Minimizing Age of Information and Energy Consumption in a Computation-Intensive Status Update System

This study examines a real-time status update system featuring energy-harvesting sensing devices and intensive packet processing. Diverging from existing research in the same area, our approach employs partial offloading, where packets are split between local server and mobile edge computing server. Based on the available energy, the device determines the offloading ratio in order to minimize both the age of information (AoI) and the energy consumption. Due to the randomness of the environment and the need to take sequential decisions, the problem is formulated using a Markov decision process. The state transition over subsequent decision epochs is modelled using a multi-dimensional Markov chain to facilitate writing the Bellman equation and finding the optimal offloading ratio. The numerical results show that the partial offloading scheme exploits the available energy more effectively to decrease the AoI compared with both the total and binary offloading schemes unless the channel is in a good state with high probability.