Maintaining freshness of data collection in Internet-of-Things (IoT) networks
has attracted increasing attention. By taking into account age-of-information
(AoI), we investigate the trajectory planning problem of an unmanned aerial
vehicle (UAV) that is used to aid a cluster-based IoT network. An optimization
problem is formulated to minimize the total AoI of the collected data by the
UAV from the ground IoT network. Since the total AoI of the IoT network depends
on the flight time of the UAV and the data collection time at hovering points,
we jointly optimize the selection of hovering points and the visiting order to
these points. We exploit the state-of-the-art transformer and the weighted A*,
which is a path search algorithm, to design a machine learning algorithm to
solve the formulated problem. The whole UAV-IoT system is fed into the encoder
network of the proposed algorithm, and the algorithm’s decoder network outputs
the visiting order to ground clusters. Then, the weighted A* is used to find
the hovering point for each cluster in the ground IoT network. Simulation
results show that the trained model by the proposed algorithm has a good
generalization ability to generate solutions for IoT networks with different
numbers of ground clusters, without the need to retrain the model. Furthermore,
results show that our proposed algorithm can find better UAV trajectories with
the minimum total AoI when compared to other algorithms.
The article discusses the importance of maintaining freshness of data collection in Internet-of-Things (IoT) networks and investigates the trajectory planning problem of an unmanned aerial vehicle (UAV) used to aid a cluster-based IoT network. The focus is on minimizing the total age-of-information (AoI) of the collected data by optimizing the selection of hovering points and the visiting order to these points.
Multi-disciplinary Nature:
This content delves into the multi-disciplinary nature of IoT networks and trajectory planning for UAVs. It combines concepts from computer science (machine learning, path search algorithms), aerospace engineering (UAV trajectory planning), and telecommunications (IoT network management).
The integration of transformer architecture, a state-of-the-art technique from natural language processing, into the machine learning algorithm demonstrates the cross-pollination of ideas from different domains. By leveraging transformer and weighted A* algorithms, the proposed solution addresses the complex problem of optimizing UAV trajectories within IoT networks.
Expert Analysis:
The formulation of an optimization problem to minimize the total AoI is a significant advancement in the field. By jointly optimizing the selection of hovering points and the visiting order, the proposed algorithm aims to improve efficiency in data collection. This approach takes into account both flight time and data collection time, considering the trade-off between these two factors.
The use of a machine learning algorithm to solve the formulated problem is a logical choice, given the available data and the need for adaptable solutions. By training the model on various scenarios, the algorithm demonstrates good generalization ability, eliminating the need for retraining when faced with different numbers of ground clusters in IoT networks.
The application of weighted A* to find hovering points for each cluster further enhances the efficiency of the solution. This path search algorithm is known for its effectiveness in finding optimal routes, making it suitable for the context of UAV trajectory planning within IoT networks.
Predictions for the Future:
The research presented in this article paves the way for further advancements in optimizing data collection in IoT networks. With the increasing deployment of IoT devices and the reliance on UAVs for support, efficient trajectory planning becomes crucial.
In the future, we can expect further integration of machine learning techniques, such as reinforcement learning, to improve the optimization process. Additionally, advancements in path search algorithms and optimization algorithms tailored specifically for UAV trajectory planning within IoT networks are likely to emerge.
The multi-disciplinary nature of this topic also highlights the need for collaborative efforts between different fields. By leveraging expertise from computer science, aerospace engineering, and telecommunications, researchers can develop comprehensive solutions that address the challenges inherent in IoT network management and UAV trajectory planning.