带移动节点的无线传感器网络节能路由算法

为解决无线传感器网络( WSN)的“热点”问题,避免因网络局部突发事件造成网络过早失效,提出一种带移动节点的WSN节能路由算法。该算法基于网格思想,根据节点的剩余能量以及节点到簇重心坐标距离的加权和来选举簇头,通过可控移动策略调度sink节点接收簇头所收集的数据,节省网络能耗。同时引入能量不受限的中继节点,调度该节点服务于信息传输大的区域,延长网络生存时间。通过仿真实验分析sink节点的移动速度以及加权系数对算法性能的影响,结果表明,该算法在网络的生存时间、总能耗和sink节点接收数据量3个方面均优于TTDD和MSEERP算法。当sink节点的移动速度为5 m/s、加权系数为0.6时,算法性能最佳。     

WSN中一种基于多sink的快速数据收集算法

数据收集问题是无线传感器网络中的研究热点之一。数据收集方式会影响数据到达sink的准确度、延迟以及网络的能量消耗。针对时间响应和数据准确度要求高的应用,提出了一种基于多sink的快速数据收集算法（QDGA）。sink利用已知的全局信息和计算能力构建出基于最小度的数据收集森林进行任务分发,得到网格粒度最优的数据收集策略,网格内的普通节点通过时隙分配来进行数据收集,并可以根据自身的局部信息动态调整数据收集路径。仿真实验表明,相对于已有的方法,QDGA在保证网络生命周期的前提下,能够有效降低延迟以及提高数据收集的准确率。     

优化网络生命周期和最短化路径的WSN移动sink路径规划算法

为了缓解无线传感器网络（WSN）中传感器节点分布不均匀、传感器节点感知数据量不同而造成能耗不均衡、"热区"等问题,提出一种优化网络生命周期和最短化路径的WSN移动sink路径规划算法（MSPPA）。首先,通过监测区域网格化,在每个网格内分布若干个移动sink候选访问站点,sink在每个网格中选择一个站点停留收集网格中节点数据;然后,分析所有传感器节点的生命周期与sink站点选择的关系,建立权衡网络生命周期和sink移动路径的优化模型;最后,使用双链遗传算法规划移动sink遍历网格的顺序和选择每个网格中移动sink访问站点,得到移动sink节点遍历所有网格收集数据的路径。仿真结果显示,与已有的低功耗自适应分簇（LEACH）算法与基于移动sink节点与集合节点（RN）的优化LEACH分簇算法（MS-LEACH-RN）相比,MSPPA在网络生命周期方面提高了60%,且具有良好的能耗均衡性。实验结果表明,MSPPA能有效缓解能量不均衡、"热区"问题,延长网络生命周期。     

WSN中一种基于网格的并行数据收集方案

数据收集问题是无线传感器网络中的研究热点之一。数据收集方式会影响数据到达sink的准确度、延迟以及网络的能量消耗。针对时间响应和数据准确度要求高的应用,提出一种基于网格的并行数据收集方案(DGSG)。首先,sink利用已知的全局信息和计算能力构建出基于最小度的数据收集森林进行任务分发,得到网格粒度最优的数据收集策略,然后网格内的普通节点通过时隙分配来进行数据收集,并可以根据自身的局部信息动态调整数据收集路径。仿真实验表明,相对于已有的方法,DGSG在保证网络生命周期的前提下,能够有效降低延迟以及提高数据收集的准确率。     

无线传感器网络路径寻优的仿真研究

研究无线传感器网络路径寻优问题.针对无线传感器网络路径寻优同时涉及到数据传输路径的长度、传感器节点能量以及整个网络的能量均量均衡,传统的数学模型对其进行求解存在求解时间长,速度慢,得到的路径并非最优,导致网络的能量不均衡,网络生命周期短.为了快速找到传感器网络最优路径,提出一种传感器路径混合寻优方法.算法首先利用遗传算法进行全局寻优,使网络最优路径稳定地分布在解空间区域,然后采用禁忌算法进行网络路径局部寻优,最后找到无线传感器最优路径.仿真结果表明,混合算法能快速找到无线传感器网络最优路径,且消耗的能量最少,有效实现了网络负载均衡,延长了网络的生命周期.     

基于消息冗余度动态测算的数据机会转发策略

针对由移动传感器节点组成的移动传感器网络数据转发能耗高、有效性低等问题,提出了基于消息冗余度动态测算的数据机会转发策略。该策略结合节点与sink间位置关系、运动模式、剩余能耗等局部信息构建三维特征向量来描述节点转发消息的能力,并利用预期的消息成功传输到sink的概率和节点当前的机会概率动态调整消息冗余度,使转发消息的机会概率越低转发冗余度越高,反之亦然,以此在提高消息成功传输率的同时控制消息副本数,进而降低网络平均能耗。与直接传输和原路返回机会数据转发相比,新策略传输有效性高、能耗低,更适合移动传感网,符合移动传感网对数据转发策略高有效性、低能耗、延时容忍的要求。     

无线传感器网络中基于μ律爆炸算子的烟花虚拟力混合覆盖策略

针对烟花算法在无线传感器网络节点部署过程中易陷入局部最优导致节点分布不均匀、后期收敛速度慢等问题,本文提出一种基于μ律爆炸算子的烟花虚拟力混合算法(μFW–VFA).首先,采用μ律特性曲线重新定义爆炸算子,增强烟花间的差异性,通过动态调整μ值使烟花爆炸的数目和幅度随迭代次数动态调整,以平衡烟花局部和全局的寻优能力.其次,引入虚拟力调节停滞烟花内传感器节点的位置信息,加速烟花种群进化,增强算法跳出局部最优的能力,提高算法收敛速度.仿真实验表明,经μFW–VFA部署后,网络的重叠区域和监测盲区显著减少,有效提升了网络覆盖率并压缩节点移动距离.     

无线传感器网络能量均衡的多sink分簇路由算法

针对无线传感器网络中传感器节点能量有限以及节点能耗不均衡的问题,提出了一种基于能量均衡的多sink分簇路由算法（EBMCR）。该算法在簇头选择阶段,综合考虑了节点的剩余能量级和节点到sink的距离等因素选择簇头节点;在簇间通信过程,采用多跳传输的方式,综合考虑了路径能量消耗、路径最小剩余能量和节点到sink的跳数等因素,选择节点到多个sink的最优路径。仿真结果表明,该算法能够有效地均衡网络能量,延长网络生命周期。     

感知受限的移动传感器节点扫描覆盖优化算法

移动无线传感器网络（WSN）的应用中,因为传感器节点的感知范围受限,其覆盖分析就是一个针对目标区域的扫描覆盖问题。提出了一种基于多目标优化的扫描覆盖算法。在目标区域中,采用双目标优化策略对单个移动传感器节点进行路径规划,一方面使节点的覆盖面最大化,另一方面使扫描覆盖的路径最短。仿真实验在含有障碍物和不含障碍物的情况下进行,与多节点的编队覆盖算法相比,所提算法在适度降低覆盖率的情况下,可大幅降低移动能耗。     

随机分布的无线传感器网络中移动sink的路径规划

针对大量节点正态分布的无线传感器网络,为了提高网络的寿命,提出了一种移动sink的高效路径规划方案。首先由节点的分布规律将网络划分为多个子区域,然后在此基础上以最大化网络寿命为目标找到sink的最佳转折点,最后得到一条最优路径。通过NS-2中大量的仿真实验结果表明,与已有的类似方案相比,该方案可以有效均衡网络能耗,延长网络的生命周期,同时取得较好的网络性能。     

移动Sink的传感器网络路径优化策略

在无线传感器网络（WSNs）中引入移动 Sink 可以避免网络拥塞和能量空洞并降低网络能耗,但由于移动速度的限制导致时延较大。针对这一问题,提出了时延约束下的移动 Sink 路径优化策略,根据时延和网络能耗之间的关系设计了可调节的节点权重,通过模拟退火遗传算法得到最优节点权重,并依据此权重通过迭代得到汇聚节点和最佳移动路径。仿真结果表明：该策略能保证在满足时延约束的前提下降低网络能耗,且收敛速度快。     

Sink轨迹固定传感器网络的高效数据采集机制

在sink移动轨迹固定的传感器网络中,由于sink点有限的通信时间和节点的随机分布,使得很难兼顾数据采集量的提高和整体能耗的降低.为了解决该问题,提出了一种最大数据量最短路径(maximum amount shortest path,简称MASP)数据采集方法.MASP对网络中成员节点与sub-sink节点之间的匹配关系进行集中式优化.采用0-1线性规划方法对MASP问题进行形式化描述,提出了一种基于二维染色体编码的遗传算法进行求解,并给出了相应的数据通信协议设计.另外,MASP可以扩展支持低密度网络和多sink点网络.基于OMNET++的仿真结果表明,MASP在能耗利用率方面要远远优于最短路径树方法(shortest path tree,简称SPT)及固定sink数据采集方法.     

数据传输时延和跳数受限的Sink节点移动路径选择算法

考虑实际无线传感网系统中数据传输时延和跳数受限情况,且为降低算法的时间复杂度,提出一种移动无线传感网的Sink节点移动路径选择算法（MPSA）。在MPSA算法中,Sink节点采用分布式最短路径树算法收集k+1跳通信范围内传感节点的相关信息和感知数据,采用虚拟力理论计算边界、障碍物和空洞区域的虚拟斥力、第k+1跳未覆盖传感节点的虚拟引力和所有虚拟力的合力,根据停留次数、合力大小和方向等信息计算当前网格中心的停留时间和下一个停留网格中心。仿真结果表明：MPSA算法根据传感节点的位置、剩余能量等信息,寻找到一条较优的移动路径,从而提高Sink节点的数据收集量和节点覆盖率,降低传感节点的感知数据丢弃量。总之,在数据传输时延和跳数受限下,MPSA算法比RAND算法、GMRE算法和EASR算法更优。     

VD-PSO: An efficient mobile sink routing algorithm in wireless sensor networks

Efficient energy consumption is crucial for energy constrained networks such as Wireless Sensor Networks (WSN). Using a mobile sink to collect the data of the nodes is a good method to balance the energy level of the nodes and prolong the lifetime of the whole network. For the mobile sink, an efficient path planning can make the mobile sink visit significantly more nodes during a limited period and shorten the latency of information gathering. Considering the communication range of the nodes, we can deduce this routing problem as a special case of traveling salesman problem with neighborhoods (TSPN), which is a NP-hard problem [1]. In this paper, we propose a novel routing design algorithm based on Variable Dimension Particle Swarm Optimization (VD-PSO). In this algorithm, every feasible path solution of TSPN is expressed as a particle. Each dimension of the particle is the coordinates of a rendezvous point (RP, the point where the mobile sink stays to gather data). The dimensionality of the particle is equal to the number of the rendezvous points in the path. Using the evolutionary method of the particles, we can derive the optimal path of the mobile sink. Simulation results show that the proposed algorithm has fast convergence speed, and the result is quite approximate to the optimal solution.     

Termite-hill: Performance optimized swarm intelligence based routing algorithm for wireless sensor networks

A wireless sensor network (WSN) is a large collection of sensor nodes with limited power supply, constrained memory capacity, processing capability, and available bandwidth. The main problem in event gathering in wireless sensor networks is the formation of energy-holes or hot spots near the sink. Due to the restricted communication range and high network density, events forwarding in sensor networks is very challenging, and require multi-hop data forwarding. Improving network lifetime and network reliability are the main factors to consider in the research associated with WSN. In static wireless sensor networks, sensors nodes close to the sink node run out of energy much faster than nodes in other parts of the monitored area. The nodes near the sink are more likely to use up their energy because they have to forward all the traffic generated by the nodes farther away to the sink. The uneven energy consumption results in network partitioning and limit the network lifetime. To this end, we propose an on-demand and multipath routing algorithm that utilizes the behavior of real termites on hill building termed Termite-hill which support sink mobility. The main objective of our proposed algorithm is to efficiently relay all the traffic destined for the sink, and also balance the network energy. The performance of our proposed algorithm was tested on static, dynamic and mobile sink scenarios with varying speed, and compared with other state-of-the-art routing algorithms in WSN. The results of our extensive experiments on Routing Modeling Application Simulation Environment (RMASE) demonstrated that our proposed routing algorithm was able to balance the network traffic load, and prolong the network lifetime.     

Efficient data-forwarding method in delay-tolerant P2P networking for IoT services

These days Internet of Things (IoT), which consists of smart objects such as sensor nodes is the most important technology for providing intelligent services. In the IoT ecosystem, wireless sensor networks deliver collected information from IoT devices to a server via sink nodes, and IoT services are provided by peer-to-peer (P2P) networking between the server and the IoT devices. Particularly, IoT applications with wide service area requires the mobile sink nodes to cover the service area. To employ mobile sink nodes, the network adopts delay-tolerant capability by which delay-tolerant nodes try to transmit data when they connect to the mobile sink node in the application service field. However, if the connection status between a IoT device and a mobile sink node is not good, the efficiency of data forwarding will be decreased. In addition, retransmission in bad connection cause high energy consumption for data transmission. Therefore, data forwarding in the delay-tolerant based services needs to take the connection status into account. The proposed method predicts the connection status using naïve Bayesian classifier and determines whether the delay tolerant node transmits data to the mobile sink node or not. Furthermore, the efficiency of the proposed method was validated through extensive computer simulations.     

An improved ant colony optimization-based approach with mobile sink for wireless sensor networks

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms.     

基于口令应答的协作式WSNs移动复制节点检测方法研究

目前,针对无线传感器网络复制节点攻击研究主要集中在对静态网络中复制节点的检测。WSNs的应用中,节点部署在一定区域形成静态网络并采集信息,为了减少节点间通信量、降低能耗,若干个节点形成一个簇,簇内选举簇头节点作为簇间通信人。静态网络采集的信息通常由汇聚节点回收,为了方便,汇聚节点通常采用移动形式加入网络,收集完后离开。如果这类在移动中收集信息的节点是复制节点,对整个WSNs的威胁比静态网络中的复制节点威胁更大。在借鉴已有的移动网络检测方案的基础上,针对静态网络分簇和移动节点位置经常变换的特点,提出了基于口令应答的协作式WSN移动复制节点检测方法CRCDS（Challenge/Response and Collaborative Detection Scheme）,有效利用静态网络的存储空间,采取静态网络和移动节点相互协作的方式,规避因移动节点位置变化对检测结果的影响,并从理论和实验上分析了该检测方法的安全性和可行性。     

蚁群优化算法在WSN路由中的应用研究

介绍了基本蚁群算法的原理和适用范围,总结出了基本蚁群算法在求解最优路径问题时,虽然具有很强的发现较优解的能力,但是存在容易陷入局部最优解和收敛时间过长等问题。考虑到基本蚁群算法在无线传感器网络路由上应用的不足,提出了一种改进后的蚁群算法,并将其应用到传感器网络路由中。该算法不仅在状态转移概率公式中引入罚函数和动态权重因子,而且采用局部信息素更新和全局信息素更新结合的方式更新路径信息,充分考虑到传感器节点与节点间的传输距离,并且充分考虑传感器节点的剩余能量。最后通过仿真实验,得到了基本蚁群算法和改进后的蚁群算法在传感器节点剩余能量和传输数据包时网络延迟的不同曲线,验证了改进后的蚁群算法在无线传感器网络路由选择上的高效性。     

基于方向决策的移动锚节点动态路径规划方法

在基于移动锚节点的无线传感器网络定位过程中,移动锚节点的路径规划问题对定位性能有着重要的影响,但现有的路径规划方法没有充分考虑到网络内未知节点的密度以及分布情况,定位效率低且成本大,因此提出了一种基于方向决策的移动锚节点动态路径规划方法CWDP(Dynamic Path Planning Based on Orientation Decision-Classed Weighted).首先网络内的未知节点根据连通度阈值对自身进行分级处理,当移动锚节点进入网络区域后,根据通信范围内未知节点的反馈信息,再利用分级权重系数实时决策下一目标的移动方向.仿真结果表明,该方法有效地提高了网络内未知节点的定位覆盖率和降低了定位误差,并节约了定位成本.