A UAV gathers and recharges Every single sensor inside a length of two seconds [four]. The total energy employed by the UAV inside the DOCEM problem is the sum of your Strength useful for data downloads, traveling for the sensors, and sensor node recharging. For that sake of simplicity, we suppose the UAV, touring at 55 km h − one

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The agent could be the entity performing steps throughout the atmosphere (In cases like this, a UAV). The present-day point out of your program in a presented time action t is represented as being a partial solution ( C t

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when averting all of the hurdles in its path. Also, the UAV demands to arrive at Every sensor j within a specified deadline d j

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To handle these worries, this paper proposes a novel method determined by Deep Q-Mastering (a reinforcement Mastering procedure) for optimizing UAV trajectories in constrained WSN environments when accumulating data from sensors and recharging them. By leveraging the capabilities of Deep Q-learning, the proposed algorithm allows the UAV to generate smart detour decisions in real-time.

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is some time used although gathering data from sensor i. Looking at the proposed system product, the UAV’s full Electrical power use E u a v