research-article

A Privacy Preserving Mobile Crowdsensing Architecture for a Smart Farming Application

Authors:

Nils AschenbruckAuthors Info & Claims

CrowdSenSys '17: Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications

Pages 62 - 67

https://doi.org/10.1145/3139243.3139250

Published: 06 November 2017 Publication History

Abstract

Smart Farming refers to the act of utilizing modern information and sensor technology in conventional industrial farming. An important plant parameter that can be estimated by sensor technology in the context of Smart Farming is the leaf area index (LAI) which is a key variable used to model processes such as photosynthesis and evapotranspiration. Nowadays, leveraging the enhanced sensor peripherals of current devices and their computing capabilities, smartphone applications present a fast and economical alternative to estimate the LAI compared to traditional methods. This paper exemplarily extends such an application, namely Smart fLAIr, with features of Mobile Crowdsensing (MCS) in order to create a system for a crowd-sensed LAI enabling an increased spatio-temporal resolution of LAI estimations. Besides the system design, this paper conducts a threat analysis for user privacy in the application-specific scenario which can be transferred to general Smart Farming scenarios. As a consequence, a perturbation based privacy mechanism is developed and applied in conjunction with a Trusted Third Party (TTP) architecture to ensure user privacy. Subsequently, its impact is demonstrated. Moreover, the energy consumption of the extended Smart fLAIr application is evaluated showing negligible additional costs of the proposed MCS extension.

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Cited By

Louta MBanti KKarampelia I(2024)Emerging Technologies for Sustainable Agriculture: The Power of Humans and the Way AheadIEEE Access10.1109/ACCESS.2024.342840112(98492-98529)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3428401
Ali IBukhari WAdnan MKashif MDanish ASikander A(2024)Security and privacy in IoT-based Smart Farming: a reviewMultimedia Tools and Applications10.1007/s11042-024-19653-3Online publication date: 26-Jun-2024
https://doi.org/10.1007/s11042-024-19653-3
Amiri-Zarandi MDara RDuncan EFraser E(2022)Big Data Privacy in Smart Farming: A ReviewSustainability10.3390/su1415912014:15(9120)Online publication date: 25-Jul-2022
https://doi.org/10.3390/su14159120
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A Privacy Preserving Mobile Crowdsensing Architecture for a Smart Farming Application

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cover image ACM Conferences

CrowdSenSys '17: Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications

November 2017

81 pages

ISBN:9781450355551

DOI:10.1145/3139243

Editor:
Rasit Eskicioglu
University of Manitoba, Canada

Copyright © 2017 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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Published: 06 November 2017

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November 6 - 8, 2017

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https://doi.org/10.1109/ACCESS.2024.3428401
Ali IBukhari WAdnan MKashif MDanish ASikander A(2024)Security and privacy in IoT-based Smart Farming: a reviewMultimedia Tools and Applications10.1007/s11042-024-19653-3Online publication date: 26-Jun-2024
https://doi.org/10.1007/s11042-024-19653-3
Amiri-Zarandi MDara RDuncan EFraser E(2022)Big Data Privacy in Smart Farming: A ReviewSustainability10.3390/su1415912014:15(9120)Online publication date: 25-Jul-2022
https://doi.org/10.3390/su14159120
Yu RYang WYang C(2022)Differentially Private XGBoost Algorithm for Traceability of Rice VarietiesApplied Sciences10.3390/app12211103712:21(11037)Online publication date: 31-Oct-2022
https://doi.org/10.3390/app122111037
Safi MDadkhah SShoeleh FMahdikhani HMolyneaux HGhorbani A(2022)A Survey on IoT Profiling, Fingerprinting, and IdentificationACM Transactions on Internet of Things10.1145/35397363:4(1-39)Online publication date: 6-Sep-2022
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Tran QTurnbull BWu Hde Silva AKormusheva KHu J(2021)A Survey on Privacy-Preserving Blockchain Systems (PPBS) and a Novel PPBS-Based Framework for Smart AgricultureIEEE Open Journal of the Computer Society10.1109/OJCS.2021.30530322(72-84)Online publication date: 2021
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Yang XShu LChen JFerrag MWu JNurellari EHuang K(2021)A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy ChallengesIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2020.10035368:2(273-302)Online publication date: Feb-2021
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Lama RKarmakar S(2021)3-way Authentication Approach for Agricultural IOT using IFTTT application2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT51525.2021.9579958(1-7)Online publication date: 6-Jul-2021
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Scheuren SBauer JGehrs FGrothaus HJatzlau MLauer ALöpmeier MNiesse UVogel P(2021)Offene Software-Plattform für Dienstleistungsinnovationen in einem Wertschöpfungsnetz in der LandwirtschaftDienstleistungsinnovationen durch Digitalisierung10.1007/978-3-662-63099-0_12(483-531)Online publication date: 30-Sep-2021
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Mendes JPinho TNeves dos Santos FSousa JPeres EBoaventura-Cunha JCunha MMorais R(2020)Smartphone Applications Targeting Precision Agriculture Practices—A Systematic ReviewAgronomy10.3390/agronomy1006085510:6(855)Online publication date: 16-Jun-2020
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