Role of Deep Learning in Mobile Ad-hoc Networks

The portable capability of MANETs has specially delighted in an unexpected expansion. A massive need for dynamic ad-hoc basis networking continues to be created by advancements in hardware design, high-speed growth in the wireless network communications infrastructure, and increased user requirements for node mobility and regional delivery processes. There are several challenging issues in mobile ad-hoc networks, such as machine learning method cannot analyze features like node mobility, channel variation, channel interference because of the absence of deep neural layers. Due to decentralized nature of mobile ad hoc networks, its necessitate to concentrate over some extremely serious issues like stability, scalability, routing based problems such as network congestion, optimal path selection, etc. and security

Dependability-based clustering in mobile ad-hoc networks

Clustering is an effective solution to handle challenges due to lack of centralized controllers and infrastructure in ad-hoc networks. Clustered network topologies offer better scalability and reliability when compared with flat topologies. In this paper, a novel clustering algorithm, Dependability-based Clustering Algorithm (DCA) is presented. In DCA, we focus on the dependability of clusters themselves rather than considering only individual nodes to maintain the clustered structure of a network. We validated the performance of our proposal by discrete-event simulations and showed that our approach outperforms its opponents in terms of stability, energy efficiency and quality of service in stationary and mobile scenarios. Moreover, we performed sensitivity analysis of different metrics that are used to select cluster heads and compute the cluster dependability score proposing a complete optimization framework to analyze such metrics for weighted clustering algorithms. As a result of the analysis, we found that while topology-related metrics have negative impact, in many cases, energy-related metrics (e.g., residual energy) have varying effects on different objective