Blockchain-Based Smart Farm Security Framework for the Internet of Things

Abstract

Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies and the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables the automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms. The number of accepted blockchain transactions on smart farming requests fell from 189,000 to 109,450 after carrying out the first three tests while the next three testing phases showed a rise in the number of blockchain transactions accepted on smart farming requests from 176,000 to 290,786. We further observed that the lesser the time taken to induce the device alarm, the higher the number of blockchain transactions accepted on smart farming requests, which demonstrates the efficacy of blockchain-based poisoning attack mitigation in smart farming.

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

“…DL algorithm has developed in [22] for discovering DDoS attacks. Blockchain technology was discussed in [23] for security threats. Honey badger algorithm with an optimal hybrid deep belief network (HBA-OHDBN) technique was utilized [24] to enhance network security.…”

Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control

“…In rural farming communities where information technology (IT) infrastructure is sometimes lacking, efficient data encryption is crucial to protect data collected through various IoT devices while in use and at rest [7]. Additionally, in remote areas with higher risks of local network breaches, advanced encryption technologies ensure that data remains unintelligible to unauthorized users [36].…”

“…Conventionally, in smart farming, IoT sensors, cloud servers, and human experts are the three primary nodes that must be managed effectively. IoT sensors positioned systematically throughout the farm serve as the primary data collection points, gathering accurate details about crops and the surrounding environment [7]. On the other hand, cloud sensors act as the primary hub for data processing and storage, utilizing AI algorithms to analyze the gathered data and produce informative results.…”

Integrated Route-Planning System for Agricultural Robots

“…Smart farming, as an evolution of precision agriculture, is an approach that goes beyond the optimization of specific farming tasks to a data-driven and system-based approach to optimize the entire farming system [1][2][3]. Under a systems perspective, smart farming encompasses various components and processes, including sensors (proximal and remote), data processing and data analysis, decision support, automation and control including robotics [4][5][6][7], and connectivity and communication (including cloud and IoT technologies) [8,9] up to a supply chain level [10][11][12], while the whole approach should also consider the sustainability [13][14][15][16], data security [17][18][19], and privacy perspectives [20][21][22].…”