Resource and Trajectory Optimization for Secure Communications in Dual Unmanned Aerial Vehicle Mobile Edge Computing Systems

Lu, Weidang; Ding, Yu; Gao, Yuan; Hu, Su; Wu, Yuan; Zhao, Nan; Gong, Yi

doi:10.1109/tii.2021.3087726

Cited by 94 publications

(46 citation statements)

References 30 publications

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“…The position of UAV S is related to the origin of the three-dimensional Cartesian coordinate system. The position information can be get by GPS positioning or other methods [34], [36]- [37]. We assume that UAV S has known the position of all GUs and GJ, and the channel state information of the corresponding links in advance by means of synthetic aperture radar, etc [34], [37].…”

Section: System Modelmentioning

confidence: 99%

“…Li et al studied the rate improving design in case of imperfect eavesdropping channels [35]. Since UAV has the unique advantages of high mobility and easy concealment, it can be easily explored by malicious users to eavesdrop the legitimate signals [36]- [37]. Zhou et al proposed a security computation offloading scheme in a traditional communication mode with UAV acting as an eavesdropper, and the signals from other users are treated as interference during the transmission [36].…”

Section: Introductionmentioning

confidence: 99%

“…Zhou et al proposed a security computation offloading scheme in a traditional communication mode with UAV acting as an eavesdropper, and the signals from other users are treated as interference during the transmission [36]. Lu et al proposed a scheme to enhance the Dual-UAV-MEC system's security performance via optimizing the UAV server trajectory and system resource with TDMA [37]. Comparing with the ground eavesdroppers, which are deployed at the fixed locations in the existing works, UAV eavesdroppers will have much better channel condition due to the LoS transmission.…”

Section: Introductionmentioning

confidence: 99%

“…UAV S carries the MEC server to assist K GUs to compute the offloading tasks information. The potential mobile UAV E eavesdrops the tasks information from GUs offloading to UAV S. In order to enhance the system's security computation performance, GJ sends the artificial jamming signal to disturb the eavesdropping of UAV E. Compared with [30]- [37], our novelty is that we consider UAV eavesdropping on GUs' offloading information for the NOMA-based UAV-MEC system towards a flying eavesdropper. We formulate an optimization problem to maximize the average security computation capacity of the system.…”

Section: Introductionmentioning

confidence: 99%

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Secure NOMA-Based UAV-MEC Network Towards a Flying Eavesdropper

Ding

Gao

et al. 2022

IEEE Trans. Commun.

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Non-orthogonal multiple access (NOMA) allows multiple users to share link resource for higher spectrum efficiency. It can be applied to unmanned aerial vehicle (UAV) and mobile edge computing (MEC) networks to provide convenient offloading computing service for ground users (GUs) with largescale access. However, due to the line-of-sight (LoS) of UAV transmission, the information can be easily eavesdropped in NOMA-based UAV-MEC networks. In this paper, we propose a secure communication scheme for the NOMA-based UAV-MEC system towards a flying eavesdropper. In the proposed scheme, the average security computation capacity of the system is maximized while guaranteeing a minimum security computation requirement for each GU. Due to the uncertainty of the eavesdropper's position, the coupling of multi-variables and the non-convexity of the problem, we first study the worst security situation through mathematical derivation. Then, the problem is solved by utilizing successive convex approximation (SCA) and block coordinate descent (BCD) methods with respect to channel coefficient, transmit power, central processing unit (CPU) computation frequency, local computation and UAV trajectory. Simulation results show that the proposed scheme is superior to the benchmarks in terms of the system security computation performance.

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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Secure NOMA-Based UAV-MEC Network Towards a Flying Eavesdropper

Ding

Gao

et al. 2022

IEEE Trans. Commun.

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“…The next generation of mobile networks (5G) is being deployed [1][2][3], which will bring new challenges and opportunities, enabling the creation and integration of new networks such as the Internet of Things (IoT), meeting the explosive growth in data traffic and lower latency requirements demanded in today's communications and enabling and improving the quality of services in a multitude of applications, such as social networking, media streaming, video calls, and other broadband services [4][5][6][7][8][9]. In this sense, 5G cellular communication networks are expected to have a more reliable transmission link, better connectivity, and to approach the challenges involved with the mobility scenarios.…”

Section: Introductionmentioning

confidence: 99%

Design and Synchronization Procedures of a D&F Co-Operative 5G Network Based on SDR Hardware Interface: Performance Analysis

Peña

Alonso

2022

Sensors

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Software defined radio (SDR) is a commonly used platform for its ease of operation and cost-effectiveness for the development and testing of real wireless communication systems. By supporting high transmission rates and enabling fast and cost-effective deployments, mainly in millimeter-wave (mmWave), the co-operative 5G network has been standardized by 3GPP Release 16. In this paper, a decode-and-forward (D&F) co-operative hardware network is proposed as one of the key technologies for future 5G/6G wireless networks. The proposed system consists of an emulated base station processing unit (gnodeB), a D&F protocol and the user equipment (UE). In particular, the design of the D&F relay node is based on an MIMO layer 2 relay technology. A testbed based on an SDR platform and MatlabTM software, in which the physical broadcast channel (PBCH) transmission, physical downlink control channel (PDCCH), physical downlink shared channel (PDSCH), and downlink shared channel (DL-SCH) for transport channel coding, according to the 3GPP standardized 5G downlink signal, has been designed. The key performance indicators (KPIs), namely EVM, BER, and throughput, were measured for 5G signals with 64-QAM and 256-QAM modulation schemes. The obtained results show that the D&F co-operative 5G network achieves substantially improved KPIs in the communication between the gnodeB and the UE in an outdoor-to-indoor scenario. Furthermore, it has been demonstrated that the D&F protocol presents a good performance and behavior being compared to one commercial equipment.

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Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

Wang

et al. 2022

Satell Commun Network

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Summary With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min‐Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.

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Resource and Trajectory Optimization for Secure Communications in Dual Unmanned Aerial Vehicle Mobile Edge Computing Systems

Cited by 94 publications

References 30 publications

Secure NOMA-Based UAV-MEC Network Towards a Flying Eavesdropper

Secure NOMA-Based UAV-MEC Network Towards a Flying Eavesdropper

Design and Synchronization Procedures of a D&F Co-Operative 5G Network Based on SDR Hardware Interface: Performance Analysis

Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

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