传感器网络中基于路线的隐私保护数据聚集算法

针对现有隐私保护数据聚集算法依赖某种网络拓扑结构和加解密次数过多的问题,本文提出了一种基于同心圆路线的隐私保护数据聚集算法PCIDA （Privacy-preserving and Concentric-circle Itinerary-based Data Aggregation algorithm）.PCIDA沿着设计好的理想路线执行数据聚集,使得算法不依赖网络拓扑结构.PCIDA利用安全通道保证数据的隐私性,避免了数据聚集过程中的加解密运算.PCIDA沿着同心圆并行处理,使得算法数据处理延迟较小.理论分析和实验结果显示,PCIDA在较低通信量和能耗的情况下获得较高的数据隐私性和聚集精确度.     

A Secret Confusion Based Energy-Saving and Privacy-Preserving Data Aggregation Algorithm

To reduce communication overhead on the premise of privacy protection, this study presents a novel secret Confusion based energy-saving and privacy-preserving data aggregation algorithm (CESPT). In con-fusion phase, CESPT confuses real sensory data and their sources by positive-negative pairs and a confusion factor is introduced to determine the quantity of pairs generated by a sensor, the exchange rounds and the threshold of data ex-change, which aff ect communication overhead and privacy intensity of a Wireless sensor network (WSN). In aggre-gation phase, CESPT adopts a positive-negative neutral-ization strategy and a well-designed time slice allocation mechanism to reduce network traffic and message collision. In a word, CESPT can greatly reduce data traffic and en-ergy consumption and obtain accurate statistical results on the basis of data privacy.     

安全的WSN数据融合隐私保护方案设计

针对无线传感器网络数据融合过程中的数据隐私和完整性保护问题,提出一种安全的数据融合隐私保护方案(SPPDA),把节点的私密因子与原始数据构成复数,采用同态加密方法对复数进行加密,实现在密文不解密的情况下进行数据融合,同时采用基于复数的完整性验证方法,确保数据的可靠性。理论分析和仿真结果表明,SPPDA方案的计算代价和通信开销较少,数据融合的精确度高。     

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.     

无线自组传感器网络中基于能量的超宽带多路径路由算法

The Energy based Ultra-Wideband Multipath Routing (EUMR) algorithm for Ad hoc sensor network is proposed. It utilizes the function of UWB positioning to reduce the network communication delay and route overhead. Furthermore, the algorithm considers energy consumption, the residual energy and node hops of communication paths to make energy consumption more balanced and extend the network lifetime. Then routing which is stable, energy-saving and low-delay is realized. Simulation results show that the algorithm has better performance on saving energy, route overhead, stability and extending network lifetime.     

SCT Based Adaptive Data Aggregation for Wireless Sensor Networks

Communication overhead is a major concern in wireless sensor networks because of inherent behavior of resource constrained sensors. To degrade the communication overhead, a technique called data aggregation is employed. The data aggregation results are used to make crucial decisions. Certain applications apply approximate data aggregation in order to reduce communication overhead and energy levels. Specifically, we propose a technique called semantic correlation tree, which divides a sensor network into ring-like structure. Each ring in sensor network is divided into sectors, and each sector consists of collection of sensor nodes. For each sector, there will be a sector head that is aggregator node, the aggregation will be performed at sector head and determines data association on each sector head to approximate data on sink node. We propose a doorway algorithm to approximate the sensor node readings in sector head instead of sending all sensed data. The main idea of doorway algorithm is to reduce the congestion and also the communication cost among sensor nodes and sector head. This novel approach will avoid congestion by controlling the size of the queue and marking packets. Specifically, we propose a local estimation model to generate a new sensor reading from historic data. The sensor node sends each one of its parameter to sector head, instead of raw data. The doorway algorithm is utilized to approximate data with minimum and maximum bound value. This novel approach, aggregate the data approximately and efficiently with limited energy. The results demonstrate accuracy and efficiency improvement in data aggregation.     

Scalable privacy-preserving big data aggregation mechanism

As the massive sensor data generated by large-scale Wireless Sensor Networks (WSNs) recently become an indispensable part of ‘Big Data’, the collection, storage, transmission and analysis of the big sensor data attract considerable attention from researchers. Targeting the privacy requirements of large-scale WSNs and focusing on the energy-efficient collection of big sensor data, a Scalable Privacy-preserving Big Data Aggregation (Sca-PBDA) method is proposed in this paper. Firstly, according to the pre-established gradient topology structure, sensor nodes in the network are divided into clusters. Secondly, sensor data is modified by each node according to the privacy-preserving configuration message received from the sink. Subsequently, intra- and inter-cluster data aggregation is employed during the big sensor data reporting phase to reduce energy consumption. Lastly, aggregated results are recovered by the sink to complete the privacy-preserving big data aggregation. Simulation results validate the efficacy and scalability of Sca-PBDA and show that the big sensor data generated by large-scale WSNs is efficiently aggregated to reduce network resource consumption and the sensor data privacy is effectively protected to meet the ever-growing application requirements.     

基于连通支配集的无线传感网Top-k查询最优支撑树研究

构建底层逻辑树能有效降低集中式top-k查询带来的巨大通信开销,针对现有逻辑树都以固定汇聚节点为根节点,导致其附近节点能耗太大、过早死亡的问题,本文在无固定汇聚节点的网络背景下,基于连通支配集,提出一种能耗均衡的top-k查询最优支撑树构建方法,综合节点能量、度数以及与邻节点通信开销,选取能量代价小的作为支配节点负责查询中间数据处理,在每次查询中,节点基于地理位置ID轮流作为根节点,有效均衡节点的能耗.仿真实验表明,与其他逻辑拓扑树相比,基于最优支撑树的top-k查询具有相近的查询时间,但其平均每轮查询能耗更小,多次查询后各节点能耗达到均衡,有效延长了网络生命周期.     

A new load balancing and data collection algorithm for energy saving in wireless sensor networks

Data gathering is a major function of many applications in wireless sensor networks. The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirements of special applications or users. Wireless sensor networks are characterized by centralized data gathering, multi-hop communication and many to one traffic pattern. These three characteristics can lead to severe packet collision, network congestion and packet loss, and even result in hot-spots of energy consumption thus causing premature death of sensor nodes and entire network. In this paper, we propose a load balance data gathering algorithm that classifies sensor nodes into different layers according to their distance to sink node and furthermore, divides the sense zone into several clusters. Routing trees are established between sensor node and sink depending on the energy metric and communication cost. For saving energy consumption, the target of data aggregation scheme is adopted as well. Analysis and simulation results show that the algorithm we proposed provides more uniform energy consumption among sensor nodes and can prolong the lifetime of sensor networks.     

无线传感器网络一种基于聚合收益的动态成簇算法

无线传感器网络资源有限,通常采用分簇聚合减少传输数据,本文提出了一种基于聚合收益的动态成簇算法.首先,针对网络整体能耗最优化问题,建立一个非线性整数规划模型,进而提出一种近似最优、低复杂度的启发式簇头选举算法.在此基础上,提出一种分布式的、基于聚合收益的动态成簇算法,可分布式实现该簇头选举算法并进行成簇.理论分析和实验仿真表明,基于聚合收益的动态成簇算法能较好地解决节点负载均衡问题,提高网络能耗效率,延长网络生命周期.     

Reversible digital watermarking-based protocol for data integrity in wireless sensor network

For the contradiction between high energy consumption of WSN privacy protection algorithm and constrained resources of sensor network,a recoverable data fusion protocol that ensures data integrity and confidentiality based on reversible digital watermarking and homomorphic encryption technology was proposed.On the one hand,the data from the sensor was embedded by the difference expansion method by using the reversible digital watermarking technique,and original data could be recovered by using a reversible watermark to ensure the integrity check of the fusion data when the fusion data were destroyed.On the other hand,elliptic curve homomorphic encryption encrypted data to prevent sensor data from being perceived during data transmission.Security results show that the proposed protocol performs well against cluster head node compromise as well as tampering from an attack.Performance analysis shows that the protocol has significant advantages over other algorithms in terms of computation,communication overhead and propagation delay.The experimental results show that the protocol has a low resource overhead and improves network performance.     

WSN中一种基于数据融合的能量高效分簇路由协议

基于无线传感器网络中监测数据具有较高时空相关性的应用场景。提出了一种基于数据融合的局部能量高效汇聚分簇协议LEEAC，该协议通过反映局部空间相关性的数据相异度对节点剩余能量进行约束，并使用约束后的预测能量作为竞选簇头的主要依据，被选举的簇头在传感器网络中具有良好的分布性。同时通过引入数据鉴定码，减少了簇内数据传输阶段的通信量以及簇头数据融合的工作量，从而大大节约了能量消耗。实验结果表明，LEEAC协议能够有效均衡网络能量消耗。延长网络生存时间。     

DABPR: a large-scale internet of things-based data aggregation back pressure routing for disaster management

In this paper, we propose a data aggregation back pressure routing (DABPR) scheme, which aims to simultaneously aggregate overlapping routes for efficient data transmission and prolong the lifetime of the network. The DABPR routing algorithm is structured into five phases in which event data is sent from the event areas to the sink nodes. These include cluster-head selection, maximization of event detection reliability, data aggregation, scheduling, and route selection with multi attributes decision making metrics phases. The scheme performs data aggregation on redundant data at relay nodes in order to decrease both the size and rate of message exchanges to minimize communication overhead and energy consumption. The proposed scheme is assessed in terms of packet delivery, network lifetime, ratio, energy consumption, and throughput, and compared with two other well-known protocols, namely “information-fusion-based role assignment (InFRA)” and “data routing for in-network aggregation (DRINA)”, which intrinsically are cluster and tree-based routing schemes designed to improve data aggregation efficiency by maximizing the overlapping routes. Meticulous analysis of the simulated data showed that DABPR achieved overall superior proficiency and more reliable performance in all the evaluated performance metrics, above the others. The proposed DABPR routing scheme outperformed its counterparts in the average energy consumption metric by 64.78% and 51.41%, packet delivery ratio by 28.76% and 16.89% and network lifetime by 42.72% and 20.76% compared with InFRA and DRINA, respectively.

     

Connected sensor cover: self-organization of sensor networks for efficient query execution

Spatial query execution is an essential functionality of a sensor network, where a query gathers sensor data within a specific geographic region. Redundancy within a sensor network can be exploited to reduce the communication cost incurred in execution of such queries. Any reduction in communication cost would result in an efficient use of the battery energy, which is very limited in sensors. One approach to reduce the communication cost of a query is to self-organize the network, in response to a query, into a topology that involves only a small subset of the sensors sufficient to process the query. The query is then executed using only the sensors in the constructed topology. The self-organization technique is beneficial for queries that run sufficiently long to amortize the communication cost incurred in self-organization. In this paper, we design and analyze algorithms for suchself-organization of a sensor network to reduce energy consumption. In particular, we develop the notion of a connected sensor cover and design a centralized approximation algorithm that constructs a topology involving a near-optimal connected sensor cover. We prove that the size of the constructed topology is within an O(logn) factor of the optimal size, where n is the network size. We develop a distributed self-organization version of the approximation algorithm, and propose several optimizations to reduce the communication overhead of the algorithm. We also design another distributed algorithm based on node priorities that has a further lower communication overhead, but does not provide any guarantee on the size of the connected sensor cover constructed. Finally, we evaluate the distributed algorithms using simulations and show that our approaches results in significant communication cost reductions.     

最大化最小能耗概率的移动Sink无线传感器网络数据收集方法

在基于移动sink传感器网络中,传感器节点能量受限,数据收集的能耗问题一直是研究的热点.通过建立最大化最小能耗概率模型,提出一种最大化最小能耗概率(Maximizing Minimum Probability of Energy Consumption,MMPEC)数据收集方法.MMPEC对网络中子节点与汇聚节点之间的路径长度进行分布式优化,使得整个网络的能耗达到最低的概率最大化.仿真结果表明,MMPEC在能耗方面优于同类基于移动sink的WSN分层数据收集方法.     

Achieving Energy Conservation by Cluster Based Data Aggregation in Wireless Sensor Networks

In Wireless Sensor Networks (WSN), one of the major issues is to maximize the network lifetime. Since all sensor nodes directly send the data to the Base station, the energy requirement is very high. This reduces the lifetime of the network. One of the solutions is to partition the network into various clusters which avoids direct communication. In this paper we propose an Energy efficient Cluster Based Data Aggregation (ECBDA) scheme for sensor networks. In this algorithm, Cluster members send the data only to its corresponding local cluster head, there by communication overhead is reduced. Data generated from neighboring sensors are often redundant and highly correlated. So the cluster head performs data aggregation to reduce the redundant packet transmission. In our approach, clusters are formed in a non-periodic manner to avoid unnecessary setup message transmissions. Re-clustering is performed only when CH needs to balance the load among the nodes. The simulation results show that our approach effectively reduces the energy consumption and hence the network lifetime is also increased.     

NLDA non-linear regression model for preserving data privacy in wireless sensor networks

Recently, the application of Wireless Sensor Networks (WSNs) has been increasing rapidly. It requires privacy preserving data aggregation protocols to secure the data from compromises. Preserving privacy of the sensor data is a challenging task. This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data. The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes. Instead of sending the complete data to the cluster head, the sensor nodes only send the coefficients of the non-linear function. This will reduce the communication overhead of the network. The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data. The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead, enhance data aggregation accuracy, and preserve data privacy.     

An efficient energy consumption model using data aggregation for wireless sensor network

Wireless sensor networks (WSNs) have become increasingly important in recent years. Small and low-power sensor nodes make up these sensor networks. A random distribution of nodes is made throughout an unmanaged target region. One of WSN's key challenges is its limited and irreplaceable energy supply. In most situations, sensor nodes cannot be replaced since they operate in a hostile physical environment. The act of gathering and aggregating usable data from different sensor nodes situated to perceive almost the same attribute of the occurrence is known as data aggregation. The mathematical model is used in this research study to generate cluster-based data aggregation, which is an effective technique to increase energy usage by minimising the number of data transfers. The proposed mathematical model-based data aggregation (MM-DA) attains a 97% packet delivery ratio with minimal energy consumption. The MM-DA outperforms other existing approaches in terms of packet delivery ratio (PDR), energy consumption (EC), network lifetime and control overhead.     

低能耗的隐私数据安全融合方法

为解决物联网安全数据融合过程中,数据隐私保护与节点计算能力及能量受限之间的矛盾,在对现有方法优缺点分析的基础上,提出一种低能耗的隐私数据安全融合方法(LCSDA, low energy-consuming secure data aggregation),该方法根据最短路径原则选择邻居节点,并且采用Prim最小生成树算法建立簇内数据融合路径。仿真结果表明,该方法可以有效降低节点能耗和簇头节点被捕获的概率,同时保证节点数据的隐私性。     

Energy-efficient routing for correlated data in wireless sensor networks

In this paper, we investigate the reduction in the total energy consumption of wireless sensor networks using multi-hop data aggregation by constructing energy-efficient data aggregation trees. We propose an adaptive and distributed routing algorithm for correlated data gathering and exploit the data correlation between nodes using a game theoretic framework. Routes are chosen to minimize the total energy expended by the network using best response dynamics to local data. The cost function that is used for the proposed routing algorithm takes into account energy, interference and in-network data aggregation. The iterative algorithm is shown to converge in a finite number of steps. Simulations results show that multi-hop data aggregation can significantly reduce the total energy consumption in the network.