17,028 research outputs found
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Investigating IoT Middleware Platforms for Smart Application Development
With the growing number of Internet of Things (IoT) devices, the data
generated through these devices is also increasing. By 2030, it is been
predicted that the number of IoT devices will exceed the number of human beings
on earth. This gives rise to the requirement of middleware platform that can
manage IoT devices, intelligently store and process gigantic data generated for
building smart applications such as Smart Cities, Smart Healthcare, Smart
Industry, and others. At present, market is overwhelming with the number of IoT
middleware platforms with specific features. This raises one of the most
serious and least discussed challenge for application developer to choose
suitable platform for their application development. Across the literature,
very little attempt is done in classifying or comparing IoT middleware
platforms for the applications. This paper categorizes IoT platforms into four
categories namely-publicly traded, open source, developer friendly and
end-to-end connectivity. Some of the popular middleware platforms in each
category are investigated based on general IoT architecture. Comparison of IoT
middleware platforms in each category, based on basic, sensing, communication
and application development features is presented. This study can be useful for
IoT application developers to select the most appropriate platform according to
their application requirement
Smart Cities: An In-Depth Study of AI Algorithms and Advanced Connectivity
The goal of smart city development is to improve the quality of life by incorporating technology into daily activities. Artificial intelligence (AI) is critical to the ongoing development of future smart cities. The Internet of Things (IoT) idea connects every internet-enabled device for improved access and control. AI in various domains has changed ordinary towns into highly equipped smart cities. Machine learning and deep learning algorithms have proven indispensable in a variety of industries, and they are now being implemented into smart city concepts to automate and improve urban activities and operations on a large scale. IoT and machine learning technology are frequently used in smart cities to collect data from various sources. This article delves deeply into the significance, scope, and developments of AI-based smart cities. It also addresses some of the difficulties and restrictions associated with smart cities powered by AI. The goal of the study is to inspire and encourage academics to create original smart city solutions based on AI technologies
After the Gold Rush: The Boom of the Internet of Things, and the Busts of Data-Security and Privacy
This Article addresses the impact that the lack of oversight of the Internet of Things has on digital privacy. While the Internet of Things is but one vehicle for technological innovation, it has created a broad glimpse into domestic life, thus triggering several privacy issues that the law is attempting to keep pace with. What the Internet of Things can reveal is beyond the control of the individual, as it collects information about every practical aspect of an individual’s life, and provides essentially unfettered access into the mind of its users. This Article proposes that the federal government and the state governments bend toward consumer protection while creating a cogent and predictable body of law surrounding the Internet of Things. Through privacy-by-design or self-help, it is imperative that the Internet of Things—and any of its unforeseen progeny—develop with an eye toward safeguarding individual privacy while allowing technological development
Distributed Systems for Artificial Intelligence in Cloud Computing: A Review of AI-Powered Applications and Services
The synergy of distributed frameworks with Artificial Intelligence (AI) is pivotal for advancing applications in cloud computing. This review focuses on AI-powered applications in distributed systems, conducting a thorough examination. Analyzing foundational studies and real-world applications, it extracts insights into the dynamic interplay between AI and distributed frameworks. Quantitative measures allow a nuanced comparison, revealing diverse contributions. The survey provides a broad overview of the state-of-the-art, spanning applications like performance optimization, security, and IoT integration. The ensuing discussion synthesizes comparative measures, significantly enhancing our understanding. Concluding with recommendations for future research and collaborations, it serves as a concise guide for professionals and researchers navigating the challenging landscape of AI-powered applications in distributed cloud computing platforms
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