事件驱动的带执行器无线传感器网络分簇算法研究

带有执行器的无线传感器网络是指在传统无线传感器网络中加入执行节点,形成传感器节点、执行节点和基站共同构成的三层监控网络。根据执行器在能量、计算能力和感知能力方面的优势,提出建立应用于事件调度的双环分簇算法。算法将执行器连接成双环结构,提升网络在线扩展能力的同时,也为无线传感器网络满足事件驱动构建基础。仿真实验证明,此算法能够有效降低网络能耗,随着节点数目的增加和监控领域的扩大,表现更加凸出。     

基于离散多目标优化粒子群算法的多移动代理协作规划

无线传感器网络中多移动代理协作能快速高效地完成感知数据汇聚任务,但是随着移动代理访问数据源节点数的增加,移动代理携带的数据分组会逐渐增大,导致传感器节点能量负载不均衡,部分数据源节点能耗过快,网络生存期缩短。目前,针对该问题所设计的能耗均衡算法,多以降低多移动代理总能耗为目标,却未充分考虑部分数据源节点能量消耗过快对网络生存期造成的影响。提出离散多目标优化粒子群算法,以网络的总能耗和移动代理负载均衡作为适应度函数,在多移动代理协作路径规划中寻求近似最优解。通过仿真实验验证,所提出的多移动代理协作路径规划,在网络总能耗和网络生存期方面的性能优于同类其他算法。     

基于改进微粒群算法的WSN节点调度

为了降低无线传感器网络监测区域节点能耗和延长网络生命周期,设计了一种基于改进微粒群算法的节点调度方法.首先,以网络覆盖率和休眠工作节点数为目标建立了系统的数学模型,然后设计了粒子的编码方式、适应度函数以及自适应动态惯性权重,并定义了使用改进的微粒群算法对传感器网络节点调度的具体算法.仿真实验表明,该方法能正确地实现无线传感器网络监测区域的节点调度,在迭代次数较少时,就能以较少的节点获得较高的网络覆盖率,且与其他方法相比,具有收敛速度快和全局寻优能力强的优点.     

基于移动Agent的JPEG2000分布式编码算法研究

该文针对无线传感器网络节点处理能力、存储能力和能量供应均有限的特点,提出一种基于移动Agent的JPEG2000分布式编码算法。采用多个节点对图像进行分布式编码,通过引入移动Agent机制来实现网络节点间的信息交互,在保证图像编码性能不变的情况下,降低单个节点能耗,使系统不因单个节点能量耗尽而瘫痪,以延长系统的生命周期。仿真实验结果表明,该文所提出的基于移动Agent的分布式编码算法在无线传感器网络环境中可以保证编码后的图像质量没有下降,并能有效均衡系统能耗、延长网络工作寿命达3倍左右。     

基于无线传感器网络的业务流调度算法

实时性要求是无线传感器网络调度算法性能评价的重要内容。对于实时性要求很高的应用场合,调度算法的首要标准是降低数据包的传输时延。针对无线传感器网络的业务流调度问题,结合传统的加权循环调度算法WRR,着眼于解决无线传感器网络中业务流突发引起的时延性能下降问题,提出了新的调度算法-WSWRR。新算法合理分配传感器节点的数据感知和传输时间,使节点在不需要工作时转入低功耗的休眠模式,且能在满足应用要求服务质量的前提下,高效利用节点能量,延长整个传感器网络的生命周期。通过仿真实验,验证了新算法在调度突发数据包时性能得到了很好的改善,且没有增加网络的整体能耗,证明了WSWRR算法的有效性。     

无线传感器网络中移动目标跟踪算法研究

由于单个传感器节点的通信、处理和感知能力有限,所以传感器节点必须协同工作才能够完成任务。本文介绍了无线传感器网络的研究现状,阐述了目前成形的无线传感器网络协议与体系结构,阐述了针对面向目标跟踪的无线传感器网络涉及到的一些关键技术,并对现有的一些无线传感器网络中移动目标协同跟踪算法以及未来的发展方向进行了论述。     

无线传感网络移动分簇路由策略

无线传感网络(WSN)路由协议中,分簇路由具有拓扑管理方便、能量高效和数据融合简单等优点,成为当前重点研究的路由技术。通过研究各种环境下的移动传感器网络,有效地降低能耗则是研究移动无线传感器网络的重要目的之一。针对无线传感网络中移动性问题,基于LEACH协议,利用移动传感器网络中节点距离、速度和剩余能量等因素提出了能量高效的移动分簇路由算法。实验结果表明此算法能够较好地支持节点移动,从而降低网络能耗,延长网络生存时间。     

分簇无线传感器网络中的频谱感知方案

无线传感器网络中的节点众多,节点之间仅依靠频谱资源管理机制分配的频段进行数据传输会造成频谱资源紧张。针对这一问题,将认知无线电技术引入分簇的无线传感器网络,并且考虑分簇无线传感器网络节点的能量受限问题提出了一种节能的频谱感知方案。此方案首先让簇内的节点采取双门限能量检测的协作频谱感知方法,在满足目标检测概率和虚警概率的条件下,确定进行频谱感知的最少节点数,降低系统能耗。其次,通过选择合适的簇内节点进行频谱感知,使簇内节点的能耗均衡,簇的生命周期得到延长。     

基于能量预测的无线传感器网络节点覆盖调度算法

针对无线传感器网络中存在的热区问题,采用非均匀部署方案,并在此基础上提出基于能量预测的无线传感器网络节点覆盖调度算法。并对算法进行了仿真实验和性能分析。结果表明该算法可以延长的网络生命周期并在较长的时间里保持较高的网络覆盖率,能够达到网络内大多数节点能耗均衡的设计目标。     

基于移动模型的传感器网络覆盖控制算法

无线传感网络覆盖控制直接反应了传感器网络的感知能力。移动目标检测概率和发现移动目标时间长短直接反应了覆盖质量的好坏。而在环境恶劣、传感器网络采用随机部署方式的情况下,目标区域内的节点分布杂乱无章,传感器网络不能很好地满足覆盖的要求。针对传感器网络随机部署的不足,文章在部署中引入RCCM移动模型,并分析了移动目标检测率、检测时间与节点运行速度、节点数量的关系。     

Prolonging lifetime for wireless rechargeable sensor networks through sleeping and charging scheduling

Prolonging network lifetime is a fundamental requirement in wireless sensor network (WSN). Existing charging scheduling algorithms suffer from high node redundancy and energy consumption issues. In this paper, we study WSN charging problem from the perspectives of energy conservation combined with energy replenishment scheduling. Firstly, we detect the redundant nodes whose energy is wasted in the network functionality and develop a K‐covering redundant nodes sleeping scheduling algorithm (KRSS) for reducing energy. Secondly, we employed multiple wireless charging vehicles (WCVs) for energy replenishment and optimize the charging scheduling algorithm to prevent any exhaustion of nodes, and we proposed a distance and energy–oriented charging scheduling algorithm (DECS) with multiple WCVs. Simulation experiments are conducted to show the advantages of the proposed KRSS+DECS, confirming that our scheme is capable of removing redundant nodes, lowering node failures, and prolonging network lifetime.     

Power Efficient Video Multipath Transmission over Wireless Multimedia Sensor Networks

This paper proposes a power efficient multipath video packet scheduling scheme for minimum video distortion transmission (optimised Video QoS) over wireless multimedia sensor networks. The transmission of video packets over multiple paths in a wireless sensor network improves the aggregate data rate of the network and minimizes the traffic load handled by each node. However, due to the lossy behavior of the wireless channel the aggregate transmission rate cannot always support the requested video source data rate. In such cases a packet scheduling algorithm is applied that can selectively drop combinations of video packets prior to transmission to adapt the source requirements to the channel capacity. The scheduling algorithm selects the less important video packets to drop using a recursive distortion prediction model. This model predicts accurately the resulting video distortion in case of isolated errors, burst of errors and errors separated by a lag. Two scheduling algorithms are proposed in this paper. The Baseline scheme is a simplified scheduler that can only decide upon which packet can be dropped prior to transmission based on the packet’s impact on the video distortion. This algorithm is compared against the Power aware packet scheduling that is an extension of the Baseline capable of estimating the power that will be consumed by each node in every available path depending on its traffic load, during the transmission. The proposed Power aware packet scheduling is able to identify the available paths connecting the video source to the receiver and schedule the packet transmission among the selected paths according to the perceived video QoS (Peak Signal to Noise Ratio—PSNR) and the energy efficiency of the participating wireless video sensor nodes, by dropping packets if necessary based on the distortion prediction model. The simulation results indicate that the proposed Power aware video packet scheduling can achieve energy efficiency in the wireless multimedia sensor network by minimizing the power dissipation across all nodes, while the perceived video quality is kept to very high levels even at extreme network conditions (many sensor nodes dropped due to power consumption and high background noise in the channel).     

Energy‐Efficient Adaptive Dynamic Sensor Scheduling for Target Monitoring in Wireless Sensor Networks

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy‐efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.     

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.     

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.

     

一种基于冗余度的无线传感器网络节能策略

在无线传感器网络中,传感节点由于采用电池供电,因此寿命有限。如何有效节省传感器节点的能量,延长网络的使用寿命,一直是广泛研究的焦点。文章提出一种适用于高冗余度布置的无线传感器网络结构中,节省传感器节点能量消耗的方法-接续调度法。该方法通过协调点对小区域内节点的调度,使区域内节点依次分时段工作。通过这种接续调度,避免了节点间的冲突和串扰,达到延长整体网络寿命的效果。     

监测无线传感器网络休眠调度算法

休眠调度设计是无线传感器网络一种重要的通信节能方法。针对监测典型应用,为了实现长时间的监测应用要求,充分利用冗余部署提供的能量资源,提出了一种能量相关的分布式自适应休眠调度算法。算法利用极大独立集构建思想,结合节点层次级别、实时的能量消耗、连通度等信息动态选择连通支配节点集作为网络骨干,使得网络活跃节点数量最小化。仿真试验分析表明,算法能够有效地利用冗余节点提供的能量资源,扩展了网络的生命周期。     

A new load balancing and data collection algorithm for energy saving in wireless sensor networks

Data gathering is a major function of many applications in wireless sensor networks. The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirements of special applications or users. Wireless sensor networks are characterized by centralized data gathering, multi-hop communication and many to one traffic pattern. These three characteristics can lead to severe packet collision, network congestion and packet loss, and even result in hot-spots of energy consumption thus causing premature death of sensor nodes and entire network. In this paper, we propose a load balance data gathering algorithm that classifies sensor nodes into different layers according to their distance to sink node and furthermore, divides the sense zone into several clusters. Routing trees are established between sensor node and sink depending on the energy metric and communication cost. For saving energy consumption, the target of data aggregation scheme is adopted as well. Analysis and simulation results show that the algorithm we proposed provides more uniform energy consumption among sensor nodes and can prolong the lifetime of sensor networks.     

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.