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AI-BPO: Adaptive incremental BLE beacon placement optimization for crowd density monitoring applications

Authors:

Masato Sugasaki,

Yoshihiro Kawahara,

Kota Tsubouchi,

Matthew Ishige,

Masamichi ShimosakaAuthors Info & Claims

SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems

Pages 301 - 304

https://doi.org/10.1145/3474717.3483964

Published: 04 November 2021 Publication History

Abstract

With the pandemic of COVID-19, indoor crowd density monitoring has become one of the most critical responsibilities of public space managers. Beacon placement optimization has been tackled as fundamental research work as the performance of crowd density monitoring highly depends on how BLE beacons are allocated. In this research, we propose a novel beacon placement optimization approach to incrementally place the beacon on the updated detection status adaptively in favor of Bayesian optimization, which can help to provide the optimal beacon placement. Our proposed method can optimize the beacon placement effectively to improve the signal coverage quality in the given environment and minimize human workload.

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Cited By

Shimokawa TTsubouchi KKawahara YMurakami HShimosaka M(2023)Data-driven Simulation of Wireless Communication Signal Strength in Indoor Environments2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN57070.2023.10332523(1-6)Online publication date: 25-Sep-2023
https://doi.org/10.1109/IPIN57070.2023.10332523
Zakaria CTrivedi ACecchet EChee MShenoy PBalan R(2022)Analyzing the Impact of COVID-19 Control Policies on Campus Occupancy and Mobility via WiFi SensingACM Transactions on Spatial Algorithms and Systems10.1145/35165248:3(1-26)Online publication date: 22-Sep-2022
https://dl.acm.org/doi/10.1145/3516524

Index Terms

AI-BPO: Adaptive incremental BLE beacon placement optimization for crowd density monitoring applications
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Sensor networks
2. Networks
  1. Network types
    1. Mobile networks

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Published In

cover image ACM Conferences

SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems

November 2021

700 pages

ISBN:9781450386647

DOI:10.1145/3474717

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2021

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SIGSPATIAL '21

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SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems

November 2 - 5, 2021

Beijing, China

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Cited By

Shimokawa TTsubouchi KKawahara YMurakami HShimosaka M(2023)Data-driven Simulation of Wireless Communication Signal Strength in Indoor Environments2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN57070.2023.10332523(1-6)Online publication date: 25-Sep-2023
https://doi.org/10.1109/IPIN57070.2023.10332523
Zakaria CTrivedi ACecchet EChee MShenoy PBalan R(2022)Analyzing the Impact of COVID-19 Control Policies on Campus Occupancy and Mobility via WiFi SensingACM Transactions on Spatial Algorithms and Systems10.1145/35165248:3(1-26)Online publication date: 22-Sep-2022
https://dl.acm.org/doi/10.1145/3516524

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