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Wireless sensor networks play a pivotal role in a myriad of applications, including agriculture, health monitoring, tracking and structural health monitoring. One crucial aspect of these applications involves accurately determining the positions of the sensors. In this paper, we study a novel Nystrom based sampling protocol in which a selected group of anchor nodes, with known locations, establish communication with only a subset of the remaining sensor nodes. Leveraging partial distance information, we present an efficient algorithm for estimating sensor locations. To demonstrate the effectiveness of our approach, we provide empirical results using synthetic data and underscore the practical advantages of our sampling technique for precision agriculture.
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Scalable Privacy-preserving Geo-distance Evaluation for Precision Agriculture IoT Systems
Precision agriculture has become a promising paradigm to transform modern agriculture. The recent revolution in big data and Internet-of-Things (IoT) provides unprecedented benefits including optimizing yield, minimizing environmental impact, and reducing cost. However, the mass collection of farm data in IoT applications raises serious concerns about potential privacy leakage that may harm the farmers’ welfare. In this work, we propose a novel scalable and private geo-distance evaluation system, called SPRIDE, to allow application servers to provide geographic-based services by computing the distances among sensors and farms privately. The servers determine the distances without learning any additional information about their locations. The key idea of SPRIDE is to perform efficient distance measurement and distance comparison on encrypted locations over a sphere by leveraging a homomorphic cryptosystem. To serve a large user base, we further propose SPRIDE+ with novel and practical performance enhancements based on pre-computation of cryptographic elements. Through extensive experiments using real-world datasets, we show SPRIDE+ achieves private distance evaluation on a large network of farms, attaining 3+ times runtime performance improvement over existing techniques. We further show SPRIDE+ can run on resource-constrained mobile devices, which offers a practical solution for privacy-preserving precision agriculture IoT applications.
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- PAR ID:
- 10393839
- Date Published:
- Journal Name:
- ACM Transactions on Sensor Networks
- Volume:
- 17
- Issue:
- 4
- ISSN:
- 1550-4859
- Page Range / eLocation ID:
- 1 to 30
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3463575
