In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.
Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review
Purple Mountain Laboratories（紫金山实验室）；Northwestern Polytechnical University（西北工业大学）；Wuhan University of Technology（武汉理工大学）；Peking University（北京大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Carleton University（卡尔顿大学）
Internet of Things (IoT) is emerging as a significant technology in shaping the future by connecting physical devices or things with internet. It also presents various opportunities for intersection of other technological trends which can allow it to become even more intelligent and efficient. In this paper we focus our attention on the integration of Intelligent Conversational Software Agents or Chatbots with IoT. Literature surveys have looked into various applications, features, underlying technologies and known challenges of IoT. On the other hand, Chatbots are being adopted in greater num…
Macau University of Science and Technology（澳门科技大学）；Norwegian University of Science & Technology（挪威科技大学）；Tianjin University（天津大学）；South China University of Technology（华南理工大学）；King Saud University（沙特国王大学）
The recent advances in information and communication technology (ICT) have promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing. Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytic…
Empowering Tabular Data Preparation with Language Models: Why and How?
Antai College of Economics and Management, Shanghai Jiao Tong University（上海交通大学安泰经济管理学院）；The University of New South Wales（新南威尔士大学）；Zhejiang Gongshang University（浙江工商大学）
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction
Badji Mokhtar-Annaba University（巴吉·穆克塔尔-安纳巴大学）；University of La Rochelle（拉罗谢尔大学）；National Higher School of Technology and Engineering -Annaba（安纳巴国家技术与工程高等学校）
Center for Robotics MINES ParisTech, PSL Research University（巴黎文理研究大学巴黎高等矿业学院机器人中心）
Artificial Intelligence represents many things: a new market to conquer or a quality label for tech companies, a threat for traditional industries, a menace for democracy, or a blessing for our busy everyday life. The press abounds in examples illustrating these aspects, but one should draw not hasty and premature conclusions. The first successes in AI have been a surprise for society at large-including researchers in the field. Today, after the initial stupefaction, we have examples of the system reactions: traditional companies are heavily investing in AI, social platforms are monitored duri…
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence
Sun Yat-sen University（中山大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；Midea Group（美的集团）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Peng Cheng Laboratory（鹏城实验室）；Terminus Group（特斯联集团）；The University of Waterloo（滑铁卢大学）
Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. But new challenges are emerging every day. In this paper, we present recent algori…
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
Department of Electrical and Electronic Engineering, University of Hong Kong（香港大学电子与电气工程系）；HKU Musketeers Foundation Institute of Data Science, University of Hong Kong（香港大学穆斯奎特基金会数据科学研究所）；School of Computer Science, Fudan University（复旦大学计算机学院）
Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database of known intrusion experiences, known as signature-based, or on learning the normal behavior of the system and reporting whether some anomalous events occur, named anomaly-based. This work is dedicated to survey the application of IDS to the Internet of Things (IoT) networks, where also the edge computing is used to support the IDS implementation. New c…
