An approach for near-optimal distributed data fusion in wireless sensor networks

In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent’s itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs near-optimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool.     

基于Bayes序贯估计的无线传感器网络数据融合算法

移动代理被认为是无线传感器网络中解决数据融合的有效方法,但代理访问节点的次序以及总数对算法有较大影响,为此该文提出一种基于Bayes序贯估计的移动代理数据融合算法.该算法通过构造特定数据结构的报文,在多跳环境中由Bayes序贯估计调整梯度向量,据此动态决定移动代理的访问路径,使移动代理有选择地在传感器节点之间移动,且在节点处由移动代理对数据进行融合,将多余的感知数据剔除,而不是把原始数据传输到Sink节点。理论分析和模拟实验表明,该算法有较小的能量消耗和传输延时。     

Building trees based on aggregation efficiency in sensor networks

Sensor network protocols must minimize energy consumption due to their resource-constrained nature. Large amounts of redundant data are produced by the sensors in such networks; however, sending unnecessary data wastes energy. One common technique used to reduce the amount of data in sensor networks is data aggregation. Therefore, we consider the impact and cost of data aggregation in sensor networks to achieve energy-efficient operation. We propose a new notion of energy efficiency that can be used to decide where aggregation points in the network should be placed. The optimal choice of these points is determined by the aggregation efficiency, which determines the amount of data reduction, and the cost in terms of energy to perform the aggregation. We present our aggregation tree algorithm “Oceanus” that produces energy-efficient aggregation trees by taking into account both of these factors. Our evaluation shows that Oceanus provides higher energy efficiency compared to existing solutions.     

Distributed Algorithm for En Route Aggregation Decision in Wireless Sensor Networks

In sensor networks, enroute aggregation decision regarding where and when aggregation shall be performed along the routes has been explicitly or implicitly studied extensively. However, existing solutions have omitted one key dimension in the optimization space, namely, the aggregation cost. In this paper, focusing on optimizing over both transmission and aggregation costs, we develop an online algorithm capable of dynamically adjusting the route structure when sensor nodes join or leave the network. Furthermore, by only performing such reconstructions locally and maximally preserving existing routing structure, we show that the online algorithm can be readily implemented in real networks in a distributed manner requiring only localized information. Analytically and experimentally, we show that the online algorithm promises extremely small performance deviation from the offline version, which has already been shown to outperform other routing schemes with static aggregation decision.     

D2F: A Routing Protocol for Distributed Data Fusion in Wireless Sensor Networks

Data fusion can be distributed into network and executed on network nodes, to reduce data from redundant sensor nodes, to fuse the information from complementary sensor nodes and to get the complete view from cooperative nodes. Consequently only the inference of interest is sent to end user. This distributed data fusion can significantly reduce the data transmission cost and there is no need for a powerful centralized node to process the collected information. However, to achieve the advantages of distributed data fusion and better utilization of network resources, each fusion function needs to be performed at particular network node for minimizing energy cost of data fusion application, both data transmission cost and computation cost. In this paper, distributed data fusion routing (D2F) is proposed, which is designed for deploying distributed data fusion application in wireless sensor networks. D2F can find the optimal route path and fusion placements for a given data fusion tree, which obtains the optimal energy consumption for in-network data fusion. D2F can also handle different link failures and maintain the optimality of energy cost of data fusion by adapting to the dynamic change of network.     

A Rate-Distortion Based Aggregation Method Using Spatial Correlation for Wireless Sensor Networks

Today we are witnessing an amazing growth of wireless sensor networks due to many factors including but limited to reducing cost of semiconductor components, rapid deployment of wireless networks, and attention to low-power aspect that makes these networks suitable for energy sensitive applications to a large extent. The power consumption requirement has raised the demand for the new concepts such as data aggregation. Data correlation plays an important role in an efficient aggregation process. This paper introduces a new correlation-based aggregation algorithm called RDAC (Rate Distortion in Aggregation considering Correlation) that works based on centralized source coding. In our method, by collecting correlated data at an aggregation point while using the Rate-Distortion (RD) theory, we can reduce the load of data transmitted to the base station by considering the maximum tolerable distortion by the user. To the best of our knowledge, nobody has yet used the RD theory for the data aggregation in wireless sensor networks. In this paper, a mathematical model followed by implementations demonstrates the efficiency of the proposed method under different conditions. By using the unique features of the RD theory, the correlation matrix and observing the behavior of the proposed method in different network topologies, we can find the mathematical upper and lower bounds for the amount of aggregated data in a randomly distributed sensor network. The bounds not only determine the upper and lower limits of the data compressibility, it also makes possible the estimation of the required bit count of the network without having to invoke the aggregation algorithm. This method therefore, allows us to have a good estimation of the amount of energy consumed by the network.     

高效的攻击检测与数据融合算法

给出了一种高效的无线多媒体传感器网络攻击检测和数据融合算法EIDSART。该算法从节点的多元属性方面对节点行为特征进行界定,通过选择合适的邻居节点集合,可以运用于任意规模的多媒体传感器网络;另外,在经过精确检测攻击行为的情况下,对传感数据进行了融合,降低了网络通信开销。仿真结果表明,EIDSART在攻击检测精度和误报率等方面具有优势,并能得到精确的数据融合结果。     

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.     

Coverage in Hybrid Mobile Sensor Networks

This paper considers the coverage problem for hybrid networks which comprise both static and mobile sensors. The mobile sensors in our network only have limited mobility, i.e., they can move only once over a short distance. In random static sensor networks, sensor density should increase as O(log L + k log log L) to provide k-coverage in a network with a size of L. As an alternative, an all-mobile network can provide k-coverage with a constant density of O(k), independent of network size L. We show that the maximum distance for mobile sensors is O( 1/sqrt(k) log^(4/3)(kL)). We then propose a hybrid network structure, comprising static sensors and a small fraction of O( 1/sqrt(k)) of mobile sensors. For this network structure, we prove that k-coverage is also achievable with a constant sensor density of O(k). Furthermore, for this hybrid structure, we prove that the maximum distance which any mobile sensor has to move is bounded as O(log^(3/4)L). We then propose a distributed relocation algorithm, where each mobile sensor only requires local information in order to optimally relocate itself. We verify our analysis via extensive numerical evaluations and show an implementation of the mobility algorithm on real mobile sensor platforms.     

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.     

基于APF的二元WSN运动目标跟踪算法

针对传感节点计算、通信及资源受限的特点,引入二元WSN模型,提出了一种基于辅助粒子滤波(APF)的集中式算法,以实现运动目标的实时跟踪。由于每个二元传感器只对目标是否进入其感知区域做出反应(向数据融合中心报告0或1),粒子滤波算法的复杂运算集中在融合中心完成,因此节点结构简单、通信代价低廉,有助于延长监测网络的生存周期。仿真实验结果表明,该算法对随机部署和规则部署的两种方案,均具有良好的跟踪性能,能满足一般机动目标实时跟踪的应用要求。     

A reliable and security method for data aggregation in WSNs

Data aggregation in wireless sensor networks (WSNs) eliminates the data redundancy and is widely accepted as an essential paradigm for energy efficient routing in sensor networks. In this paper, we describe a protocol called RDA which associates a packet's reliability in data transmission with the amount of information it contains and gives the packet containing more information higher reliability in data transmission by adjust the degree of redundancy. Our algorithm is not associated with some special routing schemes, hence it can be used in any kinds of routing schemes for wireless sensor networks especially where data aggregation takes place and ensure a data packet with more information be transmitted more reliably and in that way reliability per energy could be improved.     

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion,aggregation, and routing scheme

Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.     

An Efficient Approach for Big Data Aggregation Mechanism in Heterogeneous Wireless Connected Sensor Networks

Recently and due to the impressive growth in the amounts of transmitted data over the heterogeneous sensor networks and the emerged related technologies especially the Internet of Things in which the number of the connected devices and the data consumption are remarkably growing, big data has emerged as a widely recognized trend and is increasingly being talked about. The term big data is not only about the volume of data, but also refers to the high speed of transmission and the wide variety of information that is difficult to collect, store and process using the available classical technologies. Although the generated data by the individual sensors may not appear to be significant, all the data generated through the many sensors in the connected sensor networks are able to produce large volumes of data. Big data management imposes additional constraints on the wireless sensor networks and especially on the data aggregation process, which represents one of the essential paradigms in wireless sensor networks. Data aggregation process can represent a solution to the problem of big data by allowing data from different sources to be combined to eliminate the redundant ones and consequently reduce the amounts of data and the consumption of the available resources in the network. The main objective of this work is to propose a new approach for supporting big data in the data aggregation process in heterogeneous wireless sensor networks. The proposed approach aims to reduce the data aggregation cost in terms of energy consumption by balancing the data loads on the heterogeneous nodes. The proposal is improved by integrating the feedback control closed loop to reinforce the balance of the data aggregation load on the nodes, maintaining therefore an optimal delay and aggregation time.

     

传感器网络中一种基于估计代价的数据聚合树生成算法

无线传感器网络是一种全新的技术,能够广泛应用于恶劣环境和军事领域.传感器网络在数据收集中,为减少冗余数据的传输耗能,降低延迟,需要采用数据聚合技术.本文采用定向传输方式,在消息路由机制基础上提出了一种基于估计代价的数据聚合树生成算法.该算法主要思想在于将节点能耗、传输距离与聚合收益三方面作为估计代价,优化聚合路径,实现数据聚合在能量与时延上的折中.     

A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter

We address the Bayesian sensor fusion approach for distributed location estimation in the wireless sensor network. Assume each sensor transmits local calculation of target position to a fusion center, which then generates under a Bayesian framework the final estimated trajectory. We study received signal strength indication-based approach using the unscented Kalman filter for each sensor to compute local estimation, and propose a novel distributed algorithm which combines the soft outputs sent from selected sensors and computes the approximated Bayesian estimates to the true position. Simulation results demonstrate that the proposed soft combining method can achieve similar tracking performance as the centralized data fusion approach. The computational cost of the proposed algorithm is less than the centralized method especially in large scale sensor networks. In addition, it is straightforward to incorporate the proposed soft combining strategy with other Bayesian filters for the general purpose of data fusion.

     

传感器网络中基于树的感知器分布优化

无线传感器网络中,感知节点的合理分布对于提高网络的感知能力和信息收集能力以及提高网络的生存期限都具有重要的作用。对于随机分布方式产生的感知网络,可以利用节点的移动性对特定感知节点的位置进行调整从而改善网络整体的感知覆盖范围。为此,利用 Voronoi 图以及相关 Delaunay 三角网定义了传感器网络中以sink 节点为中心的伸展树,并提出了基于遗传算法的感知节点分布优化算法。仿真结果表明,算法能够以较小代价对传感器网络进行节点的分布优化,从而有效提高网络整体的感知能力。     

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。     

Designing Routes for Source Coding With Explicit Side Information in Sensor Networks

In this paper, we study the problem of designing routes for source coding with explicit side information (i.e., with side information at both the encoder and the decoder) in sensor networks. Two difficulties in constructing such data-centric routes are the lack of reasonably practical data aggregation models and the high computational complexity resulting from the coupling of routing and in-network data fusion. Our data aggregation model is built upon the observation that in many physical situations the side information providing the most coding gain comes from a small number of nearby sensors. Based on this model, we formulate an optimization problem to minimize the communication cost, and show that finding the exact solution of this problem is NP-hard. Subsequently, two suboptimal algorithms are proposed. One is inspired by the balanced trees that have small total weights and reasonable distance from each sensor to the fusion center . The other separately routes the explicit side information to achieve cost minimization. Bounds on the worst-case performance ratios of two methods to the optimal solution are derived for a special class of rate models, and simulations are conducted to shed light on their average behaviors.     

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.