无线传感器网络中利用随机网络编码的低能耗可靠机会路由

无线传感器网络中节点大多采用电池供电,让节点以低能耗将采集的数据传递到信宿,对无线传感器网络有效运行极为重要.该文提出了能量有效的可靠机会路由EROR（Energy-efficient Reliable Opportunistic Routing）,它利用结合节点剩余能量和链路上收发双方的总能耗的转发代价,选择转发节点集合（简称“转发集”）、主转发节点和协助转发节点,让节点调节发射功率并利用随机线性编码把数据包分片编码发送到转发集,进而以多跳方式把数据可靠低能耗地传递到信宿.仿真结果表明：在网络生存时间和能耗方面,EROR比已有路由策略CodePower更优.     

一种面向高速路车联网场景的自适应路由方法

车载自组织网络中节点的高速移动性使得网络拓扑频繁变化,造成路由效率低下.本文提出了一种面向高速路车联网场景的自适应路由方法.本方法采用了贪婪机会转发（GOF）算法,在选择下一跳转发节点时,同时考虑到目的节点的距离计算、节点间的链路状态以及下一跳的有效节点度状况来找出最优转发节点,并提出新的计算连通概率的方法.仿真实验和实际道路场景的测试表明,与相关算法相比在路由稳定性方面表现出较好的效果.     

一种多跳无线网络中的高效广播算法

广播是多跳无线网络中的一种基本操作。现有的广播算法中普遍存在转发冗余过多的问题。该文首先分析了覆盖网络所需的最少转发节点数目,然后以此为基础,提出了一种简单高效的广播算法。该算法中,每个节点最多只需选择3个转发节点,从而明显地减少了广播的转发次数,提高了节点能量和网络资源的利用率;同时,所有转发节点实现了对整个网络接近双重的覆盖,能够保证较高的传输可靠性;此外,对不同的网络规模和拓扑的动态变化,该算法具有较好的可扩展性。仿真结果显示,该算法在多种常见的网络环境下具有比现有方法更优越的性能。     

WSN中基于能量感知的最小跳数路由算法

无线传感器网络的拓扑往往由于节点死亡而发生变化。网络拓扑的重新构建加速了剩余传感器节点的死亡,缩短了网络的生存时间。针对无线传感器网络对网络生存时间的苛刻要求,提出了一种基于能量感知的最小跳数路由算法。建立路由时,该算法综合考虑了节点剩余能量和该节点潜在的转发能力。仿真结果显示,该算法在生存时间、存活节点数和吞吐量方面的性能要远优于LEACH算法和HEED算法。     

一种基于节点能量的机会网络概率路由算法

在机会网络中,节点之间可能不存在端到端的路径.为了节省网络中节点消耗的能量,在概率路由算法的基础上提出了一种基于节点能量的概率路由算法(Energy-based Probabilistic Routing Protocol using History of Encounters and Transitivity,EBPROPHET).首先,计算两个节点相遇时节点的通信开销,然后相遇节点的剩余能量作为一个参量引入算法,选择节点剩余能量多的节点作为转发节点,最后,进行消息的转发.仿真表明,EB-PROPHET算法具有良好的路由性能,降低了整个网络的能量消耗,延长了网络的生存周期.     

ZigBee转发节点选择算法的研究及实现

提出一种Zig Bee网络转发节点选择算法Zi FA,每个广发节点选择一跳邻节点集合的子集覆盖部分两跳邻节点,降低了算法时间复杂度和空间复杂度,较易得到转发节点的最小值,由于Zig Bee分层地址空间,只需要一跳邻节点的信息,部分被覆盖的两跳邻节点是由不进行信息交换的节点转化的。仿真结果表明,Zig Bee转发节点选择算法是高效实用的。     

k跳受限泛洪的能量平衡组播路由算法

组播路由算法(Energy-Balanced Multicast Routing,EBMR)把无线传感器网络节点的剩余能量作为建立组播路由的重要因子,在不引入过长路径的同时优先选择剩余能量高的节点作为组播数据转发节点,构建组播能量平衡树(EB-Tree),从网络能量均衡消耗的角度来延长了无线传感器网络的生存时间.针对EBMR算法路由开销较大的问题,提出了k跳受限泛洪的能量平衡组播路由算法k-EBMR,控制组播路由报文在k跳范围内传播,并且研究了影响算法性能的关键因子的选取.仿真实验表明,与EBMR算法相比,k-EBMR算法较大程度上降低了路由控制报文的传输,提高了节点能量有效使用性,进一步延长了网络生存时间.     

无线传感器网络的中继节点自适应选择休眠机制

针对无线传感器网络节点能量有限的问题,设计一种中继节点自适应休眠方法,对网络节点的工作时间进行优化调度。在路由链路建立中,根据网络中节点的地理位置、剩余能量选择中继节点的方法进行节点休眠调度。网络工作时通过计算下一跳最佳节点位置,选取其附近区域内剩余能量多的节点作为下一跳的转发节点,设计出中继节点自适应选择的节点休眠机制。仿真结果表明,所设计的休眠机制提高了节点能量利用效率、延长了网络工作时间。     

分布式能量均衡的WSN动态数据转发策略

针对无线传感器网络中存在的能量空洞问题,该文提出一种能量均衡的层次型数据转发策略。根据节点可用能量、节点之间相对位置及不同网络区域簇头能量消耗速率构建非均匀层次化结构。进而,综合考虑节点簇内通信开销和节点关系选取中继转发节点,并在多跳簇间数据转发时执行数据融合进一步降低转发数据量以实现簇间多跳通信的能量有效性。数值结果表明,所提机制可以有效地均衡网络负载,延长网络生命周期,改善网络数据转发性能。     

一种能量平衡的无线体域网络AODV多播路由发现协议

在动态网络拓扑中,AODV协议通过数据源节点S泛洪广播RREQ消息请求到任意目标节点D的路由,而在无线体域网络中,只有一个sink目标节点,除最短跳数路由上的节点外,其他参与RREQ接收和转发的节点浪费了能量。提出了一种能量平衡的无线体域网络AODV多播路由发现协议,通过在节点广播的hello消息中增加到sink的最小跳数hops、到sink的下一跳节点next和节点本身是否具备转发能力isforward 3 个参数,只选择能到达sink节点的邻居节点参与转发RREQ消息,变广播为多播,有效地降低了路由发现的能量开销,并通过能量平衡延长了WBAN的使用寿命。性能分析与模拟实验表明,该协议在RREQ数量、数据传输率和能量消耗等方面优于相似协议EAAODV。     

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。     

基于煤矿监测的低功耗无线传感器节点的研究与设计

无线传感器网络是通过无线通信方式形成的一个多跳自组织网络.传感器节点是无线传感器网络的基本单元,节点设计的好坏直接影响到整个网络的质量.为满足无线传感器网络可靠性和持久性的要求,尽可能地降低传感器节点的功耗成为设计的核心要素.通过对节点功耗的分析研究,选择以MSP430 F149作为处理器的传感器,满足了低功耗设计要求,可以实现无线传感器在煤矿监测以及复杂地理环境下的应用.     

A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks

In wireless sensor network, a large number of sensor nodes are distributed to cover a certain area. Sensor node is little in size with restricted processing power, memory, and limited battery life. Because of restricted battery power, wireless sensor network needs to broaden the system lifetime by reducing the energy consumption. A clustering‐based protocols adapt the use of energy by giving a balance to all nodes to become a cluster head. In this paper, we concentrate on a recent hierarchical routing protocols, which are depending on LEACH protocol to enhance its performance and increase the lifetime of wireless sensor network. So our enhanced protocol called Node Ranked–LEACH is proposed. Our proposed protocol improves the total network lifetime based on node rank algorithm. Node rank algorithm depends on both path cost and number of links between nodes to select the cluster head of each cluster. This enhancement reflects the real weight of specific node to success and can be represented as a cluster head. The proposed algorithm overcomes the random process selection, which leads to unexpected fail for some cluster heads in other LEACH versions, and it gives a good performance in the network lifetime and energy consumption comparing with previous version of LEACH protocols.     

Hybrid Chameleon Search and Remora Optimization Algorithm-based Dynamic Heterogeneous load balancing clustering protocol for extending the lifetime of wireless sensor networks

Clustering is an indispensable strategy that helps towards the extension of lifetime of each sensor nodes with energy stability in wireless sensor networks (WSNs). This clustering process aids in sustaining energy efficiency and extended network lifetime in sensitive and critical real-life applications that include landslide monitoring and military applications. The dynamic characteristics of WSNs and several cluster configurations introduce challenge in the process of searching an ideal network structure, a herculean challenge. In this paper, Hybrid Chameleon Search and Remora Optimization Algorithm-based Dynamic Clustering Method (HCSROA) is proposed for dynamic optimization of wireless sensor node clusters. It utilized the global searching process of Chameleon Search Algorithm for selecting potential cluster head (CH) selection with balanced trade-off between intensification and extensification. It determines an ideal dynamic network structure based on factors that include quantity of nodes in the neighborhood, distance to sink, predictable energy utilization rate, and residual energy into account during the formulation of fitness function. It specifically achieved sink node mobility through the integration of the local searching capability of Improved Remora Optimization Algorithm for determining the optimal points of deployment over which the packets can be forwarded from the CH of the cluster to the sink node. This proposed HCSROA scheme compared in contrast to standard methods is identified to greatly prolong network lifetime by 29.21% and maintain energy stability by 25.64% in contrast to baseline protocols taken for investigation.     

Adaptive directed evolved NSGA2 based node placement optimization for wireless sensor networks

Wireless sensor network (WSN) is a wireless network composed of a large number of static or mobile sensors in a self-organizing and multi-hop manner. In WSN research, node placement is one of the basic problems. In view of the coverage, energy consumption and the distance of node movement, an improved multi-objective optimization algorithm based on NSGA2 is proposed in this paper. The proposed algorithm is used to optimize the node placement of WSN. The proposed algorithm can optimize both the node coverage and lifetime of WSN while also considering the moving distance of nodes, so as to optimize the node placement of WSN. The experiments show that the improved NSGA2 has improvements in both searching performance and convergence speed when solving the node placement problem.

     

基于ZigBee的自愈组网与协议实现

ZigBee技术是一种低功耗、网络容量大、时延小且具有自组织自愈功能的无线通讯技术。利用ZigBee技术组建的网络是一种低数据传输速率的无线个域网。在简要阐述传感器网络节点的基本体系结构的基础上,介绍了基于CC2530的ZigBee传感器节点的硬件组成。主要是对多跳自组路由协议和网状网的自愈的研究,并在此硬件平台上进行组网,通过对生理信息数据的传递来进行检验。     

Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata

In wireless sensor networks, when each target is covered by multiple sensors, we can schedule sensor nodes to monitor deployed targets in order to improve lifetime of network. In this paper, we propose an efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the proposed scheduling method can better prolong the lifetime of the network in comparison to similar existing methods.     

Efficient Energy Utilization Through Optimum Number of Sensor Node Distribution in Engineered Corona-Based (ONSD-EC) Wireless Sensor Network

An optimum sensor node deployment in wireless sensor network can sense the event precisely in many real time scenarios for example forests, habitat, battlefields, and precision agriculture. Due to these applications, it is necessary to distribute the sensor node in an efficient way to monitor the event precisely and to utilize maximum energy during network lifetime. In this paper, we consider the energy hole formation due to the unbalanced energy consumption in many-to-one wireless sensor network. We propose a novel method using the optimum number of sensor node Distribution in Engineered Corona-based wireless sensor network, in which the interested area is divided into a number of coronas. A mathematical models is proposed to find out the energy consumption rate and to distribute the optimum number of sensor node in each corona according to energy consumption rate. An algorithm is proposed to distribute the optimum number of sensor nodes in corona-based networks. Simulation result shows that the proposed technique utilized 95 % of the total energy of the network during network lifetime. The proposed technique also maximizes the network lifetime, data delivery and reduce the residual energy ratio during network lifetime.     

低能耗节点位置未知无线传感器网络控制方案

介绍了一种低能耗节点位置未知的网络控制方案,根据不同的网络运行轮数设定网络节点的通信半径,使网络具有良好的能量有效性.网络中基站经过构建阶段的启动过程、节点信息收集过程和节点信息上报过程,获得了整个网络节点的相对位置分布,然后整合节点-节点信息支路,得到具有回路链接的簇首节点集,其他节点根据自己邻居信息选择簇首节点,实现网络近似最小能耗拓扑的构建.通过仿真与同类典型算法LEACH-C、MCLB进行比较,结果显示该方案应用于网络运行时具有更长的网络生命周期、更少的信息总数和更低的网络构建代价.     

Research on WSN lifetime optimization game algorithm combined power and channel

Due to the problem of shortening the network lifetime which was caused by the big energy consumption for wireless sensor network (WSN) whose energy and computing power was limit,a lifetime optimization game algorithm combined power control and channel allocation (LOAPC) was proposed.The influence of node power and residual energy on the node interference was explored to construct an interference affection measurement model.Then,expected transmission times was introduced to establish a novel node lifetime model.Finally,LOAPC aimed at reducing interference and prolonging lifetime,and the node power was limited by an optional power set which ensured the network connectivity and economized energy consumption,so as to prolong the network lifetime effectively.At the same time,the simulation results show that the algorithm has the characteristics of low interference,low energy consumption and effectively prolonging the lifetime of the network.