一种基于蚁群算法的能量有效WSN分簇算法

由于无线传感器网络节点的能量限制,如何延长网络和节点的寿命成为其核心问题之一。LEACH协议是WSN中的低功耗白适应分层路由算法,但由于其随机性的特点,难以形成最优拓扑结构,同时分簇时未考虑簇头节点剩余能量,节点能耗分布不平均。本文提出一种考虑能量有效的基于ACO的分簇算法,通过群集作用动态选择簇头节点,延长了网络寿命,仿真结果表明其性能优于LEACH。     

Particle swarm optimization based clustering algorithm with mobile sink for WSNs

Wireless sensor networks with fixed sink node often suffer from hot spots problem since sensor nodes close to the sink usually have more traffic burden to forward during transmission process. Utilizing mobile sink has been shown as an effective technique to enhance the network performance such as energy efficiency, network lifetime, and latency, etc. In this paper, we propose a particle swarm optimization based clustering algorithm with mobile sink for wireless sensor network. In this algorithm, the virtual clustering technique is performed during routing process which makes use of the particle swarm optimization algorithm. The residual energy and position of the nodes are the primary parameters to select cluster head. The control strategy for mobile sink to collect data from cluster head is well designed. Extensive simulation results show that the energy consumption is much reduced, the network lifetime is prolonged, and the transmission delay is reduced in our proposed routing algorithm than some other popular routing algorithms.     

基于群体智能和遗传算法的WSNs能耗优化关键技术研究

节点能耗是决定无线传感器网络(WSNs)生存期的重要参数,设计良好的网络通信协议可以很大程度上减少和平衡能量消耗。网络协议设计簇头和簇间路由的计算过程是多项式时间无法解答的NP问题,该文讨论了5种自然元启发算法,既4种群体智能算法和遗传算法应用于WSNs能耗优化的关键技术,给出了不同网络能量结构模型的簇间单跳和多跳场景的设计建议,旨在为搭建大规模WSNs网络提供参考和借鉴。     

无线传感网中一种智能数据融合算法的实现及仿真分析

在无线传感网中,传感器节点一般都由自身装配的电池供电,难以进行电量补充,因此节约电量对于无线传感网来说至关重要.为了提高无线传感网能量使用效率,延长网络生存时间,提出了一种结合遗传算法和粒子群算法优化BP神经网络的智能数据融合算法 GAPSOBP(BP Neural Network Data Fusion algorithm optimized by Genetic algorithm and Particle swarm).GAPSOBP算法将无线传感网的节点类比为BP神经网络中的神经元,通过神经网络提取无线传感网采集的感知数据并结合分簇路由对收集的传感数据进行融合处理,从而大幅减少发往汇聚节点的网络数据量.仿真结果表明,与经典LEACH算法和PSOBP算法相比,GAPSOBP算法能有效减少网络通信量,节约节点能量,显著延长网络生存时间.     

无线传感器网络中能量有效自适应路由算法研究

针对无线传感器网络寿命最大化问题,基于无线传感器节点能耗分布特点和数据传输能耗模型,建立无线传感器网络生存周期的数学优化模型,并针对最小能耗路由的能耗不均衡问题和能量均衡路由的能耗开销问题,综合考虑网络中节点的剩余能量和节点间发送数据的能耗,提出一个适合无线多跳传感器网络的自适应路由算法。仿真结果表明,提出的路由算法能充分地利用有限的能量资源,较大地延长网络生存周期。     

可充电无线传感网络能量均衡路由算法

针对可充电无线传感网络中的能量均衡路由问题,提出在稳定功率无线充电和监测数据收集网络场景下的多路径路由算法和机会路由算法,以实现网络的能量均衡。首先,通过电磁传播理论构建了无线传感节点的充电和接收功率关系模型;然后,考虑网络中无线传感节点的发送能耗和接收能耗,基于上述充电模型将网络能量均衡的路由问题转化为网络节点运行时间的最大最小化问题,通过线性规划得到的各链路流量用以指导路由中数据流量分配;最后,考虑一种更加现实的低功耗的场景,并提出了一种基于机会路由的能量均衡路由算法。实验结果表明,与最短路径路由（SPR）和期望周期最短路由（EDC）算法相比较,所提出的两种路由算法均能有效提高采集能量的利用率和工作周期内的网络生命周期。     

An energy-saving routing architecture with a uniform clustering algorithm for wireless body sensor networks

Wireless body sensor networks are expected to extend human-centered applications in large-scale sensing and detecting environments. Energy savings has become one of the most important features of the sensor nodes to prolong their lifetime in such networks. To provide reasonable energy consumption and to improve the network lifetime of wireless body sensor network systems, new and efficient energy-saving schemes must be developed. An energy-saving routing architecture with a uniform clustering algorithm is proposed in this paper to reduce the energy consumption in wireless body sensor networks. We adopted centralized and cluster-based techniques to create a cluster-tree routing structure for the sensor nodes. The main goal of this scheme is to reduce the data transmission distances of the sensor nodes by using the uniform cluster structure concepts. To make an ideal cluster distribution, the distances between the sensor nodes are calculated, and the residual energy of each sensor node is accounted for when selecting the appropriate cluster head nodes. On the basis of the uniform cluster location, the data transmission distances between the sensor nodes can be reduced by employing an adaptive multi-hop approach. The energy consumption is reduced, and the lifetime is extended for the sensor nodes by balancing the network load among the clusters. Simulation results show that the proposed scheme outperforms the previously known schemes in terms of the energy consumption and the network lifetime for the wireless body sensor networks.     

Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks

Wireless sensor networks are rapidly evolving technological platforms with tremendous applications in several domains. Since sensor nodes are battery powered and may be used in dangerous or inaccessible environments, it is difficult to replace or recharge their power supplies. Clustering is an effective approach to achieve energy efficiency in wireless sensor networks. In clustering-based routing protocols, cluster heads are selected among all sensor nodes within the network, and then clusters are formed by simply assigning each node to the nearest cluster head. The main drawback is that there is no control on the distribution of cluster heads over the network. In addition to the problem of generating unbalanced clusters, almost all routing protocols are designed for a certain application scope, and could not cover all applications. In this paper, we propose a swarm intelligence based fuzzy routing protocol (named SIF), in order to overcome the mentioned drawbacks. In SIF, fuzzy c-means clustering algorithm is utilized to cluster all sensor nodes into balanced clusters, and then appropriate cluster heads are selected via Mamdani fuzzy inference system. This strategy not only guarantees to generate balanced clusters over the network, but also has the ability to determine the precise number of clusters. In fuzzy-based routing protocols in literature, the fuzzy rule base table is defined manually, which is not optimal for all applications. Since tuning the fuzzy rules very affects on the performance of the fuzzy system, we utilize a hybrid swarm intelligence algorithm based on firefly algorithm and simulated annealing to optimize the fuzzy rule base table of SIF. The fitness function can be defined according to the application specifications. Unlike other routing protocols which have been designed for a certain application scope, the main objective of our methodology is to prolong the network lifetime based on the application specifications. In other words, SIF not only prolongs the network lifetime, but also is applicable to any kind of application. Obtained simulation results over 10 heterogeneous networks show that SIF outperforms the existing clustering-based protocols in terms of generating balanced clusters and prolonging the network lifetime.     

基于混合量子进化算法的高效节能无线传感器网络路由算法

在无线传感器网络中,层次型路由算法能减少节点能量消耗和延长网络生存周期.因此在LEACH算法和PEGASIS算法的基础上,提出了一种基于混合量子进化算法的高效节能的无线传感器网络路由算法HERA.该算法中把网络分为多个簇,每个簇中的节点连接成为一条多跳通讯链路,并使用混合量子进化算法来得到最优的分簇组链方式,以减少链路...     

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)。     

一种能量高效的无线传感网分簇路由算法

针对无线传感网节点能耗不均造成的生命周期短的问题,提出一种能量高效的无线传感网分簇路由算法（NUC&GDF）。该算法从三个方面对无线传感网的路由进行优化;a）改进簇首选举机制,选择合适的簇首;b）改进簇首节点成簇半径规则,形成合理的簇规模大小;c）在簇首与基站的稳定数据传输过程中,引入改进的梯度下降法强化学习来计算权值最小的自适应无线簇间路由。实验结果分析表明,提出的算法性能比LEACH算法、LEACH-C算法以及DEBUC算法更优;在网络规模为100 m×100 m时,网络生命周期分别提高约50.3%、21.5%、16.4%,能更有效地延长网络生命周期。     

WSN中基于改进樽海鞘群算法的分簇路由协议

针对无线传感器网络能耗不均衡、网络生存期短的问题,提出了一种基于改进樽海鞘群算法的分簇路由协议;所提协议分别从分簇阶段和稳定传输阶段进行优化;分簇阶段,首先对樽海鞘群算法进行改进,引入了精英反向学习策略和动态学习机制,克服了基本樽海鞘群算法局部勘探和全局开发能力不平衡的问题;其次考虑节点的剩余能量和地理位置设计高效的适应度函数,以选出最优簇首,平衡簇间负载;稳定传输阶段,设计了基于最小生成树的簇间路由算法,为簇首构建最优传输路径,在缓解簇首负载的同时提高网络能量利用率;簇内通信阶段,引入轮询控制机制帮助节点构建传输调度,提高时隙利用率;对所提协议进行仿真实验,并与几种前沿协议进行对比;对比结果表明,所提出的基于改进樽海鞘群算法的分簇路由协议能够有效地改善网络性能,从而延长传感网对于目标区域的监测时间,并提高基站接受到的数据包总量。     

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。     

基于传感器网络节点配置优化仿真研究

研究无线传感网络节点配置覆盖优化问题。由于无线传感网络存在着热区问题,对网络的覆盖性能造成严重的影响,同时影响网络配置优化。为了有效的提高无线传感网络的覆盖率,提出了一种改进的粒子群算法优化无线网络节点覆盖。针对粒子群算法存在易陷入局部极值和早熟的缺陷,引入遗传算法中的交叉算子和变异算子,优化传感网络节点的混合粒子群算法,在严格确保无线传感器网络连通性的条件下,传感器节点配置数目达到要求的覆盖度,并进行仿真。仿真结果表明混合粒子群算法能快速收敛到更精确的解,使网络节点配置达到覆盖的优化要求。     

无线传感器网络中能量有效自适应路由算法研究

针对无线传感器网络寿命最大化问题,基于无线传感器节点能耗分布特点和数据传输能耗模型,建立无线传感器网络生存周期的数学优化模型,并针对最小能耗路由的能耗不均衡问题和能量均衡路由的能耗开销问题,综合考虑网络中节点的剩余能量和节点间发送数据的能耗,提出一个适合无线多跳传感器网络的自适应路由算法。仿真结果表明,提出的路由算法能充分地利用有限的能量资源,较大地延长网络生存周期。     

基于贪婪算法无线传感器网络中继节点布局的研究

为实现远距离的无线通信, 在网络中添加中继节点, 采用多跳路由传输数据。对于中继节点的布局问题, 依据线性结构使网络整体能量消耗最小的特征, 提出一种中继节点贪婪布局算法。该算法通过最近贪婪策略、中继节点通信容量、传感器节点数据转发跳数等约束方法限制中继节点的布局位置。理论分析和实验验证了该算法能够有效减少能量消耗, 延长网络寿命。     

WSN中一种能量均衡的路由协议

无线传感器节点的自身特点决定了网络的能量消耗成为评价无线传感器网络路由协议优劣的重要指标.但是,仅仅追求能量消耗最小化是不够的,因为这有可能会使部分节点由于频繁的处理数据而比网络中其它节点提早死亡,导致网络分割或者"洞"的出现,从而严重影响网络的寿命.本文提出了一种能量均衡的路由协议,它能够平衡网络能量消耗并延长网络寿命.     

基于进化算法的无线传感器网络任播路由算法

在无线传感器网络WSN(Wireless Sensor Networks)中存在无线链路容易失效的现象,但大多数学者在设计路由算法时较多地关注网络生存期问题,而忽略路由健壮性问题.提出一种基于进化算法的WSN任播路由算法.该算法以网络生存期和路由健壮性为优化目标,并通过多目标进化算法寻找到两者的最佳适应值.实验验证了该算法的有效性,实验数据表明:相比较基于单目标优化(网络生存期)的任播路由算法,所提算法的网络生存期及路由健壮性两个性能的综合优化值优于前者;相比较传统单路径任播路由算法,所提算法的网络生存期、路由健壮性和可扩展性优于前者.     

WSN中基于能量代价的能量优化路由算法

针对无线传感器网络路由算法中的节点能耗问题,提出了一种基于能量代价的能量优化路由算法。算法综合考虑数据传输中节点能耗的有效性和均衡性,设计了一种新的能量代价函数,实现了二者的优化匹配。传感器节点按此函数计算前向部居节点的能量代价,选择能量代价最小的节点作为下一跳。算法基于部居节点信息进行路由选择,具有较低的计算时间复杂度。最后,对路由算法的性能进行了仿真分析,并与典型的路由算法进行了对比,结果表明,本路由算法能够有效延长网络生存时间,节约并均衡节点的能量消耗。     

Improvement of battery lifetime in software-defined network using particle swarm optimization based cluster-head gateway switch routing protocol with fuzzy rules

The software-defined network (SDN) is one of the network architectures, in which the data plane and control plane is divided from each other, and the network can be handled using a sensibly centralized controller and this method is adopted to reconfigure the wireless sensor network automatically. In this article, to implement the SDN in MANET, in which control nodes can be chosen in SDN dynamically for the activation of MANET function to allocate the works to other mobile nodes to the base station. However, in the field of mobile ad hoc networks, the network lifetime, and battery lifetime is one of the major problems and the energy consumption can play a significant rule for the transmission of data in the SDN. Therefore, in this article, particle swarm optimization (PSO) based CGSR (cluster-head gateway switch routing protocol) algorithm with fuzzy rules is proposed to increase the network lifetime of battery powered mobile nodes by reducing the energy consumptions of each node in software-defined MANET. In this proposed method, a routing method that can permit various mobile nodes with low battery power to transmits the data from source node to base station. We design a PSO based CGSR routing protocol by selecting the routing mobile nodes using fuzzy rules for packet transmission. In CGSR process, the formation of cluster and selection of cluster head is executed depending on the particle swarm optimization method. This proposed routing protocol can be used to enhance the battery lifetime by extension of the network lifetime with numerical analysis for efficient route node selection.