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Policy controlled self-configuration in unattended wireless sensor networks

机译:无人值守的无线传感器网络中策略控制的自配置

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With the decreasing cost and size of sensors, their importance has grown rapidly in the field of unattended network systems, which have a significant and vital role in military and security applications where human access is difficult once the nodes are deployed. A major issue in wireless sensor networks (WSN) is the limited availability of energy supply. The nodes have to cooperate and self-organize to provide an active backbone, making optimal use of energy by putting off the radio of the nodes which are not required. We propose the Policy Controlled Self-Configuration for Unattended Wireless Sensor Networks (PCSSN) scheme for self-organization in WSN. The algorithm consists of two phases: topology discovery and topology maintenance. Based on the concepts of Markov Decision Processes (MDP) we design a policy to activate the optimum number of nodes such that the application fidelity is not affected. The policy is based on the distance between neighboring nodes, the residual energy, and the neighbor count, which are indicators of the hop count, the network lifetime, and the state of connectivity of the network, respectively. We also consider the case of node failures, since failures are quite common in harsh unattended environments. In this paper, we show that our proposed scheme increases the utility function ranging from about 7% to 50% as the number of nodes increases. Further, we show that despite the overhead involved in the proposed scheme, the energy spent is much less than the active case, in which all the nodes stay active forever, without going to sleep.
机译:随着传感器成本和尺寸的不断降低,它们在无人值守网络系统领域的重要性迅速提高,而无人值守网络系统在军事和安全应用中起着至关重要的作用,一旦部署节点,这些应用将难以实现人员访问。无线传感器网络(WSN)的一个主要问题是能源供应的有限性。节点必须合作并自组织以提供活动的主干,通过推迟不需要的节点的无线电来优化能源利用。我们为WSN中的自组织提出了无人参与的无线传感器网络(PCSSN)方案的策略控制自配置。该算法包括两个阶段:拓扑发现和拓扑维护。基于马尔可夫决策过程(MDP)的概念,我们设计了一种策略来激活最佳数量的节点,从而不会影响应用程序的保真度。该策略基于相邻节点之间的距离,剩余能量和邻居计数,它们分别是跳数,网络生存期和网络连接状态的指标。我们还考虑节点故障的情况,因为故障在恶劣的无人值守环境中非常普遍。在本文中,我们表明,随着节点数量的增加,我们提出的方案将效用函数从大约7%增加到50%。此外,我们表明,尽管所提出的方案涉及开销,但所花费的能量要比活动情况少得多,在活动情况下,所有节点永远保持活动状态,而不会进入睡眠状态。

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