基于压缩网络编码的WSN数据传输技术

介绍了应用于无线传感器网络（Wireless Sensor Networks,WSN）中的一种数据传输方案--压缩网络编码（Compressed Network Coding,CNC）。在WSN中,通常应用网络编码（Network Coding,NC）来适应拓扑结构的动态变化并提高数据传输效率。考虑到传感器网络中节点测量值之间的相关性,与随机线性网络编码（Random Linear Network Coding,RLNC）方案中的编码操作与压缩感知（Compressed Sensing,CS）中随机投影操作之间的相似性,CNC方案将CS引入到NC中,通过对测量值数据包以及NC局部编码向量的设计,来解决传统NC译码存在的“全有或全无”问题。在汇聚节点收集到的数据包个数小于网络中源节点个数的情况下,CNC方案仍能以高概率精确重构感知数据。仿真结果表明,在合理的误差容许范围内重构测量值,所需的数据包个数仅为传统NC方案所需个数的一半,与传统NC技术相比,CNC方案将数据传输效率提升了20%以上。     

基于分簇机制的移动无线传感器网络数据采集协议*

为了均衡无线传感器网络的能量消耗,提出了一种基于分簇机制的移动无线传感器网络数据采集协议。该协议中,整个网络使用网格均匀分簇,节点根据加权能量—邻居规则选出分布在簇中间区域的簇头,簇头负责收集簇内兴趣事件并进行数据融合,移动sink依次运动到簇的中心点位置收集簇内兴趣事件。仿真结果表明,该协议有效地均衡了网络的能量消耗,延长了网络的生存时间。     

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.     

SERA: a secure energy reliability aware data gathering for sensor networks

Wireless sensor networks are used in many applications in military, ecology, health, and other areas. These applications often include the monitoring of sensitive information making the security issue one of the most important aspects to consider in this field. However, most of protocols optimize for the limited capabilities of sensor nodes and the application specific nature of the networks, but they are vulnerable to serious attacks. In this paper, a Secure Energy and Reliability Aware data gathering protocol (SERA) is proposed, which provides energy efficiency and data delivery reliability as well as security. The proposed protocol’s security was confirmed by a formal verification carried out using the AVISPA tool and analysis of the most common network layer attacks such as selective forwarding, sinkhole, Sybil, wormhole, HELLO flood, and acknowledgment spoofing attacks. Additionally, a visual simulation environment was developed to evaluate the performance of the proposed protocol.     

Delay-bounded data gathering in urban vehicular sensor networks

Vehicular sensor networks are an emerging network paradigm, suitable for various applications in vehicular environment making use of vehicles’ sensors as data sources and Inter-Vehicle Communication systems for the transmissions. We present a solution, based on vehicular sensor networks, for gathering data from a certain geographic area while satisfying with a specific delay bound. The method leverages the time interval during which the query is active in order to make the gathering process efficient, properly alternating data muling and multi-hop forwarding strategies like in delay-bounded routing protocols. Simulations show that our proposed solution succeeds in performing efficient data gathering outperforming other solutions.     

传感器网络中基于区间小波变换的混合熵数据压缩算法

考虑传感器网络的数据特性及小波变换在流数据压缩方面的良好性能,提出了一种基于区间小波变换的混合熵数据压缩方法。理论分析和仿真结果表明,结合传统的DC(Data Centric)算法-DD (Directed Diffusion)路由算法,新算法能对传感器网络中的数据流进行有效压缩,可更大程度地降低DD路由算法下节点数据传输的能耗,从而可进一步延长整个网络的生命周期。     

Ant-based routing for wireless multimedia sensor networks using multiple QoS metrics

In wireless sensor networks, most routing protocols consider energy savings as the main objective and assume data traffic with unconstrained delivery requirements to be a given. However, the introduction of video and imaging sensors unveils additional challenges. The transmission of video and imaging data requires both energy efficiency and QoS assurance (end-to-end delay and packet loss requirements), in order to ensure the efficient use of sensor resources as well as the integrity of the information collected. This paper presents a QoS routing model for Wireless Multimedia Sensor Networks (WMSN). Moreover, based on the traditional ant-based algorithm, an ant-based multi-QoS routing metric (AntSensNet) is proposed. The AntSensNet protocol builds a hierarchical structure on the network before choosing suitable paths to meet various QoS requirements from different kinds of traffic, thus maximizing network utilization, while improving its performance. In addition, AntSensNet is able to use a efficient multi-path video packet scheduling in order to get minimum video distortion transmission. Finally, extensive simulations are conducted to assess the effectiveness of this novel solution and a detailed discussion regarding the effects of different system parameters is provided. Compared to typical routing algorithms in sensor networks and the traditional ant-based algorithm, this new algorithm has better convergence and provides significantly better QoS for multiple types of services in wireless multimedia sensor networks.     

EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations

In this paper, we present an approximate data gathering technique, called EDGES, for sensor networks that utilizes temporal and spatial correlations. The goal of EDGES is to efficiently obtain the sensor reading within a certain error bound. To do this, EDGES utilizes the multiple model Kalman filter, which is for the non-linear data distribution, as an approximation approach. The use of the Kalman filter allows EDGES to predict the future value using a single previous sensor reading in contrast to the other statistical models such as the linear regression and multivariate Gaussian. In order to extend the lifetime of networks, EDGES utilizes the spatial correlation. In EDGES, we group spatially close sensors as a cluster. Since a cluster header in a network acts as a sensor and router, a cluster header wastes its energy severely to send its own reading and/or data coming from its children. Thus, we devise a redistribution method which distributes the energy consumption of a cluster header using the spatial correlation. In some previous works, the fixed routing topology is used or the roles of nodes are decided at the base station and this information propagates through the whole network. But, in EDGES, the change of a cluster is notified to a small portion of the network. Our experimental results over randomly generated sensor networks with synthetic and real data sets demonstrate the efficiency of EDGES.     

基于动态数据压缩的能量采集无线传感网络数据收集优化

针对能量采集无线传感网络（WSN）中的数据收集优化问题,考虑传感器节点能量采集的时空变化特性,提出一种基于节点动态采样速率和数据压缩的策略,以实现网络中采样数据总量的最大化。首先,提出一种根据节点的邻居信息决定其最优压缩策略的本地压缩算法,基于节点在数据汇聚树中的拓扑位置考虑其数据接收和转发能耗,逐渐增加其采样速率直到其总能耗到达采集能耗阈值。接着构造网络性能的全局优化问题并提出一种启发式的算法,通过迭代求解线性规划问题计算最优的采样速率和压缩策略。实验结果表明,与现有的自适应传感和压缩率选择方案相比,所提出的两种数据收集优化算法能够维持更加稳定的传感器节点电量水平并实现更高的网络性能。     

无线传感器网络中的新型数据收集协议

数据收集是无线传感器网络最基本的应用,数据收集协议设计的首要目标是有效节约能源,延长网络生命周期。提出了一种网内数据融合和网络区分服务的思想,在此基础上实现了一种既能采纳网内数据融合技术,高效周期性地收集检测数据,又能满足紧急数据处理要求的具有区分服务机制的数据收集算法DSDG（Differentiated Service in-network data aggregation Data Gathering algorithm）。该算法对网络中数据流采用区分服务,在数据传输过程中采用高效的网内数据融合技术。仿真实验表明,与LEACH、HEED协议比较,DSDG数据收集协议能使WSN具有更长的网络寿命和更好的可扩充性。     

Energy-aware topology control for wireless sensor networks using memetic algorithms

Cost-effective topology control is critical in wireless sensor networks. While much research has been carried out in this aspect using various methods, no attention has been made on utilizing modern heuristics for this purpose. This paper proposes a memetic algorithm-based solution for energy-aware topology control for wireless sensor networks. This algorithm (called ToCMA), using a combination of problem-specific light-weighted local search and genetic algorithms, is able to solve the minimum energy network connectivity (MENC) this NP-hard problem in an approximated manner that performs better than the classical minimum spanning tree (MST) solution. The outcomes of ToCMA can also be utilized for various network optimization and fault-tolerant purposes.     

Adaptive design optimization of wireless sensor networks using genetic algorithms

We present a multi-objective optimization methodology for self-organizing, adaptive wireless sensor network design and energy management, taking into consideration application-specific requirements, communication constraints and energy-conservation characteristics. A precision agriculture application of sensor networks is used as an example. We use genetic algorithms as the optimization tool of the developed system and an appropriate fitness function is developed to incorporate many aspects of network performance. The design characteristics optimized by the genetic algorithm system include the status of sensor nodes (whether they are active or inactive), network clustering with the choice of appropriate clusterheads and finally the choice between two signal ranges for the simple sensor nodes. We show that optimal sensor network designs constructed by the genetic algorithm system satisfy all application-specific requirements, fulfill the existent connectivity constraints and incorporate energy-conservation characteristics. Energy management is optimized to guarantee maximum life span of the network without lack of the network characteristics that are required by the specific application.     

Distributed routing in wireless sensor networks using energy welfare metric

There are several requirements for a routing algorithm in wireless sensor networks. First, it should achieve both energy-efficiency and energy-balancing together, in order to prolong the lifetime of sensor networks. Second, the algorithm should follow a distributed control scheme so that it is applicable to large-scale networks. Third, it needs to be robust to diverse potential event generation patterns. The routing algorithm, MaxEW, designed in this study satisfies such requirements. It adopts the social welfare function from social sciences to compute energy welfare as a goodness measure for energy populations. When each sensor tries to maximize energy welfare of its local society, it collectively leads to globally efficient energy-balancing. This emergent property consequently supports preparedness and hence robustness to diverse event generation patterns. We demonstrate the effectiveness of the proposed routing algorithm through extensive simulation-based experiments, by comparing with other existing algorithms as well as optimal routing solutions.     

基于混合计算模型的分布式传感器网络数据收集

首先分析了传感器网络中两种计算模型的特点;然后针对装甲集群状态监控的实际需求,提出了基于无线传感器网络的装备状态监控系统的基本结构.分析了用于前端数据收集和传输的两层传感器网络的不同特点,结合client/server和移动agent模型的优点,提出基于混合计算模型的分布式传感器网络数据收集机制,在相对独立的车载监测网络中基于client/server模型建立数据收集机制,在车载监测网络之间基于移动agent模型建立数据收集机制.     

Exact and approximate balanced data gathering in energy-constrained sensor networks

We consider the problem of gathering data from a wireless multi-hop network of energy-constrained sensor nodes to a common base station. Specifically, we aim to balance the total amount of data received from the sensor network during its lifetime against a requirement of sufficient coverage for all the sensor locations surveyed. Our main contribution lies in formulating this balanced data gathering task, studying the effects of balancing, and proposing an approximation algorithm for the problem. Based on an LP network flow formulation, we present experimental results on both optimal and approximate data routing designs, in open transmission ranges and with impenetrable obstacles between the nodes.     

Data aggregation in wireless sensor networks using ant colony algorithm

Data aggregation is important in energy constraint wireless sensor networks which exploits correlated sensing data and aggregates at the intermediate nodes to reduce the number of messages exchanged network. This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. The ant colony system provides a natural and intrinsic way of exploring search space in determining data aggregation. Moreover, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore all possible paths from the source node to the sink node. The data aggregation tree is constructed by the accumulated pheromone. Simulations have shown that our algorithm can reduce significant energy costs.     

Network lifetime maximization for time-sensitive data gathering in wireless sensor networks

Energy-constrained sensor networks have been widely deployed for environmental monitoring and security surveillance purposes. Since sensors are usually powered by energy-limited batteries, in order to prolong the network lifetime, most existing research focuses on constructing a load-balanced routing tree rooted at the base station for data gathering. However, this may result in a long routing path from some sensors to the base station. Motivated by the need of some mission-critical applications that require all sensed data to be received by the base station with minimal delay, this paper aims to construct a routing tree such that the network lifetime is maximized while keeping the routing path from each sensor to the base station minimized. This paper shows that finding such a tree is NP-hard. Thus a novel heuristic called top-down algorithm is presented, which constructs the routing tree layer by layer such that each layer is optimally extended, using a network flow model. A distributed refinement algorithm is then devised that dramatically improves on the load balance for the routing tree produced by the top-down algorithm. Finally, extensive simulations are conducted. The experimental results show that the top-down algorithm with balance-refinement delivers a shortest routing tree whose network lifetime achieves around 85% of the optimum.     

撒布型无线传感器网络节点定位算法

针对撒布型无线传感器网络提出了基于非度量多维标度的NMDS-MAP算法及NMDS-MAP（P）算法,两种方法采用TDOA等测距技术测量节点间距,利用非度量多维标度技术对未知节点进行定位,前者是集中式算法,后者是分布式算法。理论分析与仿真实验表明,两种算法具有较高的定位精度与健壮性。     

Active node determination for correlated data gathering in wireless sensor networks

In wireless sensor network applications where data gathered by different sensor nodes is correlated, not all sensor nodes need to be active for the wireless sensor network to be functional. Given that the sensor nodes that are selected as active form a connected wireless network, the inactive sensor nodes can be turned off. Allowing some sensor nodes to be active and some sensor nodes inactive interchangably during the lifecycle of the application helps the wireless sensor network to have a longer lifetime. The problem of determining a set of active sensor nodes in a correlated data environment for a fully operational wireless sensor network can be formulated as an instance of the connected correlation-dominating set problem. In this work, our contribution is twofold; we propose an effective and runtime-efficient iterative improvement heuristic to solve the active sensor node determination problem, and a benefit function that aims to minimize the number of active sensor nodes while maximizing the residual energy levels of the selected active sensor nodes. Extensive simulations we performed show that the proposed approach achieves a good performance in terms of both network lifetime and runtime efficiency.