TY - GEN
T1 - Transmission Probability Strategies for Cluster-Based Event-Driven Wireless Sensor Networks
AU - Rivero-Angeles, Mario E.
AU - Rubino, Gerardo
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - In the literature, it is common to consider that sensor nodes in a clustered-based event-driven Wireless Sensor Network (WSN) use a Carrier Sense Multiple Access (CSMA) protocol with a fixed transmission probability to control data transmission. However, due to the highly variable environment in these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies for event-driven WSNs are studied: optimal, fixed and adaptive. As expected, the optimum strategy achieves the best results in terms of energy consumption but its implementation in a practical system is not feasible. The commonly used fixed transmission strategy is the simplest but does not adapt to changes in the system's conditions and achieves the worst performance. In the paper, we find that the adaptive transmission strategy, pretty easy to implement, achieves results very close to the optimal one. The three strategies are analyzed in terms of energy consumption, and cluster formation latency.
AB - In the literature, it is common to consider that sensor nodes in a clustered-based event-driven Wireless Sensor Network (WSN) use a Carrier Sense Multiple Access (CSMA) protocol with a fixed transmission probability to control data transmission. However, due to the highly variable environment in these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies for event-driven WSNs are studied: optimal, fixed and adaptive. As expected, the optimum strategy achieves the best results in terms of energy consumption but its implementation in a practical system is not feasible. The commonly used fixed transmission strategy is the simplest but does not adapt to changes in the system's conditions and achieves the worst performance. In the paper, we find that the adaptive transmission strategy, pretty easy to implement, achieves results very close to the optimal one. The three strategies are analyzed in terms of energy consumption, and cluster formation latency.
KW - Transmission probability
KW - clustering
KW - event-driven WSNs
UR - http://www.scopus.com/inward/record.url?scp=85050198962&partnerID=8YFLogxK
U2 - 10.1109/CyberC.2017.92
DO - 10.1109/CyberC.2017.92
M3 - Contribución a la conferencia
AN - SCOPUS:85050198962
T3 - Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017
SP - 416
EP - 419
BT - Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017
Y2 - 12 October 2017 through 14 October 2017
ER -