基于传输距离和Sink移动的扩延网络寿命算法

在无线传感网络WSN(Wireless Sensor Network)中,传感节点通常以多跳方式向信宿Sink传输感测数据.由于邻近信宿Sink的传感节点需要承担数据转发的任务,比其他节点消耗更多的能量,缩短了网络寿命.为此,提出一种扩延网络寿命的新算法,记为NLTA(Network LifeTime Augmentation).NLTA算法采用了节点传输距离自适应调整和信宿Sink移动两个策略.节点依据能量情况,调整传输距离,减少能量消耗,然后根据路径容量值,调整Sink的位置,平衡网内的节点能量消耗,避免信宿Sink的周围节点能量过度消耗.仿真结果表明,提出的NLTA方案能够有效地提高网络寿命.     

WSNs中基于粒子群优化的信宿移动路径规划算法

感测数据,再将数据传输至信宿是无线传感网络(WSNs)中节点的首要任务。传感节点由电池供电,它们的多数能量用于传输数据,越靠近信宿的节点,传输的数据量越大。因此,这些节点的能耗速度快,容易形成能量-空洞问题。而通过移动信宿收集数据能够缓解能量-空洞问题。为此,提出基于粒子群优化的信宿移动路径规划(PSO-RPS)算法。PSO-RPS算法结合数据传递时延和信息速率两项信息选择驻留点,并利用粒子群优化算法选择最优的驻留点,进而构建时延有效的信宿收集数据的路径。仿真结果表明,提出的PSO-RPS算法有效地控制路径长度,缩短了收集数据的时延。     

WSNs中优化移动信宿路径的数据收集算法

收集数据是部署无线传感网络(WSNs)的根本目的。采用移动信宿策略可有效缓解WSNs的能耗问题,信宿的移动路径是该策略的关键。为此,提出基于伪驻留点的数据收集(VRDC)算法。VRDC算法先依据驻留点规划信宿路径,再依据路径选择伪驻留点(VRPs)。VRPs可通过一跳直接向移动信宿传输数据,而其他的节点则将数据传输至最近的VRPs,进而减少传输跳数,降低能耗。仿真结果表明,提出的VRDC算法能有效降低能耗,并平衡节点间的能耗。     

基于传输功率和数据包尺寸的优化网络寿命的研究

在无线传感网络中,数据包长度和传输功率等级成为影响能量效率和网络寿命的关键因素。为此,提出基于传输功率和数据包长度的联合优化网络寿命算法,记为TP-PS-INL。首先,依据Mica2传感节点的能量消耗特性和无线信道模型,构建链路层模型,然后再利用链路层模型,建立混合整数规划MIP模型,进而联合优化传输功率和数据包长度,最大化网络寿命。数值仿真分析表明,MIP模型能较准确地反映数据包长度和传输功率对网络寿命的影响,并且TP-PS-INL算法能够最大化网络寿命。     

一种无线多媒体传感器网络路径最优路由算法

介绍了无线多媒体传感网络(WMSN)路由的设计原则与策略,描述了路径最优的基本原理,最后从最优路径的选择、信息转发策略、节点能量消耗3个方面考虑,设计出路径最优路由算法。该算法与蚁群算法进行比较,实验结果表明,该算法在节点能量消耗、信息传送速度上都优于蚁群算法,尤其传速率提高了30%左右。     

无线传感器网络动态分簇路由BWAS的算法研究

为加快无线传感器网络(WSN)路径搜索速度,减少了路径寻优能量消耗,提出了基于最优-最差蚂蚁系统(BWAS)算法的无线传感器网络动态分簇路由算法。该算法是基于WSN动态分簇能量管理模式,在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径,以多跳接力方式将数据发送至汇聚节点。BWAS算法在路径搜寻过程中评价出最优-最差蚂蚁,引入奖惩机制,加强搜寻过程的指导性。结合动态分簇能量管理,避免网络连续过度使用某个节点,均衡了网络节点能量消耗。通过与基于蚁群算法(ACS)路由算法仿真比较,本算法减缓了网络节点的能量消耗,延长了网络寿命,在相同时间里具有较少的死亡节点,具有较强的鲁棒性。     

基于ERPMT改进启发式方法的WSN寿命最大化算法

针对传感器节点的电池容量限制导致无线传感网络寿命低的问题,基于容量最大化(CMAX)、线上最大化寿命(OML)两种启发式方法以及高效路由能量管理技术(ERPMT),提出了基于ERPMT改进启发式方法的无线传感网络寿命最大化算法。首先,通过启发式方法初始化每个传感器节点,将节点能量划分为传感器节点起源数据和其它节点数据延迟;然后利用加入的一种优先度量延迟一跳节点的能量消耗;最后,根据路径平均能量为每个路由分配一个优先级,并通过ERPMT实现最终的无线传感网络优化。针对不同分布类型网络寿命的实验验证了本文算法的有效性及可靠性,实验结果表明,相比较为先进的启发式方法CMAX及OML,本文算法明显增大了无线传感网络的覆盖范围,并且大大地延长了网络的寿命。     

WSANs中基于跳步数自适应的路由算法

无线传感反应网络是由大量传感节点和少量（可移动）资源丰富的反应节点构成.传感节点 负责收集外部环境信息,而反应节点对外部环境实施相应的操作.本文分析节点能量消耗模 型,求解出理想条件下单条路径上总能耗最小时的最优跳步数.在此基础上,提出了一种跳 步数自适应的路由算法HAR,用来实现数据收集的实时需求与能量消耗之间的平衡.仿真实验 表明：HAR算法在平衡能量消耗与时间延迟的性能上优于HBMECT算法,而且能较好地适用于 反应节点移动的情形.     

基于移动锚节点的无线传感网络节点定位算法

在无线传感网络定位算法中,锚节点位置决定了节点定位精度。为此,提出基于高斯-Markov模型的移动锚节点的节点定位(GM-MAL)算法。GM-MAL算法基于高斯-Markov移动模型,提出自适应锚节点的移动路径规划,通过速度调整策略、垂直平分线策略、虚斥力策略以及虚引力策略规划路径。在定位阶段,将非凸优化问题转化为双凸形式,再利用交替最小算法(AMA)求解,进而获取更短的锚节点移动路径。实验数据表明,引入虚引力策略提高了路径规划精度,覆盖了更多的监测区域。此外,相比于线性算法,GM-MAL的定位精度得到提高。     

基于BWAS的无线传感器网络静态分簇路由算法

为提高路径搜索效率,避免动态分簇较多的能量消耗,提出了基于最优-最差蚂蚁系统(BWAS)的无线传感器网络静态分簇路由算法.BWAS是对蚁群算法的改进,在路径搜寻过程中评价出最优最差蚂蚁,引入奖惩机制,加快了路径搜索速度.通过无线传感器网络静态分簇、簇内动态选举簇头,在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径,能减少路径寻优能量消耗,实现均衡能量管理,延长网络寿命,且具有较强的鲁棒性.通过与基于BWAS的动态分簇和基于蚁群算法的动态分簇路由的仿真实验相比较,证实了本算法的有效性.     

Distributed trajectory design for data gathering using mobile sink in wireless sensor networks

Several studies have demonstrated the benefits of using a mobile sink (MS) to reduce energy consumption resulting from multi-hop data collection using a static sink in wireless sensor networks (WSNs). However, using MS may increase data delivery latency as it needs to visit each sensor node in the network to collect data. This is a critical issue in delay-sensitive applications where all sensed data must be gathered within a given time constraint. In this paper, we propose a distributed data gathering protocol utilizing MS for WSNs. The proposed protocol designs a trajectory for the MS, which minimizes energy consumption and delay. Our protocol operates in four main phases: data sensing, rendezvous point (RP) selection, trajectory design, and data gathering. In data sensing, a number of deployed sensor nodes keep sensing the target field for a specific period of time to capture events. Then, using a cluster-based RP selection algorithm, some sensor nodes are selected to become RPs based on local information. The selected RPs are then used to determine a trajectory for the MS. To do so, we propose three trajectory design algorithms that support different types of applications, namely reduced energy path (REP), reduced delay path (RDP), and delay bound path (DBP). The MS moves through the constructed path to accomplish its data gathering according to an effective scheduling technique that is introduced in this work. We validate the proposed protocol via extensive simulations over several metrics such as energy, delay, and time complexity.     

无线传感网络中基于蚁群的路由算法

In the wireless sensor networks, high efficient data routing for the limited energy resource networks is an important issue. By introducing Ant-colony algorithm, this paper proposes the wireless sensor network routing algorithm based on LEACH. During the construction of sensor network clusters, to avoid the node premature death because of the energy consumption, only the nodes whose residual energy is higher than the average energy can be chosen as the cluster heads. The method of repeated division is used to divide the clusters in sensor networks so that the numbers of the nodes in each cluster are balanced. The basic thought of ant-colony algorithm is adopted to realize the data routing between the cluster heads and sink nodes, and the maintenance of routing. The analysis and simulation showed that the proposed routing protocol not only can reduce the energy consumption, balance the energy consumption between nodes, but also prolong the network lifetime.     

Energy efficient path selection for mobile sink and data gathering in wireless sensor networks

Mobile sink (MS) has drawn significant attention for solving hot spot problem (also known as energy hole problem) that results from multihop data collection using static sink in wireless sensor networks (WSNs). MS is regarded as a potential solution towards this problem as it significantly reduces energy consumption of the sensor nodes and thus enhances network lifetime. In this paper, we first propose an algorithm for designing efficient trajectory for MS, based on rendezvous points (RPs). We next propose another algorithm for the same problem which considers delay bound path formation of the MS. Both the algorithms use k-means clustering and a weight function by considering several network parameters for efficient selection of the RPs by ensuring the coverage of the entire network. We also propose an MS scheduling technique for effective data gathering. The effectiveness of the proposed algorithms is demonstrated through rigorous simulations and comparisons with some of the existing algorithms over several performance metrics.     

Energy efficient dispatch strategy for the dual-functional mobile sink in wireless rechargeable sensor networks

Scavenging energy from radio-frequency (RF) signals has drawn significant attention in recent years. By introducing the technology of RF energy harvesting into wireless sensor networks, a new type of network named mobile data gathering based wireless rechargeable sensor network (MGWRSN) is considered in this paper. In the MGWRSN, a dual-functional mobile sink (MS) which has the abilities of data collecting and RF energy generating is employed. Data sensed by sensor nodes is gathered at several selected head nodes (HNs). Through using the RF energy supplied by the MS, the HNs deliver the gathered data to the MS arriving at the corresponding rendezvous points (RPs). In our works, the network energy consumption model of the MGWRSN is built, and the energy efficient dispatch strategy for the MS is studied, aiming at cutting down the total network energy consumption. For the simplest case, i.e., the one-HN MGWRSN, the optimal location of the RP is provided to minimize the total network energy consumption. After that, the researches are extended into the case of multi-HN MGWRSN and a heuristic dispatch strategy named HEEDS is proposed. Theoretical analysis and numerical results show that: (1) in the one-HN MGWRSN, the optimal location of the RP is close related to the data bulk to be transmitted, the unit mobility energy cost, the required bit error rate, the modulation scheme, and the departure position of the MS; (2) comparing with the existing algorithm WRP which directly dispatches the MS to the locations of HNs to collect data, the proposed strategy HEEDS is shown to be more energy efficient. Moreover, when a high energy transfer power is available at the MS, HEEDS renders shorter packet delay compared to WRP.     

一种能耗均衡的无线传感器网络路由算法

为了延长采用电池供电的无线传感器网络的生命周期,提出了一种综合考虑单个节点能耗和节点传输信息至汇集节点所需总能耗的路由算法.该算法首先根据网络中节点到汇集节点从小到大的距离顺序选择待规划节点,然后计算各对应候选节点的评价参数,该参数由单节点能耗和节点传输信息至汇集节点所需总能耗加权得到,最后选择评价参数最小的候选节点作为待规划节点的中继节点.仿真结果表明,该算法的生命周期明显长于LEACH(Low Energy Adaptive Clustering Hier-archy)算法.     

Dual head static clustering algorithm for wireless sensor networks

Clustering of nodes is often used in wireless sensor networks to achieve data aggregation and reduce the number of nodes transmitting the data to the sink. This paper proposes a novel dual head static clustering algorithm (DHSCA) to equalise energy consumption by the sensor nodes and increase the wireless sensor network lifetime. Nodes are divided into static clusters based on their location to avoid the overhead of cluster re-formation in dynamic clustering. Two nodes in each cluster, selected on the basis of the their residual energy and their distance from the sink and other nodes in the cluster, are designated as cluster heads, one for data aggregation and the other for data transmission. This reduces energy consumption during intra-cluster and inter-cluster communication. A multi-hop technique avoiding the hot-spot problem is used to transmit the data to the sink. Experiments to observe the energy consumption patterns of the nodes and the fraction of packets successfully delivered using the DHSCA suggest improvements in energy consumption equalisation, which, in turn, enhances the lifetime of the network. The algorithm is shown to outperform all the other static clustering algorithms, while being comparable with the performance of the best dynamic algorithm.     

Nature inspired discrete firefly algorithm for optimal mobile data gathering in wireless sensor networks

Nowadays wireless sensor networks enhance the life of human beings by helping them through several applications like precision agriculture, health monitoring, landslide detection, pollution control, etc. The built-in sensors on a sensor node are used to measure the various events like temperature, vibration, gas emission, etc., in the remotely deployed unmanned environment. The limited energy constraint of the sensor node causes a huge impact on the lifetime of the deployed network. The data transmitted by each sensor node cause significant energy consumption and it has to be efficiently used to improve the lifetime of the network. The energy consumption can be reduced significantly by incorporating mobility on a sink node. Thus the mobile data gathering can result in reduced energy consumption among all sensor nodes while transmitting their data. A special mobile sink node named as the mobile data transporter (MDT) is introduced in this paper to collect the information from the sensor nodes by visiting each of them and finally it sends them to the base station. The Data collection by the MDT is formulated as a discrete optimization problem which is termed as a data gathering tour problem. To reduce the distance traveled by the MDT during its tour, a nature-inspired heuristic discrete firefly algorithm is proposed in this paper to optimally collect the data from the sensor nodes. The proposed algorithm computes an optimal order to visit the sensor nodes by the MDT to collect their data with minimal travel distance. The proposed algorithm is compared with tree-based data collection approaches and ant colony optimization approach. The results demonstrate that the proposed algorithm outperform other approaches minimizing the tour length under different scenarios.     

Minimum spanning tree–based delay‐aware mobile sink traversal in wireless sensor networks

Energy efficient data collection in a delay‐bound application is a challenging issue for mobile sink–based wireless sensor networks. Many researchers have proposed the concept of rendezvous points (RPs) to design the path for the mobile sink. Rendezvous points are the locations in the network where the mobile sink halts and collects data from the nearby sensor nodes. However, the selection of RPs for the design of path has a significant impact on timely data collection from the network. In this paper, we propose an efficient algorithm for selection of the RPs for efficient design of mobile sink trajectory in delay‐bound applications of wireless sensor networks. The algorithm is based on a virtual path and minimum spanning tree and shown to maximize network lifetime. We perform extensive simulations on the proposed algorithm and compare results with the existing algorithms to demonstrate the efficiency of the proposed algorithm of various performance metrics.     

移动无线传感网的生存时间优化算法研究

当sink节点位置固定不变时,分布在sink 节点周围的传感节点很容易成为枢纽节点,因转发较多的数据而过早失效。为解决上述问题,提出移动无线传感网的生存时间优化算法（LOAMWSN）。LOAMWSN算法考虑sink节点的移动,采用减聚类算法确定sink节点移动的锚点,采用最近邻插值法寻找能遍历所有锚点的最短路径近似解,采用分布式非同步Bellman-Ford算法构建sink节点k跳通信范围内的最短路径树。最终,传感节点沿着最短路径树将数据发送给sink节点。仿真结果表明：在节点均匀分布和非均匀分布的无线传感网中,LOAMWSN算法都可以延长网络生存时间、平衡节点能耗,将平均节点能耗保持在较低水平。在一定的条件下,比Ratio_w、TPGF算法更优。     

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.