Privacy-Preserving Three-Factor Authentication Protocol for Wireless Sensor Networks Deployed in Agricultural Field

Abstract

The agriculture is the backbone of economic system, and it plays an essential part in the survival of a nation’s prosperity. In addition to providing raw materials and food, it also offers numerous employment opportunities. Consequently, agriculture necessitates modern technology to increase productivity. In these circumstances, Wireless Sensor Networks (WSNs) are used to detect climatic parameters like light, humidity, carbon dioxide, acidity, soil moisture, and temperature in an agricultural field. However, the research that is being done at the moment is unable to circumvent the issue that safety and effectiveness cannot coexist. Several studies employ time-consuming cryptographic security structures, whereas the majority of lightweight systems are designed without reviewing certain security aspects like resistance to ephemeral secret leakage (ESL) attacks, perfect forward secrecy, and so on. According to our opinion, this issue may be overcome through the use of lightweight cryptographic primitives, paying particular attention to protocol weaknesses, and keeping in mind the ever-changing security needs of individuals. We present an extensive lightweight three-factor authentication protocol with diverse security criteria, along with the adaptive privacy preservation, that is suited for user-friendly situation in the WSN enviroment. This is accomplished by removing all extraneous cryptographic structures. It has been illustrated that proposed protocol is much better in terms of the privacy and security aspects via the usage of security aspects, proof of the real-or-random (ROR) model, protocols of internet security, and applications that are subjected to experimental validation utilizing automated validation. and comparison with other protocols’ security aspects. The performance analysis shows how superior our proposed protocol is to other competing protocols in terms of communication and computational overheads, with respective efficiencies of 53% and 39%. Moreover, using the ROR model reveals that this study has benefits in performance compared with other competing research.