基于压缩感知的链型无线传感器网络节能数据收集

无线传感器网络(WSNs)中节点受体积、功率、成本等限制而导致了节点能量、生命周期有限的问题.提出一种基于压缩感知算法的无线传感器网络节能优化方法,并结合无线传感器网络中的链型拓扑网络模型,给出基于压缩感知理论的节能网络数据传输模型.通过理论分析比较表明压缩感知方法在节能方面的优越性,然后在得出的网络能耗模型的基础上进行仿真.仿真结果表明:压缩感知方法有效减少了网络能耗.     

压缩感知在无线传感网络的应用综述

随着信息技术的发展,近些年压缩感知技术格外引人瞩目,在图像视频编码、雷达及微波辐射成像、气象卫星、图像加密、物联网等领域展现出强大的功能与发展前景。首先介绍了压缩感知在无线传感网络领域的发展及研究现状,然后从压缩感知仿真实验和实例、压缩感知的测量方案、压缩感知的解压缩方案、压缩感知在无线传感网络的具体应用四个方面阐明了压缩感知在无线传感网络领域的优势,最后对压缩感知的前景进行了展望。     

Neighbor discovery for wireless networks via compressed sensing

This paper studies the problem of neighbor discovery in wireless networks, namely, each node wishes to discover and identify the network interface addresses (NIAs) of those nodes within a single hop. A novel paradigm, called compressed neighbor discovery is proposed, which enables all nodes to simultaneously discover their respective neighborhoods with a single frame of transmission, which is typically of a few thousand symbol epochs. The key technique is to assign each node a unique on–off signature and let all nodes simultaneously transmit their signatures. Despite that the radios are half-duplex, each node observes a superposition of its neighbors’ signatures (partially) through its own off-slots. To identify its neighbors out of a large network address space, each node solves a compressed sensing (or sparse recovery) problem.Two practical schemes are studied. The first employs random on–off signatures, and each node discovers its neighbors using a noncoherent detection algorithm based on group testing. The second scheme uses on–off signatures based on a deterministic second-order Reed–Muller code, and applies a chirp decoding algorithm. The second scheme needs much lower signal-to-noise ratio (SNR) to achieve the same error performance. The complexity of the chirp decoding algorithm is sub-linear, so that it is in principle scalable to networks with billions of nodes with 48-bit IEEE 802.11 MAC addresses. The compressed neighbor discovery schemes are much more efficient than conventional random-access discovery, where nodes have to retransmit over many frames with random delays to be successfully discovered.     

Practical data compression in wireless sensor networks: A survey

Power consumption is a critical problem affecting the lifetime of wireless sensor networks. A number of techniques have been proposed to solve this issue, such as energy-efficient medium access control or routing protocols. Among those proposed techniques, the data compression scheme is one that can be used to reduce transmitted data over wireless channels. This technique leads to a reduction in the required inter-node communication, which is the main power consumer in wireless sensor networks. In this article, a comprehensive review of existing data compression approaches in wireless sensor networks is provided. First, suitable sets of criteria are defined to classify existing techniques as well as to determine what practical data compression in wireless sensor networks should be. Next, the details of each classified compression category are described. Finally, their performance, open issues, limitations and suitable applications are analyzed and compared based on the criteria of practical data compression in wireless sensor networks.     

Fault-resilient sensing in wireless sensor networks

Research on wireless sensor networks (WSNs) has received tremendous attention in the past few years due to their potential applications and advances in the VLSI design. In WSNs with tiny sensors, mobility of a sink may provide an energy efficient way for data dissemination. Having a mobile sink in WSN, however, creates new challenges to routing and sensor distribution modeling in the network. In this paper, based on clustering and routing optimization algorithms, we propose a new scheme called K-means and TSP-based mobility (KAT mobility). After clustering the sensor nodes, the proposed method navigates the mobile sink to traverse through the cluster centers according to the trajectory of an optimized route. The mobile sink then collects the data from sensors at the visited clusters. Simulation results have demonstrated that the proposed scheme can provide not only better energy efficiency as compared to those obtained by conventional methods which assume random waypoint for the mobile sink, but also fault-resilience in case of malfunctions of some sensors due to attacks.     

CCS-MAC: Exploiting the overheard data for compression in wireless sensor networks

Both the overhearing and overhearing avoidance in a densely distributed sensor network may inevitably incur considerable power consumption. In this paper we propose a so-called CCS-MAC (collaborative compression strategy-based MAC) MAC protocol which facilitates to exploit those overheard data that is treated useless in traditional MAC protocols for the purpose of cost and energy savings. Particularly the CCS-MAC enables different sensor nodes to perform data compression cooperatively with regard to those overheard data, so that the redundancy of data prepared for the link layer transmission can be totally eliminated at the earliest. The problem of collaborative compression is analyzed and discussed along with a corresponding linear programming model formulated. Based on it a heuristic node-selection algorithm with a time complexity of (O(N²)) is proposed to the solve the linear programming problem. The node-selection algorithm is implemented in CCS-MAC at each sensor node in a distributed manner. The experiment results verify that the proposed CCS-MAC scheme can achieve a significant energy savings so as to prolong the lifetime of the sensor networks so far.     

无线传感器网络中基于压缩感知的分簇数据收集算法

针对无线传感器网络(WSNs)能量有限、通信链路不可靠的特点,提出一种基于稀疏分块对角矩阵进行压缩感知的分簇(SBDMC)数据收集算法.该算法以稀疏分块对角矩阵作为观测矩阵以减少参与收集节点数目;采用分布式分簇路由实现数据的分布式收集;通过分析能耗模型得到最优簇头数目以减少网络能耗.在此基础上,给出一种有效的分簇路由数据收集算法.仿真分析表明:提出的算法较之已有算法可以减少通信能耗、延长网络寿命,同时均衡能耗负载.     

Adaptive image compression technique for wireless sensor networks

When using wireless sensor networks for real-time image transmission, some critical points should be considered. These points are limited computational power, storage capability, narrow bandwidth and required energy. Therefore, efficient compression and transmission of images in wireless sensor network is considered. To address the above mentioned concerns, an efficient adaptive compression scheme that ensures a significant computational and energy reduction as well as communication with minimal degradation of the image quality is proposed. This scheme is based on wavelet image transform and distributed image compression by sharing the processing of tasks to extend the overall lifetime of the network. Simulation results are presented and they show that the proposed scheme optimizes the network lifetime, reduces significantly the amount of the required memory and minimizes the computation energy by reducing the number of arithmetic operations and memory accesses.     

应用压缩感知的无线传感器网络数据处理综述

研究了压缩感知在无线传感器网络数据处理方面的应用。介绍了压缩感知技术和无线传感器网络的发展及研究现状,并从数据融合、信号采集、信号路由传输以及信号重构4个方面,对近年来基于压缩感知的无线传感器网络数据处理研究进行了详尽的分析,提出数据安全的重要性。总结并展望了压缩感知技术未来的研究方向。     

An energy-efficient compression scheme for wireless multimedia sensor networks

Multimedia Tools and Applications - In this paper, we propose an energy-efficient compression scheme for wireless multimedia sensor networks. To do this, we analyze the characteristics of...     

无线传感器网络安全数据汇聚协议

无线传感器网络中,如何将数据汇聚技术与路由技术结合,设计高效的数据汇聚协议,以减少传输过程中的数据量,减轻数据汇聚过程中的网络拥塞,协助路由协议延长网络的生存时间是一个重要的问题。尽管数据汇聚能有效地减小通信开销,但数据汇聚技术也不可避免的带来了一系列安全问题,譬如传感数据外泄等,因而安全问题成为数据汇聚中一个重要研究方向。目前,安全数据汇聚技术及其实现协议已经成为传感器网络安全的研究中一个重要课题。详细介绍安全数据汇聚协议这个领域内的研究进展和成果,从安全角度对比分析各种现有协议,最后结合该领域内的研究现状以及作者的研究体会指出了该领域未来研究的一些重要方向。     

UAV-assisted data gathering in wireless sensor networks

An unmanned aerial vehicle (UAV) is a promising carriage for data gathering in wireless sensor networks since it has sufficient as well as efficient resources both in terms of time and energy due to its direct communication between the UAV and sensor nodes. On the other hand, to realize the data gathering system with UAV in wireless sensor networks, there are still some challenging issues remain such that the highly affected problem by the speed of UAVs and network density, also the heavy conflicts if a lot of sensor nodes concurrently send its own data to the UAV. To solve those problems, we propose a new data gathering algorithm, leveraging both the UAV and mobile agents (MAs) to autonomously collect and process data in wireless sensor networks. Specifically, the UAV dispatches MAs to the network and every MA is responsible for collecting and processing the data from sensor nodes in an area of the network by traveling around that area. The UAV gets desired information via MAs with aggregated sensory data. In this paper, we design a itinerary of MA migration with considering the network density. Simulation results demonstrate that our proposed method is time- and energy-efficient for any density of the network.     

QoS and data relaying for wireless sensor networks

In this paper we study the effects of data relaying in wireless sensor networks (WSNets) under QoS constraints with two different strategies. In the first, data packets originating from the same source are sent to the base station possibly along several different paths, while in the second, exactly one path is used for this purpose. The two strategies correspond to splitting and not splitting relaying traffic, respectively. We model a sensor network architecture based on a three-tier hierarchy of nodes which generalizes to a two-tier WSNet with multiple sinks. Our results apply therefore to both types of networks. Based on the assumptions in our model, we describe several methods for computing relaying paths that are optimal with respect to energy consumption and satisfy QoS requirements expressed by the delay with which data are delivered to the base station(s). We then use our algorithms to perform an empirical analysis that quantifies the performance gains and losses of the splittable and unsplittable traffic allocation strategies for WSNets with delay-constrained traffic. Our experiments show that splitting traffic does not provide a significant advantage in energy consumption, but can afford strategies for relaying data with a lower delay penalty when using a model based on soft-delay constraints.     

Energy-efficient cooperative data aggregation for wireless sensor networks

Recently, cooperative communication mechanism is shown to be a promising technology to improve the transmit diversity only by a single transceiver antenna. Using this communication paradigm, multiple source nodes are able to coordinate their transmissions so as to obtain energy savings. As data aggregation is one of the most important operations in wireless sensor networks, this paper studies the energy-efficient data aggregation problem through cooperative communication. We first define the cooperative data aggregation (CDA) problem, and formally prove that this problem is NP-Hard. Due to the difficult nature of this problem, we propose a heuristic algorithm MCT for cooperative data aggregation. The theoretical analysis shows that this algorithm can reach the approximate performance ratio of 2. Moreover, the distributed implementation DMCT of the algorithm is also described. We prove that both centralized and distributed algorithms can construct the same topology for cooperative data aggregation. The experimental simulations show that the proposed algorithms will decrease the power consumption by about 12.5% and 66.3% compared with PEDAP and PEGASIS algorithms respectively.     

Adaptive residual-based distributed compressed sensing for soft video multicasting over wireless networks

Recently, multicasting of video signals has become a useful technology in wireless networks, in which the main challenge is to scalably serve multiple receivers that have different channel characteristics. In this paper, we propose an adaptive residual-based distributed compressed-sensing scheme for soft video multicast (ARDCS-cast). At the encoder, we first adaptively determine if a block in a non-reference frame should be measured directly or predictively during compressed-sensing. The resulting adaptive measurements from non-reference frames are then packeted together with the measurements of the reference frames. We further derive the optimal power allocation scheme for the measurements from each frame within each packet. The packets are then transmitted over the wireless channel. At the decoder, the receivers with different channel characteristics obtain different numbers of packets and reconstruct videos with different quality. Experimental results show that the proposed ARDCS-cast is more effective than the state-of-the-art SoftCast-2D, SoftCast-3D and DCS-cast schemes in both unicast and multicast scenarios.     

Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity.     

无线传感器网络中基于分簇的数据聚合机制

在无线传感器网络(WSNs)中如何有效聚合数据是当前物联网应用中的重要挑战.然而在已有的若干解决方案中,传感器的计算和通信过程消耗了较高的能量,数据的隐私性和完整性得不到有效保障.针对上述问题,提出一种基于分簇的数据聚合机制,采用代数方程计算传感器的中间数据聚合值,采用数字签名进行簇间传感器的身份认证.实验表明:该机制能有效降低传感器计算和通信的耗能,保证数据的隐私性和完整性.     

Energy optimal data propagation in wireless sensor networks

We propose an algorithm to compute the optimal parameters of a probabilistic data propagation algorithm for wireless sensor networks (WSN). The probabilistic data propagation algorithm we consider was introduced in previous work, and it is known that this algorithm, when used with adequate parameters, balances the energy consumption and increases the lifespan of the WSN. However, we show that in the general case achieving energy balance may not be possible. We propose a centralized algorithm to compute the optimal parameters of the probabilistic data propagation algorithm, and prove that these parameters maximize the lifespan of the network even when it is not possible to achieve energy balance. Compared to previous work, our contribution is the following: (a) we give a formal definition of an optimal data propagation algorithm: an algorithm maximizing the lifespan of the network. (b) We find a simple necessary and sufficient condition for the data propagation algorithm to be optimal. (c) We constructively prove that there exists a choice of parameters optimizing the probabilistic data propagation algorithm. (d) We provide a centralized algorithm to compute these optimal parameters, thus enabling their use in a WSN. (e) We extend previous work by considering the energy consumption per sensor, instead of the consumption per slice, and propose a spreading technique to balance the energy among sensors of a same slice. The technique is numerically validated by simulating a WSN accomplishing a data monitoring task and propagating data using the probabilistic data propagation algorithm with optimal parameters.     

Distributed data source verification in wireless sensor networks

In-network data aggregation is favorable for wireless sensor networks (WSNs): It allows in-network data processing while reducing the network traffic and hence saving the sensors energy. However, due to the distributed and unattended nature of WSNs, several attacks aiming at compromising the authenticity of the collected data could be perpetrated. For example, an adversary could capture a node to create clones of the captured one. These clones disseminated through the network could provide malicious data to the aggregating node, thus poisoning/disrupting the aggregation process. In this paper we address the problem of detecting cloned nodes; a requirement to be fulfilled to provide authenticity of the data fusion process.First, we analyze the desirable properties a distributed clone detection protocol should meet. Specifically: It should avoid having a single point of failure; the load should be totally distributed across the nodes in the network; the position of the clones in the network should not influence the detection probability. We then show that current solutions do not meet the exposed requirements. Next, we propose the Information Fusion Based Clone Detection Protocol (ICD). ICD is a probabilistic, completely distributed protocol that efficiently detects clones. ICD combines two cryptographic mechanisms: The pseudo-random key pre-distribution, usually employed to secure node pairwise communications, with a sparing use of asymmetric crypto primitives. We show that ICD matches all the requirements above mentioned and compare its performance with current solutions in the literature; experimental results show that ICD has better performance than existing solutions for all the cost parameters considered: Number of messages sent, per sensor storage requirement, and signature verification. These savings allow to increase the network operating lifetime. Finally, note that ICD protocol could be used as an independent layer by any data aggregation mechanism.     

On-demand Geographic Forwarding for data delivery in wireless sensor networks

A purely on-demand network protocol, called On-demand Geographic Forwarding (OGF), is proposed for data delivery in large-scale and resource-constrained static wireless sensor networks with unreliable sensors. OGF is a cross-layer protocol that employs (i) an explicit contention scheme to establish a next-hop node in a distributed fashion, (ii) a simple scheme using a forwarding table to maintain the learned next-hop information, and (iii) a novel partial source routing scheme to handle communication voids. The operation of our protocol is based on the actual demands of the application, data traffic, and network dynamics. Packet-level simulations show that OGF exhibits a superior performance in terms of energy consumption, scalability, and void handling. Our study demonstrates that OGF is viable for efficient data delivery in the targeted sensor networks.