改进的基于蚁群算法的非均匀分簇路由协议

针对无线传感器网络中传感器节点随机分布造成能耗不均和“热区”等问题,提出了一种改进的基于蚁群算法的非均匀分簇路由协议。该协议也采用“轮”方式运行,每轮簇首选举开始阶段,根据节点剩余能量、节点密度,结合节点到Sink节点的距离来构造不均匀的竞选半径,每个节点根据竞选半径范围内邻居节点计算剩余能量比及距离偏差平均值,从而计算出其簇首竞争等待时间,采用时间等候簇首竞选机制来选举出簇首,平衡簇内的通信能耗;数据传输阶段,考虑剩余能量、通信能耗、链路质量、传输时延等因素,采用改进的蚁群算法构造最优传输路径,数据传输的同时更新信息素,从而达到自适应、动态优化地建立和维护传输路径。仿真结果表明,该路由协议能有效节约能量和均衡能耗,延长网络生命周期,改善链路质量,减少传输时延。     

基于DIJKSTRA的无线传感器网络分簇路由算法

为加快无线传感器网络最优路径搜索速度、减少路径寻优能量消耗和延长网络寿命,提出了基于改进的DIJKSTRA算法的无线传感器网络分簇路由算法;运用DIJKSTRA算法在无线传感器网络内以多跳接力的方式来搜寻从源节点到目的节点的最短路径;结合能耗优化策略,避免网络能耗热点问题,实现网络能耗均衡;通过与基于蚁群算法的路由算法对比分析,基于Dijkstra的网络分簇路由算法能优化网络分簇并建立较优传输路径,其快速收敛性能减缓了网络中簇头节点的能耗,延长了网络寿命,提高了网络鲁棒性。     

一种基于可变扇区的非均匀分簇算法

针对LEACH协议中簇首分布不均匀,网络能耗不均衡的问题,提出一种基于可变扇区的非均匀分簇的算法（UCBVS）。首先,利用可变扇区和同心圆将网络合理的动态划分,使得距离基站近的区域分区较小;其次,选取簇内权值大的节点作为簇头,根据权值的大小判断是否进行簇头轮换;最后采用单跳和多跳相结合的方式进行网络通信。仿真结果表明,改进算法能够保证簇头节点能耗均匀,延长了网络生命周期。     

基于非均匀分簇的无线传感器网络路由协议

在无线传感器网络中,分簇技术是一种有效延长网络生命周期的方法。但是这种多跳的网络模型,如果节点均匀分布并且簇的大小相等,则靠近基站的簇头由于要中继更多的数据,则会导致能量空洞现象。因此提出一种非均匀分簇方法来缓解能量空洞问题。首先,通过节点的剩余能量、到基站的距离以及邻居节点数量来选择簇头。簇一旦形成之后,通过单跳和多跳的混合机制将数据发送到基站。实验结果表明,此协议能有效的延长网络的生命周期,均衡网络能耗,有效延缓能量空洞的形成速度。     

一种无线传感器网络多级异构分簇路由协议

JP+1]研究了无线传感器网络,提出了一种适合大规模应用的无线传感器网络体系结构,并提出了一种新的路由协议——多级异构分簇路由协议;该协议将无线传感器网络节点分成4种类型,根据簇头间平均跳数、簇头能耗等因素选举出第一级簇头节点;利用通信能耗、节点能耗以及跳数3个方面作为启发因子,寻找第一级簇头到第二级簇头间的最佳路径;基于NS2平台对该路由协议进行了仿真实验,并与LEACH协议进行比较;仿真结果表明:在大规模的应用中,该路由协议能有效地降低节点的平均能耗、延长网络生存时间。     

基于改进蚁群算法的WSN分簇路由机制研究

针对现有的用于无线传感器网络(WSN)的分簇路由协议,存在着所有簇头直接与汇聚节点通信、远离汇聚节点的簇头能量消耗过快等一系列的问题,根据蚁群算法(ACA)及WSN分簇路由算法的特点,对ACA进行改进并引入到WSN分簇路由机制中,提出一种基于改进蚁群算法的WSN分簇路由算法;该算法将到汇聚节点的距离设定为启发函数以找到簇头下沉的最佳路径和提高蚁群算法的效率,同时,在选择节点概率公式时将该节点的剩余能量考虑在内,在数据传输过程中,减少了簇头节点的能量消耗,进而实现节点能量的高效利用,增强网络的使用寿命,以实现网络通信的高效;通过仿真,结果表明,该算法是可行的、有效的。     

基于LEACH路由协议的多跳节能路由算法

当无线传感器网络部设在不同环境中时,需要提出新的算法以适应特殊环境,减少节点能量消耗;算法针对LEACH路由算法的局限性,提出了一种适用网络覆盖范围较大,节点间距离较远,需要远距离传输的路由算法;本算法利用节点到基站的距离因素,修改簇头阈值信息;并利用簇头竞争重新设定簇头,使剩余能量较高的节点成为簇头;同时,运用多跳的方式传输数据,这样可以适应远距离传输;仿真结果表明,相对LEACH算法,算法将节点死亡时间推后了300~400轮,网络存活周期延长了400轮左右,很明显的减少了网络的能量消耗,延长了网络的生存周期和稳定性。     

基于区域差异化的WSNs能效优化研究

随着“互联网 ”概念进一步加深,无线传感器网络(WSNs)技术也得到飞速发展,目前在环保、工业、军事等领域均有大量涉及,而能效问题一直是WSNs发展中重要的制约因素。文章对原有面向簇集的WSNs中存在的簇头能量损失过快的问题进行分析,发现该问题主要由节点过量转发数据所引起,因此从簇内和簇间两部分入手,并对路由策略进行了改进,将网络划分为热点和非热点区域,动态调整区域覆盖范围,结合虚拟多输入输出(VMIMO)和路由跨跳技术减少不必要的节点中继,最后对该方案进行仿真测试,发现改进后的方案可以更加有效地平衡优化WSNs内系统能量损耗。     

基于人工蜂群的无线传感器网络能耗均衡算法

由于无线传感器网络中的节点链路状况、数据传输能耗及节点剩余能量的限制,造成网络中部分感知节点寿命缩短,影响网络生存周期,提出了一种基于人工蜂群算法的WSNs能耗均衡算法,优化网络能耗均衡,从而提高网络寿命;文章给出了网络能耗相应的数学模型及优化求解算法,介绍人工蜂群算法的寻找食物过程,阐述了人工蜂群算法在网络能耗均衡方面的实现步骤;通过实验仿真证明,文章提到的算法与LEACH分簇算法、蚁群优化算法相比,具有更好的能耗和负载均衡能量、丢包率和时延性,有效地提高了网络生存周期。     

基于QoS和分簇机制的WMSNs路由算法研究

针对无线多媒体传感器网络中如何设计具有服务质量保证的路由算法问题,综合考虑的了节点间标准化后的丢包率、延迟、剩余能量、可用存储四个参数,提出算法ED-ACO(Energy and best distribution of Cluster Head Distance-Ant colony optimization )。ED-ACO算法采用基于剩余能量和簇首最佳距离分布的分簇结构,均匀划分网络,将节点的丢包率,延迟,剩余能量,可用存储标准化为具体参数,考虑到蚁群算法的状态转移概率公式中,利用该公式去选择下一跳路径传送感知数据,同时满足了服务质量要求。NS2仿真结果表明,与经典的AODV算法相比,在丢包率,延迟上保证了服务质量要求。     

基于改进蚁群算法的LEACH协议研究

针对LEACH协议在数据传输阶段,簇首与汇聚节点之间采用单跳模式传输数据使得能量消耗快并且不均衡的问题,提出一种基于改进蚁群算法的新型路由协议。该协议利用了能耗因子对蚁群转移概率以及信息素更新进行改进,充分考虑了节点的剩余能量和节点间距离,通过信息素的建立和更新,寻找簇首节点和基站之间的最优传输路径,进行多跳传输模式,从而均衡簇首节点能量消耗。仿真实验结果表明,改进后的ACO-BEC协议较之于LEACH协议,能够有效降低了整个网络能量消耗,延长了网络寿命。     

Energy-Efficient Clustering Mechanism of Routing Protocol for Heterogeneous Wireless Sensor Network Based on Bamboo Forest Growth Optimizer

In wireless sensor networks (WSN), most sensor nodes are powered by batteries with limited power, meaning the quality of the network may deteriorate at any time. Therefore, to reduce the energy consumption of sensor nodes and extend the lifetime of the network, this study proposes a novel energy-efficient clustering mechanism of a routing protocol. First, a novel metaheuristic algorithm is proposed, based on differential equations of bamboo growth and the Gaussian mixture model, called the bamboo growth optimizer (BFGO). Second, based on the BFGO algorithm, a clustering mechanism of a routing protocol (BFGO-C) is proposed, in which the encoding method and fitness function are redesigned. It can maximize the energy efficiency and minimize the transmission distance. In addition, heterogeneous nodes are added to the WSN to distinguish tasks among nodes and extend the lifetime of the network. Finally, this paper compares the proposed BFGO-C with three classic clustering protocols. The results show that the protocol based on the BFGO-C can be successfully applied to the clustering routing protocol and can effectively reduce energy consumption and enhance network performance.     

Adaptive multimode transmission in wireless sensor networks with energy harvesting

Limited energy has always been an important factor restricting the development of wireless sensor networks. The unbalanced energy consumption of nodes will accelerate the death of some nodes. To solve the above problems, an adaptive routing algorithm for energy collection sensor networks based on distributed energy saving clustering (DEEC) is proposed. In each hop of data transmission, the optimal mode is adaptively selected from four transmission modes: single-hop cooperative, multi-hop cooperative, single-hop non-cooperative and multi-hop non-cooperative, so as to reduce and balance the energy consumption of nodes. The performance of the proposed adaptive multi-mode transmission method and several benchmark schemes are evaluated and compared by computer simulation, where a few performance metrics such as the network lifetime and throughput are adopted. The results show that, the proposed method can effectively reduce the energy consumption of the network and prolong the network lifetime; it is superior to various benchmark schemes.     

Adaptive coding clustered routing protocol for energy efficient and reliable WSN

In Wireless Sensor Networks (WSNs), the sensor nodes are expected to remain autonomous for a long time and the communication must be successful. So, energy efficiency and communication reliability are the main challenges for WSN. Routing protocols and, in particular, clustering is the key to energy constraints in WSNs. In a loss milieu, data shared between nodes is susceptible to corruption by errors caused by signal fading, random noise, and some other forces. For this reason, channel coding is necessary to assure efficient and reliable transmission. Therefore, energy efficiency and reliability of communications are the most crucial factors in the design of an efficient cluster-based routing protocol. In this context, to raise the reliability of the communication link and minimize the necessary transmission energy, a new adaptive coding routing protocol is proposed, which implements RS and LDPC codes to a routing protocol. Simulation results show that our proposed protocol improves energy consumption, network lifetime, stability, and throughput compared to LEACH and BRE-LEACH. In addition, it reaches a good BER performance and coding gain by using the LDPC code.     

Modeling wireless sensor networks using random graph theory

A critical issue in wireless sensor networks (WSNs) is represented by limited availability of energy within network nodes. Therefore, making good use of energy is necessary in modeling sensor networks. In this paper we proposed a new model of WSNs on a two-dimensional plane using site percolation model, a kind of random graph in which edges are formed only between neighbouring nodes. Then we investigated WSNs connectivity and energy consumption at percolation threshold when a so-called phase transition phenomena happen. Furthermore, we proposed an algorithm to improve the model; as a result the lifetime of networks is prolonged. We analyzed the energy consumption with Markov process and applied these results to simulation.     

基于质心理念的无线传感网络路由协议的研究与改进*

在无线传感器网络中存在诸如节点能量损耗以及数据传输时所存在的时延等问题,因此在深入研究PEGASIS以及相关改进算法的基础上,并结合力学中质心的概念而提出一种基于质心理念的链式算法CMC-PEGASIS(Center of mass concept-PEGASIS)。该算法将整个传感器区域分成等宽的五个子区域,通过计算每个子区域内节点至基站的距离并按照距离与基站的远近来成链。其次引入质心的概念找到每个区域节点的能量中心,同时结合节点自身的能耗以及每个节点与基站的距离关系,得到每个子区域中的最佳的簇头节点,最后每个区域的簇头节点直接与基站完成信息的传输。经过理论分析和仿真结果得知：CMC-PEGASIS算法降低了全局能耗、减少了信息延迟率同时延长了网络的生命周期。     

A random walk evolution model of wireless sensor networks and virus spreading

In this paper, considering both cluster heads and sensor nodes, we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure, and discuss the spreading dynamic behavior of viruses in the evolution model. A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance, but also can dynamically balance the energy loss of the entire network. It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency, it can effectively inhibit the spread of viruses. In addition, the heterogeneity of the network improves the network efficiency and enhances the virus prevalence. We confirm all the theoretical results with sufficient numerical simulations.     

A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes.