Active Neighbor Exploitation for Fast Data Aggregation in IoT Sensor Networks

Fast data aggregation is crucial for facilitating critical Internet of Things services as it enables the collection of sensory data within strict volume and time constraints. Over the past decades, the data aggregation scheduling problem for minimum latency has garnered significant research attentio...

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Bibliographic Details
Published in:IEEE internet of things journal 2024-04, Vol.11 (8), p.13199-13216
Main Authors: Vo, Van-Vi, Le, Duc-Tai, Raza, Syed M., Kim, Moonseong, Choo, Hyunseung
Format: Article
Language:English
Subjects:
Coloring method
Data aggregation
Data management
Data transmission
Delays
duty cycle
Dynamic scheduling
Exploitation
Interference
Internet of Things
Internet of Things (IoT)
multichannel
Network latency
Nodes
Optimal scheduling
Receivers
Schedules
Scheduling
wireless sensor networks (WSNs)
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Summary:Fast data aggregation is crucial for facilitating critical Internet of Things services as it enables the collection of sensory data within strict volume and time constraints. Over the past decades, the data aggregation scheduling problem for minimum latency has garnered significant research attention. Existing approaches to this problem typically schedule all data transmissions based on an aggregation tree, which is constructed without secondary interference. However, such interference can introduce delays when scheduling a transmission from a node to its parent in the tree. To this end, this study proposes an approach called active neighbor exploitation (ANEX) that enables sensor nodes to switch their parents by identifying active neighbors for potential connectivity, irrespective of the receivers established in the tree. Additionally, the scheme prioritizes scheduling nodes with the fewest unscheduled active neighbors, thereby allowing for more concurrent transmissions. ANEX is evaluated through theoretical analysis and extensive simulations under various scenarios. The results demonstrate that ANEX achieves up to 86% faster aggregation compared to the state-of-the-art approach while maintaining an equivalent time complexity.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3354730