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Low latency and energy efficient routing-aware network coding-based data transmission in multi-hop and multi-sink WSN

机译:多跳和多槽WSN中基于低延迟和节能路由感知网络编码的数据传输

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摘要

Multi-hop and multi-sink wireless sensor networks have the potential to provide network performance through efficient data exchanges. In multi-sink phenomena, clusters of nodes are defined using distance vector and thereby specific node that lies at the center of the cluster is identified as a sink. The performance of multi-hop and multi-sink wireless networks is significantly affected by sink node placement and routing of data packets within the cluster. In this paper, the authors propose an application of three different algorithms to improve the performance of a sensor network in terms of sink node placement along with route construction and optimization using nature-inspired computational methods. Furthermore, at potential relays, opportunistic coding is used to reduce the number of transmissions. Hence, the proposed implementation integrates three algorithms, which combines the merits of each for significant enhancement in data transmission. First is the placement of sink node through particle swarm optimization, second is the route construction from sensors and sink of the particular cluster using minimum wiener spanning tree, which further optimized by artificial bee colony technique and third is opportunistic packet amalgamation before transmitting to neighbors. Finally, the proposed work is evaluated and validated for coded transmissions and non-coded transmissions through comparisons of evaluation metrics like throughput, energy conservation, packet delivery ratio and average hop-count between sensor and sink node. (C) 2020 Elsevier B.V. All rights reserved.
机译:多跳和多汇无线传感器网络具有通过有效数据交换提供网络性能。在多汇现象中,使用距离矢量定义节点集群,从而识别群体中心的特定节点被识别为接收器。多跳和多汇无线网络的性能受到群节点放置和集群内的数据分组的路由的显着影响。在本文中,作者提出了三种不同算法的应用,以改善传感器网络在水槽节点放置方面的性能以及使用自然启发的计算方法的路由施工和优化。此外,在潜在的继电器中,机会编码用于减少传输的数量。因此,所提出的实现集成了三种算法,该算法结合了每个算法,用于数据传输中的显着增强。首先是通过粒子群优化的汇总节点的放置,第二是使用最小维纳生成树的传感器和沉没的路线施工,其使用最小维纳跨越树,这通过人造蜂殖民地技术进一步优化,第三是在传送到邻居之前的机会主义分组融合。最后,通过比较评估度量和传感器和汇聚节点之间的平均跳数等评估度量的比较来评估和验证所提出的工作,以进行编码传输和非编码传输。 (c)2020 Elsevier B.v.保留所有权利。

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