VoI-Aware Energy-Efficient UAV-Assisted Data Collection Using Genetic Algorithms

The growth of the Internet of Things (IoT) has triggered a technological revolution leading to significant advancements in various fields and industries, especially with the ability to connect and collect data from a wide range of devices and sensors. The collection of relevant and important data with high Value of Information (VoI) from sensor nodes (SNs), generally located in difficult-to-reach limited access areas, can allow the systems to learn from their environment, adapt, and improve the decision-making process. In our work, we consider VoI-aware energy-efficient data collection from sensor nodes using Unmanned Aerial Vehicles (UAVs). We formulate the joint UAV deployment and SN-to-UAV association problems as an optimization problem, aiming at deploying the minimum number of UAVs to collect the maximum amount of data with a maximum value of information while minimizing energy consumption, subject to latency and coverage constraints. We then propose using Genetic Algorithms (GAs) to provide real-time sub-optimal solutions while ensuring the tradeoff between different objectives. Simulations were conducted to show the performance and effectiveness of the proposed approach under different system parameters.