Metaheuristic based Data Management in IoT-Wireless Sensor Networks

The integration of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has produced smart connected systems capable of collecting a large number of data and then making decisions based on that data. These systems encounter unique challenges such as limited power, excessive redundant data, and inefficient routing protocols. This review work provides solutions to these problems by employing metaheuristic-based algorithms that could better manage data in IoT-WSNs, focusing mainly on data fusion and reduction methods, finding the best route selection, and energy conservation. This study will also compare the most common metaheuristic-based algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Firefly Algorithm (FA) and it will discuss the different constraints which they may encounter.