Journal
2020 19TH ACM/IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN 2020)
Volume -, Issue -, Pages 277-288Publisher
IEEE
DOI: 10.1109/IPSN48710.2020.00031
Keywords
LPWAN; Aggregate Queries; sensor networks; machine learning
Funding
- NSF [1942902, 1837607, 1646235]
- IoT@CyLab
- Kavcic-Moura Fund
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1837607] Funding Source: National Science Foundation
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1646235, 1942902] Funding Source: National Science Foundation
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Low-Power Wide-Area Networks (LP-WANs) are seeing wide-spread deployments connecting millions of sensors, each powered by a ten-year AA battery to radio infrastructure, often miles away. fly design, iteratively querying all sensors in an LP-WAN may take several hours or even days, given the stringent battery limits of client, radios. This precludes obtaining evert art approximate real-time view of sensed information across LP-WAN devices over a large area, say in the event of a disaster, fault or simply for diagnostics. This paper presents QuAiL(1), a system that provides a coarse aggregate view of sensed data across LP-WAN devices over a wide area within a time span of just, one LP-WAN packet. QuAiL achieves this by coordinating multiple LP-WAN radios to transmit their information synchronously in time and frequency despite their power constraints. We design each client's transmission so that the base station can retrieve an approximate heatmap of sensed data, by exploiting the spatial correlation of this data across clients. We further show how our system can be optimized for statistical and machine learning queries, all while maintaining the security and privacy of sensed data from individual clients. Our deployment over a 3 sq. km. LP-WAN deployment around CMU campus in Pittsburgh demonstrates a 4x faster information retrieval versus the state-of-the-art statistical methods to retrieve the spatial sensor heatmap at a desired resolution.
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