Abstract
In Internet of Things (IoT) networks, identifying the primary Medium Access Control (MAC) layer protocol which is suited for a service characteristic is necessary based on the requirements of the application. In this paper, we propose Energy Efficient and Group Priority MAC (EEGP-MAC) protocol using Hybrid Q-Learning Honey Badger Algorithm (QL-HBA) for IoT Networks. This algorithm employs reinforcement agents to select an environment based on predefined actions and tasks. It makes use of Q-learning method in Honey Badger Algorithm (HBA). In this algorithm, the PAN coordinator divides the network devices into multiple subgroups based on location, energy levels and the traffic type. In group priority assignment phase, a combined metric will be derived in terms of these parameters. Then a priority will be assigned to each group based on their combined metric. From each group, the optimal number of contention nodes will be selected using hybrid QL-HBA algorithm. The fitness function is derived in terms of the number of neighbours and total traffic loads of the nodes. Then transmission slots will be allotted to the group according to their group priority. The proposed EEGP-MAC protocol is implemented in NS3. Simulation results have shown that EEGP-MAC attains 11% lesser delay, 16% lesser energy consumption with 10% higher throughput, when compared to existing QL-DGMAC protocol, in various network sizes.