Random Activation Control for Priority AoI

Internet of Things (IoT) represents one of the most significant paradigm shifts recently, with several heterogeneous services and applications to the ultimate realization of connected living. In many status-sensitive IoT services, information usually has a higher value when it is fresher. A new metric, termed the age of information (AoI), was proposed to capture the freshness of status updates. In this paper, we propose a novel online control weight-based age-dependent activation control (W-ADAC) algorithm that prioritizes the activation of the devices that are divided into different classes possibly according to different AoI requirements. With the proposed W-ADAC, we introduce the concept of weighted AoI which is applied to control the activation probability of each device. In particular, higher weights are given to the devices that manifest higher priority requirements. As it is hard to obtain the system weighted AoI due to a lack of information on the distributed devices, we further introduce the capability of estimating the system weighted AoI into the proposed W-ADAC. Extensive simulations show the effectiveness of the proposed W-ADAC over multiple priorities and confirm that the proposed algorithm not only improves the overall system throughput but also provides the near-minimum AoI.

[1]  Howon Lee,et al.  Multichannel S-ALOHA-Enabled Autonomous Self-Healing in Industrial IoT Networks , 2022, IEEE Transactions on Industrial Informatics.

[2]  A. Ephremides,et al.  Timely Updates With Priorities: Lexicographic Age Optimality , 2022, IEEE Transactions on Communications.

[3]  Jung Woo Baek,et al.  Age of Information and Throughput in Random Access-Based IoT Systems With Periodic Updating , 2022, IEEE Wireless Communications Letters.

[4]  A. Munari,et al.  A Game Theoretic Approach to Age of Information in Modern Random Access Systems , 2021, 2021 IEEE Globecom Workshops (GC Wkshps).

[5]  Alexandre Graell i Amat,et al.  Age of Information in Prioritized Random Access , 2021, 2021 55th Asilomar Conference on Signals, Systems, and Computers.

[6]  Heng Wang,et al.  A Reinforcement Learning Approach for Optimizing the Age-of-Computing-Enabled IoT , 2021, IEEE Internet of Things Journal.

[7]  P. Popovski,et al.  Age of Information in Multi-hop Networks with Priorities , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[8]  Elif Uysal,et al.  Analysis of Slotted ALOHA With an Age Threshold , 2020, IEEE Journal on Selected Areas in Communications.

[9]  S. Liew,et al.  Age-of-Information Dependent Random Access for Massive IoT Networks , 2020, IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[10]  S. S. Bidokhti,et al.  Age of Information in Random Access Channels , 2019, IEEE Transactions on Information Theory.

[11]  Natarajan Gautam,et al.  Peak Age of Information in Priority Queuing Systems , 2019, IEEE Transactions on Information Theory.

[12]  Anthony Ephremides,et al.  Age of information performance of multiaccess strategies with packet management , 2018, Journal of Communications and Networks.

[13]  Roy D. Yates,et al.  Age of Information: Updates with Priority , 2018, 2018 IEEE International Symposium on Information Theory (ISIT).

[14]  Ismail Güvenç,et al.  UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges , 2017, IEEE Communications Magazine.

[15]  Giuseppe Cocco,et al.  Modern Random Access Protocols , 2016, Found. Trends Netw..

[16]  Hu Jin,et al.  Fair Channel Access in Uplink WLANs Supporting Multi-Packet Reception With Multi-User MIMO , 2016, IEEE Communications Letters.

[17]  Rahim Tafazolli,et al.  In-network caching of Internet-of-Things data , 2014, 2014 IEEE International Conference on Communications (ICC).

[18]  Victor C. M. Leung,et al.  A Terminal-Assisted Bayesian Broadcasting Algorithm for S-ALOHA Systems with Finite Population of Multi-Buffered Terminals , 2013, IEEE Communications Letters.