Link-Delay-Aware Reinforcement Scheduling for Data Aggregation in Massive IoT

Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current appro...

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Bibliographic Details
Published in:IEEE transactions on communications 2022-08, Vol.70 (8), p.5353-5367
Main Authors: Vo, Van-Vi, Nguyen, Tien-Dung, Le, Duc-Tai, Kim, Moonseong, Choo, Hyunseung
Format: Article
Language:English
Subjects:
Agglomeration
Data aggregation
Data management
Delay
Delays
duty cycle
Internet of Things
multichannel
Performance assessment
Receivers
Reinforcement
Schedules
Scheduling
Sensors
Wireless networks
Wireless sensor networks
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Summary:Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2022.3186407