Aiming to provide high-fidelity and real-time virtual replicas, a digital twin (DT) model must be dynamically updated to precisely characterize the evolution of physical objects. Unlike the existing work, this paper studies a novel edge-cloud collaborative DT update framework with adaptive model splitting and reliable crowdsourcing under uncertain data distortions. Specifically, we consider that a global DT model can be split into arbitrary subsets of its elementary components (DT units), re-forming disjoint partial-DTs. Each partial-DT is constructed on distributed edge servers (ESs) by model training using the locally collected feature data. To enhance the system reliability, being more robust against uncertain data distortions that widely occur in practice, we further improve partial-DT constructions via crowdsourcing. In other words, each partial-DT is simultaneously trained by multiple ESs, i.e., an ES crowd, with one coordinator ES intermediately aggregating all models from participating ESs into a unified one. Then, the cloud collects and integrates partial-DTs from ES crowds to update the global DT. We formulate an online joint optimization problem to adaptively determine partial-DT splitting and ES crowdsourcing across different DT evolution periods or frames, with the objective of maximizing the long-term physical-virtual mapping accuracy. To this end, we first study a simplified short-term problem in each frame, modeled as a Bayesian coalition formation game (BCFG). We then develop an uncertainty-aware crowd formation algorithm based on a particularly established believe function to solve the BCFG for short-term optimal partial-DT assignment and coordinator ES selection, given any partial-DT splitting decisions. Moreover, we modify the BCFG to accommodate dynamic settings and design a deep reinforcement learning-based algorithm integrated with this modified BCFG, called DBC. The DBC algorithm extends the short-term solution to a long-term one, which jointly and dynamically optimizes partial-DT splitting and ES crowdsourcing, thereby addressing the original problem. Simulations show the effectiveness of the introduced dynamic DT update framework, and demonstrate the superiority of the proposed DBC algorithm over counterparts in terms of increasing the average DT update accuracy while reducing the associated costs.

The statistical distribution of user geographic location data is widely used in various mobile applications. Although geo-indistinguishability (GI) has emerged as an effective privacy-preserving framework for processing location data, due to the lack of robust perturbation probability calculation and post-processing for eliminating statistical errors caused by random perturbation on user-side data, GI exhibits low accuracy when directly applied to two-dimensional continuous location data distribution estimation. To overcome this, we propose a novel and efficient location data distribution estimation mechanism by improving GI, termed gamma-based circle and uniform grids (GBC-UG). The GBC-UG mechanism consists of two key algorithms: i) the gamma-based circle (GBC) algorithm, which perturbs users' location data and ensures the calculability of perturbation probabilities on the server side, and ii) the uniform grids (UG) algorithm, which post-processes the perturbed data to accurately estimate the original distribution. We provide a theoretical analyses of the upper and lower bounds of the statistical error in distribution estimation and identify optimal parameter values to minimize this error. Experimental results on multiple real-world datasets demonstrate that the proposed GBC-UG mechanism can significantly improve the accuracy of distribution estimation, as well as the prediction accuracy of both the top-k and popularity ranking while guaranteeing user privacy, outperforming existing GI-based approaches.

This paper presents a combinatorial online learning framework for the reliable deployment of containerized Digital Twin (DT) systems in mobile edge computing, addressing challenges such as unpredictable edge server failures and variable writable-layer sharing permissions. By jointly optimizing read-only layer placement, writable layer creation, and cross-server layer loading while adhering to long-term latency and energy constraints, the framework enhances DT service reliability. We first decouple the original problem into a series of deterministic subproblems via Lyapunov optimization, and then propose a contextual bandit mechanism to explore the unknown layer sharing permission information. Theoretical analysis establishes regret bounds, while simulation experiments validate the effectiveness of the proposed framework.

In this paper, we study a mobility-aware two-timescale online optimization for constructing a digital twin (DT) -assisted task execution system under end-edge-cloud collaboration. DTs are deployed on edge servers deployed on roadside units (RSUs), and needs to be proactively migrated based on the future location of its corresponding vehicle. To minimize the task response latency executed by DT with stringent energy consumption constraint, we jointly optimize the uploading frequency of vehicle’s status data for adaptive mobility prediction, the DT migration decision along with the communication and computation resource allocations. Considering that decision variables are triggered asynchronously, we propose a novel two-timescale mobility-aware online optimization approach (TMO), which first employs an extended two-timescale Lyapunov method to decompose the problem into a series of instant subproblems and then integrates a multi-armed bandit (MAB) algorithm to dynamically determine uploading frequency of vehicle’s status data as the length of the large-timescale. After that, for each small-timescale problem, we develop a GRU-based vehicle trajectory preidiction method to predict the location of vehicles, followed by an alternate minimization (AM) based algorithm to decide remaining decision variables based on the predicted trajectory. Theoretical analyses and simulations show that the proposed approach can reach asymptotic optimum and demonstrate its superiority over counterparts.

With the rapid development of autonomous driving, how to enable unmanned vehicles (UVs) to efficiently avoid multiple dynamically moving obstacles, especially obstacle vehicles (OVs), has become a vital issue in the context of the Internet of Vehicles (IoV). This requires not only high-level adaptability to dynamic and complex traffic environments, but also extraordinarily agility in reacting to possible collision hazards with safer and proactive collision avoidance. Conventional methods, e.g., artificial potential field (APF), may overreact to distant targets which have no risk in collision, generating a false evasion direction when facing multiple OVs. To this end, we propose a novel improved APF-based algorithm along with the trajectory prediction. Specifically, to measure the safety distance for vehicle maneuvering, a trajectory prediction method integrated with unscented kalman filter (UKF) is developed. Then, an obstacle filtering method utilizing sensor information and trajectory prediction results is applied for wiping off collision-free targets. Afterwards, by employing APF method combining with avoidance strategies based on virtual forces and window-based collision detection, the potential pushing effect caused by multiple OVs is mitigated. Experimental results show that, given the scenario of collision avoidance with multiple OVs, the proposed solution can achieve an obstacle avoidance success rate of around 90%, which is about 20% higher than the best benchmark algorithms, simultaneously demonstrating advantages in efficiency and safety.

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split into and integrating from multiple functional components, i.e., partial-DTs, created at various edge servers (ESs) using feature data collected by associated sensors. Considering time-varying DT evolutions and heterogeneities among partial-DTs, we formulate an online problem that jointly and dynamically optimizes partial-DT assignments from the cloud to ESs, ES-sensor associations for partial-DT creation, and as well as computation and communication resource allocations for global-DT integration. The problem aims to maximize the constructed DT's model quality while minimizing all induced costs, including energy consumption and configuration costs, in long runs. To this end, we first transform the original problem into an equivalent hierarchical game with an upper-layer two-sided matching game and a lower-layer overlapping coalition formation game. After analyzing these games in detail, we apply the Gale-Shapley algorithm and particularly develop a switch rules-based overlapping coalition formation algorithm to obtain short-term equilibria of upper-layer and lower-layer subgames, respectively. Then, we design a deep reinforcement learning-based solution, called DMO, to extend the result into a long-term equilibrium of the hierarchical game, thereby producing the solution to the original problem. Simulations show the effectiveness of the introduced framework, and demonstrate the superiority of the proposed solution over counterparts.

In this paper, we introduce third-party intelligent reflecting surfaces (TIRSs) into the physical layer security aware wireless communication system, where a central legitimate transmitter is designed to transmit secret signals to a group of legitimate receivers in the presence of the threat from an active eavesdropper (EV). Due to the channel reshaping ability of TIRSs, they are able to not only help legitimate pairs (LPs) enhance the secure transmission rate but also assist EV in improving the eavesdropping performance. Furthermore, with the potential selfishness, TIRSs may dynamically choose to ally with LPs or EV in exchange for potential benefits (e.g., payoffs). This leads to complex dynamic ally-adversary relationships among LPs, EV, and TIRSs under unpredictable wireless channel conditions. To address this issue, we formulate a repeated coalition formation game (RCFG) with dynamic decision-making to model the long-term strategic interactions among LPs, EV, and TIRSs. In particular, we theoretically analyze the existence of Nash equilibrium in the formulated RCFG, and then propose a switch operations-based coalition selection along with a deep reinforcement learning (DRL)-based approach for obtaining such an equilibrium. Simulations examine the feasibility of the proposed approach and show its superiority over counterparts.

In this paper, a repeated coalition formation game (RCFG) with dynamic decision-making for physical layer security (PLS) in wireless communications with intelligent reflecting surfaces (IRSs) has been investigated. In the considered system, one central legitimate transmitter (LT) aims to transmit secret signals to a group of legitimate receivers (LRs) under the threat of a proactive eavesdropper (EV), while there exist a number of third-party IRSs (TIRSs) which can choose to form a coalition with either legitimate pairs (LPs) or the EV to improve their respective performances in exchange for potential benefits (e.g., payments). Unlike existing works that commonly restricted to friendly IRSs or malicious IRSs only, we study the complicated dynamic ally-adversary relationships among LPs, EV and TIRSs, under unpre-dictable wireless channel conditions, and introduce a RCFG to model their long-term strategic interactions. Particularly, we first analyze the existence of Nash equilibrium (NE) in the formulated RCFG, and then propose a switch operations-based coalition selection along with a deep reinforcement learning (DRL)-based algorithm for obtaining such equilibrium. Simulations examine the feasibility of the proposed algorithm and show its superiority over counterparts.

In this paper, a novel hierarchical game framework for physical layer security (PLS) aware wireless communications with dynamic trilateral coalitions is studied. In the considered system, legitimate users (LUs) aim to transmit secret data to associated base stations (BSs) via uplink communications under the threat of eavesdroppers (EVs), while there also exists jammers (JAs) which may choose to form coalitions with either LUs for increasing their secrecy transmission rates or EVs for increasing their eavesdropping rates in exchange for potential rewards. Different from the existing work, we explore such complicated while dynamic coalition relationships under uncertainties of wireless systems (e.g., time-varying channel conditions), and formulate a hierarchical game integrated with a dynamic trilateral coalition formation game to model strategic interactions among LUs, JAs and EVs. Particularly, we first analyze stability conditions of the trilateral coalitions and propose a hedonic coalition selection and formation algorithm for reaching the stable coalition partition in each time slot. On top of this, we propose a deep reinforcement learning (DRL) based solution, which can achieve the equilibrium with long-term performance guarantees for the hierarchical game running over multiple time slots with dynamic evolutions. Simulations evaluate the proposed solution and show its superiority over counterparts.

In this paper, a novel hierarchical game framework for physical layer security (PLS) with dynamic trilateral coalitions is studied. In the considered system, legitimate users (LUs) aim to transmit secret data to associated base stations (BSs) via uplink communications under the threat of eavesdroppers (EVs), while there also exists jammers (JAs) which may choose to form coalitions with either LUs for increasing their secrecy transmission rates or EVs for increasing their eavesdropping rates in exchange for potential rewards. Different from the existing work, we explore such complicated while dynamic coalition relationships under the uncertainties of wireless systems (e.g., time-varying channel conditions), and formulate a hierarchical game integrated with a dynamic trilateral coalition formation game to model the strategic interactions among all three parties, i.e., LUs, JAs and EVs, in PLS. Particularly, we first analyze stability conditions of the trilateral coalitions. On top of this, we further propose a deep reinforcement learning (DRL) based approach for reaching the equilibrium with long-term performance guarantees for the hierarchical game. Simulations evaluate the proposed solution and show its superiority over counterparts.