UAV-Assisted Data Collection for Internet of Things: A Survey

Wei, Zhiqing; Zhu, Mingyue; Zhang, Ning; Wang, Lin; Zou, Yingying; Meng, Zeyang; Wu, Huici; Feng, Zhiyong

doi:10.1109/jiot.2022.3176903

Cited by 168 publications

(32 citation statements)

References 140 publications

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“…In the whole UAV cluster data collection process, the blocking fading model is followed, and the accurate channel gain value can be obtained by the feedback information of the receiving node [35][36][37]. The frequency band of the radio frequency signal received by the radio frequency circuit of the receiving node is interfered with by random Gaussian white noise.…”

Section: Energy Balance Of Edc-uaviiotmentioning

confidence: 99%

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

Sun

Cai

Wang

et al. 2023

Drones

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In the case of limited endurance of unmanned aerial vehicles (UAVs), in order to further improve UAV data collection efficiency, this paper puts forward EDC-UAVIIoT: an enhanced energy-efficient data collection optimization algorithm for UAV swarm in the intelligent Internet of Things. First of all, the algorithm optimizes the UAV cruise path through the intelligent Internet of Things routing mechanism, avoids the occurrence of data errors in the packet transmission process, and uses the end-to-end transmission error probability model. The error probability of data packets in the transmission process is calculated to improve the efficiency of data collection tasks and data throughput. Secondly, considering the relationship between energy harvesting and energy consumption balance, this paper uses semi-definite programming and a convex approximation algorithm to transform the non-convex optimization problem into a convex optimization problem and realize the mapping relationship between the UAV cluster node and the target node coordinates, which reduces the computational complexity. Finally, the simulation results show that the EDC-UAVIIoT algorithm is compared with other algorithms in network energy consumption, running time, network delay, and network throughput. The numerical values are increased by 7.03%, 10.16%, 12.39%, and 8.82%, respectively, thus verifying the effectiveness and stability of the proposed EDC-UAVIIoT algorithm.

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Section: Energy Balance Of Edc-uaviiotmentioning

confidence: 99%

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

Sun

Cai

Wang

et al. 2023

Drones

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“…Multiple surveys and tutorials study various aspects of network slicing, UAVs, and UAV‐assisted networks. For instance, 12 surveys UAV‐assisted data collection for IoT devices. Furthermore, IoT in the context of network slicing is further explored in Reference 13.…”

Section: Background and Related Workmentioning

confidence: 99%

Crowd‐sourced and incentive driven UAV system to assist with network slices

Bouzid,

Chaib,

Bensaad

2023

Trans Emerging Tel Tech

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Owing to their flexibility, fast deployment, increasing affordability, and numerous application opportunities, unmanned aerial vehicles (UAVs) became an undisputed target of studies and research to discover and exploit the immense opportunities they provide. Moreover, network slicing is a critical enabler for 5G and beyond. Network slices guarantee certain network user expectations such as data rate and latency thresh‐holds. There is no doubt that crowd‐sourced and incentive driven solutions are on the rise. Crowd‐sourcing allowed companies and organizations both small and large to gather data and perform other large‐scale tasks with minimum expenses and maintenance. Indeed, the major concerns for 5G and network slicing are guaranteeing high throughput, reliability, and latency. With data rates being the most important attribute for common regular users, that is, mobile devices, video streaming, navigation, and content consumption. As a result, the focus of this work is to utilize crowd‐sourced UAVs in an incentive driven network slice assistance system to extend coverage and provide mobile users with the high throughput they require. This work presents some basic 5G cellular radio access technologies and network slicing background and proposes a novel system, namely UAV slice assistance (UAVSA), and its hybrid alternative version: UAV hybrid slice assistance (UAVHSA). The hybrid mode combines benefits from the proposed UAVSA system and the traditional base station (BS) system. The proposed algorithms address the issue of coverage and guaranteeing high data rate slices for users. The proposed systems maximize satisfaction rate of network slice users. Moreover, all systems are extensively tested and compared in various scenarios to determine their performance and provide a coherent study. Finally, future works and opportunities are highlighted in conclusion.

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“…[18]. A comprehensive survey on data collection in IoT networks by means of UAV is presented in [19]. Recently, UAVs are deployed for providing wireless coverage in scenarios where base stations are overloaded due to heavy traffic, communication facilities are sparsely distributed, and the occurrence of inevitable natural disasters or temporary malfunctions in BS is considered [20].…”

Section: Introductionmentioning

confidence: 99%

A DRL Strategy for Optimal Resource Allocation Along With 3D Trajectory Dynamics in UAV-MEC Network

et al. 2023

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Advances in Unmanned Air Vehicle (UAV) technology have paved a way for numerous configurations and applications in communication systems. However, UAV dynamics play an important role in determining its effective use. In this article, while considering UAV dynamics, we evaluate the performance of a UAV equipped with a Mobile-Edge Computing (MEC) server that provides services to End-user Devices (EuDs). The EuDs due to their limited energy resources offload a portion of their computational task to nearby MEC-based UAV. To this end, we jointly optimize the computational cost and 3D UAV placement along with resource allocation subject to the network, communication, and environment constraints. A Deep Reinforcement Learning (DRL) technique based on a continuous action space approach, namely Deep Deterministic Policy Gradient (DDPG) is utilized. By exploiting DDPG, we propose an optimization strategy to obtain an optimal offloading policy in the presence of UAV dynamics, which is not considered in earlier studies. The proposed strategy can be classified into three cases namely; training through an ideal scenario, training through error dynamics, and training through extreme values. We compared the performance of these individual cases based on cost percentage and concluded that case II (training through error dynamics) achieves minimum cost i.e., 37.75 %, whereas case I and case III settles at 67.25% and 67.50% respectively. Numerical simulations are performed, and extensive results are obtained which shows that the advanced DDPG based algorithm along with error dynamic protocol is able to converge to near optimum. To validate the efficacy of the proposed algorithm, a comparison with state-of-the-art Deep Q-Network (DQN) is carried out, which shows that our algorithm has significant improvements.

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UAV-Assisted Data Collection for Internet of Things: A Survey

Cited by 168 publications

References 140 publications

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

Crowd‐sourced and incentive driven UAV system to assist with network slices

A DRL Strategy for Optimal Resource Allocation Along With 3D Trajectory Dynamics in UAV-MEC Network

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