Unmanned aerial vehicles (UAVs) have been introduced into wireless communication systems to provide high-quality services and enhanced coverage due to their high mobility. In this paper, we study a UAV-assisted mobile edge computing (MEC) system in which a moving UAV equipped with computing resources is employed to help user devices (UDs) computing their tasks. The computing tasks of each UD can be divided into two parts: one portion is processed locally and the remaining portion is offloaded to the UAV for computing. Offloading is enabled by uplink and downlink communications between UDs and the UAV. On this basis, two types of access modes are considered, namely non-orthogonal and orthogonal multiple access. For both access modes, we formulate new optimization problems to minimize the weighted sum energy consumption of the UAV and UDs by jointly optimizing the UAV trajectory and computation resource allocation, under the constraint on the number of computation bits. These problems are non-convex optimization problems that are difficult to solve directly. Accordingly, we develop alternating iterative algorithms to solve them based on the block alternating descent method. Specifically, the UAV trajectory and computation resource allocation are alteratively optimized in each iteration. Extensive simulation results demonstrate the significant energy savings of our proposed joint design over the benchmarks.

Human digital twin (HDT) is an emerging paradigm that bridges physical twins (PTs) with powerful virtual twins (VTs) for assisting complex task executions in human-centric services. In this paper, we study a two-timescale online optimization for building HDT under an end-edge-cloud collaborative framework. As a unique feature of HDT, we consider that PTs' corresponding VTs are deployed on edge servers, consisting of not only generic models placed by downloading experiential knowledge from the cloud but also customized models updated by collecting personalized data from end devices. To maximize task execution accuracy with stringent energy and delay constraints, and by taking into account HDT's inherent mobility and status variation uncertainties, we jointly and dynamically optimize VTs' construction and PTs' task offloading, along with communication and computation resource allocations. Observing that decision variables are asynchronous with different triggers, we propose a novel two-timescale accuracy-aware online optimization approach (TACO). Specifically, TACO utilizes an improved Lyapunov method to decompose the problem into multiple instant ones, and then leverages piecewise McCormick envelopes and block coordinate descent based algorithms, addressing two timescales alternately. Theoretical analyses and simulations show that the proposed approach can reach asymptotic optimum within a polynomial-time complexity, and demonstrate its superiority over counterparts.

Ensuring reliable update and evolution of a virtual twin in human digital twin (HDT) systems depends on any connectivity scheme implemented between such a virtual twin and its physical counterpart. The adopted connectivity scheme must consider HDT-specific requirements including privacy, security, accuracy and the overall connectivity cost. This paper presents a new, secure, privacy-preserving and efficient human-to-virtual twin connectivity scheme for HDT by integrating three key techniques: differential privacy, federated multi-task learning and blockchain. Specifically, we adopt federated multi-task learning, a personalized learning method capable of providing higher accuracy, to capture the impact of heterogeneous environments. Next, we propose a new validation process based on the quality of trained models during the federated multi-task learning process to guarantee accurate and authorized model evolution in the virtual environment. The proposed framework accelerates the learning process without sacrificing accuracy, privacy and communication costs which, we believe, are non-negotiable requirements of HDT networks. Finally, we compare the proposed connectivity scheme with related solutions and show that the proposed scheme can enhance security, privacy and accuracy while reducing the overall connectivity cost.

The Internet of Things (IoT) can significantly enhance the quality of human life, specifically in healthcare, attracting extensive attentions to IoT healthcare services. Meanwhile, the human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body in the digital world and reflect its physical status in real time. Naturally, HDT is envisioned to empower IoT healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed, simulating the outcomes and guiding the practical treatments. However, successfully establishing HDT requires high-fidelity virtual modeling and strong information interactions but possibly with scarce, biased, and noisy data. Fortunately, a recent popular technology called generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data. This survey particularly focuses on the implementation of GAI-driven HDT in IoT healthcare. We start by introducing the background of IoT healthcare and the potential of GAI-driven HDT. Then, we delve into the fundamental techniques and present the overall framework of GAI-driven HDT. After that, we explore the realization of GAI-driven HDT in detail, including GAI-enabled data acquisition, communication, data management, digital modeling, and data analysis. Besides, we discuss typical IoT healthcare applications that can be revolutionized by GAI-driven HDT, namely, personalized health monitoring and diagnosis, personalized prescription, and personalized rehabilitation. Finally, we conclude this survey by highlighting some future research directions.