无线传感器网络数据收集与生存算法研究
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摘要
由大量廉价、低功率传感器节点组成的无线传感器网络具有快速部署、适用于各种恶劣环境、能够感知并获取网络覆盖区域中大量详实而可靠的数据等优点,在国防军事、环境监测、医疗卫生、交通运输、工业控制等领域有着广阔的应用前景。一方面,由于成本控制和体积限制等原因,无线传感器网中节点的能量、计算、存储、带宽等资源严重受限。为减少资源消耗,传感器节点通常采用周期性的工作/睡眠调度模式和短距离的通信方式,采集到的数据是通过节点间相互协作以多跳转发的方式传递到汇聚节点的,因而无线传感器网络具有间断连通和多跳转发的特性。特别是在移动传感器网络中,节点移动性导致的网络间断连通、拓扑变化更为频繁和明显。另一方面,无线传感器网络往往部署于恶劣甚至敌对的环境中,传感器节点会因环境中破坏性因素的作用而损毁和失效;而且传感器节点的能量有限且难于补充,一旦能量耗尽节点将死亡。因此,由网络间断连通和多跳转发产生的数据收集问题,以及由节点损毁和失效导致的数据生存问题成为了无线传感器网络大规模推广和应用必须解决的两个关键问题,是无线传感器网络研究中的重点和难点。
本文对节点静态部署的无线传感器网络以及近年来提出的延迟容忍移动传感器网络(Delay Tolerant Mobile Sensor Networks, DTMSNs)进行了系统和全面的分析与总结,在此基础上,深入研究了无线传感器网络的数据收集和数据生存算法,取得了若干创新和研究成果。本文的主要贡献包括以下方面:
1.提出了一种高效节能的延迟容忍移动传感器网络数据收集算法EDAG(an Energy-efficient Data Gathering algorithm)。EDAG一方面综合应用了基于相遇频率和运动趋势的方法来计算每个节点的递交概率;另一方面通过有效地发现和利用传感器节点在移动过程中动态形成的局部连通路径来扩展节点寻找下一跳转发节点的“视野”以改善数据传输性能。因此,EDAG算法能够以显著节约能量的单复本传输达到与现有多复本路由策略相近的高数据递交成功率;更为重要的是其良好的节能特性能够显著延长网络寿命,很好的适应了延迟容忍移动传感器网络能量受限的特点。仿真实验结果表明EDAG算法达到了预期的设计目标。
2.提出了一种自适应复本数的延迟容忍移动传感器网络数据收集算法RADG(an Replicas Adaptive Data Gathering algorithm)。DTMSNs间断连通、拓扑变化频繁,各移动传感器节点与汇聚点之间通常以机会转发的方式进行数据传输。由于机会转发并不能保证数据传输性能,在DTMSNs中采用多复本传输策略以提高消息成功到达汇聚点的概率并减少传输延迟有其合理性。然而向网络中注入过多的复本消息将消耗大量的网络资源。因此需要在消息的复本数目和网络的数据收集性能之间进行平衡。本文提出的RADG数据收集算法,通过自适应策略有效减少数据消息的冗余复本数目,并且计算节点的递交概率作为路由度量来达到提高数据传输性能的目的。仿真实验表明,与现有的几种DTMSNs数据收集策略相比,RADG算法以较少的资源消耗达到了更好的数据收集性能。
3.提出了一种快速有效的无线传感器网络数据生存算法FEDS(Fast and Efficient Data Survival scheme)。由于破坏性因素的影响通常具有较强的区域性特征,灾害发生后网络中不同区域的节点安全等级存在差异。而且从灾害发生到大部分传感器节点被破坏之间需要经过一定的时间。因此,一种提高网络数据生存能力的可行方法是:危险区域内的节点快速地将数据转移到安全区域的节点中进行保存。FEDS算法通过快速收集灾害环境下无线传感器网络中节点的安全状况信息,采用线性规划理论中的“运输问题”方法来寻求将危险区域节点中的数据向安全区域节点转移这一问题的最优解。仿真实验表明,FEDS能够达到保证高数据生存率条件下的快速数据转移的目标。
4.提出了无持续可用汇聚节点条件下一种基于虚拟引力的无线传感器网络数据生存算法VGDS(Virtual Gravity based Data Survival scheme)。VGDS是一种完全分布式的、由各传感器节点平等地相互协作来实现的数据生存算法,能够应用于不存在持续可用的汇聚节点的环境中。VGDS算法采用基于虚拟引力的方法寻求将非安全区域节点中的数据向安全区域节点转移问题的近似最优解,以提高数据生存率并减少时间消耗。仿真实验表明,VGDS达到较高的数据转移成功率且时间代价可接受,能够有效地实现在严重灾害环境下保证无线传感器网络具有较高数据生存率的目标。
Wireless sensor networks (WSNs), which are composed of a large number of low-cost, low-power, multifunctional sensor nodes, possess advantages such as fast deployment, adaptation to various challenged environments, accurate and credible data sensing and collecting in covered network regions. So WSNs have wide prospects for military, environmental, health, transportation, and commercial applications. On one side, node resources in WSNs, such as processing ability, buffer size, bandwidth and energy, are strictly limited due to cost control, volume control, and other reasons. In order to reduce resource consumption, sense nodes often adopts special scheduling models of periodic working/sleeping and short radio range. Thus sensor nodes cooperate each another to forward collected data to sink nodes through multi-hop transmission. It generates the features of opportunistic connectivity and multi-hop transmission in WSNs. On the other hand, WSNs are often deployed in harsh, even extreme environments, which may make sensor nodes smashed or invalidated by the destructive factors of environment. Moreover, sensor nodes have limited energy, and are very hard to be recharged. A sensor node will die once its energy is exhausted. Thus, the data gathering problem caused by opportunistic connectivity and multi-hop transmission, and the data survival problem caused by node corruption and invalidation, are crucial in the large-scale implemention of WSNs, and also become the key points and the chief difficulties in the research of WSNs.
This dissertation involves regular and comprehensive study and analysis on wireless sensor networks consist of static nodes and delay tolerant mobile sensor networks (DTMSNs) proposed in recent years, and, on this basis, carries out in-depth research in data gathering and data survival problems in WSNs. The original achievements and contributions of the dissertation are highlighted as follows:
1. To save node energy and prolong network life while gathering data from nodes to sink nodes, an Energy-efficient DAta Gathering (EDAG) algorithm for DTMSN is proposed. Based on community mobility models, EDAG takes two techniques as follows. First, the proposed protocol calculates the delivery probability of each node based on both the frequency, which the node meets with sinks, and its mobility trend. Second, EDAG extends the eyeshot of each node when it looks for next hop through finding and using the connected paths formed dynamically by mobile sensor nodes. So EDAG achieves high data delivery ratio closed to current multiple copy protocols but by single copy transmission which has notable energy saving advantages. Simulation results have shown that the proposed EDAG achieves the comparable message delivery ratio with the much lower transmission overhead than several main data delivering approaches for DTMSNs. What’s more, EDAG can efficiently save node energy and remarkably prolong network life, which make it well fit for the energy-limited characteristic of DTMSNs well.
2. A new data gathering scheme RADG (an Replicas Adaptive Data Gathering Scheme) for DTMSNs is proposed. Due to the inherent feature of intermitted connectivity and varying topology, DTMSNs usually gather data by the way of probabilistic forwarding. It is reasonable that DTMSNs routing employs multi-copy schemes to improve the message delivery ratio and reduce the delay, considering that probabilistic forwarding can not ensure good performance. However, the approach of injecting a large amount of message copies into the network will drain the limited network resource of DTMSNs including bandwidth, battery supply and storage space. So a proper routing method need to trade off between the number of copies of messages and the network performance. The proposed RADG economizes network resource using a self-adapting algorithm to cut down redundant copies of messages, and achieves a good network performance by leveraging the delivery probabilities of the mobile sensors as main routing metric. Simulation results have shown that RADG achieves the higher message delivery ratio with the lower transmission overhead and data delivery delay than other DTMSNs data delivering schemes.
3. A fast and efficient data survival algorithm called FEDS for WSNs is proposed. For devastating events usually only affects a limited area, there exists a big difference in the security status of sensor nodes. Moreover, there is a gap from the time when the disaster hits and the time when the time a significant portion of nodes are physically destroyed. Thus, for achieving high data survival ratio, a feasible scheme is to transfer as much data as possible from nodes in dangerous status to those in security status, preferably within as a short period as possible. According to the information collected of sensor nodes in security status, FEDS can calculate the optimal solution of data transfer according to the“Transportation Problem”in integral linear programming theory. Simulation shows that FEDS achieves high data survival ratio through fast and efficient data transfer.
4. A Virtual Gravity based Data Survival (VGDS) algorithm is proposed for unattended wireless sensor networks, in which there are not continuously available sink nodes. VGDS is a distributed algorithm, i.e., data transfer is carried out by sensor nodes in a peer-to-peer cooperative way. To improve data survival ratio and reduce evacuation time, VGDS adopts virtual gravity method to calculate the near optimum solution of data transfer. The simulation results shows that VGDS achieves high data survival ratio at acceptable time price, and effectively guarantees high data survival ratio in the networks in serious environments.
本文对节点静态部署的无线传感器网络以及近年来提出的延迟容忍移动传感器网络(Delay Tolerant Mobile Sensor Networks, DTMSNs)进行了系统和全面的分析与总结,在此基础上,深入研究了无线传感器网络的数据收集和数据生存算法,取得了若干创新和研究成果。本文的主要贡献包括以下方面:
1.提出了一种高效节能的延迟容忍移动传感器网络数据收集算法EDAG(an Energy-efficient Data Gathering algorithm)。EDAG一方面综合应用了基于相遇频率和运动趋势的方法来计算每个节点的递交概率;另一方面通过有效地发现和利用传感器节点在移动过程中动态形成的局部连通路径来扩展节点寻找下一跳转发节点的“视野”以改善数据传输性能。因此,EDAG算法能够以显著节约能量的单复本传输达到与现有多复本路由策略相近的高数据递交成功率;更为重要的是其良好的节能特性能够显著延长网络寿命,很好的适应了延迟容忍移动传感器网络能量受限的特点。仿真实验结果表明EDAG算法达到了预期的设计目标。
2.提出了一种自适应复本数的延迟容忍移动传感器网络数据收集算法RADG(an Replicas Adaptive Data Gathering algorithm)。DTMSNs间断连通、拓扑变化频繁,各移动传感器节点与汇聚点之间通常以机会转发的方式进行数据传输。由于机会转发并不能保证数据传输性能,在DTMSNs中采用多复本传输策略以提高消息成功到达汇聚点的概率并减少传输延迟有其合理性。然而向网络中注入过多的复本消息将消耗大量的网络资源。因此需要在消息的复本数目和网络的数据收集性能之间进行平衡。本文提出的RADG数据收集算法,通过自适应策略有效减少数据消息的冗余复本数目,并且计算节点的递交概率作为路由度量来达到提高数据传输性能的目的。仿真实验表明,与现有的几种DTMSNs数据收集策略相比,RADG算法以较少的资源消耗达到了更好的数据收集性能。
3.提出了一种快速有效的无线传感器网络数据生存算法FEDS(Fast and Efficient Data Survival scheme)。由于破坏性因素的影响通常具有较强的区域性特征,灾害发生后网络中不同区域的节点安全等级存在差异。而且从灾害发生到大部分传感器节点被破坏之间需要经过一定的时间。因此,一种提高网络数据生存能力的可行方法是:危险区域内的节点快速地将数据转移到安全区域的节点中进行保存。FEDS算法通过快速收集灾害环境下无线传感器网络中节点的安全状况信息,采用线性规划理论中的“运输问题”方法来寻求将危险区域节点中的数据向安全区域节点转移这一问题的最优解。仿真实验表明,FEDS能够达到保证高数据生存率条件下的快速数据转移的目标。
4.提出了无持续可用汇聚节点条件下一种基于虚拟引力的无线传感器网络数据生存算法VGDS(Virtual Gravity based Data Survival scheme)。VGDS是一种完全分布式的、由各传感器节点平等地相互协作来实现的数据生存算法,能够应用于不存在持续可用的汇聚节点的环境中。VGDS算法采用基于虚拟引力的方法寻求将非安全区域节点中的数据向安全区域节点转移问题的近似最优解,以提高数据生存率并减少时间消耗。仿真实验表明,VGDS达到较高的数据转移成功率且时间代价可接受,能够有效地实现在严重灾害环境下保证无线传感器网络具有较高数据生存率的目标。
Wireless sensor networks (WSNs), which are composed of a large number of low-cost, low-power, multifunctional sensor nodes, possess advantages such as fast deployment, adaptation to various challenged environments, accurate and credible data sensing and collecting in covered network regions. So WSNs have wide prospects for military, environmental, health, transportation, and commercial applications. On one side, node resources in WSNs, such as processing ability, buffer size, bandwidth and energy, are strictly limited due to cost control, volume control, and other reasons. In order to reduce resource consumption, sense nodes often adopts special scheduling models of periodic working/sleeping and short radio range. Thus sensor nodes cooperate each another to forward collected data to sink nodes through multi-hop transmission. It generates the features of opportunistic connectivity and multi-hop transmission in WSNs. On the other hand, WSNs are often deployed in harsh, even extreme environments, which may make sensor nodes smashed or invalidated by the destructive factors of environment. Moreover, sensor nodes have limited energy, and are very hard to be recharged. A sensor node will die once its energy is exhausted. Thus, the data gathering problem caused by opportunistic connectivity and multi-hop transmission, and the data survival problem caused by node corruption and invalidation, are crucial in the large-scale implemention of WSNs, and also become the key points and the chief difficulties in the research of WSNs.
This dissertation involves regular and comprehensive study and analysis on wireless sensor networks consist of static nodes and delay tolerant mobile sensor networks (DTMSNs) proposed in recent years, and, on this basis, carries out in-depth research in data gathering and data survival problems in WSNs. The original achievements and contributions of the dissertation are highlighted as follows:
1. To save node energy and prolong network life while gathering data from nodes to sink nodes, an Energy-efficient DAta Gathering (EDAG) algorithm for DTMSN is proposed. Based on community mobility models, EDAG takes two techniques as follows. First, the proposed protocol calculates the delivery probability of each node based on both the frequency, which the node meets with sinks, and its mobility trend. Second, EDAG extends the eyeshot of each node when it looks for next hop through finding and using the connected paths formed dynamically by mobile sensor nodes. So EDAG achieves high data delivery ratio closed to current multiple copy protocols but by single copy transmission which has notable energy saving advantages. Simulation results have shown that the proposed EDAG achieves the comparable message delivery ratio with the much lower transmission overhead than several main data delivering approaches for DTMSNs. What’s more, EDAG can efficiently save node energy and remarkably prolong network life, which make it well fit for the energy-limited characteristic of DTMSNs well.
2. A new data gathering scheme RADG (an Replicas Adaptive Data Gathering Scheme) for DTMSNs is proposed. Due to the inherent feature of intermitted connectivity and varying topology, DTMSNs usually gather data by the way of probabilistic forwarding. It is reasonable that DTMSNs routing employs multi-copy schemes to improve the message delivery ratio and reduce the delay, considering that probabilistic forwarding can not ensure good performance. However, the approach of injecting a large amount of message copies into the network will drain the limited network resource of DTMSNs including bandwidth, battery supply and storage space. So a proper routing method need to trade off between the number of copies of messages and the network performance. The proposed RADG economizes network resource using a self-adapting algorithm to cut down redundant copies of messages, and achieves a good network performance by leveraging the delivery probabilities of the mobile sensors as main routing metric. Simulation results have shown that RADG achieves the higher message delivery ratio with the lower transmission overhead and data delivery delay than other DTMSNs data delivering schemes.
3. A fast and efficient data survival algorithm called FEDS for WSNs is proposed. For devastating events usually only affects a limited area, there exists a big difference in the security status of sensor nodes. Moreover, there is a gap from the time when the disaster hits and the time when the time a significant portion of nodes are physically destroyed. Thus, for achieving high data survival ratio, a feasible scheme is to transfer as much data as possible from nodes in dangerous status to those in security status, preferably within as a short period as possible. According to the information collected of sensor nodes in security status, FEDS can calculate the optimal solution of data transfer according to the“Transportation Problem”in integral linear programming theory. Simulation shows that FEDS achieves high data survival ratio through fast and efficient data transfer.
4. A Virtual Gravity based Data Survival (VGDS) algorithm is proposed for unattended wireless sensor networks, in which there are not continuously available sink nodes. VGDS is a distributed algorithm, i.e., data transfer is carried out by sensor nodes in a peer-to-peer cooperative way. To improve data survival ratio and reduce evacuation time, VGDS adopts virtual gravity method to calculate the near optimum solution of data transfer. The simulation results shows that VGDS achieves high data survival ratio at acceptable time price, and effectively guarantees high data survival ratio in the networks in serious environments.
引文
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