Iot-Based Privacy-Preserving Anomaly Detection Model for Smart Agriculture

Abstract

Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as “smart farming”. Utilizing a variety of information and agricultural technologies, crops are observed for their general health and productivity. This requires monitoring the condition of field crops and looking at many other indicators. The goal of smart agriculture is to reduce the amount of money spent on agricultural inputs while keeping the quality of the final product constant. The Internet of Things (IoT) has made smart agriculture possible through data collection and storage techniques. For example, modern irrigation systems use effective sensor networks to collect field data for the best plant irrigation. Smart agriculture will become more susceptible to cyber-attacks as its reliance on the IoT ecosystem grows, because IoT networks have a large number of nodes but limited resources, which makes security a difficult issue. Hence, it is crucial to have an intrusion detection system (IDS) that can address such challenges. In this manuscript, an IoT-based privacy-preserving anomaly detection model for smart agriculture has been proposed. The motivation behind this work is twofold. Firstly, ensuring data privacy in IoT-based agriculture is of the utmost importance due to the large volumes of sensitive information collected by IoT devices, including on environmental conditions, crop health, and resource utilization data. Secondly, the timely detection of anomalies in smart agriculture systems is critical to enable proactive interventions, such as preventing crop damage, optimizing resource allocation, and ensuring sustainable farming practices. In this paper, we propose a privacy-encoding-based enhanced deep learning framework for the difficulty of data encryption and intrusion detection. In terms of data encoding, a novel method of a sparse capsule-auto encoder (SCAE) is proposed along with feature selection, feature mapping, and feature normalization. An SCAE is used to convert information into a new encrypted format in order to prevent deduction attacks. An attention-based gated recurrent unit neural network model is proposed to detect the intrusion. An AGRU is an advanced version of a GRU which is enhanced by an attention mechanism. In the results section, the proposed model is compared with existing deep learning models using two public datasets. Parameters such as recall, precision, accuracy, and F1-score are considered. The proposed model has accuracy, recall, precision, and F1-score of 99.9%, 99.7%, 99.9%, and 99.8%, respectively. The proposed method is compared using a variety of machine learning techniques such as the deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

“…This allows the GRU to make fewer parameter updates, thereby reducing computational complexity without significantly compromising the network's performance. For real-time NIDS applications in IoT-based EVCS, where computational resources are at a premium, this efficiency is invaluable [32]. The integration of CNN, LSTM, and GRU within the proposed model is seamless.…”

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

“…Unauthorized access to precise location data can violate the privacy of farmers and stakeholders, potentially leading to misuse or security breaches [521], [524], [525]. Robust privacy-preserving mechanisms and compliance with privacy regulations are essential to address these concerns and ensure the responsible and ethical use of location data in IoT-WSNs-based SA [72], [526], [527].…”

“…Striking a balance between optimizing agricultural practices through data utility and safeguarding user privacy is crucial [28]. Implementing transparent data usage policies, employing robust anonymization techniques, and empowering users with control over their data are vital steps in addressing these privacy and consent challenges [526], [550], [557], [558]. Effective communication of the purpose of data collection, the intended use, and potential risks to stakeholders is essential to obtain informed consent from users [13], [550], [559].…”

An Industrial Case Study on the Monitoring and Maintenance Service System for a Robot-Driven Polishing Service System under Industry 4.0 Contexts

“…For example, from the perspective of remote monitoring of manufacturing production lines, Chen [11] established a kind of intelligent production line monitoring architecture enabled by a wireless sensor network and RFID, and the architecture was able to realize real-time multithreaded production data collection/storage and workpiece tracking monitoring for discrete manufacturing production lines. Li et al [12] discussed the strategy of integrating On the other hand, advanced techniques in Industry 4.0 contexts, such as the Industrial Internet of Things [3,4], cyber-physical systems [5], digital twins [6], deep learning [7], and edge/cloud/fog computing [8], have boosted the fast development of intelligent and remote monitoring/maintenance systems together with the smart IPSS [9].…”

“…However, the reliability of the processing line is still an issue of concern, and YX must develop a kind of monitoring and maintenance service system for the polishing processing line-based IPSS. On the other hand, advanced techniques in Industry 4.0 contexts, such as the Industrial Internet of Things [3,4], cyber-physical systems [5], digital twins [6], deep learning [7], and edge/cloud/fog computing [8], have boosted the fast development of intelligent and remote monitoring/maintenance systems together with the smart IPSS [9].…”