Improvement of QoS management in wireless sensor/actuator networks using fuzzy-genetic approach

Wireless sensor/actuator networks (WSANs) are rapidly increasing due to the recent advances in radio frequency, computing and sensing technologies. In particular, quality of service (QoS) technologies management remains an important issue yet to be investigated. In this paper, a fuzzy-genetic approa...

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Bibliographic Details
Published in2009 International Conference on Networking and Media Convergence pp. 29 - 35
Main Authors Hamdy, M., El-Madbouly, H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2009
Subjects
Actuators
Computer network management
Computer networks
Environmental management
Quality of service
Radio frequency
Resource management
Sampling methods
Technology management
Wireless sensor networks
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ISBN9781424437764
1424437768
DOI10.1109/ICNM.2009.4907185

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Summary:Wireless sensor/actuator networks (WSANs) are rapidly increasing due to the recent advances in radio frequency, computing and sensing technologies. In particular, quality of service (QoS) technologies management remains an important issue yet to be investigated. In this paper, a fuzzy-genetic approach based QoS management (FG-QM) scheme is developed for WSANs with constrained resources and in dynamic and unpredictable environments. This approach deals with the impact of unpredictable changes in traffic load on the QOS of WSANs. It utilizes fuzzy-genetic controller inside each source sensor node to adapt sampling time to the deadline miss ratio associated with data transmission from the sensor to the actuator at different invocation times between the wireless sensors and fuzzy-genetic control. A pre-determined desired level for the deadline miss ratio is maintained so that the desired QOS can be achieved. Fuzzy inference mechanism has been used here for adapting the future values of the sampling period to the deadline miss ratio. The crisp consequent values of the rule-base of the previous Takagi-Sugeno fuzzy model are optimized using a genetic algorithm. The optimized crisp values of the rule-base have considerably improved the performance of the fuzzy controller. The proposed algorithm has the advantages of generality, scalability, and simplicity. Simulation results show that FG-QM can provide WSANs with the desired QOS support in several cases.
ISBN:9781424437764
1424437768
DOI:10.1109/ICNM.2009.4907185