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Moon-Il Joo, Dong-Yoon Kang, Min-Soo Kang and Hee-Cheol Kim
Edge computing can provide core functions such as data collection and analysis without connecting to a centralized server. The convergence of edge computing and IoT devices has enabled medical institutions to collect patient data in real time, improving ...
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Poornima Mahadevappa, Redhwan Al-amri, Gamal Alkawsi, Ammar Ahmed Alkahtani, Mohammed Fahad Alghenaim and Mohammed Alsamman
Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can ...
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Hanyue Xu, Kah Phooi Seng, Jeremy Smith and Li Minn Ang
In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the co...
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Yushan Li and Satoshi Fujita
This paper proposes a novel event-driven architecture for enhancing edge-based vehicular systems within smart transportation. Leveraging the inherent real-time, scalable, and fault-tolerant nature of the Elixir language, we present an innovative architec...
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Feng Zhou, Shijing Hu, Xin Du, Xiaoli Wan and Jie Wu
In the current field of disease risk prediction research, there are many methods of using servers for centralized computing to train and infer prediction models. However, this centralized computing method increases storage space, the load on network band...
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Yogeswaranathan Kalyani, Liam Vorster, Rebecca Whetton and Rem Collier
In the last decade, digital twin (DT) technology has received considerable attention across various domains, such as manufacturing, smart healthcare, and smart cities. The digital twin represents a digital representation of a physical entity, object, sys...
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Yu Dai, Jiaming Fu, Zhen Gao and Lei Yang
Due to CPU and memory limitations, mobile IoT devices face challenges in handling delay-sensitive and computationally intensive tasks. Mobile edge computing addresses this issue by offloading tasks to the wireless network edge, reducing latency and energ...
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Changhao Wu, Siyang He, Zengshan Yin and Chongbin Guo
Large-scale low Earth orbit (LEO) remote satellite constellations have become a brand new, massive source of space data. Federated learning (FL) is considered a promising distributed machine learning technology that can communicate optimally using these ...
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Tuan Phong Tran, Anh Hung Ngoc Tran, Thuan Minh Nguyen and Myungsik Yoo
Multi-access edge computing (MEC) brings computations closer to mobile users, thereby decreasing service latency and providing location-aware services. Nevertheless, given the constrained resources of the MEC server, it is crucial to provide a limited nu...
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Ruicheng Gao, Zhancai Dong, Yuqi Wang, Zhuowen Cui, Muyang Ye, Bowen Dong, Yuchun Lu, Xuaner Wang, Yihong Song and Shuo Yan
In this study, a deep-learning-based intelligent detection model was designed and implemented to rapidly detect cotton pests and diseases. The model integrates cutting-edge Transformer technology and knowledge graphs, effectively enhancing pest and disea...
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