Multi-hop similarity-based-clustering framework for IoT-Oriented Software-Defined wireless sensor networks

Abstract

The performance of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) depends on the routing protocol and the deployment technique in modern applications. In a plethora of IoT-WSNs applications, the IoT nodes are essential equipment to prolong the network lifetime with limited resources. Data similarity-based clustering protocols exploit the temporal correlation among the neighbouring sensor nodes through the subset of data. In bendy supervision, IoT-based Software Defined WSNs provide an optimistic resolution by allowing the control logic to be separated from the sensor nodes. The benefit of this SDN-based IoT architecture, allows the unified control of the entire IoT network, making it easier to implement on-demand network management protocols and applications. To this end, in this paper, we design a Multi-hop Similarity-based Clustering framework for IoT-oriented Software-Defined wireless sensor Networks (MSCSDNs). In particular, we construct data-similar application-aware clusters in order to minimise the communication overhead. Also, we adapt inter-cluster and intra-cluster multi-hop communication using adaptive normalised least mean square and merged them with the proposed MSCSDN framework that helps prolong the network lifespan. The proposed framework is compared with the state-of-the-art approaches in terms of network lifespan, stability period, instability period, report delay, report delivery, and cluster leader nodes generations. The MSCSDN achieves optimal data accuracy concerning the collected data.