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In recent years, computation offloading has become an effective way to overcome the constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive mobile application tasks to remote cloud-based data centers. Smart cities can benefit from offloading to edge points in the framework of the so-called cyber-physical-social systems (CPSS), as for example in traffic violation tracking cameras. We assume that there are mobile edge computing networks (MECNs) in more than one region, and they consist of multiple access points, multi-edge servers, and N MDs, where each MD has M independent real-time massive tasks. The MDs can connect to a MECN through the access points or the mobile network. Each task be can processed locally by the MD itself or remotely. There are three offloading options: nearest edge server, adjacent edge server, and remote cloud. We propose a reinforcementlearning-based state-action-reward-state-action (RL-SARSA) algorithm to resolve the resource management problem in the edge server, and make the optimal offloading decision for minimizing system cost, including energy consumption and computing time delay. We call this method OD-SARSA (offloading decision-based SARSA). We compared our proposed method with reinforcement learning based Q learning (RL-QL), and it is concluded that the performance of the former is superior to that of the latter. INDEX TERMS Mobile devices, edge computing, mobile edge computing, edge cloud computing, virtual machines, access points.

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High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning, a prominent method in artificial intelligence, to design an energy-preserving MAC protocol, with the aim to extend the network lifetime. Our QL-MAC protocol is derived from Q-learning, which iteratively tweaks the MAC parameters through a trial-and-error process to converge to a low energy state. This has a dual benefit of 1) solving this minimization problem without the need of predetermining the system model and 2) providing a self-adaptive protocol to topological and other external changes. QL-MAC self-adjusts the WSN node duty-cycle, reducing energy consumption without detrimental effects on the other network parameters. This is achieved by adjusting the radio sleeping and active periods based on traffic predictions and transmission state of neighboring nodes. Our findings are corroborated by an extensive set of experiments carried out on off-the-shelf devices, alongside large-scale simulations. INDEX TERMS Wireless sensor network, artificial intelligence, reinforcement learning, energy-efficient network, medium access control.