无线传感器网络中移动sink节点的路径规划

在无线传感器网络中,大量感知数据汇集到sink节点的采集方法会导致sink节点附近的节点能量耗尽,造成能量空洞。针对该问题,利用移动的sink节点进行数据收集是一种解决方法,其中移动sink的路径规划成为一个重要的问题。提出了一个移动sink路径规划算法,将无线传感器中随机分布的节点划分为不同的子区域,寻找sink节点移动的最佳转向点,最终得到最优的移动路径,以实现无线传感器网络生命周期最大化。仿真实验表明,与现有方案相比,该算法能显著延长网络的生命周期。     

时延受限的移动sink数据收集算法

在实时性要求比较高的应用中,时延要求限制了sink的移动速率与移动轨迹,sink的移动速率限制了节点与sink的通信时间,因此很难兼顾时延要求与数据收集效率。提出一种时延受限的移动sink数据收集算法MSDC,在低能耗缓存区内找到一条sink的最优移动轨迹,在有限的时间限制内利用sink的移动性来提升传感器网络的数据收集性能。仿真结果表明,与已有算法比较,该方案能够提高网络数据采集量,降低能耗,延长网络生命周期。     

无线传感器网络中多移动sink的选择策略

针对移动终端作为无线传感器网络中的移动sink情况下,提出了具有多个移动sink的稀疏无线传感器网络体系结构M2S2N.考虑到移动终端广泛存在的特性,为了有效地收集传感数据,提出了无线传感器网络中对移动sink的选择策略,并在此基础上提出了一种适合于M2S2N的基于功率控制的机会传输调度算法.仿真和分析表明选择最佳的移动sink可以有效地提高传感器节点的能量有效性和数据传输成功率.     

基于引力场的多sink传感器网络路由选择方法

根据传感器网络中数据转发与静电场中电荷移动的相似性,将传感器网络抽象成由sink节点激发的引力场.在该引力场中,数据可以在sink节点的吸引力作用下流向sink节点.基于sink节点产生的引力场,提出了一种适用于多sink传感器网络的路由选择算法,该算法具有较低的时间和空间复杂性,能适应网络规模的动态变化.理论分析与实验结果表明,提出的路由方法能够有效地减少数据传输的能量消耗,降低数据丢包率,延长网络的生命周期.     

时延受限传感器网络移动Sink路径选择方法研究

已有研究表明sink移动方案能有效提升无线传感器网络的能耗效率,但sink点移动速度的限制通常会带来较大的数据收集时延,与某些实时性要求较高的应用产生矛盾.为解决该问题,本文以满足时延要求和最小化网络整体能耗为优化目标,提出了一种基于虚拟点优先级的移动sink路径优化选择方法.仿真试验结果表明,与基准算法相比,该方法在牺牲少量能耗的前提下能显著降低算法时间复杂度,具有良好的规模可扩展性.     

移动协助传感器网络中Sink的路径优化策略

在无线传感器网络中引入移动Sink来解决。静态无线传感器网络(所有节点均为静止)存在的能量空洞、冗余覆盖和热点等问题。传感器节点将数据发送给汇聚节点(CP,collection point),移动Sink访问CP节点收集数据。提出了一种最短移动距离最小能耗的路径优化模型(MEMD)。证明了该模型是一个NP-hard问题,给出了一种基于效用的贪心启发式方法用于确定最佳的CP节点队列。为了在规定的最大传输延时的范围内访问尽可能多的CP节点,提出了一种基于CP节点访问概率的路径选择算法。通过模拟实验以及实验床的真实数据,提出的算法能很好地在满足延时要求的同时节约网络的能量。     

传感器网络中基于簇树的路由算法

针对无线传感器网络中高能效收集数据的需要,提出一种基于簇树的路由算法ETRA(energy-efficient tree-clustered Routing algorithm).首先,ETRA利用改进的最小ID算法实现对网络的平衡分簇;然后,通过簇首信息交换,构建从簇首至sink节点的数据汇聚树;最后,在数据收集阶段,数据从成员节点传送至簇首节点,并通过数据汇聚树传送至sink节点.仿真结果表明,与典型的分簇协议相比,ETRA获得了更均衡的分簇结构和更长的网络生存期.     

基于分簇的无线传感器网络路由算法

在大规模无线传感器网络中以最节能的方式将数据发送到sink是该领域研究的热点之一。针对LEACH算法的不足之处,提出了一种能耗均衡的路由算法。此算法在考虑节点剩余能量的基础上采用两分法选举簇首,然后簇首通过能耗代价函数计算出一条能耗最小的路径,以多跳转发的方式将数据传送到sink.,为了进一步减少节点的能耗,算法在簇内采用了数据聚合机制。仿真结果表明,算法有效地均衡了网络能耗,延长了网络生存期。     

无线传感器网络中基于空间相关性的移动代理路由算法

资源受限的传感器节点密集分布在无线传感器网络监控区域,sink节点通过收集节点间观测信息对监控区域内发生的事件进行感知.本文提出SCMAR(Spatial Correlation-based Mobile Agent Routing)路由算法,在移动代理架构内,利用节点观测数据的空间相关性以能量有效的方式对感知事件进行...     

一种无线传感器网络的能耗平衡覆盖模型

针对无线传感器网络节点能量有限、最小覆盖方法能耗不均衡的问题,该文提出了一种能耗平衡的连通覆盖模型,并对模型进行了分析与仿真。模型利用Voronoi划分和Delaunay三角剖分对传感器网络进行分割,判别重复覆盖目标区域的冗余传感器节点,采用节点到sink点的跳数对节点分层,进而提出选择休眠节点的方法。仿真结果表明,由模型建立的非最小连通覆盖集所导出的无线传感器网络,能够平衡节点能耗、使用优化路由、减弱路由关键点的影响。     

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.     

One-Hop Data Collection by Four Quadrants Moving Model for Mobile Sink Wireless Sensor Networks

In wireless sensor networks, the energy of sensors close to the sink within one-hop is prone to be exhausted since these sensors need to forward the data from other sensors to the sink and the energy of each sensor is limited. Once the energy of these sensors close to the sink within one-hop is exhausted, the data thus could not be sent to the sink. To address this issue, the general solution is to ensure that each sensor could consume energy to forward the data from other sensors to by using the mobile sink because the location of the mobile sink is dynamic. In this situation, each sensor may be close to the sink within one-hop. The energy consumption among all sensors thus could be balanced and the network lifespan also could be prolonged. To reduce more energy consumption of sensors by forwarding data to the sink, the one-hop data collection by mobile sink was proposed to claim the sink moved to the source sensor within one-hop to collect the data in this paper. Hence, only the source sensors send the data to the sink within one-hop and other sensors could not forward the data to the sink to save energy. In the one-hop data collection by mobile sink, the rendezvous point moving model (RPMM) and the minimum moving model (MMM) were common proposed to be used. However, the time and energy consumption for collecting data in RPMM and MMM may not be minimized with the large number of source sensors. While the source sensors increase, the mobile sink needs more time and energy to collect data. To address these issues, we proposed a one-hop data collection by four quadrants moving model, FQMM, to collect data in this paper. The implementation tool, such as simulator, was used by Java language with Java SDK to evaluate performance under our proposal and the comparing proposal. For the numerical results, the maximal moving hop count, MHC, in FQMM was 30% less than the maximal MHC in RPMM. The maximal MHC in FQMM was 36% less than the maximal MHC in MMM. The minimum MHC in FQMM was 22% less than the minimum MHC in RPMM. The minimum MHC in FQMM was 28% less than the minimum MHC in MMM. The average MHC in FQMM was 25% less than the average MHC in RPMM. The average MHC in FQMM was 31% less than the average MHC in MMM. The total energy consumption in FQMM was 55% less than that in RPMM. The total energy consumption in FQMM was 59% less than that in MMM. Since the number of source sensors is often large in the real condition, FQMM could be applied for the regarding applications of WSN with mobile sink.

     

紫外光无线传感器网络节能的研究与仿真

为了减少无线传感器网络节点的能量消耗,采用紫外光作为无线传感器网络的信息载体,研究了紫外光传感器节点的能量模型。理论分析了单跳节能和多跳节能,得出了计算最优跳数的数学表达式,并对单跳通信、多跳通信和最优跳通信的平均能量消耗进行了计算机仿真,仿真结果与理论分析一致;对于多跳通信带来的能量消耗不均匀的问题,利用移动sink节点来解决,通过仿真对比了sink节点不同移动速率对网络平均能量消耗、丢包率和端到端时延的影响。结果表明,借助移动sink节点可以降低网络的平均能量消耗,但要根据场景选择合适的移动速率。     

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.     

基于移动Agent和WSN的突发事件场景数据收集算法研究

该文针对无线传感器网络应用于突发事件监测场景的能量消耗和网络延迟问题,提出了基于移动Agent的无线传感器网络簇式数据收集算法.动态成簇过程基于事件严重程度,并由其决定簇的生命周期和覆盖范围.Sink和簇头之间形成以Sink节点为簇头的虚拟簇.移动Agent迁移路径规划过程中下一跳节点的选取基于节点剩余能量、路径损耗及受刺激强度.移动Agent通过节点遍历的方式完成对所有簇内成员节点信息的收集.仿真结果表明,相对于C/S数据收集模型,基于移动Agent的模型具有更好的节能效果,并能一定程度地减少网络延迟,尤其适用于大规模无线传感器网络应用.     

On reducing delay in mobile data collection based wireless sensor networks

In a wireless sensor network, battery power is a limited resource on the sensor nodes. Hence, the amount of power consumption by the nodes determines the node and network lifetime. This in turn has an impact on the connectivity and coverage of the network. One way to reduce power consumed is to use a special mobile data collector (MDC) for data gathering, instead of multi-hop data transmission to the sink. The MDC collects the data from the nodes and transfers it to the sink. Various kinds of MDC approaches have been explored for different assumptions and constraints. But in all the models proposed, the data latency is usually high, due to the slow speed of the mobile nodes. In this paper, we propose a new model of mobile data collection that reduces the data latency significantly. Using a combination of a new touring strategy based on clustering and a data collection mechanism based on wireless communication, we show that the delay can be reduced significantly without compromising on the advantages of MDC based approach. Using extensive simulation studies, we analyze the performance of the proposed approach and show that the packet delay reduces by more than half when compared to other existing approaches.     

Clustering Based Two Dimensional Motion of Sink Node in Wireless Sensor Networks

The wireless sensor network (WSN) is always known for its limited-energy issues and finding a good solution for energy minimization in WSNs is still a concern for researchers. Implementing mobility to the sink node is used widely for energy conservation or minimization in WSNs which reduces the distance between sink and communicating nodes. In this paper, with the intention to conserve energy from the sensor nodes, we designed a clustering based routing protocol implementing a mobile sink called ‘two dimensional motion of sink node (TDMS)’. In TDMS, each normal sensor node collects data and send it to their respective leader node called cluster head (CH). The sink moves in the two dimensional direction to collect final data from all CH nodes, particularly it moves in the direction to that CH which has the minimum remaining energy. The proposed protocol is validated through rigorous simulation using MATLAB and comparisons have been made with WSN’s existing static sink and mobile sink routing protocols over two different geographical square dimensions of the network. Here, we found that TDMS model gives the optimal result on energy dissipation per round and increased network lifetime.

     

异构传感器网络中汇聚节点位置优化路由算法

在异构无线传感器网络模型下,针对采集节点发送数据能量消耗过高及路由时分组丢失率过大等情况,对数据汇聚节点的位置优化及路由进行了研究,提出了移动汇聚节点位置优化路由算法（MLOYIH）。先根据蚁群算法的原理对移动节点与静态节点进行分组,再在组内寻找适合的位置放置汇聚节点,最后根据供电情况,选择合适的跳算进行路由。经过仿真实验与性能分析表明,MLOYIH算法与传统算法比较,能量消耗降低到64%,分组丢失率不高于3%。     

IGBDD: Intelligent Grid Based Data Dissemination Protocol for Mobile Sink in Wireless Sensor Networks

Lifetime and energy efficiency are important factors in the design of wireless sensor network. A critical issue during data collection is the formation of energy holes near the sink. Sensors which are located near the sink have to participate in relaying data on behalf of other sensors and thus their energy will be depleted very quickly. Mobile sink movement yields the significant performance gained by decreasing the amount of energy consumption. In this paper, we propose an Intelligent Grid Based Data Disseminating protocol for mobile sink in wireless sensor networks. We have utilized a virtual grid as the protocol’s substructure. In our proposed method, cell heads (CHs) will be selected based on the locations of virtual cross points (CPs) and CPs selection is needless to transfer any required data between neighbor nodes. We have optimized CPs selection using linear programming technique in order to increase network lifetime. By selecting the CHs based on our proposed algorithm, data will be disseminated toward the sink. Our data dissemination protocol is simple and has low overhead to construct and maintain. Also, we have presented a new method for sink location update which leads to the least cost in data transfer. Simulation results illustrate that by utilizing hierarchical functionality and selecting appropriate CPs and consequently selecting CHs, energy consumption will be decreased in comparison with other presented methods which directly lead to network lifetime increment. Also by determining an optimal cell size, packet delivery rate will be improved noticeably.