Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy.
Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy.
复制标题
DOI：
10.3390/s22197462
复制
发表时间：
2022-10-01
期刊：
影响因子：
--
通讯作者：
Pezaros D
中科院分区：
文献类型：
--
作者：
Alharbi N;Mackenzie L;Pezaros D
工业物联网（IIoT）的出现加速了工业无线传感器网络（IWSNs）在众多应用中的采用。这些应用中的有效通信需要减少端到端传输时间、平衡能耗和提高通信可靠性。图路由是IWSNs中的主要路由方法，在满足这些要求方面对实现有效通信具有重要影响。图路由算法涉及应用第一路径可用的方法，并使用路径冗余从源传感器节点到网关传输数据包。然而，这种方法可以通过在涉及公共传感器节点的传输之间产生冲突并由于集中管理而促进不平衡的能量消耗来影响端到端的传输时间。这些网络的特性和要求遇到了进一步的复杂性，由于需要找到最佳路径的基础上的需求，IWSNs来克服这些挑战，而不是使用可用的第一路径，这样的要求影响网络的性能和延长网络的生命周期。为了解决这个问题，我们采用协方差矩阵自适应进化策略（CMA-ES）来创建和选择图路径。首先，本文针对IWSN的需求，提出了三种单目标最佳图路由路径，传感器节点根据CMA-ES的三个目标函数选择最佳路径：基于距离的最佳路径（PODis）、基于剩余能量的最佳路径（POEng）和基于端到端传输时间的最佳路径（POE 2 E）。其次，为了提高能量消耗的平衡，并实现平衡的IWSN的要求，我们适应CMA-ES选择最佳路径与多个目标，也被称为最佳路径的图路由与CMA-ES（BPGR-ES）。利用MATLAB仿真工具对改进的图路由算法进行了不同配置和参数的仿真。此外，PODis，POEng，POE 2 E和BPGR-ES的性能与现有的国家的最先进的图路由算法进行了比较。仿真结果表明，与其他算法相比，BPGR-ES算法在网络中实现了87.53%的能量均衡，数据包的发送率达到99.86%，通信更加可靠。
The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication.
登录
查看更多内容
影响因子：
8.7
作者：
Oyewobi, Stephen S.;Hancke, Gerhard P.
通讯作者：
Hancke, Gerhard P.
基于遗传算法和果蝇优化算法相结合的无线传感器网络APTEEN路由协议优化
影响因子：
4.8
作者：
Wang, Minghao;Wang, Shubin;Zhang, Bowen
通讯作者：
Zhang, Bowen
影响因子：
2.2
作者：
Al Aghbari, Zaher;Khedr, Ahmed M.;Agrawal, Dharma P.
通讯作者：
Agrawal, Dharma P.
WirelessHartnetworks的路由和调度算法：调查。
DOI：
10.3390/s150509703
发表时间：
2015-04-24
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Nobre M;Silva I;Guedes LA
通讯作者：
Guedes LA
影响因子：
8.7
作者：
Sepulcre, Miguel;Gozalvez, Javier;Coll-Perales, Baldomero
通讯作者：
Coll-Perales, Baldomero