Type
ArticleKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Networks Laboratory (NetLab)
RISC Laboratory
Date
2020-08-05Online Publication Date
2020-08-05Print Publication Date
2020-11Embargo End Date
2022-08-19Submitted Date
2019-05-28Permanent link to this record
http://hdl.handle.net/10754/664944
Metadata
Show full item recordAbstract
The Internet of Things (IoT) paradigm envisions billions of interconnected things. This high density of things generates a huge amount of data, which results in communication delays and increases the amount of contention. Thus, it is necessary to have efficient Medium Access Control (MAC) protocols to coordinate channel access. In this paper, we consider a network throughput maximization problem in which a set of selfish nodes compete for transmission opportunities in the IoT scenario. To enhance cooperation among the nodes and to reduce the collision rate, we formulate a memory-one channel access game in which the nodes maximize their payoffs by optimizing their channel access probabilities, conditioned on their previous transmission state. A distributed learning algorithm is used by the nodes to solve the problem efficiently in order to overcome any coordination overhead. We investigate the impact of the network topology on the solution to the problem. Our simulation results show that the throughput achieved by the memory-one game outperforms the throughput achieved by other methods including IEEE 802.11 DCF protocol.Citation
Odat, E., Hamza, D., Shihada, B., & Shamma, J. S. (2020). A memory-oriented MAC-layer design for future IoT systems. Ad Hoc Networks, 108, 102276. doi:10.1016/j.adhoc.2020.102276Sponsors
The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST).Publisher
Elsevier BVJournal
Ad Hoc NetworksAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S1570870520306375ae974a485f413a2113503eed53cd6c53
10.1016/j.adhoc.2020.102276