Internet of Things最新文献

{"title":"The evolving threat landscape of botnets: Comprehensive analysis of detection techniques in the age of artificial intelligence","authors":"Arash Mahboubi ,&nbsp;Khanh Luong ,&nbsp;Hamed Aboutorab ,&nbsp;Hang Thanh Bui ,&nbsp;Seyit Camtepe ,&nbsp;Keyvan Ansari ,&nbsp;Bazara Barry","doi":"10.1016/j.iot.2025.101728","DOIUrl":"10.1016/j.iot.2025.101728","url":null,"abstract":"<div><div>Botnets represent a significant and evolving cybersecurity threat, leveraging networks of compromised devices for various malicious activities, including data exfiltration (e.g., Truebot malware), credential theft, and distributed denial-of-service (DDoS) attacks. heir increasing sophistication includes advanced evasion techniques such as domain generation algorithms (DGAs), encrypted command-and-control (C&amp;C) channels, and peer-to-peer (P2P) architectures. These innovations pose substantial challenges to conventional detection systems. Existing surveys typically examine isolated detection methodologies or specific datasets, failing to address comprehensively the broader landscape, especially regarding adversarial manipulation of machine learning (ML) and artificial intelligence (AI) feature sets. To address this critical gap, this survey introduces the first systematic adversarial-aware analysis of botnet detection strategies. It specifically evaluates how adversaries exploit ML/AI feature manipulation, such as through noise injection and feature perturbation, to evade detection, a perspective that has not been quantitatively addressed in prior literature. A core contribution is our explicit benchmarking of detection model robustness across four quantitative metrics, faithfulness, monotonicity, sensitivity, and complexity, providing novel insights into the resilience of state-of-the-art models under adversarial conditions. Additionally, we highlight persistent practical challenges including limited dataset diversity and dependence on high-quality labeled data, and propose potential mitigation approaches such as synthetic data generation, federated and semi-supervised learning, and lightweight detection architectures tailored for resource-constrained IoT deployments. Finally, we outline key future research directions emphasizing standardized robustness evaluation frameworks, explainable AI to enhance interpretability and trust, and privacy-preserving collaborative data-sharing mechanisms. By integrating this adversarial-aware perspective with a comprehensive and practical evaluation framework, this work contributes to the field’s understanding of botnet detection and supports the design of more robust and resilient cybersecurity solutions through insights relevant to both researchers and practitioners.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101728"},"PeriodicalIF":7.6,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Business continuity of Cloud-based IoT applications through a seamless continuum","authors":"Carmine Colarusso ,&nbsp;Ida Falco ,&nbsp;Eugenio Zimeo","doi":"10.1016/j.iot.2025.101723","DOIUrl":"10.1016/j.iot.2025.101723","url":null,"abstract":"<div><div>IoT systems often exploit the Cloud to process and share collected data, while pre-processing at the Edge reduces the amount of data transferred to the network and processed in the Cloud. Despite the relevant advantages of using centralized and multiplexed high-performance resources, Edge-Cloud interactions might not be possible due to transient network failures, so negatively impacting business continuity. In this paper, we extend the adoption of Edge computing by seamlessly mirroring in Edge, through a framework, some Cloud-deployed components of the business logic and related data of IoT applications to improve service availability when transient network failures impede Edge-Cloud communication. In particular, to address potential inconsistency due to data replication among Edge nodes and Cloud, we propose a new distributed consistency algorithm, called EdgeCloudWPaxos. It is based on a distributed, event-driven technique that implements reconciliation when distributed local transactions occur. The paper presents the framework, its adoption for wrapping an existing application for item tracking, and the validation through several tests conducted by emulating the tracking activity produced by a number of retail shops, which shows the effectiveness of the framework in ensuring business continuity and lower response times even in the case of network failure.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101723"},"PeriodicalIF":7.6,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT-based monitoring and control for optimized plant growth in smart greenhouses using soil and hydroponic systems","authors":"Kenza Bouarroudj ,&nbsp;Fatima Babaa ,&nbsp;Abderrahim Touil","doi":"10.1016/j.iot.2025.101710","DOIUrl":"10.1016/j.iot.2025.101710","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context:&lt;/h3&gt;&lt;div&gt;Agriculture is under mounting pressure from climate change, natural resource depletion, and the urgent need for global food security. Smart technologies, particularly IoT-enabled greenhouse systems, offer a promising pathway to sustainably intensify crop production. However, achieving seamless real-time monitoring, autonomous control, and fault detection remains technically complex, especially in remote or off-grid regions with limited infrastructure and unstable energy supply.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective:&lt;/h3&gt;&lt;div&gt;This study aims to develop a fully autonomous, intelligent greenhouse system that integrates real-time environmental monitoring, adaptive control, and embedded fault diagnosis. A key focus is on enabling continuous and reliable operation in off-grid conditions through solar energy autonomy. The system is designed to enhance crop productivity, energy efficiency, and resilience across a range of agricultural contexts, from smallholder plots to commercial-scale operations.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods:&lt;/h3&gt;&lt;div&gt;The system integrates a distributed sensor network with a centralized control platform and mobile interface to monitor key agronomic and technical variables, including temperature, humidity, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;concentration, light intensity, irrigation flow, nutrient composition, and electrical parameters. To enable predictive maintenance, an anomaly detection module based on the Isolation Forest algorithm was implemented and trained on synthetically generated multivariate time series data representing realistic fault scenarios (e.g., irradiance drop, overheating, sensor drift, voltage imbalance). The algorithm’s performance was quantitatively assessed using confusion matrices, receiver operating characteristic (ROC) curves, and classification metrics, achieving high anomaly detection accuracy. Predefined alert thresholds were assigned to each monitored parameter and integrated with machine learning outputs to enhance diagnostic reliability. The system also features a solar energy harvesting subsystem with continuous tracking of photovoltaic voltage, current, energy yield, and battery state-of-charge, supporting fully autonomous, off-grid operation.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results and Conclusions:&lt;/h3&gt;&lt;div&gt;Controlled-environment testing demonstrated the system’s ability to autonomously and precisely regulate greenhouse climate parameters while ensuring operational continuity under simulated fault and energy fluctuation scenarios. The integrated fault detection module showed high diagnostic accuracy, and visual analytics were incorporated to support interpretability by non-specialist users. Although still in the pre-deployment phase, these results confirm the system’s technical robustness and readiness for field trials. A comparative analysis with existing solutions emphasizes the system’s unique contribution—namely, the ","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101710"},"PeriodicalIF":7.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient and secure adaptive federated learning method based on CKKS for data processing in the Internet of Things","authors":"Yang Lan ,&nbsp;Lixiang Li ,&nbsp;Haipeng Peng ,&nbsp;Yeqing Ren ,&nbsp;Zhongkai Dang","doi":"10.1016/j.iot.2025.101725","DOIUrl":"10.1016/j.iot.2025.101725","url":null,"abstract":"<div><div>Federated learning (FL) provides a new paradigm for solving the security of private data in the internet of things (IoT). However, the huge consumption of computing resources and communication cost makes the FL process inefficient. To solve above problems, this paper proposes an efficient and secure adaptive federated learning method based on CKKS homomorphic encryption (HE) for data processing in IoT. Inspired by dropout, we propose an adaptive inactivation of weights strategy. Through adaptive change of inactivation parameter, part of the weights after reorganization are encrypted and uploaded in each communication. The dual protection of reorganization operation and HE can better protect the weights information. Then, to alleviate the impact of the above methods on the performance of FL, the local data distribution and the change of model accuracy are considered, we propose the federated aggregation method with reward and punishment factor, and the historical information of the local model is employed to design a weights correction strategy. Finally, we use MNIST dataset, fashion-MNIST dataset, GTSRB dataset and CSE-CIC-IDS2018 dataset to design non-independent and identically distributed data scenarios, and a large number of experiments are carried out to verify the effectiveness of the proposed method. Our method not only protects the privacy of weights information, but also reduces the communication cost and the local resource consumption caused by the encryption, which provides a good reference for the follow-up development of FL in IoT.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101725"},"PeriodicalIF":7.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated differentially private framework for risky driving behaviour assessment","authors":"Yan Zhang ,&nbsp;Guojiang Shen ,&nbsp;Huan Li ,&nbsp;Zhenhui Xu ,&nbsp;Xiangjie Kong","doi":"10.1016/j.iot.2025.101726","DOIUrl":"10.1016/j.iot.2025.101726","url":null,"abstract":"<div><div>Connected and Automated Vehicles (CAVs) offer a transformative opportunity to enhance road safety by identifying and mitigating risky driving behaviours. However, current methods for assessing these behaviours in CAVs overlook privacy concerns, as all terminal devices are required to upload original data directly to a central server for analysis. This data often includes sensitive personal information, raising the risk of potential privacy breaches. To address this challenge, we introduce Federated Learning (FL), a privacy-preserving machine learning technique, and propose a Federated Differentially Private based Risk Assessment Network (FedDPRAN) for risky driving behaviour assessment. In particular, we begin by extracting driving behaviour characteristics for each client using the Risk Assessment Network (RAN), following the adversarial principle of minimizing the difference in driving behaviour features while maximizing the difference in privacy features of the driver. Then, a new FL solution that utilizes the local RAN model is proposed to collaborate and exchange learned parameters with a cloud server without sharing actual data. To enhance privacy in the FL framework, we integrate a Differential Privacy (DP) solution for each client. Comprehensive experiments are conducted based on two real-world datasets. The results demonstrate that our approach to assessing risky driving behaviour in CAVs protects privacy while being superior to state-of-the-art schemes.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101726"},"PeriodicalIF":7.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OPC UA and MQTT performance analysis within a unified namespace context","authors":"Luis Freitas ,&nbsp;Marco Silva ,&nbsp;Gabriel Vale ,&nbsp;Camelia Avram ,&nbsp;Helena Lopes ,&nbsp;Filipe Pereira ,&nbsp;Nuno Leal ,&nbsp;José Machado","doi":"10.1016/j.iot.2025.101734","DOIUrl":"10.1016/j.iot.2025.101734","url":null,"abstract":"<div><div>One of the main obstacles to fully adopting and implementing Industry 4.0 concept and achieving effective digital transformation is interoperability. Various communication protocols, aligned with different architectures, present themselves as potential solutions. The Unified Namespace (UNS) has emerged as a promising approach for data-centric architectures aligned with reference frameworks like Reference Architectural Model Industrie 4.0 (RAMI 4.0) and The Industrial Internet Reference Architecture (IIRA). Open Platform Communications Unified Architecture (OPC UA) and Message Queuing Telemetry Transport (MQTT) are among the most widely used communication protocols applicable in a UNS. Determining which of these is most suitable for specific applications requires further exploration. This paper provides an in-depth comparison of these protocols across several key Industry 4.0 metrics, based on data from an industrial-like case study and qualitative insights from the research team. Findings indicate that a lightweight protocol like MQTT offers advantages in raw performance metrics in its efficiency and scalability; however, depending on the application and organizational philosophy, OPC UA may offer a more comprehensive solution, mainly in terms of security and interoperability. The study’s results are limited by reliance on a single case study and lack of failure or cyber-attack scenarios. Future work will expand testing with more clients, simulate network faults and attacks, and include OPC UA Pub/Sub for broader comparison.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101734"},"PeriodicalIF":7.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive IoT architecture with incremental learning for on-line solar production forecasting","authors":"Giuseppe Del Fiore,&nbsp;Teodoro Montanaro,&nbsp;Ilaria Sergi,&nbsp;Luigi Patrono","doi":"10.1016/j.iot.2025.101724","DOIUrl":"10.1016/j.iot.2025.101724","url":null,"abstract":"<div><div>Smart homes play a pivotal role in advancing energy sustainability by incorporating renewable energy sources, like photovoltaic systems. Their effectiveness depends on the closer alignment between energy production and consumption. However, forecasting solar energy remains challenging due to variability from meteorological and seasonal factors. Traditional forecasting methods primarily rely on complex models and static datasets, lacking on-line estimation based on dynamic inputs like live weather and actual production data from Internet of Things (IoT) devices. While IoT-based data acquisition has begun to enhance forecasting, the heterogeneity of these devices poses interoperability challenges, limiting their full potential. Moreover, existing models often fail to leverage incremental learning, which is essential for continuously adapting predictions as new data becomes available. To mitigate these constraints, this paper proposes a modular, interoperable, and scalable IoT architecture for solar energy forecasting. It incorporates modules to: (a) integrate heterogeneous IoT devices and external services, such as weather forecasting, to obtain real-time data; (b) incorporate a baseline model, informed by domain knowledge of photovoltaic systems, to provide initial production estimations in the absence of historical data; and (c) exploit incremental hybrid forecasting techniques able to combine a batch model for long-term trend prediction based on historical data and with a progressive refined integrated baseline for on-line short-term forecasting. The proposed architecture has been implemented and evaluated in a real-world smart home scenario. Results demonstrate its ability to predict photovoltaic energy production with over 90% accuracy while maintaining low computational complexity, underscoring its practical applicability in smart home environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101724"},"PeriodicalIF":7.6,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From lab to field: Real-world evaluation of an AI-driven Smart Video Solution to enhance community safety","authors":"Shanle Yao ,&nbsp;Babak Rahimi Ardabili ,&nbsp;Armin Danesh Pazho ,&nbsp;Ghazal Alinezhad Noghre ,&nbsp;Christopher Neff ,&nbsp;Lauren Bourque ,&nbsp;Hamed Tabkhi","doi":"10.1016/j.iot.2025.101716","DOIUrl":"10.1016/j.iot.2025.101716","url":null,"abstract":"<div><div>This article adopts and evaluates an AI-enabled Smart Video Solution (SVS) designed to enhance safety in the real world. The system integrates with existing infrastructure camera networks, leveraging recent advancements in AI for easy adoption. Prioritizing privacy and ethical standards, pose-based data is used for downstream AI tasks such as anomaly detection. A Cloud-based infrastructure and a mobile app are deployed, enabling real-time alerts within communities. The SVS employs innovative data representation and visualization techniques, such as the Occupancy Indicator, Statistical Anomaly Detection, Bird’s Eye View, and Heatmaps, to understand pedestrian behaviors and enhance public safety. Evaluation of the SVS demonstrates its capacity to convert complex computer vision outputs into actionable insights for stakeholders, community partners, law enforcement, urban planners, and social scientists. This article presents a comprehensive real-world deployment and evaluation of the SVS, implemented in a community college environment with 16 cameras. The system integrates AI-driven visual processing, supported by statistical analysis, database management, cloud communication, and user notifications. Additionally, the article evaluates the end-to-end latency from the moment an AI algorithm detects anomalous behavior in real-time at the camera level to the time stakeholders receive a notification. The results demonstrate the system’s robustness, effectively managing 16 CCTV cameras with a consistent throughput of 16.5 frames per second (FPS) over a 21-h period and an average end-to-end latency of 26.76 s between anomaly detection and alert issuance.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101716"},"PeriodicalIF":7.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bluetooth Low Energy separate-channel fingerprinting with frequency-scanned antennas","authors":"José A.López Pastor ,&nbsp;Alejandro Gil-Martínez ,&nbsp;Antonio Hernández Mateos ,&nbsp;Astrid Algaba-Brazález ,&nbsp;José Luis Gómez Tornero","doi":"10.1016/j.iot.2025.101732","DOIUrl":"10.1016/j.iot.2025.101732","url":null,"abstract":"<div><div>A novel Bluetooth Low Energy (BLE) positioning system for IoT devices that employs frequency-scanned leaky-wave antennas (FSLWAs) and the fingerprinting technique is proposed in this work. This system is based on the Received Signal Strength Indicator (RSSI) fingerprinting acquired separately from each one of the three BLE advertising channels. As a main novelty, the Separate-Channel Fingerprinting (SCFP) technique is combined with FSLWAs, specifically tuned to multiplex each BLE advertising channel in a distinct direction. In this way, FSLWAs produce increased spatial resolution in separate-channel radiomaps, which results in greater localization accuracy when compared to the use of conventional monopole antennas installed usually in BLE IoT equipment. This is demonstrated with a practical example involving four BLE beacons distributed in an indoor area of 7 m x 5 m, simulating a practical environment. The proposed system combining SCFP and FSLWAs improves the mean localization error by 39 % compared to conventional monopole antennas without SCFP and 23 % compared to the use of monopoles and SCFP. The system's performance over time and its robustness to the presence of obstacles after calibration are also analyzed, showing the benefits of using SCFP with FSLWAs.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101732"},"PeriodicalIF":7.6,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain and IoT technologies in smart farming to enhance the efficiency of the agri-food supply chain: A review of applications, benefits, and challenges","authors":"Abdennabi Morchid ,&nbsp;Abdulla Ismail ,&nbsp;Haris M. Khalid ,&nbsp;Hassan Qjidaa ,&nbsp;Rachid El Alami","doi":"10.1016/j.iot.2025.101733","DOIUrl":"10.1016/j.iot.2025.101733","url":null,"abstract":"<div><div>Innovative and sustainable agriculture encounters significant challenges due to the extensive variation in farming practices. This is also crucial for the agri-food supply chain and, therefore, for food security. To enhance the efficiency of the agri-food supply chain, it is essential to optimize the farming systems. The combination of blockchain and Internet of Things (IoT) technologies aims to revolutionize agriculture by optimizing traditional practices into more efficient, transparent, and sustainable systems. This proposed article offers an in-depth examination of this integration while addressing the global challenges of food security, resource efficiency, and environmental sustainability. This proposed article begins with an analysis of the size of the global blockchain and IoT market, focusing on their specific application in agriculture. It then presents the fundamentals of these technologies, providing an overview of IoT in smart agriculture, the basics of blockchain technology, and their convergence in this sector. The proposed article examines several key applications of these technologies, including supply chain transparency and traceability, irrigation and resource optimization, livestock monitoring and disease prevention, as well as agricultural finance and smart contracts. Finally, it examines the benefits of integrating IoT and blockchain in agriculture, including enhanced traceability, optimized resource management, and improved data security, while also highlighting the challenges associated with their implementation, such as high costs, scalability issues, energy consumption, data privacy concerns, and regulatory hurdles. This proposed study also highlights the opportunities presented by these technologies while examining the challenges to their widespread adoption in the agricultural sector.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101733"},"PeriodicalIF":7.6,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}