Improved Resource Allocation and Network Connectivity in CRSN Based Smart Grid for Efficient Grid Automation

Emmanuel U. Ogbodo, D. Dorrell, A. Abu-Mahfouz
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引用次数: 4

Abstract

The conventional smart grid (SG) uses static radio resource allocation (RRA) techniques to allocate resources to sensor nodes and communication devices in the SG network. Cognitive radio sensor Networks (CRSNs) based SGs uses dynamic RRA for resource allocation to CRSN nodes. However, the challenges associated with sensor nodes in an SG network are: energy or power constraints; poor quality of service (QoS); interference; delay; and problem of spectrum inefficiency. Thus, improvements in resource allocation criteria, such as energy efficiency, appreciable throughput, QoS guarantee, fairness, priority, interference mitigation, etc., will help in circumventing these problems in CRSN-based SGs. Hence, in this work, a new topology and algorithm for improved RRA and QoS guaranteed network connectivity in CRSN-based SGs are proposed. Bit error probability and latency are the metrics used for the investigation of the improved RRA and guaranteed network. The simulation result confirms that the improved model has a very low latency and low error rate at a given signal-to-noise ratio (SNR) compare to the conventional sensor network which has high latency and high error rate at a given SNR.
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基于CRSN的智能电网资源分配和网络连通性改进,实现高效电网自动化
传统的智能电网采用静态无线电资源分配(RRA)技术,将资源分配给SG网络中的传感器节点和通信设备。基于认知无线传感器网络(Cognitive radio sensor Networks, CRSNs)的认知无线传感器网络(SGs)采用动态RRA对CRSN节点进行资源分配。然而,与SG网络中的传感器节点相关的挑战是:能量或功率限制;服务质量差(QoS);干扰;延迟;以及频谱效率低下的问题。因此,改进资源分配标准,如能源效率、可观的吞吐量、QoS保证、公平性、优先级、干扰缓解等,将有助于在基于crsn的SGs中规避这些问题。因此,本文提出了一种新的拓扑结构和算法,用于改进基于crsn的SGs中RRA和QoS保证的网络连通性。误码率和延迟是研究改进RRA和保证网络的指标。仿真结果表明,与传统传感器网络在一定信噪比下具有较高的延迟和错误率相比,改进后的模型在一定信噪比下具有很低的延迟和错误率。
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