基于PSO的无线多媒体传感器网络覆盖增强算法

在无线多媒体传感器网络(Wireless Multimedia Sensor Networks,WMSNs)中,由于节点部署的不合理,往往存在较多的监控盲区,影响了网络的服务质量。为了提高网络的覆盖率,在有向感知模型基础的基础上,提出了一种基于粒子群算法的WMSNs覆盖增强算法PSOCE。PSOCE算法以网络覆盖率为优化目标,以粒子群算法为计算工具,同时对节点的位置与主感知方向进行调整。仿真试验表明,PSOCE算法能够有效地改进WMSNs的覆盖质量,网络的覆盖率能提高6%～12%。     

基于ERPMT改进启发式方法的WSN寿命最大化算法

针对传感器节点的电池容量限制导致无线传感网络寿命低的问题,基于容量最大化(CMAX)、线上最大化寿命(OML)两种启发式方法以及高效路由能量管理技术(ERPMT),提出了基于ERPMT改进启发式方法的无线传感网络寿命最大化算法。首先,通过启发式方法初始化每个传感器节点,将节点能量划分为传感器节点起源数据和其它节点数据延迟;然后利用加入的一种优先度量延迟一跳节点的能量消耗;最后,根据路径平均能量为每个路由分配一个优先级,并通过ERPMT实现最终的无线传感网络优化。针对不同分布类型网络寿命的实验验证了本文算法的有效性及可靠性,实验结果表明,相比较为先进的启发式方法CMAX及OML,本文算法明显增大了无线传感网络的覆盖范围,并且大大地延长了网络的寿命。     

基于进化优化的移动感知节点部署算法

 移动传感器网络中节点部署优化直接影响到网络的能量消耗、对目标区域监控的性能及整个网络的生命周期.本文从网络覆盖和能量消耗两个方面,采用多目标优化对节点部署问题建模,并从集中式角度给出了节点部署问题的遗传算法求解过程.针对一类初始中心部署模型进行实验验证,并和基于向量的算法(VEC)、基于维诺图的算法(VOR)及基于边界扩张虚拟力算法(BEVF)进行性能对比,证明了该算法在大多数情况下可使传感器网络对目标区域的覆盖率最大化,同时保证了网络的连通和网络能耗最小,进而延长了网络的生命周期.     

Design of Probability Density Function Targeting Energy Efficient Network for Coalition Based WSNs

Energy consumption is one of the important issues in wireless sensor network that rely on non chargeable batteries for power. Also, the sensor network has to maintain a desired sensing coverage area along with periodically sending of the sensed data to the base station. Therefore, coverage and the lifetime are the two important issues that need to be addressed. Effective deployment of wireless sensors is a major concern as the coverage and lifetime of any wireless sensor network depends on it. In this paper, we propose the design of a Probability Density Function (PDF) targeting the desired coverage, and energy efficient node deployment scheme. The suitability of the proposed PDF based node distribution to model the network architecture considered in this work has been analyzed. The PDF divides the deployment area into concentric coronas and provides a probability of occurrence of a node within any corona. Further, the performance of the proposed PDF is evaluated in terms of the coverage, the number of transmissions of packets and the lifetime of the network. The scheme is compared with the existing node deployment schemes based on various distributions. The percentage gain of the proposed PDF based node deployment is 32\(\%\) more than that when compared with the existing schemes. Thus, the simulation results obtained confirm the schemes superiority over the other existing schemes.     

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.

     

A Coverage-Based Scheduling Algorithm for WSNs

Node scheduling in wireless sensor networks (WSNs) plays a vital role in conserving energy and lengthening the lifetime of networks, which are considered as prime design challenges. In large-scaled WSNs, especially where sensor nodes are deployed randomly, 100 % coverage is not possible all the times. Additionally, several types of applications of WSNs do not require 100 % coverage. Following these facts, in this paper, we propose a coverage based node scheduling algorithm. The algorithm shows that by sacrificing a little amount of coverage, a huge amount of energy can be saved. This, in turns, helps to increase the lifetime of the network. We provide mathematical analysis, which verifies the correctness of the proposed algorithm. The proposed algorithm ensures balanced energy consumption over the sensor networks. Moreover, simulation results demonstrate that the proposed algorithm almost doubles the lifetime of a wireless sensor network by sacrificing only 5–8 % of coverage.     

Secure coverage-preserving node scheduling scheme using energy prediction for wireless sensor networks

With the fast development of the micro-electro-mechanical systems(MEMS),wireless sensor networks(WSNs)have been extensively studied.Most of the studies focus on saving energy consumption because of restricted energy supply in WSNs.Cluster-based node scheduling scheme is commonly considered as one of the most energy-efficient approaches.However,it is not always so efficient especially when there exist hot spot and network attacks in WSNs.In this article,a secure coverage-preserved node scheduling scheme for WSNs based on energy prediction is proposed in an uneven deployment environment.The scheme is comprised of an uneven clustering algorithm based on arithmetic progression,a cover set partition algorithm based on trust and a node scheduling algorithm based on energy prediction.Simulation results show that network lifetime of the scheme is 350 rounds longer than that of other scheduling algorithms.Furthermore,the scheme can keep a high network coverage ratio during the network lifetime and achieve the designed objective which makes energy dissipation of most nodes in WSNs balanced.     

Routing Technology in Wireless Sensor Network Based on Ant Colony Optimization Algorithm

With rapid development of wireless communication, sensor, micro power system and electronic technology, the research on wireless sensor network has attracted more and more attention. The work proposed routing algorithm in wireless sensor network based on ant colony optimization by analyzing routing protocol and utilizing advanced idea. Ant colony optimization algorithm has advantages in implementing local work, supporting multiple paths and integrating link quality into pheromone formation. In routing selection, the work calculated probability that node is selected as the next hop according to pheromone concentration on the path. With characteristics including self-organization, dynamic and multipath, ant colony optimization algorithm is suitable for routing in wireless sensor network. With low routing cost, good adaptability and multipath, the algorithm balanced energy consumption to prolong network lifetime. In terms of simulation and experiments, ant colony algorithm was proved to be suitable for finding optimal routing in wireless sensor network, thus achieving design goal of routing algorithm.     

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.     

无线传感器网络的WPCS覆盖策略

延长无线传感器网络生存时间的有效方法是让冗余节点进入休眠状态。而现有研究多是基于传感器感知模型为圆形的假设前提。该文集中讨论传感器感知模型非圆时,覆盖与连通性之间的联系,并提出适用性更广的WPCS(Well-Proportioned Coverage Strategy)覆盖策略。WPCS覆盖策略以最小化重叠面积为准则,其目的是最大化网络生存时间。仿真实验表明,WPCS性能优于CCP(Coverage Configuration Protocol),且具有一般性,并能很好地减少工作传感器数目,延长网络寿命。     

Connectivity maintenance and coverage preservation in wireless sensor networks

In wireless sensor networks, one of the main design challenges is to save severely constrained energy resources and obtain long system lifetime. Low cost of sensors enables us to randomly deploy a large number of sensor nodes. Thus, a potential approach to solve lifetime problem arises. That is to let sensors work alternatively by identifying redundant nodes in high-density networks and assigning them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In a single wireless sensor network, sensors are performing two operations: sensing and communication. Therefore, there might exist two kinds of redundancy in the network. Most of the previous work addressed only one kind of redundancy: sensing or communication alone. Wang et al. [Intergrated Coverage and Connectivity Configuration in Wireless Sensor Networks, in: Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys 2003), Los Angeles, November 2003] and Zhang and Hou [Maintaining Sensing Coverage and Connectivity in Large Sensor Networks. Technical report UIUCDCS-R-2003-2351, June 2003] first discussed how to combine consideration of coverage and connectivity maintenance in a single activity scheduling. They provided a sufficient condition for safe scheduling integration in those fully covered networks. However, random node deployment often makes initial sensing holes inside the deployed area inevitable even in an extremely high-density network. Therefore, in this paper, we enhance their work to support general wireless sensor networks by proving another conclusion: “the communication range is twice of the sensing range” is the sufficient condition and the tight lower bound to ensure that complete coverage preservation implies connectivity among active nodes if the original network topology (consisting of all the deployed nodes) is connected. Also, we extend the result to k-degree network connectivity and k-degree coverage preservation.     

A Tabu search based routing algorithm for wireless sensor networks

In this paper, a Tabu search based routing algorithm is proposed to efficiently determine an optimal path from a source to a destination in wireless sensor networks (WSNs). There have been several methods proposed for routing algorithms in wireless sensor networks. In this paper, the Tabu search method is exploited for routing in WSNs from a new point of view. In this algorithm (TSRA), a new move and neighborhood search method is designed to integrate energy consumption and hop counts into routing choice. The proposed algorithm is compared with some of the ant colony optimization based routing algorithms, such as traditional ant colony algorithm, ant colony optimization-based location-aware routing for wireless sensor networks, and energy and path aware ant colony algorithm for routing of wireless sensor networks, in term of routing cost, energy consumption and network lifetime. Simulation results, for various random generated networks, demonstrate that the TSRA, obtains more balanced transmission among the node, reduces the energy consumption and cost of the routing, and extends the network lifetime.     

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。      

An energy‐efficient node‐scheduling scheme for wireless sensor networks based on minimum dominating sets

One of the major requirements for new wireless sensor networks is to extend the lifetime of the network. Node‐scheduling techniques have been used extensively for this purpose. Some existing approaches rely mainly on location information through global positioning system (GPS) devices for designing efficient scheduling strategies. However, integration of GPS devices with sensor nodes is expensive and increases the cost of deployment dramatically. In this paper we present a location‐free solution for node scheduling. Our scheme is based on a graph theoretical approach using minimum dominating sets. We propose a heuristic to extract a collection of dominating sets. Each set consists of a group of working nodes which ensures a high level of network coverage. At each round, one set is responsible for covering the sensor field while the nodes in other sets are in sleep mode. We evaluate our solution through simulations and discuss our future research directions. Copyright © 2008 John Wiley & Sons, Ltd.     

Sleep scheduling with expected common coverage in wireless sensor networks

Sleep scheduling, which is putting some sensor nodes into sleep mode without harming network functionality, is a common method to reduce energy consumption in dense wireless sensor networks. This paper proposes a distributed and energy efficient sleep scheduling and routing scheme that can be used to extend the lifetime of a sensor network while maintaining a user defined coverage and connectivity. The scheme can activate and deactivate the three basic units of a sensor node (sensing, processing, and communication units) independently. The paper also provides a probabilistic method to estimate how much the sensing area of a node is covered by other active nodes in its neighborhood. The method is utilized by the proposed scheduling and routing scheme to reduce the control message overhead while deciding the next modes (full-active, semi-active, inactive/sleeping) of sensor nodes. We evaluated our estimation method and scheduling scheme via simulation experiments and compared our scheme also with another scheme. The results validate our probabilistic method for coverage estimation and show that our sleep scheduling and routing scheme can significantly increase the network lifetime while keeping the message complexity low and preserving both connectivity and coverage.     

无线传感器网络三维空间最佳覆盖路由协议

本文针对新兴无线传感器网络中的三维空间随机最佳覆盖NP难问题进行了研究.采用计算几何与图论着色方法建立了三维空间的随机最佳覆盖数学模型,给出了一种分布式启发算法,得到了完成最佳覆盖的低能量消耗路径.并在此基础上设计了一种可以实现无线传感器网络三维空间最佳覆盖的优化路由协议.最后进行了协议算法的性能评价以及最佳覆盖和网络生存时间的实验仿真,结果表明协议算法时间复杂度低,并具有可扩展性、有效性和鲁棒性.     

一种无线传感器网络全连通群的休眠调度算法

该文针对无线传感器网络的覆盖性和连通性问题,在假设传感器节点地理位置信息已知的条件下,设计了一种包含全连通群的建立和维护以及群内节点休眠调度的全新算法。该算法采用保证群内节点彼此一跳可达的全连通群分群方法,以及分布式节能的休眠调度策略,最大程度上减少传感器网络的能量消耗,延长了网络寿命。仿真结果表明:该算法能较好地保证无线传感器网络的覆盖性和连通性,且能耗较低。     

面向数量最少化的双层WSN中继节点部署算法

提出2种算法,目标是在监测区域部署中继节点,以便所有的传感节点都能和至少一个中继节点进行通信,同时保证中继节点数目最小化。整数线性规划最优解算法是对已选择出的CRegions通过数学上的整数线性规划的方法找到部署中继节点的最佳区域;传感网络规模较大时,提出启发式算法,将每一次排序后含传感节点个数最多的集合作为部署区域,经过多次循环排序后近似的找到部署中继节点的最佳位置。最后通过与2个同类算法在相同参数环境下进行仿真实验,比较仿真结果证明整数线性规划最优解算法及启发式算法在覆盖所有传感节点的前提下,部署中继节点最小化的性能较好,并且其部署时间也在可以接受的范围之内。     

An ant colony optimization based routing algorithm for extending network lifetime in wireless sensor networks

Reducing the energy consumption of network nodes is one of the most important problems for routing in wireless sensor networks because of the battery limitation in each sensor. This paper presents a new ant colony optimization based routing algorithm that uses special parameters in its competency function for reducing energy consumption of network nodes. In this new proposed algorithm called life time aware routing algorithm for wireless sensor networks (LTAWSN), a new pheromone update operator was designed to integrate energy consumption and hops into routing choice. Finally, with the results of the multiple simulations we were able to show that LTAWSN, in comparison with the previous ant colony based routing algorithm, energy aware ant colony routing algorithms for the routing of wireless sensor networks, ant colony optimization-based location-aware routing algorithm for wireless sensor networks and traditional ant colony algorithm, increase the efficiency of the system, obtains more balanced transmission among the nodes and reduce the energy consumption of the routing and extends the network lifetime.     

Power-Saving Scheduling for Multiple-Target Coverage in Wireless Sensor Networks

We address the multiple-target coverage problem (MTCP) in wireless sensor networks (WSNs). We also propose an energy-efficient sensor-scheduling algorithm for multiple-target coverage (MTC) that considers both the transmitting energy for collected data and overlapped targets. We introduce two algorithms: one optimal, the other heuristic. Simulation results show that the proposed algorithms can contribute to extending the lifetime of network and that the heuristic algorithm is more practical than the optimal algorithm with respect to complexity.