# Joint Optimization of Sensing and Computation for Status Update in Mobile Edge Computing Systems

arXiv: https://arxiv.org/abs/2210.17025 · HTML: https://mlsanity.com/paper/2210.17025/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2022.

## Built on by (most cited first, 3 of 3)

- [Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications](https://mlsanity.com/paper/2407.06518/) (2024)
- [Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2312.00279/) (2023)
- [Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation](https://mlsanity.com/paper/2407.13123/) (2024)

## Abstract (arXiv)

IoT devices recently are utilized to detect the state transition in the surrounding environment and then transmit the status updates to the base station for future system operations. To satisfy the stringent timeliness requirement of the status updates for the accurate system control, age of information (AoI) is introduced to quantify the freshness of the sensory data. Due to the limited computing resources, the status update can be offloaded to the mobile edge computing (MEC) server for execution to ensure the information freshness. Since the status updates generated by insufficient sensing operations may be invalid and cause additional processing time, the data sensing and processing operations need to be considered simultaneously. In this work, we formulate the joint data sensing and processing optimization problem to ensure the freshness of the status updates and reduce the energy consumption of IoT devices. Then, the formulated NP-hard problem is decomposed into the sampling, sensing and computation offloading optimization problems. Afterwards, we propose a multi-variable iterative system cost minimization algorithm to optimize the system overhead. Simulation results show the efficiency of our method in decreasing the system cost and dominance of sensing and processing under different scenarios.