高效节能的无线传感器网络数据收集协议*

现有的数据收集协议的性能不是很好,为此提出了一种基于数据收集树的数据收集协议。它通过减少数据收集树中的节点深度和中间节点数来减少制约系统生命期的节点传输数据给基站的能量消耗和节点空闲侦听的能量消耗。仿真实验表明,该协议能将系统生命期延长36%左右。     

Topology control for delay-constraint data collection in wireless sensor networks

Data collection is one of the most important operations in wireless sensor networks. Many practical applications require the real-time data transmission, such as monitoring, tracking, etc. In this paper, we import and define the topology control problem for delay-constraint data collection (TDDC), and then formalize this problem into an integer programming problem. As NP-Hardness of this problem, we present a load-aware power-increased topology control algorithm (namely LPTC) to heuristically solve the problem. The theoretical analysis shows that this algorithm can reach O(1)-approximation ratio for the linear networks. And we also analyze the impact of the delay-constraint on the worst-case for the planar networks. Moreover, this paper designs two localized algorithms, called as SDEL and DDEL, based on the area division for TDDC problem. The experimental results show that LPTC algorithm can save at least 17% power consumptions compared with HBH algorithm in many situations.     

一种移动终端辅助的传感器网络数据收集协议

提出了一种利用具有传感器通信接口的移动终端设备（如手机、手提电脑、个人数字助理等）进行辅助数据获取的传感器网络数据收集协议。网络中固定汇聚节点与移动终端共存,全部传感器节点都维护到固定汇聚节点的路由,移动终端进入网络后定期向其附近小范围内的传感器节点扩散自己的存在信息,传感器节点向距自己跳数最小的汇聚节点或移动终端发送或转发数据包,移动终端和传感器节点之间通过应答和重传的机制来保证数据的可靠传输。仿真研究证明,该协议在延长网络生存时间的同时可以获得较高的数据传输成功率和较短的数据传输延迟,而且可靠性、灵活性、可扩展性较强。     

A probabilistic and opportunistic flooding algorithm in wireless sensor networks

In wireless sensor networks, many communication protocols and applications rely on flooding for various networking purposes. Prior efforts focus on how to design efficient flooding algorithms; that is, they seek to achieve full reliability while reducing the number of redundant broadcasting across the network. To achieve efficient flooding, most of the existing protocols try to reduce the number of transmissions, which is decided without considering any online transmission result. In this paper, we propose a probabilistic and opportunistic flooding algorithm that controls rebroadcasts and retransmissions opportunistically. It seeks to achieve a target reliability required by an application. For this purpose, it makes a given node select only the subset of its one-hop neighbors to rebroadcast the same message. It considers node relations such as link error rates among nodes in selecting eligible neighbors to rebroadcast. The sender controls the number of retransmissions opportunistically by tracking the current status of message reception at its neighbors. Simulation is carried out to reveal that our proposed scheme achieves the given target reliability with less overhead than other flooding algorithms in most cases, thus prolonging the network lifetime.     

传感器网络中基于树的分布式数据收集算法

数据收集是传感器网络的根本任务,由于传感器网络节点能量有限、易失效等因素,如何设计一个低能耗的、可扩展性强的数据收集机制是传感器网络的一个关键问题。为此,提出一种基于树的分布式数据收集算法,其基本思想是：基站发送广播信息,根据节点到基站的最小跳数构造出网络的层次结构,由层次结构生成以基站为树根的树型传输网络,并基于该网络模型收集数据。理论分析和仿真实验表明该算法具有较低的复杂度,能有效地延长网络的生命周期,并具有良好的扩展性和容错性。     

Application-aware integration of data collection and power management in wireless sensor networks

Sensors are typically deployed to gather data about the physical world and its artifacts for a variety of purposes that range from environment monitoring, control, to data analysis. Since sensors are resource constrained, often sensor data is collected into sensor databases that reside at (more powerful) servers. A natural tradeoff exists between resources (bandwidth, energy) consumed and the quality of data collected at the server. Blindly transmitting sensor updates at a fixed periodicity to the server results in a suboptimal solution due to the differences in stability of sensor values and due to the varying application needs that impose different quality requirements across sensors. In order to adapt to these variations while at the same time optimizing the energy consumption of sensors, this paper proposes three different models and corresponding data collection protocols. We analyze all three models with a Markov state machine formulation, and either derive closed forms for the operation point of the data collection application or suggest algorithms for estimating this operating point to achieve a minimal energy consumption. We observe that the operating point depends on environmental characteristics and application quality requirements, which the proposed algorithms aim to accommodate. Our experimental results show significant energy savings compared to the naive approach to data collection.     

A smart node architecture for adding mobility to wireless sensor networks

Adding a few mobile nodes into the conventional wireless sensor networks can greatly improve the sensing and control capabilities of the networks and can help researchers solve many challenges such as network deployment and repair. This paper presents an enhanced node architecture for adding controlled mobility to wireless sensor networks. The structural model, the power model and the networking model of the proposed mobile node have been built respectively for better node control. And it provides a novel robotic platform for experimental research in hybrid sensor networks or other distributed measurement and control systems. A testbed has finally been created for validating the basic functions of the proposed mobile sensor node. The results of a coverage experiment show that the mobile node can provide additional support for network coverage and can ensure that the sensor network will work properly in undesirable environments.     

Optimizing data collection path in sensor networks with mobile elements

Exploiting mobile elements (MEs) to accomplish data collection in wireless sensor networks (WSNs) can improve the energy efficiency of sensor nodes, and prolong network lifetime. However, it will lead to large data collection latency for the network, which is unacceptable for data-critical applications. In this paper, we address this problem by minimizing the traveling length of MEs. Our methods mainly consist of two steps: we first construct a virtual grid network and select the minimal stop point set (SPS) from it; then, we make optimal scheduling for the MEs based on the SPS in order to minimize their traveling length. Different implementations of genetic algorithm (GA) are used to solve the problem. Our methods are evaluated by extensive simulations. The results show that these methods can greatly reduce the traveling length of MEs, and decrease the data collection latency.     

Measuring air quality in city areas by vehicular wireless sensor networks

This paper considers a micro-climate monitoring scenario, which usually requires deploying a large number of sensor nodes to capture environmental information. By exploiting vehicular sensor networks (VSNs), it is possible to equip fewer nodes on cars to achieve fine-grained monitoring. Specifically, when a car is moving, it could conduct measurements at different locations, thus collecting lots of sensing data. To achieve this goal, this paper proposes a VSN architecture to collect and measure air quality for micro-climate monitoring in city areas, where nodes’ mobility may be uncontrollable (such as taxis). In the proposed VSN architecture, we address two network-related issues: (1) how to adaptively adjust the reporting rates of mobile nodes to satisfy a target monitoring quality with less communication overhead and (2) how to exploit opportunistic communications to reduce message transmissions. We propose algorithms to solve these two issues and verify their performances by simulations. In addition, we also develop a ZigBee-based prototype to monitor the concentration of carbon dioxide (CO₂) gas in city areas.     

Energy-efficient forwarding in wireless sensor networks

For maximizing the energy efficiency in a wireless network, we propose two forwarding schemes termed single-link and multi-link energy-efficient forwarding that tradeoff delivery ratios against energy costs. Multi-link forwarding improves the network performance substantially by addressing multiple receivers at once during the packet forwarding process. If the first forwarding node does not receive a packet correctly, other nodes may act as backup nodes and perform the forwarding instead. By means of mathematical analyses, we derive how the energy efficiency of a forwarding path can be computed and how a forwarding tree is established. Routing cycles are explicitly taken into account and prevented by means of sequence numbers. Simulations and real-world experiments provide a comparison to other reference strategies, showing a superior performance of our forwarding scheme in terms of energy efficiency.     

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.     

基于模式码比较的数据融合和认证协议

致力于如何实现无线传感器网络的安全数据融合,提出了一种基于模式码的安全数据融合协议——SPDAA(Secure pattern-based data aggregation and authentication).该方案在节点的感应阶段采用差值比较法确定参与会话的节点,并采用基于两跳的认证技术在数据融合阶段进行数据(模式码)的认证,保证数据的完整性,避免了由于盲目拒绝虚假节点而造成的数据丢失.同时该方案考虑增加能量因子,有效地均衡了整个网络的能耗.仿真结果表明:SPDAA能够有效避免重要数据的丢失,保证网络数据高效安全性,均衡网络能耗,延长网络生存时间.     

An analytical study of central and in-network data processing for wireless sensor networks

This paper compares the performance of centralized and in-network data processing for wireless sensor networks (WSNs) under various deployment conditions on the real sensor hardware Sun SPOT from Sun Microsystems. We define several criteria to measure the quality of responses in WSN applications. Guided by an extensive experimental study, we discuss in detail the performance impacts of different deployment factors on algorithms that implement both centralized and in-network computing. Finally, performance guidelines are given to algorithm designers for WSN applications.     

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.     

无线传感器网络中一种分布式数据压缩算法

无线传感器网络有限的能量与通信带宽难以适应网络中大量数据的传输,需要在网络内部对传感数据进行压缩处理。基于任意支撑长度的小波函数,提出了一种分布式数据压缩算法。首先研究边界效应对传感数据重构带来的影响,然后基于虚拟网格环模型给出了一种分布式小波数据压缩算法。理论分析与实验结果表明,该算法能有效地去除传感数据中存在的空间相关性。而且,随着簇头与簇内节点距离的增加,该算法比非分布式方式更节省网络耗能。     

Optimal wake-up scheduling of data gathering trees for wireless sensor networks

In order to gather sensor data, a data gathering tree is commonly created as a subnetwork of a wireless sensor network. Power conservation is of paramount importance in such networks, and using periodic sleep–wake cycles for sensor nodes is one of the most effective methods for power conservation. This paper addresses the problem of scheduling the sleep–wake cycles of nodes in a data gathering tree under deadline constraints. After formally modeling the problem being addressed, an optimal wake-up frequency assignment (OWFA) algorithm, which takes into account the data rate at each node and the total permitted delay, is proposed. The results of simulations under various conditions showed that OWFA consumed about 8.6%∼24.3% less average power, and thus resulted in a 7.4%∼26.0% longer network lifetime, than a previously proposed method that did not consider individual data rates.     

传感器网络感知数据自适应去噪方法

在传感器网络研究领域中,去除感知数据含有的噪声是个重要的研究课题。现存的去噪算法没有考虑节点密度不均匀及信息拥塞的情况,从而过多地消耗了能量。考虑这两个因素,使用时间维加权的方法,提出了一个基于节点密度的网内自适应去噪算法－DHA（density-based hybrid approach）。DHA能够根据节点密度来进行算法决策,并且在时间维进行加权,能够对数据变化作出快速反应并且提高数据精度。实验结果表明,DHA方法能够在保证良好的去噪效果、快速响应时间的前提下,比目前最好的去噪算法WMA（weighted moving average-based）更节省能量。     

对无线传感器网络区域数据聚合有效性的研究

通过抽象出无线传感器网络中区域数据回传的网络模型,定量研究了区域数据聚合的节能条件,证明了先聚合再回传比直接进行数据回传所节省的相对路径长度,如果大于等于数据相关性与源节点个数的比值时,区域数据聚合一定可以节省能耗,并进一步给出了当数据聚合点在网络的不同位置,或数据的空间相关性不同时,区域数据聚合的节能条件。对于无线传感器网络的部署、路由协议的选择及评估数据聚合算法的能量有效性等,均具有一定的参考价值。     

Accelerated collection of sensor data by mobility-enabled topology ranks

We study the problem of fast and energy-efficient data collection of sensory data using a mobile sink, in wireless sensor networks in which both the sensors and the sink move. Motivated by relevant applications, we focus on dynamic sensory mobility and heterogeneous sensor placement. Our approach basically suggests to exploit the sensor motion to adaptively propagate information based on local conditions (such as high placement concentrations), so that the sink gradually “learns” the network and accordingly optimizes its motion. Compared to relevant solutions in the state of the art (such as the blind random walk, biased walks, and even optimized deterministic sink mobility), our method significantly reduces latency (the improvement ranges from 40% for uniform placements, to 800% for heterogeneous ones), while also improving the success rate and keeping the energy dissipation at very satisfactory levels.