Edge-based Adaptive Computation Offloading in 5G-IoV Environment

These days, curbside units play an increasingly important role in promoting software for the Internet of Connected Vehicles (RSUs). As part of the IoCV scenario, 5G is added to provide enough communication capacity to boost the RSUs' data transmission. Unloading computational work in IoCV often occurs to remote cloud servers, which lengthens the response time of the jobs. Servers, which are co-located with 5G MABS and RSUs, provide additional options for hosting the jobs. However, it is challenging to discern the unloading destination of the computational jobs in IoCV because to the complex positions of MABSs and RSUs. This motivates the development of an adaptable compute dumping technique (ACO) for use by edge devices in the IoCV envisaged by 5G, with the goal of minimizing both the offloading time at the edge and the resources it consumes. To be more precise, the accessible solutions are produced using a multi-objective evolutionary algorithm (MOEA/D). Next, a value assessment is performed to determine the best dumping strategy. Over time, experimental data prove ACO's usefulness.