Energy efficient routing algorithm for patient monitoring in body sensor networks

Wireless body sensor networks are widely used for monitoring individuals in assisted living facilities and has emerged as a promising technology in e-healthcare. Such networks consist of sensors on the body or clothing of an individual for measuring vital signals such as heart beat, body temperature...

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Veröffentlicht in:Proceedings (International Conference on Wearable and Implantable Body Sensor Networks : Print) S. 141 - 146
1. Verfasser: Rajagopalan, Ramesh
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2016
Schlagworte:
Algorithm design and analysis
Algorithms
Body sensor networks
Computer networks
Constraints
Energy consumption
Monitoring
multi-objective optimization
Networks
Optimization
Patients
Routing
Routing (telecommunications)
sensor networks
Sensors
wireless patient monitoring
ISSN:2376-8894
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Zusammenfassung:Wireless body sensor networks are widely used for monitoring individuals in assisted living facilities and has emerged as a promising technology in e-healthcare. Such networks consist of sensors on the body or clothing of an individual for measuring vital signals such as heart beat, body temperature, and electrocardiogram. This enables patients to experience greater physical mobility and independence eliminating the need to stay in the hospital. Efficient and reliable transmission of data from on body sensors to medical personnel via multi-hop routing is critical for continuous health monitoring. In this paper, we propose a new routing algorithm for energy efficient routing in body sensor networks for reliable health monitoring. We model the routing problem as a constrained multi-objective optimization problem maximizing the throughput while minimizing the energy consumption subject to a constraint on end to end latency. We have designed a new constrained multi-objective genetic algorithm (CMOGA) for obtaining energy efficient routes. Simulation results show that CMOGA demonstrates the advantages of multi-objective optimization and outperforms a widely used and well known multi-objective evolutionary algorithm.
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ISSN:2376-8894
DOI:10.1109/BSN.2016.7516248