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On-Demand Synchronous X-MAC Protocol

Authors
Kim, GayoungAhn, Jongsuk
Issue Date
2016
Publisher
IEEE
Keywords
Duty-cycle MAC Protocols; Hybrid MAC Protocol; Asynchronous Duty-cycle MAC; X-MAC Protocol
Citation
2016 13TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE), pp 234 - 239
Pages
6
Journal Title
2016 13TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE)
Start Page
234
End Page
239
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/17324
ISSN
2372-1642
Abstract
This paper proposes a hybrid duty-cycle Medium Access Control (MAC) protocol named as On-demand Synchronous X-MAC (OSX-MAC) to improve the performance of the X-MAC protocol. X-MAC, one of typical asynchronous duty-cycle MAC protocols, was designed to save energy in Wireless Sensor Networks (WSNs) by allowing awaken receivers to immediately respond in the middle of the preamble transmission. X-MAC, however, still inefficiently wastes a lot of time before delivering data frames due to two drawbacks; unavailability of the receiver's exact wake-up time and the lack of a mechanism to deal with collision frequented in congested WSNs. To solve these two problems, OSX-MAC is equipped with two techniques, an On-demand Schedule Exchange (OSE) scheme and a modified Binary Exponential Backoff (BEB) algorithm. Without the periodic schedule exchange, OSE lets the receiver explicitly provide its wakeup time whenever it acknowledges the sender. After that, the sender can transmit its preambles near the predicted wakeup time of its receiver. To reduce the degree of collisions, OSX-MAC also adopts the modified BEB algorithm that differs from the legacy BEB in that it does not reset the contention window (CW) to the predetermined initial CW but gradually decreases the CW for new transmissions. Simulation experiments confirmed that OSX-MAC outperformed X-MAC in terms of throughput and energy usage in various WSNs.
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College of Advanced Convergence Engineering > Department of Computer Science and Artificial Intelligence > 1. Journal Articles

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