The Wireless Sensor Network (WSNs) are fundamental in the contemporary applications of the IoT where the design factors that must be add in Medium Access Control (MAC) Protocol, this includes energy efficiency, low latency and dependable transfer of data. However, the conventional MAC schemes are characterized by limitations such as fixed scheduling, lack of full utilization of duty cycling, and failure to forecast energy consumption, resulting into high energy consumption and limited network life on dense deployments. The goals of this work are design an intelligent MAC protocol which should be able to utilize the energy in an optimal way and simultaneously the quality of service should be provided by dynamically adapting to the energy state of the nodes as well as the behavior of traffic. To achieve this, the present Quintessential Mapping and Adaptive Protocol of Low-Energy Transmission (Q-MAPLE) protocol uses the residual energy observing, Energy Forecasting Estimator, adaptive duty cycling and the dynamic slot allocation within one MAC-layer design. Predictive channel access and slot scheduling energy-awareness can be accomplished with the help of the algorithm in order to simplify the network load and minimize collisions. Q-MAPLE performance was experimented in large scale to compare its performance with the state of the art learning based and optimization based protocols. These results show that Q-MAPLE can significantly improve the network performances of minimum energy usage of 6.2 J at 50 nodes and simultaneously increase the throughput and reduce the end-to-end delay. In short, Q-MAPLE can provide an effective, scalable and predictive MAC system, enhancing energy efficiency, quality of service and network life and, thus, it can be used to implement dense WSN and IoT in the future.

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