Energy efficient data gathering in IoT networks with heterogeneous traffic for remote area surveillance applications: A cross layer approach

In this paper, the problem of energy-efficient data gathering in an Internet of Things (IoT) based remote area surveillance application is addressed by designing a suitable MAC layer uplink solution. We follow the 3GPP specified scheduled access scheme for narrowband IoT (NB-IoT) and consider presence of both delay sensitive and delay tolerant traffic in the network. First, we present a mixed integer non-linear programming (MINLP) based optimization framework to minimize the IoT devices\u27 cumulative transmission energy per uplink frame under the constraints of finite resource blocks, mean queue stability and residual energy levels of IoT nodes. A cross layer approach has been adopted. Precisely, using Lyapunov optimization, we propose a dynamic, distributed transmit power allocation for IoT nodes and a centralized node scheduling scheme. Further, assuming Poisson model for traffic generation at delay tolerant traffic generating nodes, we suggest a distributed probabilistic sleep scheduling scheme to improve the average delay experience of delay sensitive traffic while improving the overall energy conservation. Simulation results suggest impressive performance of our proposed solution over the existing uplink solution for NB-IoT, in terms of delay experience of delay sensitive traffic, buffer length requirement and nodes\u27 total energy consumption at high traffic load