Reinforcement learning based routing for energy sensitive wireless mesh IoT networks

With the huge growth of the Internet of Things (IoT) in manufacturing, agricultural and numerous other applications, connectivity solutions have become increasingly important especially for those covering wide remote area in the scale of kilometre squares. Although many low-power wide-area network (LPWAN) technologies such as Long Range are supposed to support long-range low-power wireless communication, the underneath star topology limits the scalability of the networks due to the need of a central hub. To provide connectivity to a wider area, the authors propose to build the mesh topology upon these LPWAN technologies. One of the challenges of meshing these networks is the routing mechanism originally designed for star networks is not energy sensitive. In this Letter, the authors address this issue by proposing a distributed as well as energy-efficient reinforcement learning based routing algorithm for the wide area wireless mesh IoT networks. They evaluate the failure rate, spectrum and power efficiencies of the proposed algorithm by simulations, which resemble the long-range IoT networks, by comparing it to that of a random routing with loop-detection algorithm and a centralised pre-programmed routing algorithm which represents the ideal scenario. They also present a progressive study to demonstrate how the learning in the algorithm reduces the power consumption of the entire network