An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting
Abstract
Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
In contemporary society, the pressing challenge of preserving user privacy clashes with the imperative for smart buildings to efficiently manage their resources, particularly in the context of occupancy monitoring for optimized energy utilization. This paper delves into the application of MMWAVE FMCW radar technology for occupancy monitoring. MMWAVE FMCW radar, unlike conventional methods that often require the use of identifiable tags or involve image analysis, operates without the need for such identifiers, mitigating privacy concerns. However, challenges arise when attempting to cover extensive indoor spaces due to the limited range of individual mmWave FMCW radar devices. The present work proposes the use of a flexible software architecture to integrate the measurements of several mmWave FMCW radar devices, so that the whole behaves as a single sensor. To validate the proposal, an example of use in a real environment in an indoor space monitored with three mmWave FMCW radar devices is also presented. The example details the whole process, from the physical installation of the devices to the use of the different software modules that allow the integration of the data into a common IOT management platform such as Home Assistant. All the elements, from the measurements captured during the test to the different software implementations, are shared publicly with the scientific community.
Results and benchmarks
In contemporary society, the pressing challenge of preserving user privacy clashes with the imperative for smart buildings to efficiently manage their resources, particularly in the context of occupancy monitoring for optimized energy utilization.
Benchmark evidence is limited
Evidence graph: 3 refs, 3 links.
Utility signals: depth 70/100, grounding 75/100, status medium.
Implementation
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Time to first repro: a few days
Developer-Y/cs-video-courses is the closest maintained adjacent implementation (Matches contextual method/domain keyword: computer science). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 83722 GitHub stars.
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Reproduction readiness
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Hardware requirements
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Validation caveat
Repositories and ecosystem
Closest related implementations
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- Developer-Y/cs-video-courses Adjacent · Confidence: Medium · 83,722 stars
Matches contextual method/domain keyword: computer science
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Hugging Face artifacts
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Research context
15
Citations
24
References
Tasks
Computer science, Internet of Things, Telecommunications, Real-time computing, occupancy monitoring, people counting, smart buildings, privacy-preserving sensing
Methods
software architecture
Domains
None detected
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