An efficient quantum based D2D computation and communication approach for the Internet of Things

Abstract

The Internet of Things is made up of a set of embedded devices such as light detection sensors, ranging (LiDAR) sensors, and millimetre-wave sensors. These sensors generate a massive amount of data in which limited communication capacity is available to share a massive amount of data to Fog-based Roadside Units (RSU) for data process and analysis service. Fog-based RSU has become an emerging paradigm in intelligent transportation but needs research attention to design intelligent decision-making methods for data communication and computation at Fog-based RSU. To address these issues, we design a two-level Quantum based D2D Computation, Communication ( $$QDC^{2}$$ ) approach. First, design a bandwidth allocation strategy based on spatial importance score factors to resolve embedded devices’ data transmission issues. Second, design an adaptive equilibrium service offloading strategy based on device-centric measurements to assess the computation capacity and performance rate for resolving Fog-node computation consistency issues. Additionally, Fog-based RSU is interconnected with LAN helps to optimize service latency. Simulation results show that our approach achieved a high service reliability rate (79.56%), low error rate (0.9%), and an execution delay of 22.5 s for 15 devices compared to state-of-the-art approaches.