无线传感器网络中基于隐私保护元的数据聚合机制

提出对聚合中的传感数据提供隐私保护的分布式机制。基于同余的代数特性定义了隐私保护元,无需通信即可实现传感数据的隐私性,且聚合值在簇内得以准确还原。给出了隐私保护元生成方法,该方法无需通信,且支持动态变化的聚合节点。分析表明,与集中式机制相比,避免了基站获取隐私数据及单点失效问题,对分组丢失环境有着更强的健壮性,且通信开销更低;与分布式机制相比,在提高隐私保护有效性的同时通信开销更低。     

舰船通信网络不完整节点数据模糊采集算法

基于改进数据采集结果冗余的目的，研究舰船通信网络不完整节点数据模糊采集算法。根据所定义的节点传输数据的概率，并结合聚类阈值，对不完整节点数据进行模糊聚类，选定聚类簇中心。采用平滑参数对舰船通信网络数据中的冗余数据进行处理，并提取不完整节点的数据特征向量。构建目标函数，通过求解最优路径来实现数据采集，同时考虑比特序列开销值，输出最优解。测试结果表明，采用提出的方法对不完整节点数据进行模糊采集时，数据集的平均冗余度为22，具备较为理想的数据采集效果。     

基于加权队列的无线智能电网通信网采集数据流量调度算法

智能电网的关键技术之一是为电力数据采集提供一个高效、可靠、安全的双向通信系统。使用具有通信能力的先进电力计量设备(智能电表)组成无线mesh网络采集数据,存在应用层数据流量对网络通信性能的挑战,当大量数据流量突发时,与本地局域网关较近的智能电表将面临较大的通信压力,可能产生严重的数据拥塞。为此,该文基于多网关联合的思想,提出一个新的基于加权队列的流量调度算法以缓解拥塞。首先,对多网关联合网络进行分析,确定影响网络性能的主要因素。其次采用队列加权的方法,提出新的流量调度算法。最后进行网络仿真,相对其它算法,该文所提算法能够极大缓解数据突发时刻的系统拥塞,有效地降低时延,同时在系统各网关吞吐量之间取得良好的平衡,能够提高采集网络的通信性能。     

一种同时保障隐私性与完整性的无线传感器网络可恢复数据聚合方案

该文针对无线传感器网络(WSNs)数据聚合与安全目标之间的矛盾,基于隐私同态和聚合消息验证码技术提出一种同时保障数据隐私性与完整性的可恢复数据聚合方案。该方案支持由聚合结果恢复出各感知数据,从而一方面能够验证感知数据和聚合数据的完整性,另一方面能够对原始数据进行任意所需的处理,不受聚合函数类型的限制。安全分析表明该方案不仅支持数据隐私性、完整性,还能够抵抗未授权聚合攻击,聚合节点俘获攻击,且能够在一定范围内检测及定位恶意节点。性能分析表明,该方案相比其他算法在通信和计算开销方面具有显著优势。为了评估方案性能和可行性,基于TinyOS给出了算法的原型实现。实验结果表明,该方案开销较低,对于资源受限的WSNs是高效可行的。     

无线传感器网络中能耗均衡的数据收集算法

无线传感器网络所具有的集中式数据收集、多跳数据传输、多对一流量模式容易导致严重的包碰撞、网络拥塞,包丢失,同样也会导致能量消耗的热点出现,使某些节点甚至整个网络过早死亡.文中提出一种能耗均衡的数据收集算法,此算法按节点离基站的距离进行分层,根据中间节点的能量参数和通信开销建立到基站的多径路由,在数据收集过程中除了利用具有最小能耗的最优路径外还偶尔使用一些次优路径;为进一步减少节点能耗,算法还采用了数据聚合机制.分析和仿真结果表明,算法在很大程度上均衡了节点间能耗并延长了网络寿命.     

基于蚁群算法的无线传感器网络数据聚合路由算法

传感器网络为减少冗余数据的传输耗能。降低延迟,需要在路由过程中采用数据聚合技术。文中采用定向传输方式,在消息路由机制基础上提出了一种基于蚁群算法的数据聚合路由算法。该算法主要思想在于将节点能耗、传输距离与聚合收益3方面作为启发因子,通过一组称为“蚂蚁”的人工代理寻找到达汇聚节点的最优路径。该算法利用蚁群算法的正反馈效应来达到数据汇集的目的,不需要网络节点维护全局信息,因此是一种实现数据聚合在能量与时延上折中的分布式路由算法。理论分析和仿真结果说明了新算法的有效性。     

面向智能电网的物联网信息聚合技术

物联网应用于智能电网是信息通信技术发展到一定阶段的必然结果,利用物联网技术将能有效整合电力系统基础设施资源,提高电力系统信息化水平,改善现有电力系统基础设施的利用效率。本文针对物联网技术和我国智能电网建设规划,研究面向智能电网应用的物联网网络架构及关键技术,总结了技术特点。在阐明网络架构的基础上,进一步针对智能电网应用中海量设备终端和海量采集信息的特点,详细论述物联网信息聚合技术,分析信息聚合技术带来的网络收益,提出信息聚合技术基本功能框架及实现方式。物联网信息聚合技术在采集原始数据的同时进行大量的信息处理和计算,从海量的、杂乱无章、难以理解的原始数据中抽取并推导出对于智能电网一体化管理平台具有特定意义和判决参考价值的数据,并且能够降低网络数据传输总量、减少网络拥塞发生、提高网络性能,是物联网发展的重要技术方向之一。本文针对智能电网目前相对薄弱的配用电环节提出配变电设备监测物联网的主要功能与信息聚合方案。     

基于多代理在微电网中拓扑识别与仿真

分布式发电的迅速发展,广泛的并网或者离网导致电网的结构改变,传统的识别方法在节点较多的情况下识别时间过长,导致电网优化性控制问题突出。为了解决这个问题,改进基于多代理(MAS)的微电网拓扑识别改进策略,设计功能不同的Agent,运用蚁群算法寻找黑板Agent,采用MAS通信机制构建拓扑识别模型。最后,利用Matlab软件编程对两种传统拓扑识别法以及智能MAS法进行仿真比较,结果表明改进后的MAS拓扑识别方法在静态和动态拓扑识别中,在网络节点数量较多时识别效率有极大的提高。     

基于凝聚型层次聚类的PBFT优化共识机制

面对粮食联盟链网络中的大量共识节点,由于传统实用拜占庭容错(practical Byzantine fault tolerance,PBFT)共识算法效率低下,导致通信能耗过高,从而极大地增加信息泄露和数据造假的风险。针对上述难题,本文提出了一种基于凝聚型层次聚类(agglomerative hierarchical clustering,AHC)的PBFT优化共识算法。首先,利用AHC算法对所有网络共识节点进行目标划分和聚类;其次,使所有簇并行发生PBFT共识;最后,通过簇间主节点共识达成消息一致。实验结果表明,该改进算法能够有效降低能量开销,并提高共识效率和吞吐量。     

NetFlow流量采集与聚合的研究实现

为实现对局域网络的流量监测与异常分析,提出一套基于NetFlow的大流量数据采集与聚合方案.该方案使用缓冲区和多线程机制米采集NetFlow数据,有效提高数据采集效率和稳定性.在原始数据的基础上,采用一整套聚合策略对数据进行处理和存储,以满足实时流量和历史流量的监测.该方案可对纷繁复杂的原始流量信息进行有效整理,为前端各种流量分析提供合理高效的数据支持.以此方案为基础的网络流量监测系统已实际运用于大型网络中.     

传感器网络中一种分布式数据汇聚层次路由算法

由于传感器网络具有能量约束,低速率冗余数据和多对一传输等特点,传统的端到端集中式路由算法一般不适合传感器网络.提出了一种分布式数据汇聚层次路由算法,该算法利用能量核的思想汇聚数据和减少传输到目的节点的信息.模拟结果表明:比较传统的端到端集中式路由算法,该算法可以显著减少能量消耗;与一般的数据汇聚算法相比,该算法在保证能量消耗少的条件下,具有复杂度低和可扩展性好的特点.     

Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications

Energy is vital parameter for communication in Internet of Things (IoT) applications via Wireless Sensor Networks (WSN). Genetic algorithms with dynamic clustering approach are supposed to be very effective technique in conserving energy during the process of network planning and designing for IoT. Dynamic clustering recognizes the cluster head (CH) with higher energy for the data transmission in the network. In this paper, various applications, like smart transportation, smart grid, and smart cities, are discussed to establish that implementation of dynamic clustering computing-based IoT can support real-world applications in an efficient way. In the proposed approach, the dynamic clustering-based methodology and frame relay nodes (RN) are improved to elect the most preferred sensor node (SN) amidst the nodes in cluster. For this purpose, a Genetic Analysis approach is used. The simulations demonstrate that the proposed technique overcomes the dynamic clustering relay node (DCRN) clustering algorithm in terms of slot utilization, throughput and standard deviation in data transmission.

     

Density Based Fuzzy C Means Clustering to prolong Network Lifetime in Smart Grids

Wireless sensor networks (WSN's) are preferred for industrial applications due to progressive increase of sensor electronics. One such application is deployment of WSN's in smart grids. Smart Grid integrates information and communication techniques with electricity network. Smart grids utilize sophisticated control and monitoring devices for improving the efficiency of the grid. For energy efficient, low cost monitoring and control in smart grid WSN's is treated as a promising technology. Advanced Metering Infrastructure (AMI) is the key technology in the distribution networks of Smart Grid. The AMI is composed of various sensors for metering purpose. The meter data is also useful for the distribution operators to manage the demand response. The network involves smart meters, smart electric gas and water meters along with digital network management appliances for optimizing the electric network with real time data management. The smart sensors are limited in terms of battery, operational power and memory. These sensors communicate with the base station in restricted range. The communication between smart grid nodes and base station (sink) is multi-hop in nature. The communication takes place within limited range of communication so the security concerns that are involved in the network are to be handled by the routing protocols. So as to make the bidirectional communication efficient between the smart sensors and utility an effective routing scheme is required for these energy limited devices to handle the heavy network traffic in smart grids. Here energy efficient routing for WSN's in NAN networks to attain load balancing is proposed through density based Fuzzy C means clustering (DFCM). The obtained simulation results show that DFCM can provide a satisfactory performance for enhancing the network life span.

     

Optimal Routing and Data Aggregation for Maximizing Lifetime of Wireless Sensor Networks

An optimal routing and data aggregation scheme for wireless sensor networks is proposed in this paper. The objective is to maximize the network lifetime by jointly optimizing data aggregation and routing. We adopt a model to integrate data aggregation with the underlying routing scheme and present a smoothing approximation function for the optimization problem. The necessary and sufficient conditions for achieving the optimality are derived and a distributed gradient algorithm is designed accordingly. We show that the proposed scheme can significantly reduce the data traffic and improve the network lifetime. The distributed algorithm can converge to the optimal value efficiently under all network configurations.      

Dual head static clustering algorithm for wireless sensor networks

Clustering of nodes is often used in wireless sensor networks to achieve data aggregation and reduce the number of nodes transmitting the data to the sink. This paper proposes a novel dual head static clustering algorithm (DHSCA) to equalise energy consumption by the sensor nodes and increase the wireless sensor network lifetime. Nodes are divided into static clusters based on their location to avoid the overhead of cluster re-formation in dynamic clustering. Two nodes in each cluster, selected on the basis of the their residual energy and their distance from the sink and other nodes in the cluster, are designated as cluster heads, one for data aggregation and the other for data transmission. This reduces energy consumption during intra-cluster and inter-cluster communication. A multi-hop technique avoiding the hot-spot problem is used to transmit the data to the sink. Experiments to observe the energy consumption patterns of the nodes and the fraction of packets successfully delivered using the DHSCA suggest improvements in energy consumption equalisation, which, in turn, enhances the lifetime of the network. The algorithm is shown to outperform all the other static clustering algorithms, while being comparable with the performance of the best dynamic algorithm.     

基于分簇的动态业务量疏导区分保护策略

To make dynamic traffic grooming faster and more efficient,and achieve an intelligent differentiated protection,a differentiated protection strategy with dynamic traffic grooming based on clustering(DPS-DTGC)was proposed.The whole network topology was allocated some clusters based on maximal independent set,in order to reduce the routing time consumption.Meanwhile,by the cooperation of layered auxiliary graph,residual capacity matrix and cluster aggregation layer,the traffic in inter- and intra- clusters would been groomed to realize the reasonable planning of resources and the higher efficiency of grooming.Furthermore,according to the proportion of different priority traffic in one wavelength ,the link importance was evaluated and a smart P-cycle was designed to give differentiated protection to the link.The simulation results show this strategy can make a better utilization of network resource.And with the increase of network load,it will gain a good performance in blocking rate.     

Double Q-learning-based adaptive trajectory selection for energy-efficient data collection in wireless sensor networks

This paper proposes a novel distributed stochastic routing strategy using mobile sink based on double Q-learning algorithm to improve the network performance in wireless sensor network with uncertain communication links. Furthermore, in order to extend network lifetime, a modified leach-based clustering technique is proposed. To balance the energy dissipation between nodes, the selected cluster head nodes are then rotated based on the newly suggested threshold energy value. The simulation results demonstrate that the proposed algorithms outperform the QWRP, QLMS, ESRP and HACDC in terms of network lifetime by 18.33%, 35.1%, 39.7% and 44.7%, respectively. Moreover, the proposed algorithms considerably enhances the learning rate and hence reduces the data collection latency.     

Fault-Tolerance Data Aggregation for Clustering Wireless Sensor Network

The hierarchical cluster-based topology is commonly accepted as an optimal structure for sensor network to increase communication scalability, prolong network lifetime, and reduce data redundancy. However, the data privacy and security are challenging the proliferation of clustering wireless sensor network (CWSN) due to its highly constrained resources and violably deployed environments, which make it infeasible to directly apply traditional cryptography and therefore vulnerable to various attacks. This article proposes a scheme that provides efficient privacy-preserving data fusion as well as malicious data tolerance by mining concealed data within groups. And the dynamically organized groups in each cluster improves resilience against large number of node compromise comparing with the existing data aggregation schemes. The simulation results and mathematical comparison show the effectiveness and fitness of our scheme for CWSN in terms of fault tolerance and process efficiency, which costs a little of additional overheads in memory and communication.