Опубликована: Май 9, 2024
Язык: Английский
IEEE Access, Год журнала: 2023, Номер 11, С. 145813 - 145852
Опубликована: Янв. 1, 2023
The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization control, early-stage pest crop disease management, energy conservation. application protocols such as ZigBee, WiFi, SigFox, LoRaWAN are commonly employed collect real-time data for monitoring purposes. Embracing advanced technology imperative ensure efficient annual production. Therefore, this study emphasizes a comprehensive, future-oriented approach, delving into IoT-WSNs, wireless network protocols, their applications since 2019. It thoroughly discusses overview IoT WSNs, architectures summarization protocols. Furthermore, addresses recent issues challenges related IoT-WSNs proposes mitigation strategies. provides clear recommendations future, emphasizing integration aiming contribute future development smart systems.
Язык: Английский
Процитировано
60
Computers in Biology and Medicine, Год журнала: 2024, Номер 170, С. 108036 - 108036
Опубликована: Янв. 28, 2024
Over the past five years, interest in literature regarding security of Internet Medical Things (IoMT) has increased. Due to enhanced interconnectedness IoMT devices, their susceptibility cyber-attacks proportionally escalated. Motivated by promising potential AI-related technologies improve certain cybersecurity measures, we present a comprehensive review this emerging field. In review, attempt bridge corresponding gap modern that deploy AI techniques performance and compensate for privacy vulnerabilities. direction, have systematically gathered classified extensive research on topic. Our findings highlight fact integration machine learning (ML) deep (DL) improves both measures speed, reliability, effectiveness. This may be proven useful improving devices. Furthermore, considering numerous advantages as opposed core counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, so on, provide structured overview current scientific trends. We conclude with considerations future research, emphasizing AI-driven landscape, especially patient data protection data-driven healthcare.
Язык: Английский
Процитировано
25
Sensors, Год журнала: 2024, Номер 24(3), С. 968 - 968
Опубликована: Фев. 1, 2024
Federated learning (FL) is a machine (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy concern. Wireless Sensor Networks (WSNs) play crucial role in IoT systems by collecting from the physical environment. This paper presents comprehensive survey integration FL, IoT, WSNs. It covers FL basics, strategies, types discusses WSNs various domains. The addresses challenges related to heterogeneity summarizes state-of-the-art research this area. also explores security considerations performance evaluation methodologies. outlines latest achievements potential directions emphasizes significance surveyed topics within context current technological advancements.
Язык: Английский
Процитировано
16
Computer Communications, Год журнала: 2024, Номер 222, С. 38 - 67
Опубликована: Апрель 21, 2024
Язык: Английский
Процитировано
15
Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 131, С. 107831 - 107831
Опубликована: Янв. 5, 2024
Язык: Английский
Процитировано
10
Neurocomputing, Год журнала: 2024, Номер 599, С. 128089 - 128089
Опубликована: Июнь 22, 2024
Язык: Английский
Процитировано
10
Опубликована: Ноя. 22, 2024
This book chapter explores the intersection of Blockchain Technology and Federated Learning, presenting a comprehensive overview their synergistic potential in creating secure privacy-preserving AI systems. With increasing demand for decentralized, collaborative models, Learning has emerged as promising paradigm. However, it brings forth concerns regarding data privacy, model integrity, trust among participating parties. In this chapter, we delve into integration blockchain offering an innovative solution to address these challenges. We discuss principles its applications across various domains, vulnerabilities exposes. Subsequently, introduce robust framework enhance security trustworthiness Systems.
Язык: Английский
Процитировано
10
Future Internet, Год журнала: 2024, Номер 16(9), С. 324 - 324
Опубликована: Сен. 6, 2024
The integration of machine learning (ML), blockchain, and the Internet Things (IoT) in smart cities represents a pivotal advancement urban innovation. This convergence addresses complexities modern environments by leveraging ML’s data analytics predictive capabilities to enhance intelligence IoT systems, while blockchain provides secure, decentralized framework that ensures integrity trust. synergy these technologies not only optimizes management but also fortifies security privacy increasingly connected cities. survey explores transformative potential ML-driven blockchain-IoT ecosystems enabling autonomous, resilient, sustainable city infrastructure. It discusses challenges such as scalability, privacy, ethical considerations, outlines possible applications future research directions are critical for advancing initiatives. Understanding dynamics is essential realizing full cities, where technology enhances efficiency sustainability resilience.
Язык: Английский
Процитировано
7
Sustainability, Год журнала: 2024, Номер 16(18), С. 7949 - 7949
Опубликована: Сен. 11, 2024
In the current era of digital transformation, among plethora technologies, blockchain (BC) technology has attracted attention, carrying weight enormous expectations in terms its applicability and benefits. BC promises immutability, reliability, transparency, security transactions, using decentralized models to scale up existing Internet Things (IoT) solutions while guaranteeing privacy. energy industry, is mainly used secure distributed power grids, which have proven be easily hackable by malicious users. Recognizing need for a preliminary analysis literature investigating role sustainability data management this study conducts bibliometric analysis, identifying implications research directions field. Specifically, performance scientific mapping are performed on 943 documents Scopus database VOSviewer software version 1.6.20. The result identification seven thematic clusters most relevant as well future actions at strategic, technical, regulatory, social levels. This extends suggesting potential opportunities regarding adoption industry; it also supports managers strategies strengthen business leveraging development new knowledge asset management.
Язык: Английский
Процитировано
7
Electronics, Год журнала: 2024, Номер 13(23), С. 4744 - 4744
Опубликована: Ноя. 30, 2024
As data become increasingly abundant and diverse, their potential to fuel machine learning models is vast. However, traditional centralized approaches, which require aggregating into a single location, face significant challenges. Privacy concerns, stringent protection regulations like GDPR, the high cost of transmission hinder feasibility centralizing sensitive from disparate sources such as hospitals, financial institutions, personal devices. Federated Learning addresses these issues by enabling collaborative model training without requiring raw leave its origin. This decentralized approach ensures privacy, reduces costs, allows organizations harness collective intelligence distributed while maintaining compliance with ethical legal standards. review delves FL’s current applications reshape IoT systems more collaborative, privacy-centric, flexible frameworks, aiming enlighten motivate those navigating confluence advancements.
Язык: Английский
Процитировано
6