Intelligent ubiquitous computing for future UAV-enabled MEC network systems

Chen, Lunyuan; Zhao, Rui; He, Ke; Zhang, Zhao; Fan, Liseng

doi:10.1007/s10586-021-03434-w

Cited by 31 publications

(25 citation statements)

References 37 publications

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“…This approach enables terrestrial users to achieve an appropriate offloading strategy that saves energy and improves processing performance. In UAV-enabled MEC networks, the authors of [106] employed RL and transfer learning algorithms to minimize latency and energy consumption. They demonstrated that combining transfer learning with RL may greatly improve system training performance when users operate dynamically.…”

Section: Uav Computingmentioning

confidence: 99%

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Alsamhi

Shvetsov

Kumar

et al. 2022

Drones

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Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research.

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Section: Uav Computingmentioning

confidence: 99%

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Alsamhi

Shvetsov

Kumar

et al. 2022

Drones

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show abstract

“…Each field has positive and negative tables, which are denoted as gf log and gf i log, respectively, on the GFð2 ω Þ field. Taking GFð2 3 Þ as an example, its table gf log and gf i log are generated as shown in Table 2 [16]:…”

Section: : ð2þmentioning

confidence: 99%

“…To solve this problem, network coding techniques have been developed, and the code with combination property (CP) is proposed: k original packets are encoded into n packets, where n > k, and any k out of these n packets are able to recover the original data. The code with CP has been widely used in distributed systems, including distributed storage (DS) [6][7][8][9][10], distributed computing (DC) [11][12][13][14][15][16], and distributed machine learning [17].…”

Section: Introductionmentioning

confidence: 99%

Multiple Dimensional Encoding/Modulation Shift-and-Addition Design for Distributed Systems

Dai

Zhang

Hong

et al. 2022

Wireless Communications and Mobile Computing

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In distributed computing/storage/machine learning system, the method of encoding and decoding combing shift-and-addition (SA) and zigzag decoding (ZD) is proposed to solve the problem of high computational complexity. However, in each encoded packet, one element takes part in the encoding only once, so the obtained overhead is extremely high. In this work, based on the idea of multidimensional encoding/modulation, we propose to employ one element of the encoding process multiple times when constructing one encoded packet based on the Cauchy matrix, thereby leveraging the favourable properties of the code based on Cauchy matrix. The overhead is reduced from square to logarithmic in certain parameters. Compared with the overhead of the existing square computational complexity, it is greatly reduced.

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“…To resolve the above disadvantages of cloud computing, mobile edge computing (MEC) has been proposed to install the calculating resources at the edge node (ENs) of the network [7][8][9]. In this way, the users can unpack the tasks to the nearby EN through wireless transmission, which leads to a decreased delay and PoC compared to the cloud computing.…”

Section: Introductionmentioning

confidence: 99%

UAV-Aided Multiuser Mobile Edge Computing Networks with Energy Harvesting

Wang

Zhu³

et al. 2022

Wireless Communications and Mobile Computing

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This article studies a mobile edge computing (MEC) with one edge node (EN), where multiple unmanned aerial vehicles (UAVs) act as users which have some heavy tasks. As the users generally have limitations in both calculating and power supply, the EN can help calculate the tasks and meanwhile supply the power to the users through energy harvesting. We optimize the system by proposing a joint strategy to unpacking and energy harvesting. Specifically, a deep reinforcement learning (DRL) algorithm is implemented to provide a solution to the unpacking, while several analytical solutions are given to the power allocation of energy harvesting among multiple users. In particular, criterion I is the equivalent power allocation, criterion II is designed through equal data rate, while criterion III is based on the equivalent transmission delay. We finally give some results to verify the joint strategy for the UAV-aided multiuser MEC system with energy harvesting.

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Intelligent ubiquitous computing for future UAV-enabled MEC network systems

Cited by 31 publications

References 37 publications

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Multiple Dimensional Encoding/Modulation Shift-and-Addition Design for Distributed Systems

UAV-Aided Multiuser Mobile Edge Computing Networks with Energy Harvesting

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