Delay and Energy Balance for Unmanned Aerial Vehicle Networks

Khalil, Muhammad I.

doi:10.1109/ccwc51732.2021.9376092

Cited by 5 publications

(7 citation statements)

References 28 publications

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“…All objectives ( 26)-( 28) must be minimized at once under the constraint of (29); however, the issue is that both (26) and (27) are in contras to (28). Multi-objective optimisation techniques, particularly the weight scalarization method [7], are some of the most reliable methods for resolving such an issue.…”

Section: Energy Allocationmentioning

confidence: 99%

“…Furthermore, works that developed the energy consumption and data transmission delay metrics, jointly or individually, ignored the relationship between these metrics and the bit error rate, which is an essential metric for evaluating the performance of UAV network applications [26]. Thus, [27] presented a specific system model that can only provide balance energy and delay transmission data for a unidirectional UAV AF relay flying in a triangle formation. Error-free reception, however, is very limed in the practical environment, particularly in multi-hop networks with varying channel conditions.…”

Section: Introductionmentioning

confidence: 99%

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Balanced Performance Between Energy-Delay and Bit Error Rate in UAV Relay Networks

Khalil¹

2022

Preprint

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This paper presents a new strategy for simultaneously reducing energy consumption, transmission delays, and bit error rate in Unmanned Aerial Vehicle (UAV) networks. A UAV is fitted with a wireless Bidirectional Relay (BR) to enable coverage network extension and increase transmission throughput. The downside of the BR advantages is the delay in data transmission caused by the UAV's movement. A consequence of this delay is increased total energy consumption, causing further degradation in bit error rate performance, especially at high SNR levels. In wireless communication, the trade-off between delay and energy consumption is, fortunately, possible to improve performance. Therefore, this study aims to enhance UAV network performance by reducing energy consumption, data transmission delay, and bit error rate. A multi-objective algorithm is employed to generate an adaptive optimal energy allocation strategy based on the balance between energy consumption and transmission delays. The results of theoretical analysis are illustrated with several examples. As herein demonstrated, the proposed solution effectively balances delay and energy efficiency in a customised system design and improves the bit error rate in UAV networks.

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Section: Energy Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Balanced Performance Between Energy-Delay and Bit Error Rate in UAV Relay Networks

Khalil¹

2022

Preprint

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“…∂F(α i , w) ∂α s ∂α r = G 2 H 2 Pα s α r Hα s GP α r + 2 + 2Gα r log(2) (Gα r + Hα s ) 2 (H (G pα s α r + α s ) + Gα r ) 2 (31)…”

Section: Appendixunclassified

“…Furthermore, works that developed the energy consumption and data transmission delay metrics, jointly or individually, ignored the relationship between these metrics and the bit error rate, which is an essential metric for evaluating the performance of UAV network applications. Thus, [31] presented an Error-free system model that can provide balance energy and delay transmission data for a unidirectional AF relay. However, in many network applications, it is an impractical scenario to consider Error-free reception in two-hop networks with varying channel conditions [32].…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancements for Air-based Networks

Khalil¹

2022

Preprint

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<p>Power allocation is essential to maximise network throughput and minimise energy consumption and bit error rate performance. Thus, a new power allocation scheme for enhancing the performance of the air-based network, named Unmanned Aerial Vehicle (UAV) networks, is presented in this paper. The proposed power allocation allows the total power to be distributed optimally among the UAV network's nodes, including source relay and destination nodes. Then, energy consumption, transmission delay, and bit error rate in UAV networks are simultaneously enhanced. The typical UAV comprises three parts: remotely controllable flight platform (fixed wing or rotary wing), transmitter and receiver wireless nodes. In the proposed UAV networks, a wireless relay is fitted with a flight platform to extend network coverage and increase transmission throughput. The downside of mobile flight communication is the delay in data transmission caused by the UAV's movement. A consequence of this delay is increased total energy consumption, causing further degradation in bit error rate performance, especially at high <em>SNR</em> levels. Fortunately, delay and energy consumption can be traded off to improve communication performance in wireless communication. The proposed power allocation in this paper is achieved using the Scalarization optimisation method, which enables the best trade-off between energy consumption and data transmission delay and enhances the bit error rate. </p>

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“…The total energy consumption of UAV is minimized by jointly optimizing the flight zone association, computation resource allocation, UAV hovering time, wireless power supply time, and flight zone order of service. In [16], they studied the energy-performance tradeoff for data transmission in UAV-enabled MEC, and they proposed a method that provides an effective scheme for realizing the energy-performance tradeoff in system design. In [17], the author investigated a multi-UAV collaborative data acquisition system, multiple UAVs perform data collection on the two-dimensional distributed devices in a flying or hovering mode to save energy.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Task Offloading and Resource Allocation with Hybrid Energy Supply for UAV-Assisted Multi-Clouds

Zhou

et al. 2022

Preprint

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Cloud computing has emerged as a promising paradigm for meeting the growing resource demands of Internet of Things (IoT) devices. Meanwhile, with the popularity of mobile aerial base stations, Unmanned Aerial Vehicle (UAV) assisted cloud computing is essential for providing diversified service at areas without available infrastructure. However, it is difficult to meet the requirements of a number of IoT devices which distribute a large area through one single UAV cloud server, and thus multi-clouds have been applied in large-scale IoT environments. Due to the limited battery capacity of UAV, hybrid energy supply has been considered as an effective approach. How to allocate the computing resources and offload the tasks to the UAV-assisted clouds is a challenging task. In this paper, we study the trade-off between the energy consumption and system performance in a UAV-assisted multi-clouds system. Considering the transmission and execution cost, a dynamic optimization problem with the objective of minimizing the power consumption of UAVs with the constraint of queue stability is formulated, which is further decomposed into three sub-problems using stochastic optimization techniques. A collaborative task offloading and resources allocation algorithm (CTORAA) based on artificial intelligent technique is proposed to jointly determine task offloading and energy harvesting. We provide corresponding mathematical analysis showing that CTORAA can reach the arbitrary profit-stability trade-off. Finally, we conduct simulation experiments to validate the efficacy of our algorithm.

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Delay and Energy Balance for Unmanned Aerial Vehicle Networks

Cited by 5 publications

References 28 publications

Balanced Performance Between Energy-Delay and Bit Error Rate in UAV Relay Networks

Balanced Performance Between Energy-Delay and Bit Error Rate in UAV Relay Networks

Performance Enhancements for Air-based Networks

Collaborative Task Offloading and Resource Allocation with Hybrid Energy Supply for UAV-Assisted Multi-Clouds

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