Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System

Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response. Cognitive Internet of things (CIoT) technologies including inherent characteristics cognitive radio (CR) potential candidates develop monitoring early warning system (MEWS) helps in efficiently utilizing response save lives during flash floods. However, CIoT devices battery-limited thus, it reduces lifetime MEWS. To tackle these problems, we propose CIoT-based MEWS slash fatalities extend by conserving limited battery energy sensors, formulate resource assignment problem maximizing efficiency. solve problem, at first, devise polynomial-time heuristic energy-efficient scheduler (EES-1). its performance can be unsatisfactory since an exhaustive search find local optimum values without consideration overall network enhance efficiency proposed EES-1 scheme, additionally optimization based on maximum weight matching bipartite graph. Then, Hungarian algorithm-based (EES-2), solvable polynomial time. The simulation results show EES-2 scheme achieves considerably high MEWS, leading extended loss throughput performance.