Affiliations: [a] Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India
| [b] Computer science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India
| [c] BioMedical Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India
Correspondence:
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Correspondence to: Singaram Chitra, Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India. E-mail: [email protected].
Abstract: Medical advancements are being made in order to extend the lifespan of mankind. In the medical field, the penetration of Wireless Sensor Networks (WSN) can aid doctors in diagnosing patients accurately and prescribing the medications accordingly. In recent times, several people have permanent implants such as face makers and it is threatening to life to keep altering this body enhancement as well as it is required to possess a system in place to improve the performance of the Wireless Body Sensors. Transmission loss and route loss are two important elements that will drag the battery energy and minimizes its life span. This research proposes optimal clustering and path selection protocol to enhance the lifetime of wireless body sensor networks. Initially, the data is collected from each body sensor through a clustering method called Glow-worm Swarm Optimization (GSO) and the Fruit-fly technique is applied to find the best path. Here, the cluster head is selected with the help of GSO that minimizes the energy consumption as well as enhances the lifetime of WBSN. Further, the best path is identified by the FFO using the fitness value that is measured within the nodes on the basis of the distance. Since hybrid technology is used here, the routing accomplished is shown to be better. The results reveal that the proposed model has improved the sensor life term (95 sec) while compared with other existing methods like PSO with FFO (78 sec), ACO with FFO (77 sec), GA with FFO (76 sec), and LEACH (68 sec) algorithm for 500 nodes.
