research-article

BLEdge: Edge-centric Programming for BLE Applications with Multi-connection Optimization

Authors:

Wei DongAuthors Info & Claims

ACM Transactions on Sensor Networks, Volume 20, Issue 6

Article No.: 126, Pages 1 - 25

https://doi.org/10.1145/3698201

Published: 22 November 2024 Publication History

Abstract

Recent years have witnessed the rapid growth of IoT (Internet of Things). Bluetooth Low Energy (BLE) is one of the most popular wireless protocols to implement IoT applications because of its energy efficiency and low-cost properties. However, the development of BLE applications is time-consuming and exhausting. Users are required to write programs for both sides of a BLE connection using complicated low-level APIs. Moreover, it needs much expertise for developers to set appropriate parameters in accordance to different application requirements, especially when there exist multiple concurrent BLE connections. To address these problems, we propose BLEdge, an edge-centric programming approach for BLE applications with multi-connection optimization. First, we propose a wireless bus abstraction for BLE programming. With this, users can write BLE applications in an edge-centric way, as if the BLE-connected peripherals are physically attached to the edge node. Second, we advocate an optimization approach for BLE connection parameters. This optimization approach considers the time slot collision problem under a multi-connection scenario. We conduct extensive experiments with the nRF52840DK platform. Experiment results show that BLEdge can reduce 62.50% to 90.55% LOC (Lines of Code) when developing BLE applications. Furthermore, our parameter optimization approach can reduce up to 42.23% energy consumption.

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Index Terms

BLEdge: Edge-centric Programming for BLE Applications with Multi-connection Optimization
1. Networks
  1. Network architectures
    1. Programming interfaces
  2. Network protocols
    1. Application layer protocols
    2. Link-layer protocols

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cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 20, Issue 6

November 2024

422 pages

EISSN:1550-4867

DOI:10.1145/3613636

Editor:
Wen Hu
University of New South Wales, Australia

Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 22 November 2024

Online AM: 01 October 2024

Accepted: 01 September 2024

Revised: 19 March 2024

Received: 16 October 2023

Published in TOSN Volume 20, Issue 6

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National Natural Science Foundation of China
“Pioneer” and “Leading Goose” R&D Program of Zhejiang
National Youth Talent Support Program

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Cited By

Zhu YZheng YLiu JLi YZha Z(2024)Noise-Resistance Learning via Multi-Granularity Consistency for Unsupervised Domain Adaptive Person Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3702328Online publication date: 2-Nov-2024
https://dl.acm.org/doi/10.1145/3702328
Cui KLiu SFeng WDeng XGao LCheng MLu HYang L(2024)Correlation-aware Cross-modal Attention Network for Fashion Compatibility Modeling in UGC SystemsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3698772Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3698772
Song PZhou YYang XLiu DHu ZWang DWang M(2024)Efficiently Gluing Pre-Trained Language and Vision Models for Image CaptioningACM Transactions on Intelligent Systems and Technology10.1145/368206715:6(1-16)Online publication date: 29-Jul-2024
https://dl.acm.org/doi/10.1145/3682067
Ye CChen WLi JZhang LMao ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Dual-path Collaborative Generation Network for Emotional Video CaptioningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681603(496-505)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681603
Wen HSong XChen XWei YNie LChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657727(229-239)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657727

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