Enhanced Resource Pooling in MCDM for Data Collection and Energy Replenishment for WSN

Resupply of energy and data collection are crucial factors in allowing wireless sensor networks to operate perpetually. In wireless sensor networks, the longer the distance the data has to travel the more the energy will be used. Due to sensors having limited energy, it is important that the energy is replenished in good time to prevent the sensors from dying. The failure of a sensor results in an inability to sense and collect data, which is detrimental to the performance of the entire wireless sensor network. In this work, we proposed a data collection and data collection method based on multi-criteria decision making. To solve these problems, we first proposed a modified <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex>-means clustering algorithm that combines sensor proximity to the base station and hop count to allocate a sensor to a particular cluster. Then, for the mobile vehicle to go charge and collect data in a particular cluster, we proposed a resource pooling method that allows a data collection and energy replenishment request to be sent to the base station so that the autonomous vehicle can travel to the requesting cluster. This results in a request being sent faster and as such having the autonomous vehicle deployed faster, resulting in a reduction in the number of dead nodes, reduced waiting time, reduced queue length, and shorter queues. Finally, a path for the vehicle to travel was created using multi-criteria greedy algorithm, more specifically, analytic hierarchy process to create priorities as to which sensor will be prioritized for data collection and energy replenishment. Our proposed method enhances performance by reducing average sensor visit density, minimizing queue length, decreasing the number of dead sensors, and shortening sensor queue waiting time.