UAVs for traffic monitoring: A sequential game-based computation offloading/sharing approach

Alioua, Ahmed; Djeghri, Houssem-eddine; Cherif, Mohammed Elyazid Tayeb; Senouci, Sidi‐Mohammed; Sedjelmaci, Hichem

doi:10.1016/j.comnet.2020.107273

Cited by 55 publications

(26 citation statements)

References 40 publications

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“…In the recent research works pertaining to the use of drones over the Internet, there is a major debate about whether the processing should be embedded at the edge level (i.e., in the drone side) [ 47 , 48 , 49 ] or it should be offloaded to the cloud [ 27 , 31 , 34 ]. Each approach has its own advantages and limitations in terms of cost, bandwidth requirement, processing speed, and energy consumption.…”

Section: Results and Experimental Analysismentioning

confidence: 99%

“…In [ 34 ], the authors considered the issue of implementing an efficient method for processing the UAV-collected data involving computer offload-sharing decision-making problems in a multi UAVs-aided traffic management scenario. The fundamental goal was to reduce computing time while reducing the operating energy and the expense of measuring and communicating.…”

Section: Related Workmentioning

confidence: 99%

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DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

Koubâa

Ammar

Alahdab

et al. 2020

Sensors

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Unmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.

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Section: Results and Experimental Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

Koubâa

Ammar

Alahdab

et al. 2020

Sensors

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show abstract

“…The work in [54] designed a new system model where UAVs can offload and/or share intensive computation tasks with other nearby network nodes. Then, the authors of [55] used the computation response time, the energy consumed for the computation, the cost of cellular communication, and the computation cost as the main system metrics to make any computation offloading/sharing decisions that optimized the system performance. The difference is herein we propose a MEC-enabled UAV-assisted VANETs architecture, based on which a number of vehicles are served by UAVs equipped with computational resources.…”

Section: Mec-enabled Uav-assisted Vanetsmentioning

confidence: 99%

Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs

Zhai

Huang

et al. 2021

Remote Sensing

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In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay.

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“…Alioua. et al [ 18 ] comprehensively considered latency, energy consumption, link quality, and other factors in a multi-UAV-enabled road traffic monitoring scenario, and obtained a computation resource allocation strategy with better overall performance based on the sequential game. EI N.N et al [ 19 ] considered device association, task assignment, and computational resource allocation problems comprehensively, and proposed an iterative algorithm based on block coordinate descent to minimize the energy consumption of mobile devices and UAVs.…”

Section: Introductionmentioning

confidence: 99%

Computation Offloading in UAV-Enabled Edge Computing: A Stackelberg Game Approach

Yuan¹,

Zhidong²,

Tan³

2022

Sensors

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This paper studies an efficient computing resource offloading mechanism for UAV-enabled edge computing. According to the interests of three different roles: base station, UAV, and user, we comprehensively consider the factors such as time delay, operation, and transmission energy consumption in a multi-layer game to improve the overall system performance. Firstly, we construct a Stackelberg multi-layer game model to get the appropriate resource pricing and computing offload allocation strategies through iterations. Base stations and UAVs are the leaders, and users are the followers. Then, we analyze the equilibrium states of the Stackelberg game and prove that the equilibrium state of the game exists and is unique. Finally, the algorithm’s feasibility is verified by simulation, and compared with the benchmark strategy, the Stackelberg game algorithm (SGA) has certain superiority and robustness.

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UAVs for traffic monitoring: A sequential game-based computation offloading/sharing approach

Cited by 55 publications

References 40 publications

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs

Computation Offloading in UAV-Enabled Edge Computing: A Stackelberg Game Approach

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