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Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第9期23卷 8649-8663页

作者： Hao, Hao Xu, Changqiao Zhang, Wei Yang, Shujie Muntean, Gabriel-Miro Qilu Univ Technol Key Lab Comp Power Network & Informat Secur Shandong Acad SciNatl Supercomp Ctr Jinan Minist EducShandong Comp Sci Ctr Jinan Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Minist Educ Jinan 250316 Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Networks Nanjing 210049 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Dublin City Univ Sch Elect Engn Performance Engn Lab Dublin Ireland

Mobile edge computing (MEC) has emerged as a solution to address the demands of computation-intensive network services by providing computational capabilities at the network edge, thus reducing service delays. Due to the flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed to assist MEC. This paper investigates the task offloading problem in a UAV-assisted MEC system with collaboration of multiple UAVs, highlighting task priorities and binary offloading mode. We defined the system gain based on energy consumption and task delay. The joint optimization of UAVs' trajectory design, binary offloading decision, computation resources allocation, and communication resources management is formulated as a mixed integer programming problem with the goal of maximizing the long-term average system gain. Considering the discrete-continuous hybrid action space of this problem, we propose a novel deep reinforcement learning (DRL) algorithm based on the latent space to solve it. The evaluation results demonstrate that our proposed algorithm outperforms three state-of-the-art alternative solutions in terms of task delay and system gain.

关键词： Task analysis Delays Autonomous aerial vehicles Optimization Trajectory Resource management Energy consumption Deep reinforcement learning mobile edge computing task offloading unmanned aerial vehicle (UAV)

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Diffraction deep neural network-based classification for vector vortex beams

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Chinese Physics B 2024年第3期33卷 387-392页

作者：彭怡翔陈兵王乐赵生妹 Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications (NJUPT)Nanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing 210003China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China

The vector vortex beam(VVB)has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum ***,a VVB is unavoidably affected by atmospheric turbulence(AT)when it propagates through the free-space optical communication environment,which results in detection errors at the *** this paper,we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT,where a diffractive deep neural network(DDNN)is designed and trained to classify the intensity distribution of the input distorted VVBs,and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the *** numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification *** energy distribution percentage remains above 95%from weak to medium AT,and the classification accuracy can remain above 95%for various strengths of *** has a faster convergence and better accuracy than that based on a convolutional neural network.

关键词： vector vortex beam diffractive deep neural network classification atmospheric turbulence

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Super-Resolution Reconstruction for Cross-Modal communications in Industrial Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2024年第13期11卷 24148-24161页

作者： Liu, Hengfa Wei, Xin Gao, Yun Zhou, Liang Qian, Yi Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Minist Educ Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Nanjing 210003 Peoples R China Univ Nebraska Dept Elect & Comp Engn Omaha NE 68588 USA

Integrating visual-haptic remote control is an important application direction in the Industrial Internet of Things (IIoT). Cross-modal communications are considered to be an effective technology to support this application. However, due to limited bandwidth and competition between modalities, the quality of visual transmission and the end user's immersive experience cannot be guaranteed in practical scenarios. To overcome this dilemma, this article proposes a super-resolution reconstruction strategy for cross-modal communications. Specifically, the sender only transmits low-resolution images and haptic signals, while a Haptic-aided Super-Resolution Reconstruction (HaSR) approach is designed at the receiver. This approach involves semantic correlation-based modal fusion and generative adversarial principle-based visual generation, which enable the reconstruction of high-resolution images using the received low-resolution images and haptic signals. Experimental results from a standard data set and a practical remote industrial control platform validate the effectiveness of the proposed strategy.

关键词： Cross-modal communications Industrial Internet of Things (IIoT) remote control super-resolution reconstruction

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Federated KD-Assisted Image Semantic communication in IoT Edge Learning

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IEEE INTERNET OF THINGS JOURNAL 2024年第21期11卷 34215-34228页

作者： Xu, Xinmian Xu, Yikai Dou, Haie Chen, Mingkai Wang, Lei Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Nanjing 210003 Peoples R China

The evolution from fifth-generation mobile communications (5G) to beyond 5G (B5G) will lead to more ubiquitous and smarter paradigms in the Internet of Things (IoT). communication will shift from the classical information theory to a semantic communication (SemCom) paradigm driven by artificial intelligence (AI) to enhance the capacity and optimize resources. Image SemCom (ISC) will empower IoT applications, such as drone image acquisition. However, ISC requires suitable devices and sufficient computing resources to support complex neural network models, posing a significant challenge. To address this, we propose a federated semantic feature distillation (FedSFD) architecture to improve the global performance of ISC by combining federated learning (FL) and feature distillation (FD) for the feature knowledge transfer. First, lightweight IoT device models in the edge group and the powerful server model alternately minimize losses to update parameters. After learning middle-layer features of all the edge models, the server can guide the individual device models. Second, we incorporate the information bottleneck (IB) concept into the design of loss functions to balance compression and reconstruction. Third, focusing on the tradeoff between the local training and knowledge interaction, FedSFD achieves image semantic reconstruction without sharing the private data, ensuring personalization and privacy protection in the FL framework. Finally, compared to the baseline, the simulation experiments show that the proposed approach achieves better ISC reconstruction and noise robustness during group ISC.

关键词： Internet of Things Image edge detection Training Semantics Data models Computer architecture Servers Beyond 5G (B5G) edge learning (EL) feature distillation (FD) federated learning (FL) image semantic communication (ISC) Internet of Things (IoT)

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Complete hyperentangled Greenberger–Horne–Zeilinger state analysis for polarization and time-bin hyperentanglement

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Chinese Physics B 2023年第6期32卷 122-126页

作者：曾志 Institute of Signal Processing and Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China

We present an efficient scheme for the complete analysis of hyperentangled Greenberger–Horne–Zeilinger(GHZ)state in polarization and time-bin degrees of freedom with two steps. Firstly, the polarization GHZ state is distinguished completely and nondestructively, resorting to the controlled phase flip(CPF) gate constructed by the cavity-assisted interaction. Subsequently, the time-bin GHZ state is analyzed by using the preserved polarization entanglement. With the help of CPF gate and self-assisted mechanism, our scheme can be directly generalized to the complete N-photon hyperentangled GHZ state analysis, and it may have potential applications in the hyperentanglement-based quantum communication.

关键词： hyperentangled state analysis polarization-time-bin hyperentanglement controlled phase flip(CPF)gate

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Trust and Trustworthiness in Information and communications Technologies

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IEEE wireless communicationS 2025年第2期32卷 84-92页

作者： Ling, Xintong Le, Yuwei Wang, Jiaheng Huang, Yongming You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China Southeast Univ Natl Mobile Commun Res Lab NCRL Nanjing Peoples R China Purple Mt Labs Nanjing Peoples R China Purple Mt Labs Pervas Commun Res Ctr Nanjing Peoples R China Purple Mt Labs Pervas Commun Res Ctr Network Secur Nanjing Peoples R China

Trust serves as the glue and cornerstone of human society and emerges as a critical enabler of information and communications technologies (lCT). Despite its importance, trust remains a debated concept with different viewpoints and limitations from diverse theories. In this study, we define the concept of trust and trustworthiness in a useful and measurable way with a rigorous mathematical basis. While our study does not resolve all disagreements about trust, we seek the fewest common elements necessary to define generic trust for achieving the minimum consensus across the broadest contexts. Based on our trust definition, we exploit the dynamic trust inference and analyze the trust transfer across different contexts, as well as the trust propagation through multiple entities. To demonstrate the applicability of our trust notion, we present two illustrative case studies: evaluating the trustworthiness of a mobile network operator communicating over a fading channel and assessing the trustworthiness of a miner in safe-guarding a blockchain from being subverted.

关键词： Information and communication technology 6G mobile communication Trusted computing Fading channels Blockchains Security Random variables Probabilistic logic Bayes methods Data mining

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A Correlation-Based Stochastic Model for Massive MIMO Channel

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China communications 2024年第1期21卷 175-187页

作者： Yang Liu Gang Li Chengxiang Wang National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast UniversityNanjing 211189China School of Internet of Things Engineering Jiangnan UniversityWuxi 214122China Pervasive Communication Research Center Purple Mountain LaboratoriesNanjing 211111China

In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the *** measurements were carried out to evaluate the performance of this proposed ***,the physical implications of this model were illustrated and the capacities are *** addition,the azimuthal power spectrum(APS)of several models was ***,the channel hardening effect was simulated and *** showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger ***,the proposed model can provide better tradeoff between accuracy and complexity in channel *** CIRM model can be used for massive MIMO design in the future communication system design.

关键词： CBSM channel capacity channel hardening channel modeling massive MIMO

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UAV-Assisted Heterogeneous Multi-Server Computation Offloading With Enhanced Deep Reinforcement Learning in Vehicular networks

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IEEE TRANSACTIONS ON network SCIENCE AND ENGINEERING 2024年第6期11卷 5323-5335页

作者： Song, Xiaoqin Zhang, Wenjing Lei, Lei Zhang, Xinting Zhang, Lijuan Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Minist Educ Nanjing 210003 Peoples R China

With the development of intelligent transportation systems (ITS), computation-intensive and latency-sensitive applications are flourishing, posing significant challenges to resource-constrained task vehicles (TVEs). Multi-access edge computing (MEC) is recognized as a paradigm that addresses these issues by deploying hybrid servers at the edge and seamlessly integrating computing capabilities. Additionally, flexible unmanned aerial vehicles (UAVs) serve as relays to overcome the problem of non-line-of-sight (NLoS) propagation in vehicle-to-vehicle (V2V) communications. In this paper, we propose a UAV-assisted heterogeneous multi-server computation offloading (HMSCO) scheme. Specifically, our optimization objective to minimize the cost, measured by a weighted sum of delay and energy consumption, under the constraints of reliability requirements, tolerable delay, and computing resource limits, among others. Since the problem is non-convex, it is further decomposed into two sub-problems. First, a game-based binary offloading decision (BOD) is employed to determine whether to offload based on the parameters of computing tasks and networks. Then, a multi-agent enhanced dueling double deep Q-network (ED3QN) with centralized training and distributed execution is introduced to optimize server offloading decision and resource allocation. Simulation results demonstrate the good convergence and robustness of the proposed algorithm in a highly dynamic vehicular environment.

关键词： Servers Task analysis Delays TV Autonomous aerial vehicles Vehicle dynamics Costs Computation offloading deep reinforcement learning Internet of Vehicles multi-access edge computing (MEC) resource allocation

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Towards fine-grained load balancing with dynamical flowlet timeout in datacenter networks

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COMPUTER networkS 2024年 255卷

作者： Hu, Jinbin Li, Ruiqian Liu, Ying Wang, Jin Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410004 Peoples R China Nanjing Univ Posts & Telecommun Minist Educ Key Lab Broadband Wireless Commun & Sensor Network Nanjing 210003 Peoples R China Hunan Univ Sci & Technol Sanya Inst Sanya 572024 Peoples R China

In modern datacenter networks (DCNs), load balancing mechanisms are widely deployed to enhance link utilization and alleviate congestion. Recently, a large number of load balancing algorithms have been proposed to spread traffic among the multiple parallel paths. The existing solutions make rerouting decisions for all flows once they experience congestion on a path. They are unable to distinguish between the flows that really need to be rerouted and the flows that potentially have negative effects due to rerouting, resulting infrequently ineffective rerouting. Fine-grained rerouting will also cause severe packet reordering, especially in asymmetric topology scenarios. To address the above issues, we present a fine-grained traffic-differentiated load balancing (TDLB) mechanism, which aims to distinguish flows that are necessarily to be rerouted and reroute traffic in fine-grained without packet reodering. Specifically, TDLB distinguishes the traffic that must be rerouted through the host pair information in the packet header, and selects an optimal path for rerouting. To prevent severe packet reodering caused by excessive path delay differences, TDLB dynamically adjusts the flowlet timeout to segment the traffic and select the optimal path for rerouting. The NS-2 simulation results show that TDLB effectively reduces tail latency and average flow completion time (FCT) for short flows by up to 49% and 46%, respectively, compared to the state-of-the-art load balancing schemes.

关键词： Datacenter networks Load balancing Traffic differentiation

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MDC²: An Integrated communication and Computing Framework to Optimize Edge-Assisted Caching for Improved Multimedia Services in UAV-Based IoT networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第20期11卷 32393-32403页

作者： Zhong, Lujie Yang, Shujie Song, Kefei Wang, Mu Jiang, Ke Muntean, Gabriel-Miro Capital Normal Univ Informat Engn Coll Beijing 100048 Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Minist Educ Nanjing 210049 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Dublin City Univ Sch Elect Engn Performance Engn Lab Dublin 9 Ireland

Multiaccess edge computing (MEC) has revolutionized the delivery of large-scale mobile multimedia services by endowing network edge with computing and caching capabilities. This not only relieves the load on core networks, but also significantly reduces data access latency. However, deploying edge data centers with a high density to accommodate the growing demand for multimedia services is not cost-effective. With the rapid development of the Internet of Things (IoT) industry, recent studies have shown that by allowing UAVs with integrated computing and communication to form a mobile device cloud (MDC) environment via UAV-to-UAV (U2U) communications in IoT networks, UAVs can play an important role in assisting cellular networks with multimedia delivery and providing excellent service for IoT devices on the ground. While a MDC environment composed of UAVs offers flexibility and cost-effectiveness, the challenge remains in allocating caching resources in a timely manner to meet the dynamic content demands. To address this challenge, we design a novel MDC-enabled Caching (MDC2) framework, which makes use of the available caching and U2U communication capabilities to enable any UAV to obtain dynamically content from other nearby UAVs via the IoT network. By modeling the dynamic network status as a fluid-based system, MDC2 employs a dynamic caching allocation algorithm to minimize both service latency and caching costs. Extensive experiments demonstrate that MDC2 outperforms a state-of-the-art MDC multimedia delivery approach by improving average cache utilization with over 40% and reducing average access latency with more than 25%.

关键词： Autonomous aerial vehicles Internet of Things Heuristic algorithms Delays Multimedia communication Cloud computing Multimedia computing Caching Internet of Things (IoT) mobile device cloud (MDC) multimedia streaming UAV

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