UAV Path Planning for Data Gathering in Wireless Sensor Networks: Spatial and Temporal Substate-Based Q-Learning

Beishenalieva, Aliia; Yoo, Sang-Jo

doi:10.1109/jiot.2023.3323921

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…An additional study 15 explores regions rich in obstacles, offering an overview image and scrutinizing deterministic and probabilistic path planning strategies for self-reliant UAV networks. The study presents algorithms amenable to both online and offline implementation, underscoring the approach's efficiency with a reduced number of images.…”

Section: Related Workmentioning

confidence: 99%

Multi‐UAV path planning utilizing the PGA algorithm for terrestrial IoT sensor network under ISAC framework

Azadur,

Pawase,

Chang

2023

Trans Emerging Tel Tech

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The integration of internet of things (IoT) technologies with unmanned aerial vehicles (UAVs) has initiated a groundbreaking revolution in data acquisition and communication systems across diverse domains. This document introduces an innovative endeavor, the integration of multi‐UAV path planning for integrated sensing and communication (ISAC) within ground‐based CAT‐M1 IoT sensor networks, accomplished through the application of the Pareto‐based genetic ant colony optimization (PGA) algorithm. The PGA algorithm is highly capable of UAV path planning due to its efficiency, adaptability, and seamless integration of domain expertise. By employing the PGA algorithm, we simultaneously minimize UAV travel distance while optimizing energy consumption, resulting in multi‐objective optimization. The synergy of ground‐based IoT sensors and seamless UAV communication, coupled with a convex optimization resource allocation algorithm, empowers real‐time data acquisition and heightens situational awareness. Our proposed UAV path planning PGA algorithm with resource allocation is crafted to maximize the efficiency of ground‐based sensor data acquisition. We stand at the forefront of advancing multi‐UAV data collection systems with this pioneering approach, promoting increased efficiency, robustness, and transformative solutions across diverse domains. The proposed system for ISAC achieves an impressive throughput of up to 95% of the system's capacity.

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Section: Related Workmentioning

confidence: 99%

Multi‐UAV path planning utilizing the PGA algorithm for terrestrial IoT sensor network under ISAC framework

Azadur,

Pawase,

Chang

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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Optimizing UAV-assisted IoT sensor networks: A multi-objective approach to data collection and routing

Mohammed,

Hassan,

Hasan

et al. 2025

Alexandria Engineering Journal

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Deep Reinforcement Learning for UAV-Based SDWSN Data Collection

Karegar,

Al-Hamid,

Chong

2024

Future Internet

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Recent advancements in Unmanned Aerial Vehicle (UAV) technology have made them effective platforms for data capture in applications like environmental monitoring. UAVs, acting as mobile data ferries, can significantly improve ground network performance by involving ground network representatives in data collection. These representatives communicate opportunistically with accessible UAVs. Emerging technologies such as Software Defined Wireless Sensor Networks (SDWSN), wherein the role/function of sensor nodes is defined via software, can offer a flexible operation for UAV data-gathering approaches. In this paper, we introduce the “UAV Fuzzy Travel Path”, a novel approach that utilizes Deep Reinforcement Learning (DRL) algorithms, which is a subfield of machine learning, for optimal UAV trajectory planning. The approach also involves the integration between UAV and SDWSN wherein nodes acting as gateways (GWs) receive data from the flexibly formulated group members via software definition. A UAV is then dispatched to capture data from GWs along a planned trajectory within a fuzzy span. Our dual objectives are to minimize the total energy consumption of the UAV system during each data collection round and to enhance the communication bit rate on the UAV-Ground connectivity. We formulate this problem as a constrained combinatorial optimization problem, jointly planning the UAV path with improved communication performance. To tackle the NP-hard nature of this problem, we propose a novel DRL technique based on Deep Q-Learning. By learning from UAV path policy experiences, our approach efficiently reduces energy consumption while maximizing packet delivery.

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UAV Path Planning for Data Gathering in Wireless Sensor Networks: Spatial and Temporal Substate-Based Q-Learning

Cited by 3 publications

References 33 publications

Multi‐UAV path planning utilizing the PGA algorithm for terrestrial IoT sensor network under ISAC framework

Multi‐UAV path planning utilizing the PGA algorithm for terrestrial IoT sensor network under ISAC framework

Optimizing UAV-assisted IoT sensor networks: A multi-objective approach to data collection and routing

Deep Reinforcement Learning for UAV-Based SDWSN Data Collection

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