Minimizing the Age of Multi-Source Information with Budget Constraint in Internet of Things

Age of Information (AoI) has become a new performance metric that quantifies the freshness of information in Internet of Things (IoT). To optimize the AoI, the latest information should be frequently sampled and timely updated by source nodes (SNs), which however contradicts with the fact that the resources of both the SNs and destination node are limited. To consider this issue, this paper formulates a more general multi-source information update problem, taking into account both the budget constraint of destination node and the limited sampling/updating capabilities of the SNs. Besides that, two different sampling models, named sampling-predetermined and sampling-at-will models, have been studied, respectively. Particularly, for information update problem under sampling-predetermined model, we prove that it is an NP-hard problem. Then, we propose a greedy algorithm to select the appropriate SNs, and theoretically analyze the bound of the AoI achieved by the proposed algorithm. For the sampling-at-will model, we theoretically derive the minimum AoI that can be achieved under given number of updates, based on which, we design an optimal SNs selection and updating time determination mechanism, to achieve the minimum AoI. Finally, we conduct simulations to evaluate the effectiveness of the proposed algorithms.