Jing Li

The Internet of Things (IoT) is gathering paces in the new era of Industry 4.0, and the Digital Twin (DT) technology bridges the gap between the bursting amounts of data generated by IoT devices and the user requirements for real-time data processing. DT services maintain living digital models of physical objects, and a DT network enables comprehensive service provisioning with the global knowledge of a group of DTs. On the other hand, exposing serverless computing at network edges, the recent advances in Mobile Edge Computing (MEC) introduce new inspirations to the DT landscape that ensure fine-grained resource management and low network-wide delay of DT services. However, social relationships among IoT devices and DT data privacy impact DT orchestrations. In this paper, we first design a differential privacy-based federated learning framework to build a DT network for DT services in response to user requests in an MEC, thereby enhancing the Quality of Services (QoS). Built upon the proposed framework, we then formulate two novel social-aware DT placement problems: the static social-aware S_DT placement problem, and the dynamic social-aware S_DT placement problem, respectively. We also show the NP-hardness of the defined problems. Then, we formulate an Integer Linear Program (ILP) solution to the static social-aware S_DT placement problem when the problem size is small; otherwise we develop an approximation algorithm with a provable approximation ratio for it. Third, we study the dynamic social-aware S_DT placement problem when requests arrive one by one without the knowledge of future request arrivals over the time horizon, for which we devise an online algorithm with a provable competitive ratio. Finally, we conduct simulations to evaluate the performance of the proposed algorithms. Simulation results show that the proposed algorithms outperform their counterparts, improving the performance compared with their baselines by no less than 14.9%.

Mobile Edge Computing (MEC) shifts powerful computing resource provisioning from remote powerful data centers to the edge of core networks. Meanwhile, Digital Twin (DT) has surfaced as a promising technology to provide comprehensive and dynamic descriptions of physical objects in cyberspace with bidirectional and real-time interactions. Moreover, Internet of Things (IoT) devices have contributed abundant, heterogeneous and continuous data from interconnected devices to the explosion of DTs. With technologies evolution, there is an increasing necessity to address the freshness of both DT states and DT data, through timely synchronizations between DTs and their objects in a highly dynamic IoT environment. In this paper, we develop innovative methodologies to improve the DT freshness while minimizing the cost of various resources consumed on the DT freshness improvement. Specifically, we first formulate two novel optimization problems of DT freshness: (i) The static DT freshness optimization problem, where all DT synchronization tasks are given in advance; and (ii) the dynamic DT freshness optimization problem without any knowledge of future DT synchronization tasks over a given finite time. We then devise an approximation algorithm with a provable approximation ratio for the static DT freshness optimization problem. Also, we develop an online algorithm with a provable competitive ratio for the dynamic DT freshness optimization problem. Finally, we evaluate the performance of the proposed algorithms through simulations. Simulation results show that the proposed algorithms outperform their comparison baselines by no less than 13.2%.

Digital twin (DT) technology enables smooth integrations of cyber and physical worlds in alignment with the Industry 4.0 initiative. DTs are virtual presentations of physical objects. Through synchronizations with physical objects in real-time, DTs can reflect the states of their objects with high fidelity. Orthogonal to the DT technology, mobile edge computing (MEC) is a promising computing paradigm that shifts computing power to the edge network, which is appropriate for delay-sensitive intelligent services. In this paper, we study fidelity-aware inference services in a DT-assisted MEC environment, where machine learning-based inference models must be continuously retrained using updated DT data in order to provide high-fidelity services for consumers. To this end, we first formulate two novel optimization problems: the initial DT and model placement problem with the aim of minimizing the total cost of various resources consumed for the placements, and the cumulative fidelity maximization problem to maximize the long-term cumulative fidelity of all service models while minimizing the cost of resource consumption on enhancements of service model fidelitiess over a given time horizon, through jointly scheduling mobile devices to synchronize with their DTs by uploading their update data and determining whether DTs and/or models to be migrated at each time slot. We then develop an efficient algorithm for the initial DT and model placement problem, through a reduction to a series of minimum-cost maximum matching problems in auxiliary graphs. We also devise an online algorithm with a provable competitive ratio for the cumulative fidelity maximization problem, by designing an elegant service request admission strategy. Finally, we evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their baselines by no less than 28%.

Inferring user status at the edge is essential for delivering personalized services, such as detecting emotional states. However, deploying large-scale models directly on user devices is impractical due to substantial computational overhead and the scarcity of labeled data. Conversely, uploading raw data to the cloud for processing raises significant privacy concerns and incurs prohibitive communication costs. To address this challenge, we propose a privacy-preserving multimodal inference framework that leverages large-scale public data while safeguarding sensitive information and optimizing computational efficiency. Specifically, we first train a teacher model in the cloud using publicly available data. Through a feature distillation process, the knowledge from this teacher model is transferred to a lightweight encoder deployed at the user end. This transfer is tailored to the user's data, ensuring that only relevant knowledge is distilled. To accommodate varying communication constraints, we introduce a feature compression mechanism that significantly reduces communication overhead without compromising inference accuracy. Extensive experiments on emotion recognition tasks demonstrate that the proposed framework effectively balances privacy preservation, resource efficiency, and inference accuracy, facilitating seamless collaboration between cloud and edge devices.

Wireless Power Transfer technology, which can charge IoT devices over the air, has become a promising technology for IoT networks. In wireless-powered IoT networks, broadcasting is a fundamental networking service for disseminating messages to the whole network. To seek a fast and collision-free broadcast schedule, the problem of Minimum Latency Broadcast Scheduling (MLBS) has been well studied when nodes are energy-abundant. However, in wireless-powered networks, a node can only receive or transmit packets after it has harvested enough energy. In such networks, it is of great importance to exploit the divergent harvested energy to reduce the broadcast latency. Unfortunately, existing works always assume a predetermined tree and a fixed transmission power for broadcast scheduling, which greatly limits their performance. Thus, in this article, we investigate the first work for the MLBS problem in wireless-powered networks without relying on predetermined trees. First, the problem is formulated and proved to be NP-hard. Then, two structure-adaptive scheduling algorithms are proposed with a theoretical bound, which can intertwine the construction of broadcast tree with the computation of an energy-aware schedule simultaneously. Furthermore, a power-aware scheduling method is also proposed to take the structure of the broadcast tree, the adjustment of nodes’ transmission powers, and the interference during transmissions into account simultaneously. Additionally, the algorithm for the MLBS problem under the physical interference model is also studied. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.