FSO–PSO based multihop clustering in WSN for efficient Medical Building Management System

• Published in: Cluster computing Vol. 22; no. Suppl 5; pp. 12157 - 12168
• Main Authors: Shanthi, G., Sundarambal, M.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2019; Springer Nature B.V
• Subjects: Algorithms; Automation; Blood pressure; Body temperature; Building management systems; Clustering; Communication; Computer Communication Networks; Computer Science; Control equipment; Effectiveness; Efficiency; Electric equipment; Energy consumption; Nodes; Operating Systems; Oxygenation; Particle swarm optimization; Privacy; Processor Architectures; Sensors; Ventilation; Wireless sensor networks; Wireless Sensor Network (WSN); Fish Swarm Optimization (FSO) and Particle Swarm Optimization (PSO); Energy; Clustering; Medical Building Management System (BMS)
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-017-1569-x

A Wireless Sensor Network (WSN) is the one that is formed keeping a maximum number of sensor nodes that have been positioned in any application or an environment for monitoring the physical entities in the target area. The main challenge is the organizing of sensor networks with efficacy of energy. This need for efficacy of energy is owing to the capacity of the sensor node being limited and their replacement not being viable. An efficient technique to prolong the lifetime of WSNs is by means of adapting clustering algorithm. This includes the grouping of sensor nodes into clusters and the electing of Cluster Heads (CH) and the forwarding of their aggregated data to that of the Base Station (BS). A challenge involved in the WSN is the choice of suitable CH. Building Management Systems are a control system that is computer-based and installed in buildings which tend to control and also monitor the mechanical as well as the electrical equipment of the building like the ventilation, power systems, lighting etc., Medical sensor nodes have been used for examine several signals from a human body to monitor parameters like blood pressure, body temperature, blood sugar, pulse oxygenation of the blood etc. The work proposed the Fish Swarm Optimization (FSO), the Particle Swarm Optimization (PSO) that is based on multi-hop clustering algorithm for the saving energy consumption in WSN. When the visual and the step dominated the FSO they are quite challenging to be set as well. The work employs the PSO formulation for modifying the FSO and make is free from step. Also, visual along with the searching domain is formulated to improve ease of setting. The results of the experiment show that this method has better performance.