Priority‐based learning automata in Q‐learning random access scheme for cellular M2M communications

Nasir A. Shinkafi, Lawal M. Bello, Dahiru S. Shu'aibu, Paul D. Mitchell
2021 ETRI Journal  
Machine-to-Machine (M2M) communication, also called machine-type communication (MTC), is predicted to be one of the major applications of current and future cellular communications [1, 2] . M2M communication, as defined in [3] , enables communication between various devices without or with limited human intervention. Different devices such as sensors, actuators, meters, and radio frequency tags are used as M2M devices to read the status of machines and share information, either on a wireless
more » ... work, wired network, or a hybrid of both to a target destination [4, 5] . M2M devices are an important part of the emerging "Internet of Things" and "Smart City" paradigms [6, 7] , which are expected to provide solutions to current and future socioeconomic demands. In addition, M2M devices engender new applications in areas such as building and industrial automation, remote and mobile healthcare, and many more, as described in [8] . According to [9], the number of M2M devices is expected to significantly outnumber the world population [10] . This creates a significant gap and makes it practically impossible for humans to control them. Therefore, there is a need for these devices to autonomously interact among themselves. The envisaged growth of M2M applications has led to many research studies on protocols and products oriented to support M2M services. The 6LowPAN protocol suite is a popular technology for low-power devices [11] , the IEEE 802.15.4 standard is used for low-bit rate short-range transmission [12] , and Zigbee (which utilizes the 802.15.14 standard) is for M2M device interconnection in short-range
doi:10.4218/etrij.2020-0091 fatcat:bmz4qb4qv5erdcrielrbfplk34