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.     

ELPMA: Efficient Localization Algorithm Based Path Planning for Mobile Anchor in Wireless Sensor Network

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.     

Performance of some metaheuristic algorithms for localization in wireless sensor networks

In this paper we propose two novel and computationally efficient metaheuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) principles for locating the sensor nodes in a distributed wireless sensor network (WSN) environment. The WSN localization problem is formulated as a non‐linear optimization problem with mean squared range error resulting from noisy distance measurement as the objective function. Unlike gradient descent methods, both TS and PSO methods ensure minimization of the objective function without the solution being trapped into local optima. We further implement a refinement phase with error propagation control for improvement of the results. The performance of the proposed algorithms are compared with each other and also against simulated annealing based WSN localization. The effects of range measurement error, anchor node density and uncertainty in the anchor node position on localization performance are also studied through various simulations. The simulation results establish better accuracy, computational efficiency and convergence characteristics for TS and PSO methods. Further, the efficacy of the proposed methods is verified with data collected from an experimental sensor network reported in the literature. Copyright © 2008 John Wiley & Sons, Ltd.     

Wireless Sensor Network Localization Based on Cuckoo Search Algorithm

Many applications of wireless sensor networks (WSN) require information about the geographical location of each sensor node. Devices that form WSN are expected to be remotely deployed in large numbers in a sensing field to perform sensing and acting task. The goal of localization is to assign geographical coordinates to each device with unknown position in the deployment area. Recently, the popular strategy is to apply optimization algorithms to solve the localization problem. In this paper, the cuckoo search algorithm is implemented to estimate the sensor’s position. The proposed approach has been compared in terms of localization error with particle swarm optimization (PSO) and various variants of biogeography based optimization (BBO). The results show that our method outperforms the PSO and BBO variants which are recently used in the literature.     

Hybrid area exploration–based mobility‐assisted localization with sectored antenna in wireless sensor networks

In common practice, sensor nodes are randomly deployed in wireless sensor network (WSN); hence, location information of sensor node is crucial in WSN applications. Localization of sensor nodes performed using a fast area exploration mechanism facilitates precise location‐based sensing and communication. In the proposed localization scheme, the mobile anchor (MA) nodes integrated with localization and directional antenna modules are employed to assist in localizing the static nodes. The use of directional antennas evades trilateration or multilateration techniques for localizing static nodes thereby resulting in lower communication and computational overhead. To facilitate faster area coverage, in this paper, we propose a hybrid of max‐gain and cost‐utility–based frontier (HMF) area exploration method for MA node's mobility. The simulations for the proposed HMF area exploration–based localization scheme are carried out in the Cooja simulator. The paper also proposes additional enhancements to the Cooja simulator to provide directional and sectored antenna support. This additional support allows the user with the flexibility to feed radiation pattern of any antenna obtained either from simulated data of the antenna design simulator, ie, high frequency structure simulator (HFSS) or measured data of the vector network analyzer (VNA). The simulation results show that the proposed localization scheme exhibits minimal delay, energy consumption, and communication overhead compared with other area exploration–based localization schemes. The proof of concept for the proposed localization scheme is implemented using Berkeley motes and customized MA nodes mounted with indigenously designed radio frequency (RF) switch feed network and sectored antenna.     

一种基于遗传算法的无线传感器网络定位算法

在无线传感器网络中,定位系统在目标监测与跟踪、基于位置信息的路由、智能交通、流管理等许多应用中起着至关重要的作用。本文基于遗传算法设计了一种适合于无线传感器网络的定位方法。实验结果表明,该方法具有定位精度高、所需锚节点比率小、受测距误差影响小的特点。     

A path planning model based on nested regular hexagons using weighted centroid localization

In many wireless sensor network (WSN) applications, the location of a sensor node is crucial for determining where the event or situation of interest occurred. Therefore, localization is one of the critical challenges in WSNs. Mobile anchor node assisted localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL localization is that the path planning of the mobile anchor (MA) node should be done so that the localization error in the network will be minimal and that all unknown nodes in the network are covered. This paper proposes a new path planning approach called nested hexagons curves (NHexCurves) for MANAL. NHexCurves guarantees that it will receive messages from at least three non-collinear anchors to locate all unknown nodes in the network. The proposed model has compared six different path planning schemes in the literature using weighted centroid localization (WCL). In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Second, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) is observed. Then, with similar approaches, the standard deviation of errors, localization ratio, scalability performances, and finally, path lengths of the models are examined. The simulation results show that the NHexCurves static path planning model proposed in this study stands out compared to other models with high localization error and localization ratio performance, especially at low resolutions, due to its path design. At the same time, the lowest error values according to σ are obtained with the proposed model among all models considered.     

基于多维信息的无线传感器网络定位算法

在无线传感器网络中,定位系统在目标监测与跟踪、基于位置信息的路由、智能交通等许多应用中起着至关重要的作用。本文综合节点能够获得的多维信息,设计了一种适合于部署密度很疏松环境的无线传感器网络定位方法。实验结果表明,该方法具有定位精度高、所需锚节点比率小、受测距误差影响小的特点。     

无线传感器网络定位算法综述

无线传感器网络(WSN)是一个多学科的研究领域,具有很广泛的应用前景,其中,WSN的定位是非常重要的研究方向。介绍了国内外研究机构在WSN定位方面的研究进展,并对这些工作进行了归纳和总结。将每种定位算法按照需不需要测距分为两大类,而且在具体算法中讨论了其以下几个特征,包括:需要/不需要锚节点、集中式/分布式、固定/移动等。     

Range-Free Mobile Sensor Localization and a Novel Obstacle Detection Technique

Mobile sensor localization is a challenging problem in wireless sensor networks. Due to mobility, it is difficult to find exact position of the sensors at any time instance. The aim of localization is to minimize positioning errors of the mobile sensors. In this paper we propose two range-free distributed localization algorithms for mobile sensors with static anchors. Both the algorithms depend on selection of beacon points. First we assume that mobile sensors move straight during localization which helps us to provide an upper bound on localization error. Certain applications may not allow sensors to move in a straight line. Obstacles may also obstruct path of sensors. Moreover beacon point selection becomes difficult in presence of obstacles. To address these issues, we propose another localization algorithm with an obstacle detection technique which selects correct beacon points for localization in presence of obstacles. Simulation results show improvements in performance over existing algorithms.     

Prolong network lifetime and improve efficiency in WSN‐UAV systems using new clustering parameters and CSMA modification

Since unmanned aerial vehicles (UAVs) have been introduced as mobile nodes for data gathering, wireless sensor networks (WSNs) have progressed considerably. The resulting WSN‐UAV systems are employed for emergency applications and also for remote monitoring purposes. WSN‐UAV systems yield an optimum data gathering method using the WSN. In the proposed method, the nodes' data are transferred using a remotely operated vehicle (drone) rather than the conventional data transferring methods like the direct and hop‐to‐hop data transmission approaches. Then, the gathered data are delivered in the pre‐determined destination point. WSN‐UAV systems, in fact, are a special case of the systems with the mobile sink in which the sink path is previously specified and controlled. In this paper, the effects of clustering parameters on the WSNs are studied; then, the network's lifetime is prolonged by applying some parameters. In addition, the network's performance is enhanced to some extent by assigning some changes in the media access control (MAC) layer. Also, the effect of drone's path pattern on the lifetime of the network is studied.     

Distributed localization using mobile beacons in wireless sensor networks

A new distributed node localization algorithm named mobile beacons-improved particle filter (MB-IPF) was proposed. In the algorithm, the mobile nodes equipped with globe position system (GPS) move around in the wireless sensor network (WSN) field based on the Gauss-Markov mobility model, and periodically broadcast the beacon messages. Each unknown node estimates its location in a fully distributed mode based on the received mobile beacons. The localization algorithm is based on the IPF and several refinements, including the proposed weighted centroid algorithm, the residual resampling algorithm, and the markov chain monte carlo (MCMC) method etc., which were also introduced for performance improvement. The simulation results show that our proposed algorithm is efficient for most applications.     

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.     

Cooperative localization techniques for wireless sensor networks: free,signal and angle based techniques

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.     

Heterogeneous Cooperative Localization for Social Networks

Location-aware techniques has become a hot research topic with great value in commercial and military applications. Cooperative localization, which utilizes multiple sensors in portable devices to estimate locations of the mobile users in the social networks, is one of the most promising solution for the indoor geo-location. Traditional cooperative localization methods are based on ranging techniques, they are highly dependent on the distance interpreted from the received signal strength (RSS) or time of arrival from anchors. However, a precise ranging procedure demands high performance hardware which would increase the cost to the current mobile platform. In this paper, we describes four ranging-free probabilistic cooperative localization algorithms: centroid scheme, nearest neighbor scheme, kernel scheme and AP density scheme to improve the accuracy for the indoor geo-location using current mobile devices. Since the GPS sensor embedded in the smart phone is able to provide accurate location information in the outdoor area, those mobile nodes can be used as calibrated anchors. The position of the indoor mobile node can be estimated by exchanging locations and RSSs from shared wireless access points information between the target node and anchor nodes. An empirical evaluation of the system is given to demonstrate the feasibility of these cooperative localization algorithms by reporting the results in a real-world environments, e.g. suburban area and city downtown. Moreover, we compared our results with the WiFi positioning system made by Skyhook Wireless to validate the accuracy of the proposed algorithms. Meanwhile, a Monte Carlo simulation is carried out to evaluate the performance of the cooperative algorithms under different scenarios. Results show that given the same scenario setting, the AP density scheme and kernel scheme outperform than other schemes.     

基于无线传感器网络的活动目标跟踪

无线传感器网络WSN是目前通信和计算机领域的研究热点。WSN的各种应用中,活动目标跟踪在军事和民用上都具有广泛的应用前景,也是技术上具有挑战性的课题。总结了目前传感器网络目标跟踪中节点的组织和路由,目标的侦测、定位,目标轨迹的估计和预测等方面的一些研究成果,比较了各个方法的优缺点,提出了以后跟踪应用中值得注意的问题和方向。     

Decentralized and energy‐balanced algorithms for maintaining temporal full‐coverage in mobile WSNs

Coverage is one of the most important issues in Wireless Sensor Networks (WSNs). However, full coverage only can be achieved when surplus mobile sensors contribute a coverage area larger than the hole size. When there is no surplus mobile sensor to cover a big hole, previous studies have utilized mobile sensors by moving the hole from one location to another, therefore achieving temporal full‐coverage, where each location on the monitoring region has been ever covered by mobile sensors during a fixed time interval. However, with only some mobile sensors participating in the hole‐movement task, this results in an energy‐imbalance WSN. This paper considers a mobile WSN that contains a big hole where there exists no redundant mobile sensor to heal the hole. Three distributed algorithms, called Basic, Forward‐Only, and Any‐Direction movement mechanisms, are proposed to achieve the purpose of temporal full‐coverage in a way that the total energy consumption is minimized or that the energy consumption of all mobile sensors that participate in the hole‐movement task are balanced. Simulation results reveal that the proposed hole‐movement mechanisms enhance the coverage of WSNs and balance the energy consumption of mobile sensor nodes. Copyright © 2010 John Wiley & Sons, Ltd.     

An integrated framework for wireless sensor network management

Wireless sensor networks (WSNs) have significant potential in many application domains, ranging from precision agriculture and animal welfare to home and office automation. Although sensor network deployments have only begun to appear, the industry still awaits the maturing of this technology to realize its full benefits. The main constraints to large‐scale commercial adoption of WSN have been the lack of available network management and control tools, such as for determining the degree of data aggregation prior to transforming it into useful information, localizing the sensors accurately so that timely emergency actions can be taken at an exact location, routing data by reducing sensor energy consumption, and scheduling data packets so that data are sent according to their priority and fairness. Moreover, to the best of our knowledge, no integrated network management solution comprising efficient localization, data scheduling, routing, and data aggregation approaches exists in the literature for a large‐scale WSN. Thus, we introduce an integrated network management framework comprising sensor localization, routing, data scheduling, and data aggregation for a large‐scale WSN. Experimental results show that the proposed framework outperforms an existing approach that comprises only localization and routing protocols in terms of localization energy consumption, localization error, end‐to‐end delay, packet loss ratio, and network energy consumption. Moreover, the proposed WSN management framework has potential in building a future “Internet of Things”. Copyright © 2012 John Wiley & Sons, Ltd.     

MAX–MIN aggregation in wireless sensor networks: mechanism and modeling

In‐network aggregation is crucial in the design of a wireless sensor network (WSN) due to the potential redundancy in the data collected by sensors. Based on the characteristics of sensor data and the requirements of WSN applications, data can be aggregated by using different functions. MAX—MIN aggregation is one such aggregation function that works to extract the maximum and minimum readings among all the sensors in the network or the sensors in a concerned region. MAX—MIN aggregation is a critical operation in many WSN applications. In this paper, we propose an effective mechanism for MAX—MIN aggregation in a WSN, which is called Sensor MAX—MIN Aggregation (SMMA). SMMA aggregates data in an energy‐efficient manner and outputs the accurate aggregate result. We build an analytical model to analyze the performance of SMMA as well as to optimize its parameter settings. Simulation results are used to validate our models and also evaluate the performance of SMMA. Copyright © 2010 John Wiley & Sons, Ltd.     

Analysis of weighted centroid-based localization scheme for wireless sensor networks

Weighted centroid-based schemes provide a cost-effective alternative to locate sensors in a Wireless Sensor Network (WSN). In this paper, we describe mathematical characteristics of weighted centroid localization in a WSN. We provide an expression to compute the distance between the weighted and unweighted centroids of a set of points. We present algorithms to compute the weighted centroid in an iterative and non-iterative manner. We provide expressions for the distance between weighted centroids during successive iterations. The analytical framework presented in this paper is general and may incorporate any criterion for assigning weights to locations of anchors involved in the computation of location of a sensor using the weighted centroid. Simulations are carried out to evaluate the performance of weighted centroid localization where weights are assigned using proximities based on the distance and the received signal power. We observed that the normalized error for weights assigned using the received signal power based proximity is less than the weights assigned using distance based proximity.