Structure-Free Data Aggregation in Sensor Networks

Data aggregation protocols can reduce the communication cost, thereby extending the lifetime of sensor networks. Prior works on data aggregation protocols have focused on tree-based or cluster-based structured approaches. Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for event-based applications. The goal of our work is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure. As packets need to converge spatially and temporally for data aggregation, we propose two corresponding mechanisms - data-aware anycast at the MAC layer and randomized waiting at the application layer. We model the performance of the combined protocol that uses both the approaches and show that our analysis matches with the simulations. Using extensive simulations and experiments on a testbed with implementation in TinyOS, we study the performance and potential of structure-free data aggregation.     

Optimal Query-Driven Data Forwarding for Delay-Sensitive Wireless Sensor Networks

Most of existing works on the topic of real-time routing for wireless sensor networks suffer from void forwarding paths (cannot reach the destination, but have to backtrack) and time overhead of handling isolated nodes. Designing a desired real-time data forwarding protocol as well as achieving a good tradeoff between real time and energy efficiency (as well as energy balance) for delay-sensitive wireless sensor networks remains a crucial and challenging issue. In this paper, we propose an optimal query-driven data forwarding framework that each sensor gets its optimal data forwarding paths (directed acyclic graphs) based on the query messages flooded by the base station without extra overhead. Furthermore, First Forwarding Nodes and Second Forwarding Nodes schemes are developed for data forwarding. In addition, two greedy distributed data forwarding algorithms are provided base on hybrid link cost model trying to achieve energy balance and congestion avoidance in data forwarding. Our framework is fully distributed and practical to implement, as well as robust and scalable to topological changes. The extensive simulations show that our framework has significantly outperformed the existing routing protocols in terms of real time and energy efficiency.     

Maximizing Lifetime for Data Aggregation in Wireless Sensor Networks

This paper studies energy efficient routing for data aggregation in wireless sensor networks. Our goal is to maximize the lifetime of the network, given the energy constraint on each sensor node. Using linear programming (LP) formulation, we model this problem as a multicommodity flow problem, where a commodity represents the data generated from a sensor node and delivered to a base station. A fast approximate algorithm is presented, which is able to compute (1−ε)-approximation to the optimal lifetime for any ε > 0. Then along this baseline, we further study several advanced topics. First, we design an algorithm, which utilizes the unique characteristic of data aggregation, and is proved to reduce the running time of the fastest existing algorithm by a factor of K, K being the number of commodities. Second, we extend our algorithm to accommodate the same problem in the setting of multiple base stations, and study its impact on network lifetime improvement. All algorithms are evaluated through both solid theoretical analysis and extensive simulation results. Yuan Xue received her B.S. in Computer Science from Harbin Institute of Technology, China in 1994 and her M.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2002, and 2005. Currently she is an assistant professor at the Department of Electrical Engineering and Computer Science of Vanderbilt University. Her research interests include wireless and sensor networks, mobile systems, and network security. Yi Cui received his B.S. and M.S. degrees in 1997 and 1999, from Department of Computer Science, Tsinghua University, China, and his Ph.D. degree in 2005 from the Department of Computer Science, University of Illinois at Urbana-Champaign. Since then, he has been with the Department of Electrical Engineering and Computer Science at Vanderbilt University, where he is currently an assistant professor. His research interests include overlay network, peer-to-peer system, multimedia system, and wireless sensor network. Klara Nahrstedt (M ' 94) received her A.B., M.Sc degrees in mathematics from the Humboldt University, Berlin, Germany, and Ph.D in computer science from the University of Pennsylvania. She is an associate professor at the University of Illinois at Urbana-Champaign, Computer Science Department where she does research on Quality of Service(QoS)-aware systems with emphasis on end-to-end resource management, routing and middleware issues for distributed multimedia systems. She is the coauthor of the widely used multimedia book ‘Multimedia:Computing, Communications and Applications’ published by Prentice Hall, and the recipient of the Early NSF Career Award, the Junior Xerox Award and the IEEE Communication Society Leonard Abraham Award for Research Achievements, and the Ralph and Catherine Fisher Professorship Chair. Since June 2001 she serves as the editor-in-chief of the ACM/Springer Multimedia System Journal. An erratum to this article is available at .     

Multi-Source Temporal Data Aggregation in Wireless Sensor Networks

Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes.This paper proposes an energy-efficient multi-source temporal data aggregation model called MSTDA in WSNs. In MSTDA model, a feature selection algorithm using particle swarm optimization (PSO) is presented to simplify the historical data source firstly. And then a data prediction algorithm based on improved BP neural network with PSO (PSO-BPNN) is proposed. This MSTDA model, which helps to find out potential laws according to historical data sets, is deployed at both the base station (BS) and the node. Only when the deviation between the actual and the predicted value at the node exceeds a certain threshold, the sampling value and new model are sent to BS. The experiments on the dataset which comes from the actual data collected from 54 sensors deployed in the Intel Berkeley Research lab made a satisfied performance. When the error threshold greater than 0.15, it can decrease more than 80% data transmissions.     

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.     

An Energy Efficient Scalable Clustering Protocol for Dynamic Wireless Sensor Networks

The issue of energy constraint has always been a challenging task in the research field of wireless sensor networks. Clustering is the most effective approach for enhancing the performance of wireless sensor networks to a great extent in terms of energy consumption, network lifetime and throughput. However, the uneven formation of clusters can lead to faster energy depletion of few nodes, and thus results in premature failure of the wireless sensor network. This paper proposes an energy-efficient scalable clustering protocol (EESCP) which considers inter-cluster and intra-cluster distances to generate balanced clusters. A novel Dragonfly algorithm based particle swarm optimization technique is proposed to optimize the selection of cluster heads. Further, extensive simulations have been carried out by varying node densities and network sizes to demonstrate the full potential of EESCP.

     

Malleability Resilient Concealed Data Aggregation in Wireless Sensor Networks

The objective of concealed data aggregation is to achieve the privacy preservation at intermediate nodes while supporting in-network data aggregation. The need for privacy preservation at intermediate nodes and the need for data aggregation at intermediate nodes can be simultaneously realized using privacy homomorphism. Privacy homomorphism processes the encrypted data without decrypting them at intermediate nodes. However, privacy homomorphism is inherently malleable. Although malicious adversaries cannot view transmitted sensor readings, they can manipulate them. Hence, it is a formidable challenge to realize conflicting requirements, such as end-to-end privacy and end-to-end integrity, while performing en route aggregation. In this paper, we propose a malleability resilient concealed data aggregation protocol for protecting the network against active and passive adversaries. In addition, the proposed protocol protects the network against insider and outsider adversaries. The proposed protocol simultaneously realizes the conflicting objectives like privacy at intermediate nodes, end-to-end integrity, replay protection, and en route aggregation. As per our knowledge, the proposed solution is the first that achieves end-to-end security and en route aggregation of reverse multicast traffic in the presence of insider, as well as outsider adversaries.     

Data Aggregation in Wireless Sensor Networks Using Firefly Algorithm

Wireless Personal Communications - The challenging issue of data aggregation in wireless sensor networks (WSNs) is of high significance for reducing network overhead and traffic. The majority of...     

Data Aggregation Routing for Rechargeable Wireless Sensor Networks in Forest Monitoring

In rechargeable wireless sensor networks (r-WSNs), higher data transmitted efficiency is required because sensor have to operate in a very low duty cycle owing to sporadic availability of energy. In r-WSNs, Data collected by many sensors is based on common phenomena, and hence there is a high probability that this data has some redundancy. In this work, we address the problem of jointly optimizing data aggregation and routing so that the network workload can be maximized. Establish the relationship model between data aggregation rate and throughput, so that the balanced was set up between the data aggregation rate and maximum network data traffic. Through the use of optimal candidate sample allocation, the algorithm can coverage efficiently and can make the maximum data aggregation rate flow to the network while maximizing network workload. Simulations are carried out to show that the proposed algorithm can significantly improve workload.     

A Path-Based Approach for Data Aggregation in Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network(WSN) are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme(PBDAS) for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station(BS), we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.     

Delay-Constrained Optimal Data Aggregation in Hierarchical Wireless Sensor Networks

A lot of realistic applications with wireless sensor networks adopt hierarchical architecture in which sensor nodes are grouped into clusters, with each cluster relying on a gateway node for local data aggregation and long-distance radio transmission. Compared to normal sensor nodes, the gateway nodes, also called application nodes (ANs), are equipped with relatively powerful transceivers and have more energy. Nevertheless, since an AN is the main gateway for sensor nodes within its clusters, its energy may be depleted more quickly than normal sensor nodes. As such, it is important to find methods to save energy for ANs. This paper presents a Delay-Constrained Optimal Data Aggregation (DeCODA) framework that considers the unique feature of traffic patterns and information processing at ANs for energy saving. Mathematical models and analytical results are provided, and simulation studies are performed to verify the effectiveness of the DeCODA framework.     

基于分簇的无线多媒体传感器网络数据聚合方案研究

该文提出了一种基于分簇的无线多媒体传感器网络(WMSNs)数据聚合方案(Cluster-based Data Aggregation Algorithm, CDAA)。利用新的分簇方法和数据聚合策略,CDAA可以有效延长网络生命期。根据多媒体节点数据采集的方向性和节点剩余能耗,该文提出新的无线多媒体传感器网络的分簇方法,并基于该分簇方法进行网内多媒体数据聚合。仿真结果表明,该方法能够有效减少冗余数据的传送,与LEACH, PEGASIS等传统WSNs路由协议和针对WMSNs的AntSensNet协议相比,在能耗均衡和节能方面表现出更好的性能。     

无线传感器网络中有效的安全数据融合机制

在无线传感器网络中,数据融合是实现有效传输和节省能源的一个重要途径,许多应用都需要可靠并且可信的数据来进行融合.针对上述要求,提出了一个新的安全数据融合算法来保证融合数据的机密性和完整性.算法使用端到端加密和逐跳加密相结合的方式进行数据传输,通过认证过程进行恶意节点及伪造数据的检测.仿真表明,提出的算法能够有效地检测出恶意节点,并保证融合结果的准确性.     

Optimal Stochastic Policies for Distributed Data Aggregation in Wireless Sensor Networks

The scenario of distributed data aggregation in wireless sensor networks is considered, where sensors can obtain and estimate the information of the whole sensing field through local data exchange and aggregation. An intrinsic tradeoff between energy and aggregation delay is identified, where nodes must decide optimal instants for forwarding samples. The samples could be from a node's own sensor readings or an aggregation with samples forwarded from neighboring nodes. By considering the randomness of the sample arrival instants and the uncertainty of the availability of the multiaccess communication channel, a sequential decision process model is proposed to analyze this problem and determine optimal decision policies with local information. It is shown that, once the statistics of the sample arrival and the availability of the channel satisfy certain conditions, there exist optimal control-limit-type policies that are easy to implement in practice. In the case that the required conditions are not satisfied, the performance loss of using the proposed control-limit-type policies is characterized. In general cases, a finite-state approximation is proposed and two on-line algorithms are provided to solve it. Practical distributed data aggregation simulations demonstrate the effectiveness of the developed policies, which also achieve a desired energy-delay tradeoff.     

A Multi-Level Strategy for Energy Efficient Data Aggregation in Wireless Sensor Networks

In this paper, we have proposed energy efficient multi-level aggregation strategy which considers data sensing as continuous stochastic process. Our proposed strategy performs filtration of sensed data by removing the redundancy in the sensed data pattern of the sensor node using Brownian motion. Further, the filtered data at the sensor node undergoes entropy-based processing prior to the transmission to cluster head. The head node performs wavelet-based truncation of the received entropy in order to select higher information bearing packets before transmitting them to the sink. Overall, our innovative approach reduces the redundant packets transmissions yet maintaining the fidelity in the aggregated data. We have also optimized the number of samples that should be buffered in an aggregation period. In addition, the power consumption analysis for individual sensors and cluster heads is performed that considers the communicational and computational cost as well. Simulation of our proposed method reveals quality performance than existing data aggregation method based on wavelet entropy and entropy based data aggregation protocols respectively. The evaluation criteria includes—cluster head survival, aggregation cycles completed during simulation, energy consumption and network lifetime. The proposed scheme reflects high potential on practical implementation by improving the life prospects of the sensor network commendably.     

Data Aggregation using Difference transfer for Load Reduction in Periodic Sensor Networks

One of the basic challenges in wireless sensor networks is energy conservation. Sensor nodes are energy constrained and prudent energy usage is of utmost importance. Data aggregation aims to reduce amount of data communicated across the network without loss in information, thereby reducing the energy costs, and increasing network lifetime. In this paper, we propose a novel, simple and easy to implement method to reduce the amount of periodic data transferred from the sensor nodes to the sink. Instead of sending a set of measures at the end of every time period, we propose sending the first measure, and for every subsequent measure in that time period, we send the difference with respect to first measure. Differences are represented by a group of binary bits. Differences are also chosen in an adaptive manner in order to maintain precision between the data measured at sensor nodes and data reconstructed from binary bit patterns at sink. We evaluated our technique against two real world data-sets with vastly different properties. Results indicate 85–88.5% reduction in amount of data sent and transmission energy.

     

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.     

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。     

Trajectory-Based Data Forwarding Schemes for Vehicular Networks

This paper explains trajectory-based data forwarding schemes for multihop data delivery in vehicular networks where the trajectory isthe GPS navigation path for driving in a road network. Nowadays, GPS-based navigation is popular with drivers either for efficient driv-ing in unfamiliar road networks or for a better route, even in familiar road networks with heavy traffic. In this paper, we describe howto take advantage of vehicle trajectories in order to design data-forwarding schemes for information exchange in vehicular networks.The design of data-forwarding schemes takes into account not only the macro-scoped mobility of vehicular traffic statistics in road net-works, but also the micro-scoped mobility of individual vehicle trajectories. This paper addresses the importance of vehicle trajectoryin the design of multihop vehicle-to-infrastructure, infrastructure-to-vehicle, and vehicle-to-vehicle data forwarding schemes. First, weexplain the modeling of packet delivery delay and vehicle travel delay in both a road segment and an end-to-end path in a road net-work. Second, we describe a state-of-the-art data forwarding scheme using vehicular traffic statistics for the estimation of the end-to-end delivery delay as a forwarding metric. Last, we describe two data forwarding schemes based on both vehicle trajectory and vehicu-lar traffic statistics in a privacy-preserving manner.     

Towards Secure Data Forwarding with ANFIS and Trust Evaluation in Wireless Sensor Networks

Wireless Personal Communications - The rapid growth of Wireless Networking and MEMS augmented the human lifestyle. The combination of Wireless Sensor Networks (WSN) with Artificial Intelligence...