无线传感器网络中一种分布式安全存储方案

对无线传感器网络的数据存储进行分析和研究,提出了一种以数据为中心的无线传感器网络中的分布式数据存储方案.该方案可有效解决集中式数据存储方案造成的中心节点的高负荷瓶颈问题,同时也避免了由于中心节点失效导致的数据丢失或泄漏问题.相对于现有的分布式数据存储方案,该方案运用Reed Solomon Code对数据进行加密,并且...     

无线传感器网络的一种数据管理底层协议

以数据为中心(data-centric)的数据管理是当今公认的无线传感器网络数据管理模式,由于其通讯量小、可扩展性好、适应网络规模和拓扑变化等优良性能而受到日益广泛的关注。提出一套适用于无线传感器网络的数据管理底层协议,其基本功能是利用传感器节点的数据通讯和存储能力,使某区域的数据(包括历史数据)总保存在离该区域最近的传感器节点中,而不随最初采集该数据节点的关闭或移动而丢失或移动。该协议在网络中建立起的结构稳定性,使网络中的数据查询过程避免了因网络拓扑的动态变化造成的访问中断和路由更换,为以数据为中心(data-centric)的数据管理的可靠实现提供了支持。     

货运列车车载网络轻量级身份认证协议研究

随着铁路运输不断发展,对货运列车的安全提出了更高要求。目前的地对车安全监控体系由于技术限制,无法满足列车实时的安全需求。车载传感器可以实时收集车辆信息,保证列车安全运行。车载传感器通信基础是车载网络。安全车载网络组成的基础是网络节点的身份认证,提出适用于货运列车车载网络节点身份认证的轻量级身份认证协议,轻量级身份认证保证所有网络节点均为可信节点,并且减少与可信第三方的交互。     

大规模无线传感器网络中基于振荡轨迹的数据存储与发现机制

在大规模无线传感器网络中,在不存在基站节点的情况下,节点组成对等网络,任何一个节点都有可能成为数据消费者节点或者数据生产者节点.传感器网络是一种资源受限的自组织网络,节点的能量和计算能力不足以支持复杂协议的设计.如何让随机产生的消费者节点和生产者节点能够有效迅速地发现对方并进行数据查询工作是传感器网络研究中的一个难点.利用数据为中心的存储策略,提出了一种振荡轨迹的数据存储发现机制.该方案要求消费者节点和生产者节点将查询或者数据存储到相应的振荡路径上.该方案不需要节点存储全局的网络信息,每个节点根据局部信息和预设的反射角度进行路由选择和数据转发.理论上,所有的振荡轨迹满足两两相交的特性,保证了数据查询成功率,而且消费者节点在查询数据时所需要的跳步距离是有界的,同时该方案能够保证数据负载的平衡.     

一种适合无线传感器网络的混合编码数据压缩算法

传感器节点通常由电池供电,充电和更换电池比较困难.如何有效地利用能源,减少损耗已经成为无线传感器网络的研究热点之一.传感器节点的能量大部分损耗在无线传输的过程中,因此,对数据压缩后再传输,能有效的减少传输的流量并提高节点的生存期.本文分析了传感数据的分布规律,指出当节点采集的数据符合慢变性质时,其分布将服从以0为中心的正态分布.依此提出了一种基于混合编码的传感器节点数据无损压缩算法,与传感器网络中的经典无损压缩算法相比,本算法以更少的性能开销获得了更高的压缩率.实验结果也证明了这一点.     

基于时延期望的车载自组织网络机会路由算法

针对车载自组织网络中,车辆随机运动的环境下源节点、目的节点均为运动中的车辆时数据传输效率低下的问题,提出了一种基于时延期望的机会路由算法.算法融合了概率论和统计学知识,综合考虑目的节点轨迹预测和数据时效性两方面需求,得到时延期望参数,以该参数作为整个数据传输过程中每一次数据转发中继节点选择标准,保证数据能够及时、有效地由移动中的源节点转发至移动中的目的节点.     

一种新的基于漫游代理的定向路由树算法

分析了以数据为中心的无线传感器网络中的定向扩散路由算法,提出了一种基于随机漫游的具有路由修复agent的定向路由树算法.该算法用泛洪策略构造一棵以汇聚节点为根的定向汇聚树,随后agent以等概率的方式在各个相邻传感器节点之间移动,当agent发现某两节点之间不再相邻时,即采取以该节点为中心的、逐步增加搜索范围的定向汇聚树的层次修复工作.研究表明,该算法具有良好的路由修复功能,同时也能节省能量并延长无线传感器网络的寿命.     

无线传感器网络中基于关联度的多查询优化

无线传感器网络是一种以数据为中心的网络,用户通过基站向网络提出查询请求获取所需数据。如何通过多查询的优化来减少传感器节点的能耗以延长网络生命期是无线传感器网络中需要解决的关键问题之一。提出了基于关联度的多查询优化算法,其基本思想是节点通过节点与候选父亲节点之间的关联度来选择父节点,从而被相同查询覆盖的节点聚集成一个组,多个查询间共享组中节点的数据,在网络中对查询数据进行有效的融合,充分减少了网络的数据传输量,延长了网络的生命期。理论分析和模拟实验表明该算法可以充分减少数据传输量,从而达到节能的目的。     

基于无线传感器网络的农业精准控制系统研究

根据设施农业应用环境复杂的情况,利用无线传感器网络多网络整合技术、无线传感器网络通信协议、无线传感器网络节点技术、无线传感器网络数据管理技术,设计出以传感数据管理为中心,结合数据库技术和计算机网络技术,从逻辑概念和软硬件技术两个方面实现高性能的以数据为中心的控制系统。     

基于二部图匹配的车载网络分布式存储机制

现有的车载网络中对数据存储机制的研究大多以移动车载节点作为数据载体,然而车载节点的快速移动、存储空间有限、存在安全风险等特性,限制了车载网络数据存储性能的进一步优化.针对部署有路边基础设施的车载网络场景,以路边单元作为存储节点,提出了基于二部图匹配的车载网络分布式存储机制（distributed storage scheme,简称DSS）.在车载网络中,以最大化数据响应率为目标,路边单元的数据存储问题是NP完全问题.首先,依据请求分割规则将原问题转化为二部图最大匹配问题,其中,二部图左顶点代表车载节点的请求,右顶点代表路边单元的存储单元;进而,利用Hungarian算法在多项式时间内求得最优解.由于问题转化可能造成不同路边单元存储相同数据的冗余问题,设计了冗余副本清理算法,依据不同副本的响应因子排序,检查并清理冗余副本.实验结果表明：DSS能够提高数据响应率,降低响应时延,并保持较小的网络资源开销.     

无线传感器网络中能量高效的自适应分发协议

能源效率在设计无线传感器网络时是非常重要的考虑因素,提供部分节点进入通信休眠状态的功能因此变得异常重要。提出一种基于类SRM（Scalable Reliable Multicast）抑制机制的EEA（Energy-Efficient Adaptive）分发协议,通过动态调整发射频率,抑制不必要的数据重复发送,根据制定的规则来关闭无线射频通信来达到节约节点能耗的目的;此外,在选择发送节点时,引入节点剩余能量参数,可以在一定程度上满足能量均衡;最后分别通过实验对泛洪协议和SPIN进行了相关比较,结果表明：EEA协议发送的次数更少,网络寿命更长。     

基于概率传输的无线传感网数据汇聚方案

针对传统无线传感网数据汇聚中通信开销较大的问题,提出了基于概率传输的无线传感网数据汇聚方案。由于簇内节点有限,汇聚误差难以避免,在可容忍汇聚误差下,簇内节点按概率进行数据发送,可有效减少簇内消息传输量,降低通信开销。同时,采取狄克逊准则对小样本中的粗大误差进行剔除提供高的簇内汇聚可靠性。实验结果表明,在可容忍误差下进行概率发送可有效降低簇内消息传输量,所提方案的簇内数据汇聚通信开销约为传统方案的27.5%;概率发送的汇聚误差与所有节点进行数据发送的汇聚误差基本在同一水平,都是传感器网络可接受的。     

Adaptive decentralized re-clustering protocol for wireless sensor networks

Wireless sensor networks are composed of a large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way since the energy is limited. The clustering algorithm is a technique used to reduce energy consumption. It can improve the scalability and lifetime of wireless sensor network. In this paper, we introduce an adaptive clustering protocol for wireless sensor networks, which is called Adaptive Decentralized Re-Clustering Protocol (ADRP) for Wireless Sensor Networks. In ADRP, the cluster heads and next heads are elected based on residual energy of each node and the average energy of each cluster. The simulation results show that ADRP achieves longer lifetime and more data messages transmissions than current important clustering protocol in wireless sensor networks.     

Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics

The restricted energy of nodes is one of the most important challenges in wireless sensor networks. Since data transmissions among nodes consume most of the nodes' energy, thus, minimizing the unnecessary transmissions reduces the consumed energy. One of the sources of this problem is the redundancy of raw data that can be eliminated at the aggregation points. As a result, data aggregation can be considered as an effective strategy to tackle the mentioned issue and to optimize the communication energy consumption. In this paper, the sensor nodes are organized in a tree structure, and the data aggregation are done in intermediate nodes at the junction of tree branches. One of the main characteristics of tree protocols is reduction of energy consumption through optimizing the structure of a data aggregation tree. For this, this paper proposes to apply a swarm intelligent algorithm named river formation dynamics. The simulation results show that the proposed algorithm outperforms in comparison to the famous ant colony optimization algorithm in terms of network lifetime. Simulations show that the proposed algorithm makes nearly 4% and 50% improvement in lifetime of wireless sensor networks than ant colony optimization and shortest path routing, respectively.     

无线传感器网络中基于区域再生树汇聚的路由算法

传感器网络具有严格的能量限制,冗余的低速数据流和多对一的通信方式等不同于传统Ad Hoc网络的特点,针对这些特点,提出一种区域再生树汇聚的路由算法。算法中将传感区域内部的所有传感器节点采集的数据沿区域再生树的父子关系层层汇聚到传感区域内离Sink点最近的区域汇聚点,再将汇聚的数据通过全局路由树形成的最短路径传递给Sink节点。仿真结果显示区域再生树的数据汇聚能够减少数据传输量,并具有较小的传输时延。     

Analytical modeling and mitigation techniques for the energy hole problem in sensor networks

In this paper we investigate the problem of uneven energy consumption in a large class of many-to-one sensor networks. In a many-to-one sensor network, all sensor nodes generate constant bit rate (CBR) data and send them to a single sink via multihop transmissions. This type of sensor network has many potential applications such as environmental monitoring and data gathering. Based on the observation that sensor nodes sitting around the sink need to relay more traffic compared to other nodes in outer sub-regions, our analysis verifies that nodes in inner rings suffer much faster energy consumption rates (ECR) and thus have much shorter expected lifetimes. We term this phenomenon of uneven energy consumption rates as the “energy hole” problem, which may result in severe consequences such as early dysfunction of the entire network. We proposed analytical modeling for this problem, which can help understand the relevance of different factors on energy consumption rates. Using this model, we study the effectiveness of several existing approaches towards mitigating the “energy hole” problem, including deployment assistance, traffic compression and aggregation. We have used simulation results to validate our analysis.     

无线传感器网络的数据汇聚机制

针方法是首先将传感区域内部的所有传感器节点采集的数据都传送对传感器网络的特点，提出了一种最小传输成本生成树的数据汇聚机制。具体实现给传感区域内离汇聚点最近的节点，经过数据汇聚后，将汇聚的数据通过最短路径传递给汇聚点。仿真结果显示最小传输代价生成树的数据拒聚能够减少数据传输量50％-80％，并具有较小的传输时延。     

A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks

With the increasing presence and adoption of wireless sensor networks (WSNs), the demand of data acquisition and data fusion are becoming stronger and stronger. In WSN, sensor nodes periodically sense data and send them to the sink node. Since the network consists of plenty of low-cost sensor nodes with limited battery power and the sensed data usually are of high temporal redundancy, prediction- based data fusion has been put forward as an important issue to reduce the number of transmissions and save the energy of the sensor nodes. Considering the fact that the sensor node usually has limited capabilities of data processing and storage, a novel prediction-based data fusion scheme using grey model (GM) and optimally pruned extreme learning machine (OP-ELM) is proposed. The proposed data fusion scheme called GM-OP-ELM uses a dual prediction mechanism to keep the prediction data series at the sink node and sensor node synchronous. During the data fusion process, GM is introduced to initially predict the data of next period with a small number of data items, and an OPELM- based single-hidden layer feedforward network (SLFN) is used to make the initial predicted value approximate its true value with extremely fast speed. As a robust and fast neural network learning algorithm, OP-ELM can adaptively adjust the structure of the SLFN. Then, GM-OP-ELM can provide high prediction accuracy, low communication overhead, and good scalability. We evaluate the performance of GM-OP-ELM on three actual data sets that collected from 54 sensors deployed in the Intel Berkeley Research lab. Simulation results have shown that the proposed data fusion scheme can significantly reduce redundant transmissions and extend the lifetime of the whole network with low computational cost.     

Energy efficient ant colony algorithms for data aggregation in wireless sensor networks

In energy-constrained wireless sensor networks, energy efficiency is critical for prolonging the network lifetime. A family of ant colony algorithms called DAACA for data aggregation are proposed in this paper. DAACA consists of three phases: initialization, packets transmissions and operations on pheromones. In the transmission phase, each node estimates the remaining energy and the amount of pheromones of neighbor nodes to compute the probabilities for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustments are performed, which take the advantages of both global and local merits for evaporating or depositing pheromones. Four different pheromones adjustment strategies which constitute DAACA family are designed to prolong the network lifetime. Experimental results indicate that, compared with other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last, the features of DAACA are analyzed.     

Heuristic routing with bandwidth and energy constraints in sensor networks

Most of the routing algorithms devised for sensor networks considered either energy constraints or bandwidth constraints to maximize the network lifetime. In the real scenario, both energy and bandwidth are the scarcest resource for sensor networks. The energy constraints affect only sensor routing, whereas the link bandwidth affects both routing topology and data rate on each link. Therefore, a heuristic technique that combines both energy and bandwidth constraints for better routing in the wireless sensor networks is proposed. The link bandwidth is allocated based on the remaining energy making the routing solution feasible under bandwidth constraints. This scheme uses an energy efficient algorithm called nearest neighbor tree (NNT) for routing. The data gathered from the neighboring nodes are also aggregated based on averaging technique in order to reduce the number of data transmissions. Experimental results show that this technique yields good solutions to increase the sensor network lifetime. The proposed work is also tested for wildfire application.