Designing effective incentive mechanisms in mobile crowdsensing (MCS) networks is crucial for engaging distributed mobile users (workers) to contribute heterogeneous data for various applications (tasks). In this paper, we propose a novel stagewise trading framework to achieve efficient and stable task-worker matching, explicitly accounting for task diversity (e.g., spatio-temporal limitations) and network dynamics inherent in MCS environments. This framework integrates both futures and spot trading stages. In the former, we introduce the futures trading-driven stable matching and pre-path-planning mechanism (FT-SMP3), which enables long-term taskworker assignment and pre-planning of workers' trajectories based on historical statistics and risk-aware analysis. In the latter, we develop the spot trading-driven DQN-based path planning and onsite worker recruitment mechanism (ST-DP2WR), which dynamically improves the practical utilities of tasks and workers by supporting real-time recruitment and path adjustment. We rigorously prove that the proposed mechanisms satisfy key economic and algorithmic properties, including stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Extensive experiements further validate the effectiveness of our framework in realistic network settings, demonstrating superior performance in terms of service quality, computational efficiency, and decision-making overhead.

The deep exploration potential of basic-ultrabasic rock masses associated with Panzhihua-type vanadium-titanium magnetite (VTM) deposits are closely tied to the occurrence of deep-seated rock bodies. In this study, we utilized newly acquired 1:50000 scale aeromagnetic data from the Panxi region to perform a 3-D magnetization inversion using an improved regularized focusing conjugate gradient approach to achieve high-resolution 3-D magnetic imaging of basic-ultrabasic rock masses within the “Panzhihua-type” VTM concentration zone at depths reaching 10 km. The inversion results reveal that the 3-D magnetic anomalies of strong magnetic sources correspond with the distribution of the NS fault zones in the study area. However, these anomalies are predominantly located within narrow zones between the fault zones rather than directly along the fault lines. It also suggests that during the Late Huashan period, two rift regions might have developed in the Panxi area: the Anninghe Rift and the Panzhihua Rift. The deep and large faults within these confined rift valleys likely controlled the eruption and intrusion of mantle-derived magma, facilitating the emplacement of basic-ultrabasic strong magnetic rock masses along these zones. Additionally, the local shear structures within the paleo-rift zones may have provided ample space and a relatively stable environment conducive to the formation of VTM deposits.

This paper explores an interesting worker recruitment challenge where the mobile crowd sensing and computing (MCSC) platform hires workers to complete tasks with varying quality requirements and budget limitations, amidst uncertainties in worker participation and local workloads. We propose an innovative hybrid worker recruitment framework that combines offline and online trading modes. The offline mode enables the platform to overbook long-term workers by pre-signing contracts, thereby managing dynamic service supply. This is modeled as a 0-1 integer linear programming (ILP) problem with probabilistic constraints on service quality and budget. To address the uncertainties that may prevent long-term workers from consistently meeting service quality standards, we also introduce an online temporary worker recruitment scheme as a contingency plan. This scheme ensures seamless service provisioning and is likewise formulated as a 0-1 ILP problem. To tackle these problems with NP-hardness, we develop three algorithms, namely,i)exhaustive searching,ii)unique index-based stochastic searching with risk-aware filter constraint,iii)geometric programming-based successive convex algorithm. These algorithms are implemented in a stagewise manner to achieve optimal or near-optimal solutions. Extensive experiments demonstrate our effectiveness in terms of service quality, time efficiency, etc.

Task scheduling as an effective strategy can improve application performance on computing resource-limited devices over distributed networks. However, existing evaluation mechanisms for application completion fail to depict the complexity of diverse applications and time-varying networks, which involve dependencies among tasks, computing resource requirements, multi-dimensional quality of service (QoS) constraints, and limited contact duration among devices. Furthermore, traditional QoS-oriented task scheduling strategies struggle to meet the performance requirements without considering differences in satisfaction and acceptance of the application, leading to application failures and resource wastage. To tackle these issues, a quality of experience (QoE) cost model is designed to evaluate application completion, depicting the relationship among application satisfaction, communications, and computing resources over the time-varying distributed networks. Specifically, considering the sensitivity and preference of QoS, we model the different dimensional QoS degradation cost functions for dependent tasks, which are then integrated into the QoE cost model. Based on the QoE model, the dependent task scheduling problem is formulated as the minimization of overall QoE cost, aiming to improve the application performance over the time-varying distributed networks, which is proven Np-hard. Moreover, a heuristic Hierarchical Multi-queue Task Scheduling (HMTS) algorithm is proposed to address the QoE-oriented task scheduling problem among multiple dependent tasks, which utilizes hierarchical multiple queues to determine the optimal task execution order and location according to different dimensional QoS priorities. Finally, extensive experiments demonstrate that the proposed algorithm can significantly improve the satisfaction of applications.

The “Lala-type” copper-iron deposits in the Lala area are primarily located in the central part of the Kangdian tectonic zone, distributed along an East-West (EW) trending Neoproterozoic gabbro-dolerite belt that is prominently controlled by regional EW-oriented tectonic structures. The genesis of these deposits is widely believed to be closely related to ancient volcanic structures, but their relationship with deep-seated basic intrusive rocks remains highly controversial. This article proposes an improved 3-D re-weighting regularized conjugate gradient (RRCG) focusing inversion method, constrained by the background field to recover the anomaly with a high resolution. The magnetic structure in a real case indicates the presence of a distinctly oriented basic intrusive rock mass in the deep part of the “Lala-type” Shilong copper deposit. Large-scale, high-precision magnetic profiles and magnetotelluric inversion results show that this strongly magnetic and high-resistance intrusive rock mass, which extends to a depth of 1.5 km, has transformed the ore-bearing basement into a clearly imaged anticlinal uplift and fold structure. Trenches and drill holes reveal that the ore bodies are primarily located in the fold and detachment spaces formed at the intersections of EW and North-South (NS) faults. The breccia formed by the basic rock intrusion along the faults provides favorable conditions for the occurrence and enrichment of ore bodies. The multiple intrusive thermal events in the Lala area not only supplied the fluids necessary for the enrichment of the deposits but also facilitated the transformation of the basement and the formation of ore-bearing spaces.

Federated learning (FL) is a prominent paradigm of 6G edge intelligence (EI), which mitigates privacy breaches and high communication pressure caused by conventional centralized model training in the artificial intelligence of things (AIoT). The execution of multimodal federated perception (MFP) services comprises three sub-processes, including sensing-based multimodal data generation, communication-based model transmission, and computing-based model training, ultimately competitive on available underlying multi-domain physical resources such as time, frequency, and computing power. How to reasonably coordinate the multi-domain resources scheduling among sensing, communication, and computing, therefore, is vital to the MFP networks. To address the above issues, this article explores service-oriented resource management with integrated sensing, communication, and computing (ISCC). Specifically, employing the incentive mechanism of the MFP service market, the resources management problem is defined as a social welfare maximization problem, where the concept of “expanding resources” and “reducing costs” is used to enhance learning performance gain and reduce resource costs. Experimental results demonstrate the effectiveness and robustness of the proposed resource scheduling mechanisms.

By opportunistically engaging mobile users (workers), mobile crowdsensing (MCS) networks have emerged as important approach to facilitate sharing of sensed/gathered data of heterogeneous mobile devices. To assign tasks among workers and ensure low overheads, we introduce a series of stable matching mechanisms, which are integrated into a novel hybrid service trading paradigm consisting offutures tradingandspot tradingmodes, to ensure seamless MCS service provisioning. In futures trading, we determine a set of long-term workers for each task through anoverbooking-enabledin-advancemany-to-manymatching (OIA3M) mechanism, while characterizing the associated risks under statistical analysis. In spot trading, we investigate the impact of fluctuations in long-term workers' resources on the violation of service quality requirements of tasks, and formalize a spot trading mode for tasks with violated service quality requirements under practical budget constraints, where the task-worker mapping is carried out viaonsitemany-to-manymatching (O3M) andonsitemany-to-onematching (OMOM). We theoretically show that our proposed matching mechanisms satisfy stability, individual rationality, fairness, and computational efficiency. Comprehensive evaluations confirm the satisfaction of these properties in practical network settings and demonstrate our commendable performance in terms of service quality, running time, and decision-making overheads, e.g., delay and energy consumption.

Implementing either Federated learning (FL) or split learning (SL) over clients with limited computation/communication resources faces challenges on achieving delay-efficient model training. To overcome such challenges, we investigate a novel distributedClustering-basedHybrid fEdEratedSplit lEarning (CHEESE) framework, consolidating distributed resources among clients by device-to-device (D2D) communications, working in an intra-serial inter-parallel manner. InCHEESE, each learning client can form a cluster with its neighboring helping clients via D2D communications to train an FL model collaboratively. Inside each cluster, the model is split into multiple segments via a model splitting and allocation (MSA) strategy, while each cluster member trains one segment. After completing intra-cluster training, a transmission client (TC) is determined from each cluster to upload a complete model to the base station for global model aggregation under allocated bandwidth. Accordingly, an overall training delay cost minimization problem is formulated, involving the following subproblems: client clustering, MSA, TC selection, and bandwidth allocation. Due to its NP-Hardness, the problem is decoupled and solved iteratively. The client clustering problem is first transformed into a distributed clustering game based on potential game theory, where each cluster further investigates the remaining three subproblems to evaluate the utility of each clustering strategy. Specifically, a heuristic algorithm is proposed to solve the MSA problem under a given clustering strategy, while a greedy-based convex optimization approach is introduced to solve the joint TC selection and bandwidth allocation problem. Extensive experiments on practical models and datasets demonstrate thatCHEESEcan significantly reduce training delay costs.

Recently many efforts have been devoted to learning non-linear predictions from neighboring samples with deep neural networks. However, existing methods mainly generate predictions with local reference samples, regardless of nonlocal self-similarity. In this paper, we aim to incorporate local and nonlocal correlations for intra prediction and propose a two-stage coarse-to-fine network (CTFN), which is integrated into VVC codec as an optional intra prediction mode. The prediction process of CTFN is decomposed into two stages. In the first stage, we train a set of networks to generate a coarse result with local reference samples. In the second stage, we extract sufficient features from nonlocal region using the coarse result as priors and transform the features into a fine prediction result. In particular, a patch-wise attention layer (PAL) is designed in the second stage that can fully explore nonlocal correlations in feature domain and assign weights to each nonlocal feature adaptively, as shown in Fig. 1. As such, the proposed CTFN can not only learn a non-linear mapping from local context, but also explicitly borrow similar features from nonlocal region in a weighted form. Different from image inpainting tasks, the patch synthesis problem is converted to patch matching problem with the CTFN, yielding more reliable predictions. More-over, we construct a classified dataset based on Pearson Correlation Coefficient for network training to better handle contents that are highly correlated. Experiments on VTM-11.0 show that the proposed network achieves 1.77% ED-rate reductions under all intra configuration, which outperforms the state-of-the-art methods.

Fog computing is an energy-efficient and cost-effective paradigm to help alleviate the pressure of resource-constrained mobile devices (MDs) running computation-intensive applications. In this article, we investigate the joint task partitioning and power control problem in a fog computing network with multiple MDs and fog devices (FDs), where each MD has to complete a periodic computation task under the constraints of delay and energy consumption. Each task can be partitioned into multiple subtasks and offloaded to the FDs according to the task partition strategy and transmission power strategy to reduce task execution delay and energy consumption. To this end, we present a multiagent deep deterministic policy gradient (MADDPG)-based task offloading algorithm for MDs to maximize the long-term system utility including the execution delay and energy consumption. Each MD inputs the local information, e.g., the task requirements, the available communication, and computation resources of the FDs, the computation resources, and the battery level of the MD into a distributed actor network to generate a task offloading policy, while a centralized critic network is used to update the weights of the actor networks to improve offloading performance. Numerical simulation results demonstrate the effectiveness of the proposed scheme in improving the system utility, reducing the average execution delay as well as the average energy consumption.

In this paper, we investigate the multi-task federated learning in mobile edge computing (MEC) networks where a central server assigns different federated learning tasks to different MEC servers and select feasible clients to participate in the federated learning training process. The problem is formulated as a joint client selection and task assignment problem to maximize the total utility of all tasks, subject to the trained model quality and total training latency. Since the above-mentioned problem is NP-Hard, it poses challenges to obtain the optimal solution within polynomial time, the problem is transformed into a many-to-one-to-one 3D matching problem. To further reduce the computation while ensuring the matching stability, we first adopt the spectral clustering algorithm to cluster the clients into multiple client clusters. Then we reformulate the problem as a 3-Partite weighted hypergraph total weight maximization problem. Finally, we propose a greedy and local search (GLS) based algorithm to resolve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm as compared with baseline schemes.

Mobile edge computing (MEC) enabled user-centric ultra-dense network (UDN) is a promising solution to the energy constrained mobile users with delay-sensitive and computation intensive applications. Due to the high density of access points and MEC servers in UDN, both task offloading decision, power control, communication and computation resource allocation need to be addressed. To this end, we consider the joint problem of task offloading, uplink transmission power control, communication and computation resource allocation in a UDN, where the task of each user can be partitioned into several subtasks and offloaded to different access points. To handle the continuous action space of task partitioning and power control, we propose a multi-agent deep deterministic policy gradient (MADDPG) approach to solve this problem. Simulation results reveal the effectiveness of the proposed method.

With the advantages of Millimeter wave in wireless communication network, the coverage radius and inter-site distance can be further reduced, the ultra dense network (UDN) becomes the mainstream of future networks. The main challenge faced by UDN is the serious inter-site interference, which needs to be carefully addressed by joint user association and resource allocation methods. In this paper, we propose a multi-agent Q-learning based method to jointly optimize the user association and resource allocation in UDN. The deep Q-network is applied to guarantee the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method and different performances under different simulation parameters are evaluated.

The heterogeneous vehicular network (HetVNET), which consists of multiple radio access networks (RANs), is a promising paradigm to provide a variety of services on the road. However, with the diverse quality of experience (QoE) requirements for vehicles, how to choose the optimal network for the vehicles pose great challenges. In this paper, we investigate the network selection problem in a HetVNET, which includes LTE-vehicle-to-anything (LTE-V2X), dedicated short-range communications (DSRC), WiFi. The network selection problem is formulated as a stable matching between the vehicles and different RANs. A two-sided one-to-many matching algorithm is presented based on the preference lists of both vehicles and RANs. Numerical simulation results show that the proposed method can improve the total throughput of the system and reduce the total network switch time compared to some existing algorithms.

LCC high voltage generator is widely adopted for X-ray beam excitation and control. It has the requirement of wide load range, fast transients, high efficiency and reduced system size. Therefore, SiC based high voltage generator switching at 2X-3X of current frequency will be a better option. Accurate prediction of SiC switching characteristics as well as comprehensive switching behavior evaluation are important prerequisites for thermally optimized and efficient SiC system design. This paper proposed on a novel miller capacitor modeling approach: Segmented-and-Cascaded approach. The mismatch between modeled capacitor curve and measurements data from datasheet is within 15%, which is three times as less as the SPICE model. A comprehensive switching characterization is also revealed. The hard turn on loss is 157.7μ J at 500V/40A and will be significantly reduced under ZVS-ON. The turn off energy loss under hard switching is 48.1μ J at 500V/40A. It is reduced to 4.43μ J with 3.2nF snubber capacitor at soft turn off. Good consistency is observed of predicted data vs. measurements with maximum deviation of 17.2%. SiC power transistor with ZVS turn on and soft turn off has the lowest switching loss of 9.12μ J. Therefore, designing the LCC inverter at switching frequency higher than resonant frequency will achieve both high efficiency and high power density.