期刊
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
卷 9, 期 2, 页码 904-917出版社
IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2021.3140023
关键词
Internet of Things; Costs; Scheduling; Markov processes; Delays; Cameras; Wireless communication; Age of information; information update; Internet of Things
资金
- Key Project of Science and Technology Innovation 2030, Ministry of Science and Technology of China [2018AAA0101300]
- National Natural Science Foundation of China [61872150, 61972448]
- Guangdong Basic and Applied Basic Research Foundation [2020A1515011209]
- Science and Technology Program of Guangzhou [202002030426]
This paper investigates the performance metric of Age of Information (AoI) in the Internet of Things (IoT) and addresses the challenges in optimizing the freshness and revenue of information updates. Two optimization problems, AoI-aware Multi-source Information Update problem (AoI-MSIU) and AoI-Reduction-aware Multi-source Information Update problem (AoIR-MSIU), are formulated and efficient algorithms are proposed to solve them. The performance of the proposed schemes is evaluated through simulations.
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 (AoI-MSIU) and AoI-Reduction-aware Multi-source Information Update problem (AoIR-MSIU) 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.
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