Sharing Semantic Information Among Vehicles to Reduce Computation and Communication Energy Consumption

In this paper, we propose a framework for semantic information sharing between vehicles to reduce energy consumed by computation related to processing of sensor data. The energy consumption is reduced via avoiding redundant semantics extraction by multiple vehicles. We develop an algorithm that allows sharing semantic information derived by one vehicle with neighboring vehicles, thereby reducing the need for individual semantics extraction. Of course, semantic sharing introduces communication energy overhead. Thus, in our work, we consider not only computation but also communication energy. We propose graph representation of the problem to allow mapping of a part of the energy consumption minimization to the maximum independent set problem, which we further combine with a greedy and recursive approach. Simulations demonstrate that the proposed algorithm can save up to 61% of energy compared to state-of-the-art approaches.