SALA-IoT: Self-Reduced Internet of Things With Learning Automaton Sleep Scheduling Algorithm

The extensive development of wireless communications has led to the popularity of self-powered Internet-of-Things (SPIoT) networks. Even though significant advances made in keeping energy consumption at a low level, making such networks live longer is still one of the biggest challenges. In particular, a significant question is how to cover the maximum range of the environment by the sensor nodes with the lowest amount of energy. In this research, we have proposed a sleep scheduling algorithm based on learning automaton for IoT (SALA-IoT), utilizing machine-learning approaches to find the optimal set of sensor nodes that can cover a wide range of the environment. This algorithm consists of learning and covering phases, in which the essential sensor nodes are activated, and the rest are turned off. Although the implementation of learning algorithms requires a high energy level, after the learning phase's performance, the network gathers complete information about the status of the nodes. It can meet the needs of the network with a limited amount of energy. We have evaluated the proposed algorithm with several simulation scenarios and considered the coverage area and the number of active sensor nodes as the main evaluation metrics. The simulation results show that the sensing and covering ranges of the sensors are directly related to their transmitted power, which can be adjusted to match the expected network requirements in terms of primary coverage factors; the proposed method improved by 6.743 for 50 nodes and 5.204 for 100 nodes, respectively, for distance and intervals in a different number of nodes.