Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning

By Seongah Jeong, Osvaldo Simeone, Joonhyuk Kang. IEEE Transactions on Vehicular Technology, 2016

596 citations recorded by the source corpus.

Unmanned aerial vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or no infrastructure. In this paper, a UAV-based mobile cloud computing system is studied in which a moving UAV is endowed with computing capabilities to offer computation offloading opportunities to MUs with limited local processing capabilities. The system aims at minimizing the total mobile energy consumption while satisfying quality of service requirements of the offloaded mobile application. Offloading is enabled by uplink and downlink communications between the mobile devices and the UAV, which take place by means of frequency division duplex via orthogonal or nonorthogonal multiple access schemes. The problem of jointly optimizing the bit allocation for uplink and downlink communications as well as for computing at the UAV, along with the cloudlet's trajectory under latency and UAV's energy budget constraints is formulated and addressed by leveraging successive convex approximation strategies. Numerical results demonstrate the significant energy savings that can be accrued by means of the proposed joint optimization of bit allocation and cloudlet's trajectory as compared to local mobile execution as well as to partial optimization approaches that design only the bit allocation or the cloudlet's trajectory.

Subfields: Aerospace Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b515d0557f4f3619ceeb2ca3061b86684bf7848c

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Citations in the corpus

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “Another key application of UAVs in smart cities is their ability to act as mobile cloud computing systems [40].”
Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems (2019, methodology): “, flying computing cloudlets [18],2 UAV-aided data collection [19], [20], and mobile relays [21].”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “…data injection attacks consist to modify the exchanged data, causing several damages, such as taking control of UAVs by deviating them from their paths, interfering with ground BSs, disseminating false information ( e.g., weather conditions, false events, etc. ) to their neighbors [305]–[307].”
Energy Tradeoff in Ground-to-UAV Communication via Trajectory Design (2017, background): “To this end, the authors in [16] developed a mathematical model for the propulsion energy consumption of fixed-wing UAVs, based on which energyefficient UAV trajectories were designed in various U2G/G2U communication systems [16], [17].”
Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks (2017, background): “Meanwhile, extensive research efforts from the academia have also been devoted to employing UAVs as different types of wireless communication platforms [9], such as aerial mobile base stations (BSs) [10]–[14], mobile relays [15], [16], and flying computing cloudlets [17], [18].”
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey (2021, background): “3) Trajectory Design: In [173], the authors studied a UAVmounted cloudlet assisted MEC system to minimize the”
Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey (2018, background): “Drones may also serve as cloudlets and relieve energy consumption of mobile devices by taking over computations, which are energy consuming; they may also perform data storage and provide video on demand services at camping sites [119, 120].”
UAV Communications for 5G and Beyond: Recent Advances and Future Trends (2019, background): “[123] presented extension to the multiple mobile devices setting, the problem of optimizing the bit allocation and UAV’s trajectory was tackled for minimizing the mobile energy consumption subject to the latency and UAV’s energy budget constraints, where both the OMA and NOMA schemes were considered”
UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization (2018, methodology): “It is a great attempt to leverage the technology of the UAV in MEC systems, and the performance improvement of the UAV-enabled MEC architecture has been shown to be substantial [20]–[22].”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, methodology): “In [76], a UAV-mounted cloudlet was considered in a mobile edge computing system for providing offloading service to multiple ground users, where the UAV trajectory and uplink/downlink bit allocation were jointly optimized to minimize the total mobile energy consumption, including the computation, c”
Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges (2018, background): “exploring edge computing challenges and opportunities for UAVs [191].”
Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (2020, background): “Another group of studies propose speciﬁc applications of UAVs, e.g., as ﬂying base stations [32], with mobile cloudlets for computation ofﬂoading [33], and for search and rescue missions [34].”
Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2020, methodology): “)k2 2 g2 ), (12) where g denotes the gravitational acceleration. γ1 and γ2 are ﬁxed parameters related to the aircraft’s weight, wing area, air density, etc. The value of parameters is given in [21], [25]. The computing energy for executing tasks from user i in time slot k is expressed as EC,U i,k (”
Communications and Control for Wireless Drone-Based Antenna Array (2017, background): “–[15]. For instance, in [3], the authors studied the optimal 3D placement of UAVs for maximizing the number of covered users with different quality-of-service (QoS) requirements. The works in [4] and [5] studied path planning and optimal deployment problems for UAV-based communications and computing”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, methodology): “It was later successfully applied in various other setups, such as energy-efficient UAV communications [17], [18], multi-UAV-enabled DL communication [42], [150], UAVenabled data collection [149], physical-layer security for UAV communications [151]–[153], UAV-enabled mobile edge computing (MEC) [15”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “In the scenario of UAV-based MEC server [129], the UAV can act as a moving MEC server in the sky to help execute the computation tasks ofﬂoaded by multiple ground users.”
Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks (2019, methodology): “roposed by setting the UAV as an aerial BS, which ﬂies cyclically along the cell edge to ofﬂoad data trafﬁc for cell-edge users. Jointly optimizing bit allocation and UAV’s trajectory, the authors in [43] and [44] optimized the total mobile energy consumption while satisfying QoS requirements of the”
Terahertz Band: The Last Piece of RF Spectrum Puzzle for Communication Systems (2019, methodology): “In [281], the total mobile energy consumption was reduced and the QoS requirements of the offloaded mobile application were fulfilled upon jointly optimizing bit allocation and UAV’s trajectory.”