FL-Sleep: Temperature adaptive multi-attribute sleep-scheduling algorithm using hesitant fuzzy logic for Wireless Sensor Networks

The sustainable operation of sensor nodes in Wireless Sensor Network depends on the nodes’ adaptability with the environment. A sensor node strives to live longer using periodic sleep/awake activity. But it fails to achieve considerable success due to the node’s inability to make the sleep/awake strategy adaptive to the environment. To this end, we propose an algorithm, ‘FL-Sleep’ which makes every node in the network to observe the ambient temperature and status of their parameters after every round of operation. Depending on their perception of the parameters, the nodes execute a sleep-scheduling strategy in the subsequent round. It makes the node evaluate its current state and decide the required action (’Active’, ‘Listen’ or ‘Sleep’) to perform. A node working in a favorable condition would decide the action with an optimistic attitude towards the parameters. In contrast, a critical condition of a node compels it to decide pessimistically. This qualitative measurement provides a precise understanding of the environment. ‘FL-Sleep’ works on hesitant fuzzy logic-based Multi-Criteria Decision Making method and is found to improve the network’s lifetime by 247.11% compared to BMAC, by 68.56% compared to SOPC, and by 77.2% compared to RL-Sleep. The best lifetime of nodes is obtained when the network is organized in spiral topology. ‘FL-Sleep’ shows better performance in terms of packet-delivery-ratio, energy efficiency, and the number of active nodes in the network compared to BMAC, SOPC, and RL-Sleep.