Energy Efficient Edge Computing Enabled by Satisfaction Games and Approximate Computing

In this paper, we introduce an energy efficient edge computing solution to collaboratively utilize Multi-access Edge Computing (MEC) and Fully Autonomous Aerial Systems (FAAS) to support the computing demands of the Internet of Things (IoT) nodes residing in Areas of Interest (AoIs) and executing machine learning tasks. The Satisfaction Games are adopted to determine whether the nodes' optimal partial task should be offloaded to the MEC server or to a hovering FAAS above the AoI. The decision is taken by considering IoT nodes' latency, energy consumption, and acceptable level of Deep Neural Network (DNN) inference accuracy drop constraints. We exploit the error resilience of DNNs and we enhance the FAAS with a heterogeneous approximate DNN accelerator that supports different computational precision and throughput, thus allowing to intelligently adapt to different computing demands. A reinforcement learning-based technique is introduced to enable the FAAS to autonomously optimize its trajectory, aiming at increasing the IoT nodes' satisfaction of their computing demands, while accounting for its flying and data processing energy cost. Our experimental results show the benefits of FAAS, MEC, and approximate computing in terms of increasing the number of satisfied users by 40% under a maximum accuracy drop of only 1%.