Optimizing the Age of Information for Multi-Source Information Update in Internet of Things
Weijian Pan, Zhaoji Deng, Xiumin Wang, Pan Zhou, Weiwei Wu
Abstract
Age of Information (AoI) has recently become a new performance metric that captures the freshness/timeliness of information at end users in Internet of Things (IoT). Most existing information update mechanisms mainly consider the status information from a single Source Node (SN) or from multiple SNs with independent observations. However, in a practical IoT system, each status could be observed by multiple SNs, which brings new challenge in optimizing the AoI. To consider this issue, this paper formulates two optimization problems, named AoI-aware Multi-source Information Update problem (<i>AoI-MSIU</i>) and AoI-Reduction-aware Multi-source Information Update problem (<i>AoIR-MSIU</i>) problem, respectively. Specifically, AoI-MSIU problem assumes that the status are the first time to be uploaded to the BS, while AoIR-MSIU problem supposes that the BS already maintains the status that it has received before. Besides maximizing the revenue of information update, both problems consider how to optimize the AoI of the status. For AoI-MSIU problem, we prove that it is NP-hard, and propose an efficient greedy algorithm with a guaranteed approximation ratio. For AoIR-MSIU problem, we propose a polynomial-time optimal solution, which is based on a maximum weight bipartite matching on an auxiliary graph. Finally, we evaluate the performance of the proposed schemes through simulations.