Kaiyi Zhang
Papers
2
Total Citations
9
H-Index
2
About
Kaiyi Zhang is a researcher at the forefront of mobile edge computing and the Internet of Things (IoT), with a focus on optimizing energy-constrained systems for smart city applications. His work addresses critical challenges in task offloading and resource allocation, leveraging deep reinforcement learning to enhance the efficiency of battery-constrained mobile IoT devices. Zhang's most-cited paper, "Optimized Look-Ahead Offloading Decisions Using Deep Reinforcement Learning for Battery Constrained Mobile IoT Devices" (2020, 6 citations), proposes a novel framework that intelligently schedules computational tasks to reduce energy consumption and latency, directly supporting applications like air pollution monitoring and road safety. In a related study, "Task Offloading and Resource Allocation Using Deep Reinforcement Learning" (2020, 3 citations), he extends these principles to tackle traffic congestion and public safety issues in rapidly urbanizing environments. Though early in his career, Zhang's contributions are notable for their practical impact on sustainable smart city infrastructure, demonstrating how AI-driven decision-making can prolong device battery life while maintaining high-quality service. His work is essential reading for researchers exploring energy-efficient IoT systems and edge intelligence.
Research Focus
Key Achievements
Top Papers
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- 2