Establishing a Chain of Trust in a Sporadically Connected Cyber-Physical System

Drone based applications have progressed significantly in recent years across many industries, including agriculture. This paper proposes a sporadically connected cyber-physical system for assisting winemakers and minimizing the travel time to remote and poorly connected infrastructures. A set of representative diseases and conditions, which will be monitored by land-bound sensors in combination with multispectral images, is identified. To collect accurate data, a trustworthy and secured communication of the drone with the sensors and the base station should be established. We propose to use an Internet of Things framework for establishing a chain of trust by securely onboarding drones, sensors and base station, and providing self-adaptation support for the use case. Furthermore, we perform a security analysis of the use case for identifying potential threats and security controls that should be in place for mitigating them.

[1]  Steve Lipner,et al.  Security development lifecycle , 2010, Datenschutz und Datensicherheit - DuD.

[2]  Jerker Delsing IoT Automation : Arrowhead Framework , 2017 .

[3]  Luís Pádua,et al.  UAS, sensors, and data processing in agroforestry: a review towards practical applications , 2017 .

[4]  Dmitry Bratanov,et al.  Multi and hyperspectral UAV remote sensing: Grapevine phylloxera detection in vineyards , 2018, 2018 IEEE Aerospace Conference.

[5]  Jerker Delsing,et al.  Generic Autonomic Management as a Service in a SOA-based Framework for Industry 4.0 , 2019, IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society.

[6]  Russ Housley,et al.  Internet X.509 Public Key Infrastructure Certificate and Certificate Revocation List (CRL) Profile , 2002, RFC.

[7]  Yong Wang,et al.  A survey of security issues in wireless sensor networks , 2006, IEEE Communications Surveys & Tutorials.

[8]  Rachel R. Fern,et al.  Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland , 2018, Ecological Indicators.

[9]  Ani Bicaku,et al.  Interacting with the arrowhead local cloud: On-boarding procedure , 2018, 2018 IEEE Industrial Cyber-Physical Systems (ICPS).

[10]  A. S. Griffin,et al.  Psychological warfare in vineyard: Using drones and bird psychology to control bird damage to wine grapes , 2019, Crop Protection.

[11]  Pablo J. Zarco-Tejada,et al.  Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas , 2015, Remote. Sens..

[12]  Christian Steger,et al.  Global and Secured UAV Authentication System based on Hardware-Security , 2020, 2020 8th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud).

[13]  Matthew E. Taylor,et al.  Bird Deterrence in a Vineyard Using an Unmanned Aerial System (UAS) , 2019, Transactions of the ASABE.

[14]  Loretta Ichim,et al.  A Survey of Collaborative UAV–WSN Systems for Efficient Monitoring , 2019, Sensors.

[15]  Dmitry Bratanov,et al.  A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data , 2018, Sensors.

[16]  Gemma Hornero,et al.  Design of a low-cost Wireless Sensor Network with UAV mobile node for agricultural applications , 2015, Comput. Electron. Agric..

[17]  Massimo Satler,et al.  Towards Smart Farming and Sustainable Agriculture with Drones , 2015, 2015 International Conference on Intelligent Environments.

[18]  T. Kavitha,et al.  Security Vulnerabilities In Wireless Sensor Networks: A Survey , 2010 .

[19]  Choong Seon Hong,et al.  Security in wireless sensor networks: issues and challenges , 2006, 2006 8th International Conference Advanced Communication Technology.

[20]  Raul Morais,et al.  Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry , 2017, Remote. Sens..