Summary of Traffic Learning and Proactive Uav Trajectory Planning For Data Uplink in Markovian Iot Models, by Eslam Eldeeb et al.
Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models
by Eslam Eldeeb, Mohammad Shehab, Hirley Alves
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel learning-based framework presented in this paper estimates traffic arrival of IoT devices based on Markovian events to minimize Age of Information (AoI), energy consumption, and improve throughput. The approach uses Markovian events to predict future traffic, comparing two predictors: the Forward Algorithm (FA) and Long Short-Term Memory (LSTM). The framework then employs Deep Reinforcement Learning (DRL) to optimize UAV trajectories and scheduling policy. Simulation results show that the proposed algorithm outperforms a random-walk baseline model in terms of AoI, scheduling accuracy, and transmission power. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to manage data freshness in IoT networks using flying base stations called unmanned aerial vehicles (UAVs). The traditional approach relies on messages between devices and the base station, which causes high energy consumption and low reliability. The authors propose an innovative framework that uses machine learning to predict when devices will send or receive data. They test two different methods: one based on a mathematical algorithm and another using a type of neural network called Long Short-Term Memory (LSTM). The framework also optimizes the movement of multiple UAVs and how they schedule their flights. Simulations show that this approach can significantly reduce delays, improve accuracy, and conserve energy. |
Keywords
* Artificial intelligence * Lstm * Machine learning * Neural network * Reinforcement learning
