Multiobjective 3-D UAV Movement Planning in Wireless Sensor Networks Using Bioinspired Swarm Intelligence

Beishenalieva, Aliia; Yoo, Sang-Keun

doi:10.1109/jiot.2022.3231302

Cited by 9 publications

(3 citation statements)

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%

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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%

“…In Reference 15 STSQL introduces a method to optimize the paths of UAVs in unfamiliar terrains. Utilizing a spatial and temporal substate‐based Q‐learning algorithm.…”

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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“…In [26], the authors presented an innovative asynchronous UAV path planning mechanism for multi-objective UAV operation in large-scale WSNs. The proposed approach employed a multipurpose fitness function and Particle Swarm Optimization (PSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Data Collection and Path Planning Method for Large-Scale Terminal Access

Zhang,

He,

Peng

et al. 2023

Sensors

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In the context of the relentless evolution of network and communication technologies, the need for enhanced communication content and quality continues to escalate. Addressing the demands of data collection from the abundance of terminals within Internet of Things (IoT) scenarios, this paper presents an advanced approach to multi-Unmanned Aerial Vehicle (UAV) data collection and path planning tailored for extensive terminal accessibility. This paper focuses on optimizing the complex interplay between task completion time and task volume equilibrium. To this end, a novel strategy is devised that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration. The core idea of this work involves designing an innovative k-means algorithm capable of balancing data quantities within each cluster, thereby achieving balanced sensor node partitioning based on data volume. Then, the UAV flight trajectory paths are discretely modeled, and a grouped, improved genetic algorithm is used to solve the Multiple Traveling Salesman Problem (MTSP). The algorithm introduces a 2-opt optimization operator to improve the computational efficiency of the genetic algorithm. Empirical validation through comprehensive simulations clearly underscores the efficacy of the proposed approach. In particular, the method demonstrates a remarkable capacity to rectify the historical issue of diverse task volumes among multiple UAVs, all the while significantly reducing task completion times. Moreover, its convergence rate substantially outperforms that of the conventional genetic algorithm, attesting to its computational efficiency. This paper contributes an innovative and efficient paradigm to improve the problem of data collection from IoT terminals through the use of multiple UAVs. As a result, it not only augments the efficiency and balance of task distribution but also showcases the potential of tailored algorithm solutions for realizing optimal outcomes in complex engineering scenarios.

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

Beishenalieva,

Yoo

2024

IEEE Internet Things J.

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Multiobjective 3-D UAV Movement Planning in Wireless Sensor Networks Using Bioinspired Swarm Intelligence

Cited by 9 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

Multi-UAV Data Collection and Path Planning Method for Large-Scale Terminal Access

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

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