Doppler shift with Archimedes Optimization Algorithm for localizing unknown nodes in underwater sensor networks

The issue of underwater sensor network (UWSN) localization has led to the aim of techniques presented in recent years. In this paper, we develop Doppler shift with Archimedes Optimization Algorithm for localizing unknown nodes in UWSN. The projected method predicts that sink node plays a major function in managing the computational load contrasted with the remaining nodes in the network of underwater. This node localization is proceeding with frequency shifts of sound waves contrasted toward real, which are present once observer in addition source can be mobile as they do in a marine atmosphere. The proposed technique is utilized to compute the estimated position of an unknown sensor node; here Archimedes' optimization algorithm is utilized to reduce the error during localization of nodes in UWSNs. This proposed technique can be enhancing the accuracy of the localization of nodes in UWSNs. This proposed methodology can be implemented and evaluated with the help of performance metrics. To validate the proposed technique's efficiency, it is contrasted with conventional techniques like Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA).     

Timing-Based Mobile Sensor Localization in Wireless Sensor and Actor Networks

In this paper, localization problem in wireless sensor and actor networks (WSAN) is addressed. In WSAN, the performance of event detection and tracking highly depends on the exact location information of the events that must be reported along with the event features. Having precise location information of the sensor nodes, actors are able to execute actions more effectively in the region of detected events. In this context, the accurate localization of sensor nodes is essential with respect to the actors. Particularly, the problem becomes much more complicated when the sensor nodes as well as the anchor nodes (actors) are mobile. In order to localize the mobile sensor nodes relative to the actors, a novel Timing-based Mobile Sensor Localization (TMSL) algorithm is introduced. In TMSL, sensor nodes determine their distance from actors by using propagation time and speed of RF signal. In order to determine distance from the actors, actors actively broadcast reference beacons in a pattern of intervals adaptively defined according to the mobility of sensor nodes and the required level of localization accuracy. These reference beacons carry the interval numbers in which they were transmitted. The interval numbers are then used by the sensor nodes to calculate the start time of the beacons locally which is then used to determine the propagation time. TMSL does neither require nor assume any time synchronization among the sensor nodes or with the actors. Performance evaluations clearly show that TMSL is adaptive to velocity of mobile sensor and actor nodes and can be configured according to the required localization accuracy in order to avoid overhead raised due to high velocity.     

A three dimensional localization algorithm for underwater acoustic sensor networks

Although many localization protocols have been proposed for terrestrial sensor networks in recent years, the unique characteristics of the underwater acoustic communication channel, such as high and variable propagation delay and the three dimensional volume of the environment make it necessary to design and develop new localization algorithms. In this paper, a localization algorithm called Three-Dimensional Underwater Localization (3DUL) is introduced. 3DUL achieves networkwide robust 3D localization by using a distributed and iterative algorithm. Most importantly, 3DUL exploits only three surface buoys for localization initially. The sensor nodes leverage the low speed of sound to accurately determine the inter-node distances. Performance evaluations show that 3DUL algorithm provides high accuracy in underwater localization, which does not degrade with network size.     

Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensor nodes. It helps in identifying the geographical area where an event occurred. The event information without its position information has no meaning. In range‐free localization techniques, DV‐hop is one of the main algorithm which estimates the position of nodes using distance vector algorithm. In this paper, a multiobjective DV‐hop localization based Non‐Sorting Genetic Algorithm‐II (NSGA‐II) is proposed in WSNs. Here, we consider six different single‐objective functions to make three multiobjective functions as the combination of two each. Localization techniques based on proposed multiobjective functions has been evaluated on the basis of average localization error and localization error variance. Simulation results demonstrate that the localization scheme based on proposed multiobjective functions can achieve good accuracy and efficiency as compared to state‐of‐the‐art single‐ and multiobjective GA DV‐hop localization scheme.     

Extended Monte Carlo localization algorithm for mobile sensor networks

A real-world localization system for wireless sensor networks that adapts for mobility and irregular radio propagation model is considered. The traditional range-based techniques and recent range-free localization schemes are not well competent for localization in mobile sensor networks, while the probabilistic approach of Bayesian filtering with particle-based density representations provides a comprehensive solution to such localization problem. Monte Carlo localization is a Bayesian filtering method that approximates the mobile node＇s location by a set of weighted particles. In this paper, an enhanced Monte Carlo localization algorithm-Extended Monte Carlo Localization （Ext-MCL） is proposed, i.e., the traditional Monte Carlo localization algorithm is improved and extended to make it suitable for the practical wireless network environment where the radio propagation model is irregular. Simulation results show the proposal gets better localization accuracy and higher localizable node number than previously proposed Monte Carlo localization schemes not only for ideal radio model, but also for irregular one.     

Path planning using a mobile anchor node based on trilateration in wireless sensor networks

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

Monte Carlo localization for mobile wireless sensor networks

Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors’ radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy.     

Range‐free intersecting chord‐based geometric localization scheme for wireless sensor networks

Recent advancement in wireless sensor network has contributed greatly to the emerging of low‐cost, low‐powered sensor nodes. Even though deployment of large‐scale wireless sensor network became easier, as the power consumption rate of individual sensor nodes is restricted to prolong the battery lifetime of sensor nodes, hence the heavy computation capability is also restricted. Localization of an individual sensor node in a large‐scale geographic area is an integral part of collecting information captured by the sensor network. The Global Positioning System (GPS) is one of the most popular methods of localization of mobile terminals; however, the use of this technology in wireless sensor node greatly depletes battery life. Therefore, a novel idea is coined to use few GPS‐enabled sensor nodes, also known as anchor nodes, in the wireless sensor network in a well‐distributed manner. Distances between anchor nodes are measured, and various localization techniques utilize this information. A novel localization scheme Intersecting Chord‐Based Geometric Localization Scheme (ICBGLS) is proposed here, which loosely follows geometric constraint‐based algorithm. Simulation of the proposed scheme is carried out for various communication ranges, beacon broadcasting interval, and anchor node traversal techniques using Omnet++ framework along with INET framework. The performance of the proposed algorithm (ICBGLS), Ssu scheme, Xiao scheme, and Geometric Constraint‐Based (GCB) scheme is evaluated, and the result shows the fact that the proposed algorithm outperforms the existing localization algorithms in terms of average localization error. The proposed algorithm is executed in a real‐time indoor environment using Arduino Uno R3 and shows a significant reduction in average localization time than GCB scheme and similar to that of the SSU scheme and Xiao scheme.     

Underwater localization using stochastic proximity embedding and multi-dimensional scaling

Localization for underwater acoustic sensor networks is an active research topic where a large number of techniques have been proposed recently. This paper addresses one of the open research issues, the impact of underwater sound speed variation on the localization accuracy. In this paper, modified versions of stochastic proximity embedding and multi-dimensional scaling localization algorithms customized for underwater application are proposed. The algorithms are found to provide good performance in underwater scenario as they take into account refractive ray bending of acoustic waves. Detailed study of the algorithm performance has been done and the results are reported. Cramer Rao Lower Bound for the problem is also derived.     

Sequential Monte Carlo localization in mobile sensor networks

Node localization in wireless sensor networks is essential to many applications such as routing protocol, target tracking and environment surveillance. Many localization schemes have been proposed in the past few years and they can be classified into two categories: range-based and range-free. Since range-based techniques need special hardware, which increases the localization cost, many researchers now focus on the range-free techniques. However, most of the range-free localization schemes assume that the sensor nodes are static, the network topology is known in advance, and the radio propagation is perfect circle. Moreover, many schemes need densely distributed anchor nodes whose positions are known in advance in order to estimate the positions of the unknown nodes. These assumptions are not practical in real network. In this paper, we consider the sensor networks with sparse anchor nodes and irregular radio propagation. Based on Sequential Monte Carlo method, we propose an alterative localization method—Sequential Monte Carlo Localization scheme (SMCL). Unlike many previously proposed methods, our work takes the probabilistic approach, which is suitable for the mobile sensor networks because both anchors and unknown nodes can move, and the network topology need not be formed beforehand. Moreover, our algorithm is scalable and can be used in large-scale sensor networks. Simulation results show that SMCL has better localization accuracy and it can localize more sensor nodes when the anchor density is low. The communication overhead of SMCL is also lower than other localization algorithms.

基于连通度和加权校正的移动节点定位算法

移动节点定位问题是无线传感器网络中的研究重点。针对移动节点定位误差大的问题,提出一种基于连通度和加权校正的移动节点定位算法。在未知节点移动过程中,根据节点间连通度大小选取参与定位的信标节点,利用加权校正方法修正RSSI测距信息,然后用最小二乘法对未知节点进行位置估计。仿真分析表明,节点通信半径和信标密度在一定范围内,该算法表现出良好的定位性能,定位精度明显提升。     

Sensitivity-based data fusion for optical localization of a mobile robot

Acoustic-based techniques are the standard for localization and communication in underwater environments, but due to the challenges associated with this medium, it is becoming increasingly popular to find alternatives such as using optics. In our prior work we developed an LED-based Simultaneous Localization and Communication (SLAC) approach that used the bearing angles, needed for establishing optical line-of-sight for LED-based communication between two beacon nodes and a mobile robot, to triangulate and thereby localize the position of the robot. Our focus in this paper is on how to optimally fuse measurement data for optical localization in a network with multiple pairs of beacon nodes to obtain the target location. We propose the use of a sensitivity metric, designed to characterize the level of uncertainty in the position estimate with respect to the bearing angle error, to dynamically select a desired pair of beacon nodes. The proposed solution is evaluated with extensive simulation and experimentation, in a setting of three beacons nodes and one mobile node. Comparison with multiple alternative approaches demonstrates the efficacy of the proposed approach.     

传感网络中基于移动信标的网格扫描定位算法

节点定位是传感网络最基本的技术之一,对此提出一种基于移动信标的网格扫描定位算法(Mobile Beacon Grid-Scan,MBGS)。该算法在网格扫描定位算法基础上,利用一个移动信标巡航整个传感区域,产生大量的虚拟信标,提高网络信标覆盖率,然后普通节点利用这些信标信息减小其可能区域(Estimative Rectangle,ER),并把新可能区域网格坐标质心作为其最新估计坐标。仿真结果表明,与Bounding Box、质心定位算法以及传统的网格扫描定位算法相比,MBGS定位方法的定位精度更高,算法性能更加稳定。     

MANCL: a multi‐anchor nodes collaborative localization algorithm for underwater acoustic sensor networks

Localization is an essential and major issue for underwater acoustic sensor networks (UASNs). Almost all the applications in UASNs are closely related to the locations of sensors. In this paper, we propose a multi‐anchor nodes collaborative localization (MANCL) algorithm, a three‐dimensional (3D) localization scheme using anchor nodes and upgrade anchor nodes within two hops for UASNs. The MANCL algorithm divides the whole localization process into four sub‐processes: unknown node localization process, iterative location estimation process, improved 3D Euclidean distance estimation process, and 3D DV‐hop distance estimation process based on two‐hop anchor nodes. In the third sub‐process, we propose a communication mechanism and a vote mechanism to determine the temporary coordinates of unknown nodes. In the fourth sub‐process, we use two‐hop anchor nodes to help localize unknown nodes. We also evaluate and compare the proposed algorithm with a large‐scale localization algorithm through simulations. Results show that the proposed MANCL algorithm can perform better with regard to localization ratio, average localization error, and energy consumption in UASNs. Copyright © 2014 John Wiley & Sons, Ltd.     

Efficient Color-theory-based Dynamic Localization for Mobile Wireless Sensor Networks

Location information is critical to mobile wireless sensor networks (WSN) applications. With the help of location information, for example, routing can be performed more efficiently. In this paper, we propose a novel localization approach, Color-theory based Dynamic Localization (CDL), which is based on color theory to exploit localization in mobile WSNs. CDL makes use of the broadcast information, such as locations and RGB values, from all anchors (a small portion of nodes with GPS receivers attached), to help the server to create a location database and assist each sensor node to compute its RGB value. Then, the RGB values of all sensor nodes are sent to the server for localization of the sensor nodes. A unique feature of our color-theory based mechanism is that it can use one color to represent the distances of a sensor node to all anchors. Since CDL is easy to implement and is a centralized approach, it is very suitable for applications that need a centralized server to collect user (sensor) data and monitor user activities, such as community health-care systems and hospital monitoring systems. Evaluation results have shown that for mobile WSNs, the location accuracy of CDL (E-CDL, an enhanced version of CDL) is 40–50% (75–80%) better than that of MCL (Hu, L., & Evans, D. (2004). Localization for mobile sensor networks. In Proceedings of the 10th annual international conference on mobile computing and networking, pp. 45–57). In addition, we have implemented and validated our E-CDL algorithm on the MICAz Mote Developer’s Kit.     

A distributed range-free correction vector based localization refinement algorithm

Localization problem is an important and challenging topic in today’s wireless sensor networks. In this paper, a novel localization refinement algorithm for LAEP, which is a range-free localization algorithm by using expected hop progress, has been put forward. The proposed localization refinement algorithm, called as CVLR, is based on position correction vectors and can resolve the LAEP’s hop-distance ambiguity problem, which can lead to adjacent unknown nodes localized at the same or very close positions. CVLR can make full use of the relative position relationship of 1-hop neighboring nodes (called as CVLR1), or 1-hop and 2-hop neighboring nodes (called as CVLR2), to iteratively refine their localization positions. Furthermore, from localization accuracy and energy dissipation perspective, we optimize the communication process of CVLR2 and propose an energy-efficient improved CVLR. Simulation results show that the localization accuracy of CVLR1, CVLR2, and the improved CVLR are obviously higher than that of LAEP and DV-RND.     

LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN

The localization of the sensor nodes is a fundamental problem in wireless sensor networks. There are a lot of different kinds of solutions in the literature. Some of them use external devices like GPS, while others use special hardware or implicit parameters in wireless communications.In applications like wildlife localization in a natural environment, where the power available and the weight are big restrictions, the use of hungry energy devices like GPS or hardware that add extra weight like mobile directional antenna is not a good solution.Due to these reasons it would be better to use the localization’s implicit characteristics in communications, such as connectivity, number of hops or RSSI. The measurement related to these parameters are currently integrated in most radio devices. These measurement techniques are based on the beacons’ transmissions between the devices.In the current study, a novel tracking distributed method, called LIS, for localization of the sensor nodes using moving devices in a network of static nodes, which have no additional hardware requirements is proposed.The position is obtained with the combination of two algorithms; one based on a local node using a fuzzy system to obtain a partial solution and the other based on a centralized method which merges all the partial solutions. The centralized algorithm is based on the calculation of the centroid of the partial solutions.Advantages of using fuzzy system versus the classical Centroid Localization (CL) algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for testing localization algorithms.With this simulator, it is demonstrated that the proposed method obtains less localization errors and better accuracy than the centroid algorithm.     

Local Patches Alignment Embedding Based Localization for Wireless Sensor Networks

Localization for wireless sensor networks (WSNs) is a challenging research topic. Let the set of sensor nodes that are close to each other be a “patch”, in this paper, we propose a new manifold learning method named local patches alignment embedding (LPAE), and then present a computationally efficient range-based WSNs localization approach using LPAE. Unlike the existing range-based localization methods using “patching” techniques, LPAE approach has the following features: 1) learning local position of all sensor nodes efficiently on a set of overlapping patches, which are constructed based on anchor nodes, rather than on neighborhood of each node, 2) aligning patches with the constraints of anchor nodes thus avoiding the accumulation of error, and 3) obtaining absolute positions of all sensor nodes directly without any other refinement technology. The proposed approach has been shown to be able to achieve satisfactory performance on both accuracy and efficiency via extensive simulations.     

基于自适应采样的移动WSN定位算法

节点自定位是无线传感器网络的关键技术之一。当前对无线传感器网络定位的研究主要集中静态节点定位,移动无线传感器网络定位研究相对较少。研究了基于序列蒙特卡罗方法的移动无线传感器网络定位。针对蒙特卡罗定位采用固定样本数,计算量大的缺点,根据蒙特卡罗定位盒（MCB）算法的锚盒子大小动态设置样本数,提出一种自适应采样蒙特卡罗盒定位算法。仿真表明,该算法在保持定位精度的同时有效地减小了采样次数,节约了计算量。     

Localization of Wireless Sensor Networks Using a Single Anchor Node

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