Energy-efficient clustering in lossy wireless sensor networks

Recent experimental studies have revealed that a large percentage of wireless links are lossy and unreliable for data delivery in wireless sensor networks (WSNs). Such findings raise new challenges for the design of clustering algorithms in WSNs in terms of data reliability and energy efficiency. In this paper, we propose distributed clustering algorithms for lossy WSNs with a mobile collector, where the mobile collector moves close to each cluster head to receive data directly and then uploads collected data to the base station. We first consider constructing one-hop clusters in lossy WSNs where all cluster members are within the direct communication range of their cluster heads. We formulate the problem into an integer program, aiming at maximizing the network lifetime, which is defined as the number of rounds of data collection until the first node dies. We then prove that the problem is NP-hard. After that, we propose a metric-based distributed clustering algorithm to solve the problem. We adopt a metric called selection weight for each sensor node that indicates both link qualities around the node and its capability of being a cluster head. We further extend the algorithm to multi-hop clustering to achieve better scalability. We have found out that the performance of the one-hop clustering algorithm in small WSNs is very close to the optimal results obtained by mathematical tools. We have conducted extensive simulations for large WSNs and the results demonstrate that the proposed clustering algorithms can significantly improve the data reception ratio, reduce the total energy consumption in the network and prolong network lifetime compared to a typical distributed clustering algorithm, HEED, that does not consider lossy links.     

能量高效的WSNs分簇数据融合算法

针对无线传感器网络(WSNs)中多跳通信造成的“热区”以及数据冗余问题,提出了一种能量高效的分簇数据融合算法(EECDA).该算法在分簇阶段综合考虑节点的剩余能量、到基站的距离和邻居节点的数目,周期性地选择簇首和划分不同规模的簇;对簇内数据进行融合,利用辛普森积分法则计算预测接收数据,在保证采集数据实时性和准确性的前提下,降低数据的冗余性,减少通信负载,提高网络的能量利用率.仿真结果表明:该算法能够对数据进行高效预测,减少网络通信量,相较已有的算法,能够有效延长网络的生存周期.     

基于模糊逻辑的多跳WSNs分簇算法

针对无线传感器网络节点能量消耗不均衡和网络寿命过短的问题,提出一种基于模糊逻辑的多跳WSNs分簇算法(FLCMN).该算法综合考虑节点剩余能量、节点邻居个数、邻居节点的平均剩余能量.根据预先设定模糊规则库,利用模糊系统评估出当选簇头的满意度.额外考虑邻居节点平均剩余能量,改善了簇内热点问题,均衡了簇内能量的消耗;同时,为了改善簇间热点问题,提出一种基于斐波那契序列的多跳传输方式,延长了网络的生存时间.通过仿真验证,FLCAMN算法在网络生存时间和能量消耗方面的性能都优于LEACH、EAMMH和DFLC算法.     

HACH: Heuristic Algorithm for Clustering Hierarchy protocol in wireless sensor networks

Wireless sensor networks (WSNs) require energy management protocols to efficiently use the energy supply constraints of battery-powered sensors to prolong its network lifetime. This paper proposes a novel Heuristic Algorithm for Clustering Hierarchy (HACH), which sequentially performs selection of inactive nodes and cluster head nodes at every round. Inactive node selection employs a stochastic sleep scheduling mechanism to determine the selection of nodes that can be put into sleep mode without adversely affecting network coverage. Also, the clustering algorithm uses a novel heuristic crossover operator to combine two different solutions to achieve an improved solution that enhances the distribution of cluster head nodes and coordinates energy consumption in WSNs. The proposed algorithm is evaluated via simulation experiments and compared with some existing algorithms. Our protocol shows improved performance in terms of extended lifetime and maintains favourable performances even under different energy heterogeneity settings.     

无线传感器/执行器网络中能量有效的实时分簇路由协议

无线传感器/执行器网络(WSANs)主要应用于自动控制领域,实时性问题是其面临的首要挑战.根据实际环境中的节点部署情况,建立了系统模型;研究了分簇策略与功率控制技术对于自组织网络实时性的影响,提出了一种可适用于WSANs的能量有效的实时分簇路由协议--RECRP协议.该协议采用二级成簇策略使网络中的各类节点稳定分簇.分簇后的各类节点具有不同发射功率,利用执行器节点的强大通信能力有效降低网络延时.采用能量有效性算法使网络中的传感器节点轮换担任簇首,从而使网络能量均匀消耗,延长网络的生存时间.实验结果证明,在WSANs中RECRP协议可使网络稳定分簇,并且在网络的实时性与能量有效性方面与现有典型路由协议相比具有更优越的性能.     

传感网中带有可控阈值的优化协同覆盖算法

传统型的无线传感器网络(WSNs)覆盖受限于节点能量和数据冗余,迫使WSNs异常中断。为此,提出一种带有可控阈值的优化协同覆盖算法(OCC-CT)。该算法首先确定关注目标节点(FTNs)的位置信息,利用遗传算法(GA)给出了节点路径规划;其次,通过可控阈值参数和变异参数等特性对事件域节点成簇进行优化,使之节点成簇更为均匀,以减少节点能量的消耗,提升对全局目标节点的搜索能力;再次,利用适应函数对所覆盖目标位置及节点监测范围所形成的覆盖连续性进行优化,达到了提高网络覆盖率和延长网络生存周期的目的。最后,仿真实验结果表明,OCC-CT算法与其他三种算法相比在网络覆盖率、网络生存周期等方面平均提升了0.11、0.16,在网络能量开销方面提升了0.14,从而进一步验证了OCC-CT算法具有较强的稳定性和有效性。     

基于相对距离的多级能量异构传感器网络成簇算法

延长网络寿命并获得更好的监控质量是无线传感器网络成簇算法的重要性能指标。在分析现有主要成簇算法的基础上,提出了一种适应于多级能量异构传感器网络的基于相对距离的成簇算法RDCA(Relative Distance Clus-tering Algorithm)。算法中,节点根据通信范围内其他节点与自身的平均距离、节点自身与基站的距离以及节点当前剩余能量来确定节点成为簇头的概率。所有节点轮流成为簇头,以分摊能量消耗。仿真实验结果表明,与现有主要聚簇算法相比,在多级能量异构环境下,新的成簇算法拥有更长的生存时间和更优的网络监测质量。     

混合WSNs中基于多目标优化的覆盖控制算法

针对无线传感器网络( WSNs)随机部署产生的区域覆盖率低、节点利用率差和能量不均衡的问题,引入移动传感器节点,将快速非支配排序遗传算法Ⅱ( NSGA-Ⅱ)运用到混合无线传感器网络覆盖控制部署并进行改进,采用分层编码策略,引入删除算子避免早熟,自适应改变交叉、变异概率提高局部搜索能力,获得较优解集后基于决策者信息偏好选择最优目标.仿真实验结果表明:有效解决了WSNs覆盖控制问题,可以在网络覆盖率最大化的同时,节点利用率较大且能耗系数较低,延长网络寿命.     

基于协作传输的大范围WSNs能耗均衡分簇算法

由于大范围无线传感器网络（WSNs）节点的数量巨大,网络的能量消耗极不均,提出一种基于协作传输的分簇算法—EBBMCC—LS算法。该算法在保证网络均匀分簇的前提下,能保证网络中簇头节点的均匀分布,在簇间通信时加入协作传输策略,传感器节点之间通过协作传输构成虚拟多天线系统,改善系统性能,解决了大范围WSNs中的能耗不均现象。实验验证：该算法能够均衡大范围WSNs中的能耗,延长网络寿命,可促进大范围WSNs应用的推广。     

改进的离散果蝇优化算法在WSNs覆盖中的应用

针对无线传感器网络( WSNs)随机部署产生的区域覆盖率低、节点利用率差问题,提出一种改进的离散果蝇优化算法( FOA)对WSNs覆盖进行优化.新算法引入自适应步长的分类嗅觉随机搜索和基于移民操作及精英库的多种群协同进化机制,提高了优化精度和效率.仿真实验结果表明:新算法有效解决了WSNs覆盖问题,在确保网络覆盖率最大化的同时节点利用率较大,延长网络寿命.     

无线传感器网络中的最大生命期基因路由算法

无线传感器网络(wireless sensor networks,简称WSNs)由一组低功率且能量受限的传感器节点构成,设计此类网络的一个基本挑战便是最大化网络生命期的问题.在WSNs中,由于邻近传感器节点所收集的数据之间往往具有时空相关性,多采用数据聚合技术作为去除数据冗余、压缩数据大小的有效手段.合理地应用数据聚合技术,可以有效地减少数据传递量,降低网络能耗,从而延长网络生命期.研究了WSNs中结合数据聚合与节点功率控制的优化数据传递技术,提出了一种新的最大化网络生命期的路由算法.该算法采用遗传算法(genetic algorithm,简称GA)最优化数据聚合点的选择,并采用梯度算法进一步优化结果.该算法均衡节点能耗,并最大化网络生命期.仿真结果表明,该算法极大地提高了网络的生命期.     

无线传感器网络中的最大生命期基因路由算法

无线传感器网络(wireless sensor networks,简称WSNs)由一组低功率且能量受限的传感器节点构成,设计此类网络的一个基本挑战便是最大化网络生命期的问题.在WSNs中,由于邻近传感器节点所收集的数据之间往往具有时空相关性,多采用数据聚合技术作为去除数据冗余、压缩数据大小的有效手段.合理地应用数据聚合技术,可以有效地减少数据传递量,降低网络能耗,从而延长网络生命期.研究了WSNs中结合数据聚合与节点功率控制的优化数据传递技术,提出了一种新的最大化网络生命期的路由算法.该算法采用遗传算法(genetic algorithm,简称GA)最优化数据聚合点的选择,并采用梯度算法进一步优化结果.该算法均衡节点能耗,并最大化网络生命期.仿真结果表明,该算法极大地提高了网络的生命期.     

一种带有能量自补给节点的异构传感器网络分簇路由算法

针对现有无线传感器网络分簇路由算法的网络生命周期短、能量消耗不均衡等问题,结合节点的能量采集技术,提出了一种带有能量自补给节点的异构传感器网络分簇路由算法。考虑到实际环境中节点能量补给不稳定,根据节点的剩余能量和当前能量自补给状态,设计了能量均衡的簇头选举机制和簇间多跳机制。仿真结果表明,在延长网络生命周期和均衡全网能量消耗方面,该算法优于采用相同能量补给规律的传统分簇路由算法(LEACH算法和SEP算法)和其他基于能量自补给的分簇路由算法(PHC算法和EBCS算法)。     

Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load-balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches.     

WSN中一种基于能量的层次型拓扑生成算法

通过考虑无线传感器网络节点的能量问题确定了单层拓扑结构中簇头节点的最优个数,结合WCA算法提出了一种基于能量的无线传感器网络的层次型拓扑结构生成算法,并评估了该算法的各项性能指标。经过算法复杂度分析得出该算法的时间复杂度和网络节点的个数相关,适合生成中小型规模的网络。仿真结果表明,使用该算法可以生成具有最优簇头个数的网络拓扑结构,能大大节省网络节点能量的消耗,且延长了网络的生存周期。     

A Novel Fault Tolerance Energy-Aware Clustering Method via Social Spider Optimization (SSO) and Fuzzy Logic and Mobile Sink in Wireless Sensor Networks (WSNs)

In recent years, the application of WSNs has been remarkably increased and notable developments and advances have been achieved in this regard. In particular, thanks to smart, cheaper and smaller nodes, different types of information can be detected and gathered in different environments and under different conditions. As the popularity of WSNs has increased, the problems and issues related to networks are examined and investigated. As a case in point, routing issue is one of the main challenges in this regard which has a direct impact on the performance of sensor networks. In WSN routing, sensor nodes send and receive great amounts of information. As a result, such a system may use lots of energy which may reduce network lifetime. Given the limited power of a battery, certain method and approaches are needed for optimizing power consumption. One such approach is to cluster sensor nodes; however, improper clustering increases the load imposed on the clusters around the sink. Hence, for proper clustering, smart algorithms need to be used. Accordingly, in this paper, a novel algorithm, namely social spider optimization (SSO) algorithm is proposed for clustering sensor network. It is based on the simulation of the social cooperative behavior of spiders. In the proposed algorithm, nodes imitate a group of spiders who interact with each other according to biological rules of colony. Furthermore, fuzzy logic based on the two criteria of battery level and distance to sink is used for determining the fitness of nodes. On the other hand in WSNs with a fixed sink, since the nodes near the sink share multi-hop routes and data and integrated towards the sink, these nodes are more likely to deplete their battery energy than other nodes of the network. Also In this paper, mobile sink was suggested for dealing with this problem. For investigating and demonstrating the performance of the proposed method, we compared it with DCRRP and NODIC protocol. The results of simulation indicated better performance of the proposed method in terms of power consumption, throughput rate, end-to-end delay and signal to noise ratio and has higher failure tolerance especially in terms of sensor nodes’ failure.     

Data censoring with network lifetime constraint in wireless sensor networks

In wireless sensor networks (WSNs), senor nodes are usually battery-powered with limited energy budget. The network lifetime is directly related to the energy consumption of each node. Online censoring is an effective approach to reduce the overall energy consumption by only transmitting statistical informative data. However, the network lifetime is not proportionally extended with online censoring, since individual sensor may still suffer from energy shortage due to frequent transmission of informative data or transmission over long distance. In this paper, a parameters estimation problem is considered in WSNs, where the goal is to minimize the estimation error under the network lifetime constraint. Two censoring algorithms are developed, which allow sensor nodes to make decisions locally on whether to transmit the sampled data. The proposed algorithms can extend the network lifetime with little performance loss. Simulation results validate their effectivenesses.     

基于蚁群优化的无线传感器网络路由算法

如何在资源受限的无线传感器网络中进行高效的数据路由是无线传感器网络研究的热点之一。基于群智能优化技术的蚁群优化算法被广泛应用于网络路由算法。提出一种无线传感器网络蚁群优化路由算法,能够保持网络的生存时间最长,同时能找到从源节点到基站节点的最短路径;采用的多路数据传输也可提供高效可靠的数据传输,同时考虑节点的能量水平。仿真结果表明:提出的算法延长了无线传感器网络的寿命,实现无线传感器网络在通信过程中快速、节能的路由。     

基于学习自动机的无线传感网能量均衡分簇算法

优化簇首选择、均衡节点能量负载以延长网络存活时间,一直是无线传感器网络分簇协议研究的重点。针对无线传感器网络节点随机分布的情况,在基于学习自动机(Learning Automata, LA)的ICLA算法基础上,提出一种兼顾节点密度的能耗均衡分簇算法。在簇头选举方面,综合考虑节点剩余能量和节点密度,利用学习自动机与周围环境进行信息交互和动作奖惩,选择出相对较优的簇头;根据簇首与基站距离和其节点密度构造大小非均匀的簇,实现不同位置不同网络疏密程度下簇内和簇间能耗互补均衡;构造了基于簇首剩余能量、簇内节点密度和传输距离的评价函数,并运用贪婪算法选择出最优中转簇首进行多跳传输。仿真实验结果表明,该算法能选择出更为合理的簇头,有效地均衡网络能量负载,延长网络生存时间。     

An energy-driven unequal clustering protocol for heterogeneous wireless sensor networks

Due to the limitation of energy resources, energy efficiency is a key issue in wireless sensor networks (WSNs). Clustering is proved to be an important way to realize hierarchical topology control, which can improve the scalability and prolong the lifetime of wireless sensor networks. In this paper, an energy-driven unequal clustering protocol (EDUC) for heterogeneous wireless sensor networks is proposed. EDUC includes an unequal clustering algorithm and an energy-driven adaptive cluster head rotation method. The unequal size of clusters can balance the energy consumption among clusters, and the energy-driven cluster head rotation method can achieve the balance of energy consumption among nodes within a cluster, which reduces the waste of energy. Simulation experiments show that EDUC balances the energy consumption well among the cluster heads and prolongs the network lifetime.