无线传感器网络中移动协助的数据收集策略

利用移动数据收集器(mobile data collector,简称MDC)进行传感器网络中感知数据的收集,可以有效地减少传感器将数据发送到静止基站的传输跳数,节约网络的能量,延长网络寿命.此外,MDC通过循环收集传感器数据或承担数据转发的功能,避免节点间由于多跳传输引起的能量空洞(energy hole)以及节点失效造成的传输链路中断等问题.MDC的移动性也为无线传感器网络的研究带来新的挑战.研究基于移动协助数据收集的无线传感器网络结构,分类总结了近年来提出的一些典型的基于MDC的算法和协议,着重讨论了MDC在网络能量、延迟、路由和传输等方面带来的性能变化.最后,进行了各种算法的比较性总结,针对传感器网络中MDC的研究提出了亟待解决的问题,并展望了其未来的发展方向.     

无线传感网络中的存储节点配置

无线传感网络中的大多数应用均需要将无线传感节点收集的大量数据传输到基站以便进行数据处理或者存档,因而可以通过放置一定数目的存储节点来存储其临近节点发送的数据,如此可以减轻网络中的数据传输量,达到节能这个对于无线传感网络至关重要的目的.本文研究如何放置有限数目的存储节点,以使整个无线传感网络的能量消耗最小,这也就是存储节点配置问题.对存储节点配置问题进行规约,在这一过程中文章修正了已有算法在能量模型定义上的不足,证明无线传感网络中的存储节点配置为NP-hard问题;文章提出一个基于反向贪心策略的近似算法,并且证明了其最坏情况下的性能比为O(logn),其中n为网络中的传感节点数目.通过实验证明了这一算法的优良性能.     

Sink节点移动的三维无线传感网数据收集算法研究

为克服三维静态无线传感网中的能量空穴问题和提高网络生存时间,考虑Sink节点移动,提出一种Sink节点移动的三维无线传感网数据收集算法(DCA-TWSN),在DCA-TWSN中,提出三维环境下的正方体网格划分方法,建立包括Sink 移动路径选择约束、数据流量约束、能耗约束、链路约束等约束条件的数据收集优化模型,采用最优化方法求解已知Sink节点移动路径的数据收集优化问题,采用修正的蚁群算法求解Sink节点的移动路径问题,获得最优方案。仿真结果表明:不管Sink节点的最大数据收集跳数和传感节点数量如何变化,DCA-TWSN都能寻找到较优的移动路径和数据传输方案,从而提高了网络生存时间和传感节点的平均数据传输率,降低了移动路径长度、平均节点能耗方差和丢包率,比RAND、GREED和EDG-3D更优。     

基于SDMA应用的移动Sink节点的设计与实现

静态无线传感器网络(所有节点均为静止)不可避免地存在能量空洞(energy hole)、冗余覆盖(overlap)和热点(hot spot)等问题.在无线传感器网络中引入节点的移动性来解决上述问题.移动节点可以完成数据收集和转发等功能,有效地减少静态节点数据传输的跳数,节约静态节点的能量,延长网络的寿命.实现了一个基于ARM7内核的移动汇点DataTruck,具有容量大、速度快等特点.在此基础上引入SDMA(space-division multiple access)技术,在移动汇点上加入智能天线,使得该节点能同时接收同一频率上多个静态节点传输的数据.实验表明基于SDMA技术的移动汇点能高效地收集数据,并减少数据延时.     

无线传感器网络分簇路由协议的研究与应用

无线传感网是由许多具有计算能力的智慧无线节点组成的网络,这些无线传感节点负责收集周边环境的各类数据进行汇总处理,使得人们可以实时获得大量可靠的信息.在无线传感网络中,网络协议设计的主要目的是降低网内节点在通信上的能耗,从而延长网络的工作寿命.本文以分簇路由协议为研究对象,对国内外典型的分簇路由协议进行分析和比较,分析分簇路由协议存在的问题,并预测未来发展的趋势.     

无线传感网中一种智能数据融合算法的实现及仿真分析

在无线传感网中,传感器节点一般都由自身装配的电池供电,难以进行电量补充,因此节约电量对于无线传感网来说至关重要.为了提高无线传感网能量使用效率,延长网络生存时间,提出了一种结合遗传算法和粒子群算法优化BP神经网络的智能数据融合算法 GAPSOBP(BP Neural Network Data Fusion algorithm optimized by Genetic algorithm and Particle swarm).GAPSOBP算法将无线传感网的节点类比为BP神经网络中的神经元,通过神经网络提取无线传感网采集的感知数据并结合分簇路由对收集的传感数据进行融合处理,从而大幅减少发往汇聚节点的网络数据量.仿真结果表明,与经典LEACH算法和PSOBP算法相比,GAPSOBP算法能有效减少网络通信量,节约节点能量,显著延长网络生存时间.     

WSN中一种基于网络效益最大化的数据收集方案

数据收集问题是无线传感网中的研究热点之一。针对现有的数据收集方法能耗过大以及数据收集精度低下的问题,提出一种基于网络效益最大化的数据收集方案。首先基于压缩采样得到各个节点感知数据的测量值,然后在融合节点处采用随机高斯矩阵对测量值进行编码后传输,最后将编码后的测量值传输问题建模为网络效益最大化问题,并利用拉格朗日乘数法得到近似最优解。仿真实验结果表明,该方法是有效的,在数据重构精度以及网络生命周期等方面都要优于传统的方法。     

优化网络生存时间的Sink节点移动路径选择算法

为克服无线传感网的能量空穴问题,采用最优化方法,研究一种优化网络生存时间的Sink节点移动路径选择算法(MPSA)。在MPSA算法中,将单跳传输的无线传感网监测区域分成多个大小一致的网格,Sink节点可移动到任一网格中心,停留收集单跳最大通信范围内的传感节点数据。分析停留位置的全节点覆盖条件和所有传感节点的能耗,建立权衡网络生存时间和Sink节点移动路程的优化模型。提出一种改进的遗传算法,用于求解优化模型,即迭代执行染色体评估、选择、交叉、变异、最小覆盖处理、孤立节点处理等步骤,最终获得优化网络生存时间的Sink节点移动方案。仿真结果表明:MPSA算法能提高网络生存时间,将移动路程保持在较小范围。在提高网络生存时间方面,比RCC算法更优。     

无线传感器网络密钥分配方案改进与仿真研究

研究无线传感器网络安全中的密钥分配问题.由于传感器网络规模大、节点能源非常受限等特点,传统网络中使用的密钥分配策略并不适用于无线传感器网络.为了提高网络的安全性能,在预共享密钥和随机密钥分发方案的基础上,提出一种改进的随机密分配方案.首先通过传感节点的能量大小建立源传感节点到目的传感节点的多条传感节点不相交路径,然后根据传感节点的最小最大能量原理选择一条合适的传感节点不相交路径作为源传感节点和目的传感节点协商路径密钥的通道.仿真结果表明,改进后的方案保留了原方案的网络高安全性等优点,而且进一步节省了节点通信能量,延长了网络的生存周期,更加适用于能量非常受限的无线传感器网络.     

采用自供电无线传感网络的电线安全监测系统

电线故障会引起火灾,对电线温度、电流进行在线监测能预防事故发生。设计了采用自供电无线传感网络的电线安全监测系统,系统由传感节点、中继节点、路由节点、服务器和客户端组成。采用自供电技术解决无线传感网络监测系统中传感节点不能持续供能的问题,对传感节点工作状态进行控制以降低传感节点能耗,传感节点具有体积小、能耗低、不用更换电池等特点。分析自供电条件下,无线传感网络的网络结构,引入中继节点,延伸通信距离。将系统用于电线安全监测,该系统通信可靠、工作时间长、数据实时、人机操作方便,为电线安全的监测提供了技术支持。     

A smart node architecture for adding mobility to wireless sensor networks

Adding a few mobile nodes into the conventional wireless sensor networks can greatly improve the sensing and control capabilities of the networks and can help researchers solve many challenges such as network deployment and repair. This paper presents an enhanced node architecture for adding controlled mobility to wireless sensor networks. The structural model, the power model and the networking model of the proposed mobile node have been built respectively for better node control. And it provides a novel robotic platform for experimental research in hybrid sensor networks or other distributed measurement and control systems. A testbed has finally been created for validating the basic functions of the proposed mobile sensor node. The results of a coverage experiment show that the mobile node can provide additional support for network coverage and can ensure that the sensor network will work properly in undesirable environments.     

Mobility-assisted minimum connected cover in a wireless sensor network

All properties of mobile wireless sensor networks (MWSNs) are inherited from static wireless sensor networks (WSNs) and meanwhile have their own uniqueness and node mobility. Sensor nodes in these networks monitor different regions of an area of interest and collectively present a global overview of monitored activities. Since failure of a sensor node leads to loss of connectivity, it may cause a partitioning of the network. Adding mobility to WSNs can significantly increase the capability of the WSN by making it resilient to failures, reactive to events, and able to support disparate missions with a common set of sensor nodes. In this paper, we propose a new algorithm based on the divide-and-conquer approach, in which the whole region is divided into sub-regions and in each sub-region the minimum connected sensor cover set is selected through energy-aware selection method. Also, we propose a new technique for mobility assisted minimum connected sensor cover considering the network energy. We provide performance metrics to analyze the performance of our approach and the simulation results clearly indicate the benefits of our new approach in terms of energy consumption, communication complexity, and number of active nodes over existing algorithms.     

水下移动无线传感器网络研究综述

水下移动传感器网络是水下机器人与水下传感器网络的结合,解决了传统水下监控网络存在的无法自动实现节点投放与回收,容易存在监测盲区以及不能动态组网等问题,按照自下而上的体系结构,对水下移动传感器网络的研究内容及发展方向进行了归纳阐述,包括水下节点设计、节点互联和动态组网,即点、线、面的层次结构;着重分析了节点移动特性对水下传感器网络的影响及相应的解决方法.虽然水下移动传感器网络的研究面临很多挑战,但它必将具有广阔的应用前景.     

Optimizing data collection path in sensor networks with mobile elements

Exploiting mobile elements (MEs) to accomplish data collection in wireless sensor networks (WSNs) can improve the energy efficiency of sensor nodes, and prolong network lifetime. However, it will lead to large data collection latency for the network, which is unacceptable for data-critical applications. In this paper, we address this problem by minimizing the traveling length of MEs. Our methods mainly consist of two steps: we first construct a virtual grid network and select the minimal stop point set (SPS) from it; then, we make optimal scheduling for the MEs based on the SPS in order to minimize their traveling length. Different implementations of genetic algorithm (GA) are used to solve the problem. Our methods are evaluated by extensive simulations. The results show that these methods can greatly reduce the traveling length of MEs, and decrease the data collection latency.     

基于移动机器人的无线传感器网络数据收集方法

稀疏无线传感器网络中各传感器节点距离较远,而传统的静态数据收集方法要求各传感器节点直接通信,导致网络延迟时间长,能耗高。针对该问题,提出一种基于移动机器人的无线传感器数据收集方法。该方法首先由静态节点选择与路径最短的移动机器人作为簇头,移动机器人比较一定周期内检测到的邻居节点的平均剩余能量与整个网络传感器节点平均剩余能量,根据比较结果决定其是否移动,若移动则采用范围可控的随机移动策略;当移动机器人移动到新位置时,传感器节点更新路由,选择新的移动机器人作为簇头。仿真结果表明,与传统的静态无线传感器网络数据收集方法相比,基于移动机器人的无线传感器网络数据收集方法大大降低了数据传输延迟和节点能量消耗。     

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.     

Analysis of smartphone user mobility traces for opportunistic data collection in wireless sensor networks

The increasing ubiquity of smartphones coupled with the mobility of their users will allow the use of smartphones to enhance the operation of wireless sensor networks. In addition to accessing data from a wireless sensor network for personal use, and the generation of data through participatory sensing, we propose the use of smartphones to collect data from sensor nodes opportunistically. For this to be feasible, the mobility patterns of smartphone users must support opportunistic use. We analyze the dataset from the Mobile Data Challenge by Nokia, and we identify the significant patterns, including strong spatial and temporal localities. These patterns should be exploited when designing protocols and algorithms, and their existence supports the proposal for opportunistic data collection through smartphones.     

A detailed review of energy-efficient medium access control protocols for mobile sensor networks

This article reviews the state-of-the-art energy-efficient contention-based and scheduled-based medium access control (MAC) protocols for mobile sensor networks (MSNs) by first examining access schemes for wireless sensor networks (WSNs). Efficient and proper mobility handling in sensor networks provides a window of opportunity for new applications. Protocols, such as S-MAC, reduce energy consumption by putting nodes to sleep after losing to channel contention or to prevent idling. Sleeping is a common method for energy-efficient MAC protocols, but delay depends on sleep duration or frame time, and longer delays lead to higher packet lost rate when nodes are unsynchronized due to network mobility. MS-MAC extends S-MAC to include mobility-awareness by decreasing this sleep duration when mobility is detected. S-MAC with extended Kalman filter (EKF) reduces mobility-incurred losses by predicting the optimal data frame size for each transmission. MMAC utilizes a dynamic mobility-adaptive frame time to enhance TRAMA, a scheduled-based protocol, with mobility prediction. Likewise, G-MAC utilizes TDMA for cluster-based WSNs by combining the advantages of contention and contention-free MACs. Z-MAC also combines both methods but without clustering and allows time slot re-assignments during significant topology changes. All of the above MAC protocols are reviewed in detail.     

Energy efficient approximate self-adaptive data collection in wireless sensor networks

To extend the lifetime of wireless sensor networks, reducing and balancing energy consumptions are main concerns in data collection due to the power constrains of the sensor nodes. Unfortunately, the existing data collection schemesmainly focus on energy saving but overlook balancing the energy consumption of the sensor nodes. In addition, most of them assume that each sensor has a global knowledge about the network topology. However, in many real applications, such a global knowledge is not desired due to the dynamic features of the wireless sensor network. In this paper, we propose an approximate self-adaptive data collection technique (ASA), to approximately collect data in a distributed wireless sensor network. ASA investigates the spatial correlations between sensors to provide an energyefficient and balanced route to the sink, while each sensor does not know any global knowledge on the network.We also show that ASA is robust to failures. Our experimental results demonstrate that ASA can provide significant communication (and hence energy) savings and equal energy consumption of the sensor nodes.     

Adaptive Data Collection Strategies for Lifetime-Constrained Wireless Sensor Networks

Communication is a primary source of energy consumption in wireless sensor networks. Due to resource constraints, the sensor nodes may not have enough energy to report every reading to the base station over a required network lifetime. This paper investigates data collection strategies in lifetime-constrained wireless sensor networks. Our objective is to maximize the accuracy of data collected by the base station over the network lifetime. Instead of sending sensor readings periodically, the relative importance of the readings is considered in data collection: the sensor nodes send data updates to the base station when the new readings differ more substantially from the previous ones. We analyze the optimal update strategy and develop adaptive update strategies for both individual and aggregate data collections. We also present two methods to cope with message losses in wireless transmission. To make full use of the energy budgets, we design an algorithm to allocate the numbers of updates allowed to be sent by the sensor nodes based on their topological relations. Experimental results using real data traces show that, compared with the periodic strategy, adaptive strategies significantly improve the accuracy of data collected by the base station.