Edge Big Data-Enabled Low-Cost Indoor Localization Based on Bayesian Analysis of RSS
Abstract
Indoor localization has attracted much attention recently, due to its wide applications in location-based services(LBSs). Localization accuracy and system costs are the key issues while designing indoor localization schemes. In this paper, an edge big data-enabled indoor localization scheme is proposed. We use the radio signal strength (RSS) information that is always available wherever WiFi coverage is available, to avoid the costs on deploying and maintaining specific devices for indoor localization. Bayesian theory and edge computing are adopted in our system, so that big localization data is collected and utilized to update the prior location probabilities. A testbed, BJUTLocate, is built to evaluate the performance of the proposed scheme, and the evaluation results show its significant performance improvement.
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Edge computing is deployed to optimize indoor positioning systems and home intrusion monitoring so that they can get lower latency than using cloud computing as well as better accuracy.
“Like use cases in [118], [119], edge computing is deployed to optimize indoor positioning systems and home intrusion monitoring so that they can get lower latency than using cloud computing as well as the better accuracy.”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [118]
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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498 · see citing works
“Like use cases in [118], [119], edge computing is deployed to optimize indoor positioning systems and home intrusion monitoring so”
Method
- Authors
- Liu, S., Si, P., Xu, M., He, Y., & Zhang, Y.
- Affiliations
- Beijing University of Technology (CN)
- Beijing Normal University (CN)
- Venue
- 2017 IEEE Wireless Communications and Networking Conference (WCNC)
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 1-6
- ISSN
- 1525-3511 (ISSN-L)
- OpenAlex
- W2611986280
- Cited by
- 10
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 1 · 2021: 2 · 2020: 2 · 2018: 1
- FWCI
- 1.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Wireless Communications and Networking Conference (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Keywords
- indoor positioning, edge computing, big data, radio signal strength, Bayesian inference, Wi-Fi, location-based services, low-cost localization
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