Novel constrained QoS routing approach of energy-aware optimization based on learning automata for mobile edge computing (MEC)

Existing multi-path routing protocols can meet the service requirements of end-to-end delay and reliability between nodes in the Internet of Things, but they consume more energy. Constrained QoS routing is a new research method for routing protocols, which keeps up with the current trend. By considering the three QoS constraints of end-to-end delay, reliability, and energy consumption, we innovatively use the related techniques of mobile edge computing with machine learning automata to construct a sensor network as a multi-constrained optimal path model in this paper. At the same time, introducing the energy-aware node wake-up mechanism and the reward and punishment mechanism of learning automata, we propose an oriented mobile edge computing node energy-aware optimized QoS constrained routing algorithm. The algorithm can optimize the network energy consumption, extend the network life cycle, and accelerate the convergence by using learning automata. By experimental testing and comparison, the novel constrained Multi-QoS routing approach of energy-aware optimization based on learning automata for mobile edge computing (MQEN) proposed in this paper can reduce end-to-end delay, improve reliability, and decrease energy consumption.