Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies

Abstract By taking into account not only the node specifics but also the benefits of mobile edge computing, an integrated strategy for energy saving of Internet of Things (IoT) devices is proposed in this article. This strategy consists of two algorithms at both the end and the edge. Considering the changeable battery level and downlink communication traffic of the battery‐powered wireless nodes, an energy efficient automatic mode switching algorithm is designed at the end. Three different kinds of working modes are designed based on the features of the end nodes and the various application requirements. This algorithm tends to enable the end nodes to automatically select and switch to the proper working mode according to the actual conditions. At the edge server with much stronger processing and computing capabilities which belongs to a higher layer, the dynamic sampling rate adjustment algorithm is designed. It can adaptively adjust the sampling frequencies of the end nodes and thus reduce their working durations. The proposed integrated solution aims to decrease the energy consumption of IoT devices as much as possible and thus prolong their battery lives. The simulation results have shown both the effectiveness and the efficiency of the proposed end and edge integrated strategy in terms of energy consumption.