HiLSeR: Hierarchical Learning-based Sectionalised Routing Paradigm for Pervasive Communication and Resource Efficiency in Opportunistic IoT Network

Abstract Opportunism in the Internet of Things is the latest necessity arising in the IoT network communication development when we encompass a sustainable approach to routing. Such a requirement arises from the need for device-hop based networking to eliminate a dedicated end-to-end physical network for pervasive communication and promotes local data and computation sharing mechanisms as well as sustainably utilising the ubiquitous mobility aspect of such applications in the contemporary times. However, the functionality of opportunism runs into its own set of impasse; intermittent connectivity, limited resources, need for intelligence in routing, to name a few. Leveraging the ability of ML to tailor represent features to our advantage, this paper proposes a scheme to sectionalize the network topology based on node characteristics and employ grouping in intelligent transmission. The proposed scheme HiLSeR enables message routing using a combination of controlled-parameterized flooding and opportunistic sector-based decentralized transmission. Hierarchical learning, a multi-dimensional data conduct based soft clustering paradigm, is used for topology sectionalization and routing decision making. The performance of the proposed scheme is evaluated against contemporaneous RLPRoPH, GMMR, KNNR and Firefly PRoPHET protocols with ONE based simulations. The performance and sustainability performance is compared on various parameters such as Energy Unit per message, Dead node Percentage, Overhead Ratio, Average Latency and Success Ratio to show the enhanced performance. HiLSeR has an average successful delivery rate of 0.911 averaging out at 0.86775, in comparison to RLPRoPH, GMMR, KNNR, Firefly PRoPHET, the proposed scheme performs 12.85%, 5.59%, 61.29%, 18.50% and 88.33% better respectively.