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.