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• W2897521775 abstract "This incredibly rapid adoption of Internet of Things (IoT) and e-learning technology, a smart campus provides many innovative applications, such as ubiquitous learning, smart energy, and security services to campus users via numerous IoT devices. However, as more and more IoT devices are integrated and imported, the inadequate campus network resource caused by the sensor data transport and video streaming is also a significant problem. This paper proposes a campus edge computing network in the hardware-software co-design process. The system employs street lighting as the IoT network communication node device. The campus platform integrates campus courses service, regulatory networks, mobile wireless networks, and other computing services. Neural network learning algorithms are employed to analyze the network and compute resource required by each network node operates as a whole network resource allocation service. Moreover, the learning algorithms will be adjusted as the bidirectional IoT communication to avoid inadequate resources with many IoTs service and data streams in the overall campus network service quality. The experimental results show that the proposed mechanism that the edge computing reduces the cloud loading and predicts and adjusts the distribution of the overall network can efficiently allocate resources and maintain load balance." @default.
• W2897521775 created "2018-10-26" @default.
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• W2897521775 date "2020-03-01" @default.
• W2897521775 modified "2023-10-16" @default.
• W2897521775 title "Campus Edge Computing Network Based on IoT Street Lighting Nodes" @default.
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• W2897521775 doi "https://doi.org/10.1109/jsyst.2018.2873430" @default.
• W2897521775 hasPublicationYear "2020" @default.
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