Distributed Energy Optimization for Target Tracking in Wireless Sensor Networks

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrics are calculated by grid exclusion algorithm and Dijkstra's algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combining the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection.     

Convoy Tree Based Fuzzy Target Tracking in Wireless Sensor Network

One important application area of wireless sensor network (WSN) is tracking mobile target. When a target enters in a monitoring region and moves around it, the deployed WSN is used to collect information about the target and send it to the nearby base station. In this paper, we propose a fuzzy based target tracking algorithm (CTFTT). The algorithm constructs a convoy tree around the target and dynamically moves the tree along with the target by adding new nodes into the tree and removing old nodes from the tree. The expansion, contraction and reconfiguration of the tree is done using a fuzzy based sensing model. Important advantages are (1) convoy tree provides 100% coverage, (2) fuzzy mechanism helps to localize the evevts such as tree expansion, contraction and reconfiguration. This in turn helps to reduce the energy consumption in the network. Localized events also reduce communication overhead. Thus CTFTT is able to support the tracking of even fast moving objects. Extensive simulation shows that our algorithm performs better than the existing tree based algorithms in terms of coverage and energy.     

A pragmatic approach to area coverage in hybrid wireless sensor networks

Success of Wireless Sensor Networks (WSN) largely depends on whether the deployed network can provide desired area coverage with acceptable network lifetime. This paper seeks to address the problem of determining the current coverage achieved by the non‐deterministic deployment of static sensor nodes and subsequently enhancing the coverage using mobile sensors. We identify three key elements that are critical for ensuring effective area coverage in Hybrid WSN: (i) determining the boundary of the target region and evaluating the area coverage (ii) locating coverage holes and maneuvering mobile nodes to fill these voids, and (iii) maintaining the desired coverage over the entire operational lifetime of the network. We propose a comprehensive solution that addresses all of the aforementioned aspects of the area coverage, called MAPC (mobility assisted probabilistic coverage). MAPC is a distributed protocol that operates in three distinct phases. The first phase identifies the boundary nodes using the geometric right‐hand rule. Next, the static nodes calculate the area coverage and identify coverage holes using a novel probabilistic coverage algorithm (PCA). PCA incorporates realistic sensing coverage model for range‐based sensors. The second phase of MAPC is responsible for navigating the mobile nodes to plug the coverage holes. We propose a set of coverage and energy‐aware variants of the basic virtual force algorithm (VFA). Finally, the third phase addresses the problem of coverage loss due to faulty and energy depleted nodes. We formulate this problem as an Integer Linear Program (ILP) and propose practical heuristic solutions that achieve similar performance as that of the optimal ILP solution. A guiding principle in our design process has been to ensure that the MAPC can be readily implemented in real‐world applications. We implemented the boundary detection and PCA algorithm (i.e., Phase I) of the MAPC protocol on off‐the‐shelf sensor nodes and results show that the MAPC can successfully identify boundary nodes and accurately determine the area coverage in the presence of real radio irregularities observed during the experiments. Extensive simulations were carried out to evaluate the complete MAPC protocol and the results demonstrate that MAPC can enhance and maintain the area coverage, while reducing the total energy consumption by up to 70% as compared with the basic VFA. Copyright © 2010 John Wiley & Sons, Ltd.     

一种基于有向感知模型的传感器节点覆盖算法

覆盖控制作为无线传感器网络中的一个基本问题,反映了传感器网络所能提供的“感知”服务质量.优化传感器网络覆盖对于合理分配网络的空间资源,更好地完成环境感知、信息获取任务以及提高网络生存能力都具有重要的意义.针对无线传感器网络方向个数固定的有向感知模型提出一种覆盖增强算法,采用复杂网络社团结构算法划分对网络进行节点子集划分,重新调整节点的感知方向,增强网络的覆盖率,同时有效降低了算法的时间复杂度.     

Multi-objective Binary Reinforced Cuckoo Search Algorithm for Solving Connected Coverage target based WSN with Critical Targets

In the stream of WSN, covering the targets using sensors and communication among the sensors to forward the data packets is a prime challenge due to the sparse target locations. Dedicated sensors lead more installation cost and significant amount of maintenance needs to be charged. Coverage of multiple targets by few sensors leads to network failure in case if any sensor runs out of power. Targets in sparse region also should be considered into account while sensing the environment. Hence in this paper, an effective multi-objective connected coverage target based WSN algorithm is proposed namely Multi-Objective Binary Cuckoo Search algorithm. The proposed model also handles the critical targets in the given sensing region. The algorithms hold the potentiality to handle minimized sensor deployment, maximized coverage and connectivity cost simultaneously. The proposed model is compared with the state of art algorithms to prove its significance. Two dedicated simulation region is developed in a large scale to examine the efficiency of the proposed algorithm. The results shows the significance of the proposed model over existing algorithms.

     

分布式无线感知网络节点部署算法研究

在无线感知网络节点部署中,目标区域的覆盖率大小对信号检测的效果具有重要的意义,通过智能优化算法来提高区域覆盖率已成为当前无线感知网络节点部署领域的研究热点之一。为了提高分布式无线感知网络对目标区域内的重点区域的覆盖率和减少冗余感知节点的投放,论文提出了一种分布式无线感知网络节点部署算法。该算法首先通过随机部署满足连通性的少量感知节点后初次工作来定位和估计出重点区域,然后将估计出的重点区域融入到粒子群算法的目标函数和粒子更新方程中实现对感知节点的重新部署,从而更好的优化了重点区域的覆盖率和减少冗余感知节点数量。仿真结果表明,与标准粒子群算法及其他优化算法相比,论文所研究的算法有更高的覆盖率和更低的迭代次数。      

Potential position node placement approach via oppositional gravitational search for fulfill coverage and connectivity in target based wireless sensor networks

Wireless Sensor Network (WSN) has appeared as a powerful technological platform with tremendous and novel applications. Now-a-days, monitoring and target tracking are the most major application in WSNs. In target based WSN, coverage and connectivity are the two most important issues for definite data forwarding from every target to a remote base station. An NP entire issue is to find least number of potential or possible locations to set sensor nodes gratifying both coverage and connectivity from a given a group of target points. In this article, we propose an Oppositional Gravitational Search algorithm (OGSA) based approach to solve this problem. This approach helps that the sensor nodes are prone to failure, the proposed system provides l-coverage to all targets and n-connectivity to each sensor node. This OGSA based system is presented with agent representation, derivation of efficient fitness function along with the usual Gravitational Search algorithm operators. The approach is simulated broadly with various scenarios of Wireless Sensor Network. The experimentation results are compared with some relevant existing algorithms to demonstrate the efficiency of the proposed approach.     

视野有限的传感器网络中分布式多目标跟踪

在传感器网络的多目标跟踪研究中,大多数现有的跟踪算法通常设定网络中所有节点具有相同的视野,即所有节点都能够得到目标的测量,但在实际中,节点的感测范围通常是有限的。针对这一问题,本文提出了一种能够在感测范围有限的多传感器网络中实现多目标跟踪的分布式概率假设密度滤波算法,该算法通过融合传感器网络视野范围内的后验概率假设密度粒子集来克服传感器节点感测范围的局限。仿真结果表明,提出的算法可以在感测范围有限的情况下实现多目标状态和数目的有效跟踪,同时可以在一定程度上抑制杂波,具有较好的跟踪稳定性。      

保障监测时延的无线传感器网络感知调度算法

针对重点区域事件监测应用中,无线传感器网络需要满足不同监测时延要求,该文首先分析了点目标监测应用的时延下界,设计了多项式的最优自适应占空比分布式感知调度算法。在此基础上,设计了一个面向局部重点区域的事件监测分布式感知调度算法(LDSS)。LDSS具有较低的计算复杂度和通信复杂度。仿真结果显示,与现有的随机调度算法相比,LDSS能获得监测时延更接近于理论时延下界的性能。     

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.     

An energy efficient hole repair node scheduling algorithm for WSN

A sensor node in the wireless sensor network has limited energy and it normally cannot be replaced due to the random deployment, so how to prolong the network life time with limited energy while satisfying the coverage quality simultaneously becomes a crucial problem to solve for wireless sensor networks (WSN). In this work, we propose an energy efficient algorithm based on the sentinel scheme to reduce the sleeping node detection density by defining a new deep sleeping state for each sensor node. The average energy consumed by probing neighboring nodes is introduced as a factor to calculate the detection rate. In addition, after some theoretical analysis of the existence of coverage holes in WSN, a triangle coverage repair procedure is defined to repair coverage holes. Simulation results show that our proposed algorithm obtained better performance in terms of the coverage quality and network life time compared with some existing algorithms in the literature.     

传感器网络中基于数据融合的栅栏覆盖控制研究

该文采用概率性感知模型,并利用数据融合技术构造虚拟节点来增加节点覆盖区域。在此基础上,提出一种栅栏覆盖控制算法。算法借助分治法构造栅栏,以减少节点间通信开销;并调度传感器使冗余节点睡眠,达到减少能耗和延长网络寿命的目的。分析和实验结果表明,针对所提问题设计的模型和算法可有效增加节点覆盖范围及节点间最大间隔距离,且在栅栏数、网络寿命等性能上均优于基于节点监测数据未融合的栅栏覆盖控制算法。     

Communication Coverage in Wireless Passive Sensor Networks

System lifetime of wireless sensor networks (WSN) is inversely proportional to the energy consumed by critically energy-constrained sensor nodes during RF transmission. In that regard, modulated backscattering (MB) is a promising design choice, in which sensor nodes send their data just by switching their antenna impedance and reflecting the incident signal coming from an RF source. Hence, wireless passive sensor networks (WPSN) designed to operate using MB do not have the lifetime constraints of conventional WSN. However, the communication performance of WPSN is directly related to the RF coverage provided over the field the passive sensor nodes are deployed. In this letter, RF communication coverage in WPSN is analytically investigated. The required number of RF sources to obtain interference-free communication connectivity with the WPSN nodes is determined and analyzed in terms of output power and the transmission frequency of RF sources, network size, RF source and WPSN node characteristics.     

Performance evaluation of sensor deployment using optimization techniques and scheduling approach for K-coverage in WSNs

In the wireless sensor networks, sensor deployment and coverage are the vital parameter that impacts the network lifetime. Network lifetime can be increased by optimal placement of sensor nodes and optimizing the coverage with the scheduling approach. For sensor deployment, heuristic algorithm is proposed which automatically adjusts the sensing range with overlapping sensing area without affecting the high degree of coverage. In order to demonstrate the network lifetime, we propose a new heuristic algorithm for scheduling which increases the network lifetime in the wireless sensor network. Further, the proposed heuristic algorithm is compared with the existing algorithms such as ant colony optimization, artificial bee colony algorithm and particle swarm optimization. The result reveals that the proposed heuristic algorithm with adjustable sensing range for sensor deployment and scheduling algorithm significantly increases the network lifetime.     

有向传感器网络覆盖增强问题的贪婪迭代算法

 在有向传感器网络中,可以通过调整节点的感知方向来增强目标区域的覆盖率.提出了有向传感器网络覆盖增强问题的一种贪婪迭代算法,在每次迭代中,调整那些使得覆盖率增加最大的节点的感知方向,重复此迭代过程直至通过调整任一节点的感知方向已不能再增加覆盖率.此外,还提出了一种通过计算几何求解该算法中区域计算问题的方法,以提高计算精度和减少计算时间.大量的仿真实验结果表明,该算法的执行时间较短,收敛速度较快,能够获得比现有算法更高的目标区域覆盖率.     

Barrier coverage with line-based deployed mobile sensors

Barrier coverage of a wireless sensor network is a critical issue in military and homeland security applications, aiming to detect intruders that attempt to cross the deployed region. While a range of problems related to barrier coverage have been investigated, little effort has been made to explore the effects of different sensor deployment strategies and mechanisms to improve barrier coverage of a wireless sensor network after it is deployed. In this paper we study the barrier coverage of a line-based sensor deployment strategy and explore how to exploit sensor mobility to improve barrier coverage. We first establish a tight lower bound for the existence of barrier coverage under the line-based deployment. Our results show that the barrier coverage of the line-based deployment significantly outperforms that of the Poisson model when the random offsets are relatively small compared to the sensor’s sensing range. To take advantage of the performance of line-based deployment, we further devise an efficient algorithm to relocate mobile sensors based on the deployed line so as to improve barrier coverage. The algorithm finds barrier gaps and then relocates mobile sensors to fill the gaps while at the same time balancing the energy consumption among mobile sensors. Simulation results show that the algorithms can effectively improve the barrier coverage of a wireless sensor network for a wide range of deployment parameters. Therefore, in wireless sensor network applications, the coverage goal, possible sensor deployment strategies, and sensor mobility must be carefully and jointly considered. The results obtained in this paper will provide important guidelines and insights into the deployment and performance of wireless sensor networks for barrier coverage.     

基于感知距离调节的无线传感器网络节能区域覆盖

传感器节点能够感知的物理世界的最远距离称为节点的感知距离。该文研究了基于节点感知距离调节的无线传感器网络节能区域覆盖方案,该方案通过设定合理的节点感知距离,使得传感器网络在满足区域覆盖要求的同时,能量消耗最低。首先将区域覆盖性能和网络能量消耗模化成网络节点感知距离的函数,然后将节能覆盖问题模化成带约束条件的优化问题,最后给出了基于网络区域划分的优化模型求解方法。仿真结果表明,与传统覆盖方案比较,所提方案在满足覆盖要求的同时,有效降低了网络能量消耗。     

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

     

Coverage and Lifetime Optimization of Wireless Sensor Networks with Gaussian Distribution

A wireless sensor network (WSN) has to maintain a desirable sensing coverage and periodically report sensed data to the administrative center (i.e., base station) and the reporting period may range from months to years. Coverage and lifetime are two paramount problems in a WSN due to constraint of associated battery power. All previous theoretical analysis on the coverage and lifetime is primarily focused on the random uniform distribution of sensors or some specific network scenarios (e.g., a controllable WSN). In this paper, we provide an analytical framework for the coverage and lifetime of a WSN that follows a two-dimensional Gaussian distribution. We also study the coverage and lifetime when the dimensions of Gaussian dispersion (i.e., x, y) admit different Gaussian parameters (i.e., standard deviation, $sigma_x neqsigma_y$). We identify intrinsic properties of coverage/lifetime in terms of Gaussian distribution parameters, which is a fundamental issue in designing a WSN. Following the results obtained, we further determine the sensor deployment strategies for a WSN that could satisfy a predefined coverage and lifetime. Two deployment algorithms are developed based on using our analytical models and are shown to effectively increase the WSN lifetime.     

A distributed lightweight Redundancy aware Topology Control Protocol for wireless sensor networks

WSN consists of a large number of sensor nodes randomly deployed, and, in many cases, it is impossible to replace sensors when a node failure occurs. Thus, applications tend to deploy more nodes than necessary to cope with possible node failures and to increase the network lifetime, which leads to create some sensing and communication redundancy. However, sensors in the same region, may collect and forward the same information, which will waste more energy. In this paper, we propose a distributed Lightweight Redundancy aware Topology Control Protocol (LRTCP) for wireless sensor networks. It exploits the sensor redundancy in the same region by dividing the network into groups so that a connected backbone can be maintained by keeping a minimum of working nodes and turning off the redundant ones. LRTCP identifies equivalent nodes in terms of communication based on their redundancy degrees with respect of some eligibility rules. Simulation results indicate that, compared with existing distributed topology control algorithms, LRTCP improves network capacity and energy efficiency.