@article{(Open Science Index):https://publications.waset.org/pdf/10011618,
	  title     = {Reinforcement Learning-Based Coexistence Interference Management in Wireless Body Area Networks},
	  author    = {Izaz Ahmad and  Farhatullah and  Shahbaz Ali and  Farhad Ali and  Faiza and  Hazrat Junaid and  Farhan Zaid},
	  country	= {},
	  institution	= {},
	  abstract     = {Current trends in remote health monitoring to monetize on the Internet of Things applications have been raised in efficient and interference free communications in Wireless Body Area Network (WBAN) scenario. Co-existence interference in WBANs have aggravates the over-congested radio bands, thereby requiring efficient Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategies and improve interference management. Existing solutions utilize simplistic heuristics to approach interference problems. The scope of this research article is to investigate reinforcement learning for efficient interference management under co-existing scenarios with an emphasis on homogenous interferences. The aim of this paper is to suggest a smart CSMA/CA mechanism based on reinforcement learning called QIM-MAC that effectively uses sense slots with minimal interference. Simulation results are analyzed based on scenarios which show that the proposed approach maximized Average Network Throughput and Packet Delivery Ratio and minimized Packet Loss Ratio, Energy Consumption and Average Delay.
},
	    journal   = {International Journal of Computer and Systems Engineering},
	  volume    = {14},
	  number    = {11},
	  year      = {2020},
	  pages     = {446 - 453},
	  ee        = {https://publications.waset.org/pdf/10011618},
	  url   	= {https://publications.waset.org/vol/167},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 167, 2020},
	}