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Relevant node discovery and selection approach for the Internet of Things based on neural networks and ant colony optimization
Pervasive and Mobile Computing ( IF 4.3 ) Pub Date : 2020-12-09 , DOI: 10.1016/j.pmcj.2020.101311
Abderrahim Zannou , Abdelhak Boulaalam , El Habib Nfaoui

The Internet of Things (IoT) brings opportunities to create new services and change how services are sold and consumed. The IoT is overpopulated by a large number of networks, millions of objects and a huge number of services and interactions. Despite this, the nature of IoT networks, such as the heterogeneity of resources, the dynamic topology, and the large number of similar services, makes service discovery a complex task in terms of accuracy and the time required. Furthermore, the discovery task can offer a set of providers for a given request, so selection of the most relevant provider node must take into account the available resources, such as the power energy and the period of time. In this paper, to overcome these limitations, we propose an approach for service discovery and selection in the IoT. The discovery phase is performed by an edge server using a neural network. The selection phase is performed by nodes to select the most adequate node from the set of relevant nodes using Ant Colony Optimization (ACO). The experimental results show high performance in term of accuracy (96.5%) and a longer network lifetime for the discovery and selection phases respectively, as well as a short period of time for both phases.



中文翻译:

基于神经网络和蚁群优化的物联网相关节点发现与选择方法

物联网(IoT)带来了创造新服务和改变服务买卖方式的机会。物联网由大量网络,数百万个对象以及大量服务和交互所组成。尽管如此,物联网网络的本质(例如资源的异构性,动态拓扑和大量类似的服务)使服务发现在准确性和所需时间方面成为一项复杂的任务。此外,发现任务可以为给定请求提供一组提供程序,因此,选择最相关的提供程序节点时必须考虑可用资源,例如电能和时间段。在本文中,为了克服这些限制,我们提出了一种在物联网中进行服务发现和选择的方法。发现阶段由边缘服务器使用神经网络执行。选择阶段由节点执行,以使用蚁群优化(ACO)从相关节点集中选择最合适的节点。实验结果表明,在发现和选择阶段,分别具有较高的准确度(96.5%)和较长的网络寿命,并且两个阶段的时间较短。

更新日期:2020-12-14
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