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.     

基于模拟退火的无线传感器网络最大生命期路由*

将节点功率控制与数据聚合有机结合,为进一步降低网络能耗提供了可能,但是也给路由算法的设计带来了新的挑战。为此,针对WSNs中结合数据聚合的节能数据传递方式进行了研究,提出了一种新的最大化网络生命期的路由算法。该算法采用模拟退火算法最优化数据聚合点的选择,均衡节点能耗,最大化网络生命期。仿真结果表明该算法性能明显优于现有算法,达到了提高网络生命期的目的。     

基于蚁群算法的WSNs节点有障环境中部署优化研究

能量限制是无线传感器网络(WSNs)技术中一个关键问题.提出了一种通过无线信道能耗衰减模型利用Dijkstra算法和蚁群算法相结合的WSNs节点静态优化部署策略.通过该策略对两种不同的环境仿真部署验证表明:该策略能更好地优化节点在复杂二维地理环境中的静态部署,并可有效地减少网络的能耗,提高WSNs的使用寿命.     

基于单神经元PID的WSNs邻居节点级功率控制算法

针对无线传感器网络(WSNs)中功率控制算法不能实时地调整节点功率和能量消耗较大等问题,提出了一种基于单神经元PID的邻居节点级功率控制算法.该算法结合WSNs传输功率模型,通过实时地控制邻居节点的数量,调整每个节点的功率,动态控制网络的能耗.仿真结果表明:该算法能根据网络连通性要求调整传感器节点的功率,实时性好,收敛速度快,有效地降低了网络的整体能耗.     

基于元胞蚁群优化算法的农业无线传感器网络路由研究

无线传感器网络(WSNs)进行农业信息采集时,针对传感器节点数量多,能量消耗不均衡的特点,提出基于元胞蚁群优化(CACO)的WSNs路由算法。算法将网络中的节点映射成CACO算法中的元胞,通过改进蚂蚁信息素更新模型,引入睡眠唤醒机制,有效地减少了节点间的通信,从而改善能量消耗不均衡。仿真结果表明:该算法有效地减少了网络耗能、节点消亡数量,延长了网络生命期。     

单节点的无线传感器网络数据传输优化策略

为了提高无线传感器网络（WSNs）节点能量的利用率,延长WSNs的生存时间,提出了一种单节点的WSNs数据传输优化策略.首先对WSNs结构进行分析,并建立单个传感器节点数据传输优化的数学模型;然后采用惩罚函数法对数据传输过程中的传感器节点能耗进行优化;最后在Matlab 2012平台对其进行仿真分析.结果表明：该方法可以根据环境能量的变化对传感器节点能耗进行自适应优化,提高了节点的累积数据传输总量,可以较好适应环境能量不确定性.     

基于CS优化的双簇头分簇路由算法研究

为了减少无线传感器网络(WSNs)分簇路由中簇头的能量消耗,提出了一种基于布谷鸟搜索(CS)优化的双簇头分簇路由算法.CS通过采用节点的剩余能量和节点之间的位置关系来构造适应值函数并选举出最优双簇头.其中,主簇头将数据进行融合,副簇头将融合的数据发送给基站,缓解了以往单簇头同时负责数据融合和传输的双重压力,使得整体能耗在各个节点的分配更均衡.仿真实验表明:与LEACH算法、粒子群优化(PSO)算法相比,CS算法在减小网络能耗以及延长网络生存周期上更具优势.     

无线传感器网络网内数据融合的研究

针对目前无线传感器网络(WSNs)网内数据融合所面临的一些挑战,提出了一种基于无线传感器网络分布式K-平均聚类(DKCWSNs)算法的WSNs节点传感数据的分组策略,并采用基于自适应加权的数据融合方法对分组后的感知数据进行融合处理,从而获得更合理的结果.最后,通过实例验证了该方法的有效性.     

无线传感器网络最大生命期聚合树路由算法

提出了一种无线传感器网络最大生命期聚合树路由算法,根据能量等限制条件建立线性规划模型。考虑到网络最大生命期是NP难问题,在算法复杂度较低情况下,将网络最大生命期问题转化为网络最小归一化负载问题,在建立最大归一化负载聚合树过程中,不断调整负载较重节点的数据转发压力,最终建立一棵负载较轻的数据融合树,实现了网络生命期的最大化。通过仿真验证了算法的性能,并表明所提出算法可以有效延长网络生命期。     

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.     

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。     

Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors

Motivated by recent developments in wireless sensor networks (WSNs), we present several efficient clustering algorithms for maximizing the lifetime of WSNs, i.e., the duration till a certain percentage of the nodes die. Specifically, an optimization algorithm is proposed for maximizing the lifetime of a single-cluster network, followed by an extension to handle multi-cluster networks. Then we study the joint problem of prolonging network lifetime by introducing energy-harvesting (EH) nodes. An algorithm is proposed for maximizing the network lifetime where EH nodes serve as dedicated relay nodes for cluster heads (CHs). Theoretical analysis and extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently, and therefore help provide useful benchmarks for various centralized and distributed clustering scheme designs.     

无线传感器网络权衡生存时间与数据分组跳数的分流路由算法*

无线传感器网络的节点大多采用电池供电.因而节能对无线传感器网络就显得至关重要.该文提出一种能耗感知的优化网络生存时间的路由算法,称之为分流路由算法(DTRA,Diffluent Traffic Routing Algorithm).DTRA算法采用一个优化模型以优化每个节点发出的数据比例,从而达到权衡网络生存时间和数据分组跳数.此外,采用一个简单的遗传算法求解该优化问题.仿真结果表明:DTRA算法能显著地提高网络的生存时间,同时将数据分组平均跳数保持在一个较低的水平;在网络生存时间上,DTRA算法比一些已有的知名算法更优.     

WSN in cyber physical systems: Enhanced energy management routing approach using software agents

Recently, the cyber physical system has emerged as a promising direction to enrich the interactions between physical and virtual worlds. Meanwhile, a lot of research is dedicated to wireless sensor networks as an integral part of cyber physical systems. A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices that use sensors to monitor physical or environmental conditions. These autonomous devices, or nodes, combine with routers and a gateway to create a typical WSN system. Shrinking size and increasing deployment density of wireless sensor nodes implies the smaller equipped battery size. This means emerging wireless sensor nodes must compete for efficient energy utilization to increase the WSN lifetime. The network lifetime is defined as the time duration until the first sensor node in a network fails due to battery depletion. One solution for enhancing the lifetime of WSN is to utilize mobile agents. In this paper, we propose an agent-based approach that performs data processing and data aggregation decisions locally i.e., at nodes rather than bringing data back to a central processor (sink). Our proposed approach increases the network lifetime by generating an optimal routing path for mobile agents to transverse the network. The proposed approach consists of two phases. In the first phase, Dijkstra’s algorithm is used to generate a complete graph to connect all source nodes in a WSN. In the second phase, a genetic algorithm is used to generate the best-approximated route for mobile agents in a radio harsh environment to route the sensory data to the base-station. To demonstrate the feasibility of our approach, a formal analysis and experimental results are presented.     

Multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks using fuzzy soft sets

In this paper, a fuzzy based distributed power aware routing scheme considering both energy and bandwidth constraints, especially for query driven applications in the asynchronous duty-cycled wireless sensor networks are devised. The proposed multi-constraint, multi-objective routing optimization approach under strict resource constraints guarantees reliability and fast data delivery along with efficient power management in spite of unreliable wireless links and limited power supply. In query driven applications, the request from the sink to the individual sensor node will be a broadcast message, whereas the individual sensor nodes replies back to sink as unicast messages. In the proposed work, the fuzzy approach and “A Star” algorithm are utilized for satisfying energy and bandwidth constraints to route the broadcast messages of the sink while querying all the sensor nodes in the network. Every node will be provided with a guidance list, which is used to decide the next best neighbor node with good route quality for forwarding the received multi-hop broadcast messages. The route quality of the every node is estimated with fuzzy rules based on the network parameters such as maximum remaining energy, minimum traffic load and better link quality to increase the network lifetime. The provision of overhearing the broadcast messages and acknowledgements within the transmission range minimizes the effort to search for the active time of nodes while routing the broadcast messages with asynchronous scheduling. Further, in the proposed work only the time slot of its nearest neighbor relay node (to which packets are to be forwarded) is learnt to reduce the number of message transmissions in the network. For the unicast message replies, the fuzzy membership function is modified and devised based on the routing metrics such as higher residual energy, minimum traffic loads and minimum hop count under energy and bandwidth constraints. Also, the multi-hop heuristic routing algorithm called Nearest Neighbor Tree is effectively used to reduce the number of neighbors in the guidance list that are elected for forwarding. This helps to increase the individual sensor node’s lifetime, thereby maximizes the network lifetime and guarantees increased network throughput. The simulation results show that the proposed technique reduces repeated transmissions, decreases the number of transmissions, shortens the active time of the sensor nodes and increases the network lifetime for query driven sensor network applications invariant to total the number of sensor nodes and sinks in the network. The proposed algorithm is tested in a small test bed of sensor network with ten nodes that monitors the room temperature.     

无线传感器网络中最大化网络寿命的数据聚合路由

针对数据聚合无线传感器网络寿命最大化问题,分析了网络流量和节点能耗,提出了数据聚合路由问题的网络流量模型,并将网络最大寿命与流量模型相结合设计了一组混合整数规划代价函数.采用对偶分解的方法,获得了近似最优的中继传输速率和路由.仿真实验表明,该算法能有效减少数据通信量,均衡各个节点的能量消耗,延长网络寿命.     

无线传感器网络簇内多传感器数据融合算法

在无线传感器网络(WSNs)中,一般采用电池供电,节能是WSNs设计的研究重点。为了提高测量结果的准确度和降低网络的能耗,提出了一种两层模式数据融合方案。在传感器节点上用格林贝斯准则和顺序加权算法进行低层次数据融合,在簇头节点上用神经网络算法进行高层次数据融合。仿真实验结果表明:两层模式数据融合方案有效减少了网络中的数据传输量,提高了融合数据的精度,降低传感器节点的能耗。