Optimizing IoT-Enabled Logistics Systems Using Lightweight Neural Networks for Real-Time Predictive Scheduling
Abstract
advanced AI use. This paper presents a lightweight CNN framework based on MobileNetV2 for predictive scheduling in IoT logistics. Using
GPS, traffic, and delivery status data, it achieves 87.5% accuracy, 32% lower latency than heuristic methods, and 64% less memory than typical CNNs. Developed from the author’s expertise in IoT, AI, and logistics, the framework supports last-mile delivery, smart city logistics, and
industrial IoT. It validates the author’s master’s curriculum in AI, IoT, robotics, and cybersecurity at Hong Kong universities..
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DOI: http://dx.doi.org/10.18686/ede.v4i2.14172
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