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Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks

机译：无线传感器和Actor网络的有效Actor恢复范例

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摘要

Wireless sensor networks (WSNs) are becoming widely used worldwide. Wireless Sensor and Actor Networks (WSANs) represent a special category of WSNs wherein actors and sensors collaborate to perform specific tasks. WSANs have become one of the most preeminent emerging type of WSNs. Sensors with nodes having limited power resources are responsible for sensing and transmitting events to actor nodes. Actors are high-performance nodes equipped with rich resources that have the ability to collect, process, transmit data and perform various actions. WSANs have a unique architecture that distinguishes them from WSNs. Due to the characteristics of WSANs, numerous challenges arise. Determining the importance of factors usually depends on the application requirements.;The actor nodes are the spine of WSANs that collaborate to perform the specific tasks in an unsubstantiated and uneven environment. Thus, there is a possibility of high failure rate in such unfriendly scenarios due to several factors such as power fatigue of devices, electronic circuit failure, software errors in nodes or physical impairment of the actor nodes and inter-actor connectivity problem. It is essential to keep inter-actor connectivity in order to insure network connectivity. Thus, it is extremely important to discover the failure of a cut-vertex actor and network-disjoint in order to improve the Quality-of-Service (QoS). For network recovery process from actor node failure, optimal re-localization and coordination techniques should take place.;In this work, we propose an efficient actor recovery (EAR) paradigm to guarantee the contention-free traffic-forwarding capacity. The EAR paradigm consists of Node Monitoring and Critical Node Detection (NMCND) algorithm that monitors the activities of the nodes to determine the critical node. In addition, it replaces the critical node with backup node prior to complete node-failure which helps balances the network performance. The packet is handled using Network Integration and Message Forwarding (NIMF) algorithm that determines the source of forwarding the packets (Either from actor or sensor). This decision-making capability of the algorithm controls the packet forwarding rate to maintain the network for longer time. Furthermore, for handling the proper routing strategy, Priority-Based Routing for Node Failure Avoidance (PRNFA) algorithm is deployed to decide the priority of the packets to be forwarded based on the significance of information available in the packet. To validate the effectiveness of the proposed EAR paradigm, we compare the performance of our proposed work with state-of the art localization algorithms. Our experimental results show superior performance in regards to network life, residual energy, reliability, sensor/ actor recovery time and data recovery.
机译：无线传感器网络（WSN）正在全球范围内广泛使用。无线传感器和参与者网络（WSAN）代表WSN的特殊类别，其中参与者和传感器协作执行特定任务。 WSAN已成为最杰出的WSN新兴类型之一。节点的电源资源有限的传感器负责感测事件并将事件传输到参与者节点。 Actor是配备有丰富资源的高性能节点，这些资源具有收集，处理，传输数据和执行各种操作的能力。 WSAN具有独特的体系结构，可将其与WSN区分开。由于WSAN的特性，出现了许多挑战。确定因素的重要性通常取决于应用程序需求。参与者节点是WSAN的主干，它们可以在没有事实依据和不平衡的环境中协作执行特定任务。因此，由于诸如设备的功率疲劳，电子电路故障，节点中的软件错误或参与者节点的物理损坏以及参与者之间的连接性问题等多种因素，在这种不友好的场景中存在高故障率的可能性。保持参与者之间的连接至关重要，以确保网络连接。因此，发现割断顶点参与者和网络不相交的故障对于提高服务质量（QoS）极为重要。对于从参与者节点故障的网络恢复过程，应该进行最佳的重新定位和协调技术。在这项工作中，我们提出了一种有效的参与者恢复（EAR）范式，以确保无争用的流量转发能力。 EAR范式由节点监视和关键节点检测（NMCND）算法组成，该算法监视节点的活动以确定关键节点。此外，它在完成节点故障之前用备份节点替换关键节点，这有助于平衡网络性能。数据包使用网络集成和消息转发（NIMF）算法处理，该算法确定转发数据包的来源（来自参与者还是来自传感器）。该算法的决策能力控制着数据包的转发速率，以使网络维持更长的时间。此外，为了处理适当的路由策略，部署了基于优先级的节点故障避免路由（PRNFA）算法，根据数据包中可用信息的重要性来决定要转发的数据包的优先级。为了验证所提出的EAR范式的有效性，我们将我们提出的工作与最新的本地化算法的性能进行了比较。我们的实验结果显示出在网络寿命，剩余能量，可靠性，传感器/演员恢复时间和数据恢复方面的卓越性能。

著录项

  • 作者

    Mahjoub, Reem Khalid.;

  • 作者单位

    University of Bridgeport.;

  • 授予单位 University of Bridgeport.;
  • 学科 Computer engineering.;Computer science.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 141 p.
  • 总页数 141
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农业化学;
  • 关键词

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