distribution parameters of the NLOS errors are known. The probability of a communication of a pair of nodes in the NLOS conditions and the corresponding distribution parameters of the NLOS errors are known in the second case. The third case is the worst case, in which only knowledge about noise measurement power is obtained. The proposed algorithm is compared with the maximum likelihood-simulated annealing（ML-SA）-based localization algorithm. Simulation results demonstrate that the proposed algorithm provides good location accuracy and considerably outperforms the ML-SA-based localization algorithm for every case. The root mean square error（RMSE）of the location estimate of the NBP-based localization algorithm is reduced by about 1. 6 m in Case 1, 1. 8 m in Case 2 and 2. 3 m in Case 3 compared with the ML-SA-based localization algorithm. Therefore, in the NLOS environments,the localization algorithms can obtain the location estimates with high accuracy by using the NBP method.

A New Localization Scheme with Statistical Estimation against Wormhole Attack in Wireless Sensor Networks

Wormhole attack is one of the most devastating threats for range-free localization in wireless sensor networks. In this paper, we evaluate three statistical estimation methods with the same network model and geographic information obtailaed by the DV-Hop algorithm. We analyze the limits of Minimum Mean Square Estimate （MMSE）, Least Median of Squares （LMS） and Enhanced greedy At- tack-Resistant MMSE （EARMMSE） and propose an improved EARMMSE with the hop-distance relationship, named EARMMSE＋. Simulation results illustrate the performance of MMSE, LMS and EARMMSE＋ with different anchor fraction, the length of wormhole link and the average local neighborhood and show that EARMMSE＋ outperforms MMSE and LMS.

Analysis of Five Typical Localization Algorithms for Wireless Sensor Networks

In this paper, the self-localization problem is studied. It is one of the key technologies in wireless sensor networks (WSNs). And five localization algorithms: Centroid algorithm, Amorphous algorithm, DV-hop algorithm, APIT algorithm and Bounding Box algorithm are discussed. Simulation of those five localization algorithms is done by MATLAB. The simulation results show that the positioning error of Amorphous algorithm is the minimum. Considering economy and localization accuracy, the Amorphous algorithm can achieve the best localization performance under certain conditions.

Rigid graph-based three-dimension localization algorithm for wireless sensor networks

This paper investigates the node localization problem for wireless sensor networks in three-dimension space. A distributed localization algorithm is presented based on the rigid graph. Before location, the communication radius is adaptively increasing to add the localizability. The localization process includes three steps: firstly, divide the whole globally rigid graph into several small rigid blocks; secondly, set up the local coordinate systems and transform them to global coordinate system; finally, use the quadrilateration iteration technology to locate the nodes in the wireless sensor network. This algorithm has the advantages of low energy consumption, low computational complexity as well as high expandability and high localizability. Moreover, it can achieve the unique and accurate localization. Finally, some simulations are provided to demonstrate the effectiveness of the proposed algorithm.

Distributed Localization Algorithm for Wireless Sensor Network Based on Multidimensional Scaling and the Shortest Path Distance Correction

Sensor localization is crucial for the configuration and applications of wireless sensor network (WSN). A novel distributed localization algorithm, MDS-DC was proposed for wireless sensor network based on multidimensional scaling (MDS) and the shortest path distance correction. In MDS-DC, several local positioning regions with reasonable distribution were firstly constructed by an adaptive search algorithm, which ensures the mergence between the local relative maps of the adjacent local position regions and can reduce the number of common nodes in the network. Then, based on the relationships between the estimated distances and actual distances of anchors, the distance estimation vectors of sensors around anchors were corrected in each local positioning region. During the computations of the local relative coordinates, an iterative process, which is the combination of classical MDS algorithm and SMACOF algorithm, was applied. Finally, the global relative positions or absolute positions of sensors were obtained through merging the relative maps of all local positioning regions. Simulation results show that MDS-DC has better performances in positioning precision, energy efficiency and robustness to range error, which can meet the requirements of applications for sensor localization in WSN.

MDS and Trilateration Based Localization in Wireless Sensor Network

Localization of sensor nodes is crucial in Wireless Sensor Network because of applications like surveillance, tracking, navigation etc. Various optimization techniques for localization have been proposed in literature by different researchers. In this paper, we propose a two phase hybrid approach for localization using Multidi- mensional Scaling and trilateration, namely, MDS with refinement using trilateration. Trilateration refines the estimated locations obtained by the MDS algorithm and hence acts as a post optimizer which improves the accuracy of the estimated positions of sensor nodes. Through extensive simulations, we have shown that the proposed algorithm is more robust to noise than previous approaches and provides higher accuracy for estimating the positions of sensor nodes.

Grid-Based Localization Mechanism with Mobile Reference Node in Wireless Sensor Networks

Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location information to estimate their locations, which needs lots of time or costs. In this paper we proposed a localization mechanism using a mobile reference node （MRN） and trilateration in WSNs to reduce the energy consumption and location error. The simulation results demonstrate that the proposed mechanism can obtain more unknown nodes locations by the mobile reference node moving scheme and will decreases the energy consumption and average ocation error.

Node cooperation based location secure verification algorithm in wireless sensor networks localization

For the application of wireless sensor networks in the military field, one of the main challenges is security. To solve the problem of verifying the location claim for a node, a new location verifica- tion algorithm called node cooperation based location secure verification （NCBLSV） algorithm is proposed. NCBLSV could verify malicious nodes by contrasting neighbor nodes and nodes under beam width angle using an adaptive array antenna at a base point. Simulation experiments are con- ducted to evaluate the performance of this algorithm by varying the communication range and the an- tenna beam width angle. Results show that NCBLSV algorithm has high probability of successful ma- licious nodes detection and low probability of false nodes detection. Thus, it is proved that the NCBLSV algorithm is useful and necessary in the wireless sensor networks security.

Range-Based Localization in Wireless Networks Using Density-Based Outlier Detection

Node localization is commonly employed in wireless networks. For example, it is used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and AoA to estimate the distance between two nodes. Proximity sensing between nodes is typically the basis for range-free algorithms. A tradeoff exists since range-based algorithms are more accurate but also more complex. However, in applications such as target tracking, localization accuracy is very important. In this paper, we propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining. It requires selection of the K-nearest neighbours (KNN). DBOD assigns density values to each point used in the location estimation. The mean of these densities is calculated and those points having a density larger than the mean are kept as candidate points. Different performance measures are used to compare our approach with the linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS-SVD) algorithms. It is shown that the proposed algorithm performs better than these algorithms even when the anchor geometry about an unlocalized node is poor.

An Iterative Clustering-Based Localization Algorithm for Wireless Sensor Networks

In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most representative intersection points between every two circles and use them to estimate the position of unknown nodes.Simulation results demonstrate that the proposed algorithm outperforms other localization schemes (such as Min-Max,etc.) in accuracy,scalability and gross error tolerance.

A New Distributed Localization Scheme for Wireless Sensor Networks

在无线传感器网络(WSN ) 的节点本地化是被对待一功能目标在纸从一个新奇观点追踪双。与在 WSN 的传统的追踪问题不同，使用静态的地点商品节点估计动人的目标，活动节点被用来帮助未知节点精确地发现他们的位置。一个新节点本地化计划虚拟烽火精力比率本地化(VB-ERL ) 和它为 WSN 的精炼被介绍。在计划，活动节点基于 Gauss-Markov 活动性模型搬到监视者地并且周期性地广播信息包。每个静态的未知节点在它的察觉到的范围收到虚拟烽火和精力，并且由发现一套超球面的交叉估计它的地点。模拟结果证明建议计划是有效的。

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

Location information plays an important role in most of the applications in Wireless Sensor Network(WSN).Recently,many localization techniques have been proposed,while most of these deals with two Dimensional applications.Whereas,in Three Dimensional applications the task is complex and there are large variations in the altitude levels.In these 3D environments,the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level.For such applications,2D localization models are not reliable.Due to this,the design of 3D localization systems in WSNs faces new challenges.In this paper,in order to find unknown nodes in Three-Dimensional environment,only single anchor node is used.In the simulation-based environment,the nodes with unknown locations are moving at middle&lower layers whereas the top layer is equipped with single anchor node.A novel soft computing technique namely Adaptive Plant Propagation Algorithm(APPA)is introduced to obtain the optimized locations of these mobile nodes.Thesemobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity(Degree of Irregularity(DOI))value set to 0.01.The simulation results present that proposed APPAalgorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error,computational time,and the located sensor nodes.

A Three-Dimensional Range-Free Localization Algorithm Based on Mobile Beacons for Wireless Sensor Networks

In wireless sensor networks （WSNs） the position information of individual nodes is a matter of vital importance because allows the implementation of necessary network functions such as routing, querying and other applications. The objective of this paper is to propose an algorithm of three-dimensional distributed range-free localization for WSNs, using a mobile beacon （MB） equipped with a rotary and tilting directional antenna. This algorithm, denominated as the three-dimensional azimuthally defined area localization algorithm （3D- ADAL）, is executed in each sensor node and is based only on the analysis of the information received from the MB, therefore is energy efficient and contributes to extend the lifetime of the sensor network. Additionally the proposed algorithm has the advantage of being simple and economical. The simulation results show that the proposed algorithm is a practical, effective and accurate method for a three-dimensional location of sensor nodes in a WSN.