Affiliations: [a]
Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Tamil Nadu, India
| [b]
Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Tamil Nadu, India
Correspondence:
[*]
Corresponding author. D. Giji Kiruba, Research Scholar, Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil-629 180, Thuckalay, Kanyakumari District, Tamil Nadu, India. E-mail: [email protected].
Abstract: IoT-Mobile Wireless Sensor Networks (IMWSNs) are being employed in a variety of simulators to visually demonstrate the exposure, energy usage situation, and expected life duration of Internet of Things (IoT) mobile sensors. The majority of academics have projected and expanded routing procedures in order to extend the network’s life cycle. In IMWSNs, clustering is the most important process for improving energy efficiency. In cluster approaches, each IoT sensor node provides the acquired data to the cluster-head of their own cluster. The cluster-head embraces the conscientiousness of gathering prepared information and directing it to the arranged network’s basestation. A fuzzy based energy proficient secure clustered routing (FEPSRC) is proposed in this research effort, which takes the residue energy, remoteness from the basestation, and compactness of IoT sensor nodes in its locality as input to the Fuzzy-Inference-System. For cluster-head selection, an eligibility ratio is calculated for each IoT sensor node. This protocol guarantees energy harmonizing by electing the preeminent IoT sensor node for the position of cluster-head, velocity of IoT sensor nodes are estimated and also provides best path for routing. The simulation consequence illustrates that projected fuzzy based energy proficient secure clustered routing condensed entire power expenditure, diminishes E-to-E delay, amplifies packet deliverance percentage and accomplishes maximal network life span.
Keywords: Clustering, CH, energy effectiveness, fuzzy-logic, network life span
