Skoči na glavni sadržaj

Journal of Communications Software and Systems, Vol. 18 No. 2, 2022.

Izvorni znanstveni članak

https://doi.org/10.24138/jcomss-2021-0154

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

Asrar Ahmed Baktayan orcid id orcid.org/0000-0002-2690-1195 ; Department of Information Technology, FCIT, Sana'a University, Sana’a, Yemen
Ibrahim Ahmed Al-Baltah ; Department of Information Technology, FCIT, Sana'a University, Sana’a, Yemen
Abdul Azim Abd Ghani ; Department of Software Engineering Information Systems, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia

Puni tekst: engleski pdf 1.538 Kb

str. 111-123

preuzimanja: 368

citiraj

Sažetak

In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number of vehicles. Due to vehicle mobility and the rise of computation-intensive and delay-sensitive vehicular applications, it is challenging to achieve the rigorous latency and reliability requirements of vehicular communication. The MNO, with the MEC server mounted on an unmanned aerial vehicle (UAV), should make a profit by providing its computing services and capabilities to moving vehicles. This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles. The novelty of this paper is in how the price influences offloading demand and decides how to reduce network costs (delay and energy) while maximizing UAV operator revenue, but not the offloading benefits with the mobility of vehicles and UAV. The optimization problem is formulated as a Markov Decision Process (MDP). The MDP can be solved by the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Deterministic Policy Gradient (DDPG). Extensive simulation results demonstrate that the proposed pricing model outperforms greedy by 26%and random by 51% in terms of delay. In terms of system utility, the proposed pricing model outperforms greedy only by 17%. In terms of server congestion, the proposed pricing model outperforms random by 19% and is almost the same as greedy.

Ključne riječi

computation offloading, Dynamic Price, System Utility, Deep Reinforcement Learning (DRL), Unmanned Aerial Vehicles (UAVs), MEC

Hrčak ID:

275777

URI

https://hrcak.srce.hr/275777

Datum izdavanja:

30.6.2022.

Posjeta: 1.315 *