混合传感器网络中基于向量代数的覆盖补偿算法

针对混合传感器网络中,能量耗尽造成的覆盖空洞与休眠冗余节点能量剩余可能并存的矛盾现象,提出一种基于向量代数的移动节点覆盖补偿方法.算法包含所有可能参与补偿的冗余节点对各自位移量的计算方法,空洞边缘节点在平衡节点剩余能量的基础上,对最佳冗余节点的选择启用策略,保证网络能充分利用遗留能量资源的同时实现完全覆盖补偿.实验分析表明,本算法在平均移动距离、能量开销等方面均优于COA算法.     

UWSP：水下无线传感器网络节点休眠协议

休眠机制是传感器网络节点节约能量、延长工作寿命的重要手段之一。现存的水下传感器网络MAC协议主要考虑提高网络传输性能,对休眠机制的研究和涉及较少,并且仅有的一些休眠策略存在着因节点工作时间较为分散而导致的节点休眠-唤醒频繁的问题。节点频繁唤醒不仅会浪费额外的能量来启动电路,折损硬件寿命,还会增加数据传输冲突的概率。针对水声网络信道的独特性质,提出了一个基于树形拓扑结构的水下传感器网络节点休眠算法,该算法能够有效缩短节点唤醒次数,延长休眠时间,并保证端到端的传播延迟不受休眠时间的影响。该算法无冲突也无需预约信道,保证了较高的网络流量。最后,通过仿真实验验证了算法的可用性和效能。     

低功耗射频唤醒无线传感器网络设计

由于传统模式下为了降低无线传感器电能损耗而使用的休眠或唤醒机制有着一定的弊端,对通讯网络的咨询反馈不及时,所以文章提出了采用基于波束供电程式来运行的无线传感器网络射频唤醒技术。低功耗的射频唤醒技术对于无线传感器网络来说,其节点经过电波的流动来收集能量,准时唤醒机体自身,最终实现成效性。根据性能分析报告发现,新型的低功耗射频唤醒无线传感程式较之传统唤醒程式来说,其节点体系具备更优的稳定性和较少的耗能,对于支持往后的生活需求以及生产运营来说都有着重大意义。     

基于全网休眠的ZigBee网络节能算法

无线传感器网络节点一般由电池供电而且部署后需要长时间工作,这就导致节点的能量成为网络运行的瓶颈。针对无线传感器网络节点能量受限的问题,提出了一种基于全网休眠的节能新算法。该算法使终端节点、路由节点和汇聚节点都能得到休眠,从而为网络中每一种节点节约能量;同时,为路由节点设计了长、短两种休眠策略,在不影响数据收发的前提下减少能耗。基于CC2630芯片的实验结果表明:与现有相关算法相比,该新算法使节点能耗降低10%。     

监测无线传感器网络休眠调度算法

休眠调度设计是无线传感器网络一种重要的通信节能方法。针对监测典型应用,为了实现长时间的监测应用要求,充分利用冗余部署提供的能量资源,提出了一种能量相关的分布式自适应休眠调度算法。算法利用极大独立集构建思想,结合节点层次级别、实时的能量消耗、连通度等信息动态选择连通支配节点集作为网络骨干,使得网络活跃节点数量最小化。仿真试验分析表明,算法能够有效地利用冗余节点提供的能量资源,扩展了网络的生命周期。     

基于最大生存周期的无线传感器网络能量模型研究

在分析了无线传感器网络的应用和特性的基础上,从节点能量计算模型、节点的能量消耗模型和状态转换模型3个方面论述了无线传感器网络的系统能量模型,通过引入Flag标志和长期睡眠状态机制来防止网络中的某些节点因为过早耗尽能量而死亡,从而实现无线传感器络中节点的能量均衡和网络生存周期的最大化,对无线传感器网络的应用和研究有着深远的意义。     

无线传感器网络冗余节点休眠调度算法

提出一种冗余节点休眠调度算法来延长网络生命周期。调度过程中重点考虑两方面问题：一是采取策略防止大量节点同时从工作状态转入休眠状态以防止大量盲区同时产生;二是根据邻居表中节点的工作邻居数量,判定节点是否处于网络边界,对边界节点和内部节点采用不同的调度策略,防止边界收缩。仿真结果表明,算法能有效延长无线传感器网络的生命周期。     

基于休眠和扇区的传感器网络拓扑控制算法

拓扑控制算法对节省无线传感器网络节点能量、延长网络寿命具有重要意义。无线传感器网络节省节点能量有两种方法：层次型拓扑结构控制和功率控制；但目前还没有算法将以上两种方法相结合。丈中是结合周期性休眠和拓扑控制的一种新型拓扑控制策略：基于休眠和扇区的无线传感器网络拓扑控制算法，将拓扑控制算法和节点的休眠结合起来。仿真结果表明：算法在不降低网络吞吐量条件下节省了网络能量消耗，有效延长网络寿命。     

基于节点综合故障模型的无线传感器网络容错拓扑控制方法

鉴于无线传感器网络(WSNs)存在节点能量受限和恶劣环境造成的节点随机失效频繁的特点,该文提出了面向节点能量耗尽和随机失效综合故障优化的WSNs容错拓扑控制方法。该方法利用节点能量耗尽及随机失效信息进行综合故障建模,通过获取满足网络生命期和综合故障容忍能力双重需求的节点度取值,得到可最大限度延长网络生命期,且能够有效提高对节点能量耗尽及随机失效综合容忍能力的目标拓扑。实验结果证实了方法的有效性。     

无线传感器网络中一种协同源定位算法

针对无线传感器网络传统目标定位过程中,数据在大量传感器节点间传输使得节点能量迅速耗尽,同时远距离低信噪比节点对定位精度的影响等问题。基于分区域协同工作的思想,提出了一种协同源定位算法。对该算法进行仿真试验,结果显示,该算法明显降低了网络能耗,且具有较高的定位精度和稳健性。     

基于Voronoi的无线传感器网络覆盖控制优化策略

针对无线传感器网络运行状态中存在覆盖空洞的问题,提出了一种基于Voronoi有效覆盖区域的空洞侦测修复策略。该策略以满足一定网络区域覆盖质量为前提,在空洞区域内合理增加工作节点以提高网络覆盖率为优化目标,采用几何图形向量方法对节点感知范围和Voronoi多边形的位置特性进行理论分析,力求较准确地计算出空洞面积,找寻最佳空洞修复位置,部署较少的工作节点保证整个网络的连通性。仿真结果表明,该策略能有效地减少网络总节点个数和感知重叠区域,控制网络中冗余节点的存在,同时其收敛速度较快,能够获得比现有算法更高的目标区域空洞修复率,实现网络覆盖控制优化.     

基于冗余节点的LEACH协议的改进

无线传感器网络中LEACH协议是一种典型的能有效延长网络生命周期的节能通信协议。因为其优秀的节能效果和其简单的规程得到了广泛的认可。但是LEACH簇头算法存在簇头开销大、簇头没有确定的数量和位置等不足。而在成簇后的稳定阶段,节点通过一跳通信将数据传送给簇头,簇头也通过一跳通信将聚合后的数据传送给基站,这样会造成簇头节点...     

Data aggregation in sensor networks using learning automata

One way to reduce energy consumption in wireless sensor networks is to reduce the number of packets being transmitted in the network. As sensor networks are usually deployed with a number of redundant nodes (to overcome the problem of node failures which is common in such networks), many nodes may have almost the same information which can be aggregated in intermediate nodes, and hence reduce the number of transmitted packets. Aggregation ratio is maximized if data packets of all nodes having almost the same information are aggregated together. For this to occur, each node should forward its packets along a path on which maximum number of nodes with almost the same information as the information of the sending node exist. In many real scenarios, such a path has not been remained the same for the overall network lifetime and is changed from time to time. These changes may result from changes occurred in the environment in which the sensor network resides and usually cannot be predicted beforehand. In this paper, a learning automata-based data aggregation method in sensor networks when the environment’s changes cannot be predicted beforehand will be proposed. In the proposed method, each node in the network is equipped with a learning automaton. These learning automata in the network collectively learn the path of aggregation with maximum aggregation ratio for each node for transmitting its packets toward the sink. To evaluate the performance of the proposed method computer simulations have been conducted and the results are compared with the results of three existing methods. The results have shown that the proposed method outperforms all these methods, especially when the environment is highly dynamic.     

A distributed lightweight Redundancy aware Topology Control Protocol for wireless sensor networks

WSN consists of a large number of sensor nodes randomly deployed, and, in many cases, it is impossible to replace sensors when a node failure occurs. Thus, applications tend to deploy more nodes than necessary to cope with possible node failures and to increase the network lifetime, which leads to create some sensing and communication redundancy. However, sensors in the same region, may collect and forward the same information, which will waste more energy. In this paper, we propose a distributed Lightweight Redundancy aware Topology Control Protocol (LRTCP) for wireless sensor networks. It exploits the sensor redundancy in the same region by dividing the network into groups so that a connected backbone can be maintained by keeping a minimum of working nodes and turning off the redundant ones. LRTCP identifies equivalent nodes in terms of communication based on their redundancy degrees with respect of some eligibility rules. Simulation results indicate that, compared with existing distributed topology control algorithms, LRTCP improves network capacity and energy efficiency.     

A Learning Automata Based Area Coverage Algorithm for Wireless Sensor Networks

One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.     

Three-dimensional and two-dimensional deployment analysis for underwater acoustic sensor networks

Underwater sensor networks find applications in oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation, and tactical surveillance. In this paper, deployment strategies for two-dimensional and three-dimensional communication architectures for underwater acoustic sensor networks are proposed, and a mathematical deployment analysis for both architectures is provided. The objective is to determine the minimum number of sensors to be deployed to achieve optimal sensing and communication coverage, which are dictated by application requirements; provide guidelines on how to choose the optimal deployment surface area, given a target body of water; study the robustness of the sensor network to node failures, and provide an estimate of the number of redundant sensor nodes to be deployed to compensate for potential failures.     

Energy saving with node sleep and power control mechanisms for wireless sensor networks

Energy efficiency sleep scheduling in wireless sensor networks is one of the most crucial technologies.In this paper,we propose a simple and feasible synchronous node sleeping and waking mechanisms for small scale wireless sensor networks.Sensor nodes are divided into forwarding nodes and listening nodes.Beacon frame containing sleep command from the coordinator can be forwarded to listening nodes via forwarding nodes.All the nodes in the network can enter sleep at about the same time.Through such network s...     

Connectivity maintenance and coverage preservation in wireless sensor networks

In wireless sensor networks, one of the main design challenges is to save severely constrained energy resources and obtain long system lifetime. Low cost of sensors enables us to randomly deploy a large number of sensor nodes. Thus, a potential approach to solve lifetime problem arises. That is to let sensors work alternatively by identifying redundant nodes in high-density networks and assigning them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In a single wireless sensor network, sensors are performing two operations: sensing and communication. Therefore, there might exist two kinds of redundancy in the network. Most of the previous work addressed only one kind of redundancy: sensing or communication alone. Wang et al. [Intergrated Coverage and Connectivity Configuration in Wireless Sensor Networks, in: Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys 2003), Los Angeles, November 2003] and Zhang and Hou [Maintaining Sensing Coverage and Connectivity in Large Sensor Networks. Technical report UIUCDCS-R-2003-2351, June 2003] first discussed how to combine consideration of coverage and connectivity maintenance in a single activity scheduling. They provided a sufficient condition for safe scheduling integration in those fully covered networks. However, random node deployment often makes initial sensing holes inside the deployed area inevitable even in an extremely high-density network. Therefore, in this paper, we enhance their work to support general wireless sensor networks by proving another conclusion: “the communication range is twice of the sensing range” is the sufficient condition and the tight lower bound to ensure that complete coverage preservation implies connectivity among active nodes if the original network topology (consisting of all the deployed nodes) is connected. Also, we extend the result to k-degree network connectivity and k-degree coverage preservation.     

HDF: Hybrid Debugging Framework for Distributed Network Environments

Debugging in distributed environments, such as wireless sensor networks (WSNs), which consist of sensor nodes with limited resources, is an iterative and occasionally laborious process for programmers. In sensor networks, it is not easy to find unintended bugs that arise during development and deployment, and that are due to a lack of visibility into the nodes and a dearth of effective debugging tools. Most sensor network debugging tools are not provided with effective facilities such as real‐time tracing, remote debugging, or a GUI environment. In this paper, we present a hybrid debugging framework (HDF) that works on WSNs. This framework supports query‐based monitoring and real‐time tracing on sensor nodes. The monitoring supports commands to manage/control the deployed nodes, and provides new debug commands. To do so, we devised a debugging device called a Docking Debug‐Box (D2‐Box), and two program agents. In addition, we provide a scalable node monitor to enable all deployed nodes for viewing. To transmit and collect their data or information reliably, all nodes are connected using a scalable node monitor applied through the Internet. Therefore, the suggested framework in theory does not increase the network traffic for debugging on WSNs, and the traffic complexity is nearly O(1).     

Communication Coverage in Wireless Passive Sensor Networks

System lifetime of wireless sensor networks (WSN) is inversely proportional to the energy consumed by critically energy-constrained sensor nodes during RF transmission. In that regard, modulated backscattering (MB) is a promising design choice, in which sensor nodes send their data just by switching their antenna impedance and reflecting the incident signal coming from an RF source. Hence, wireless passive sensor networks (WPSN) designed to operate using MB do not have the lifetime constraints of conventional WSN. However, the communication performance of WPSN is directly related to the RF coverage provided over the field the passive sensor nodes are deployed. In this letter, RF communication coverage in WPSN is analytically investigated. The required number of RF sources to obtain interference-free communication connectivity with the WPSN nodes is determined and analyzed in terms of output power and the transmission frequency of RF sources, network size, RF source and WPSN node characteristics.