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

Abstract

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.

Keywords

• internet of things
• multi‐hoping
• similarity‐based clustering
• software defined networking