Blockchain-enabled mobile edge computing (MEC) must jointly optimizetask offloadingandconsensus finalityunder highly heterogeneous AIoT devices, where latency/energy constraints and fairness-sensitive incentives coexist with time-varying validator reliability. We proposeFE-CTDE, a unified framework that couples (1) a Stackelberg pricing-and-allocation layer that reaches a unique equilibrium and reduces utility disparity, (2) a reliability-aware dynamic BFT committee and block-packing mechanism that stabilizes confirmation delay under intermittent connectivity, and (3) a centralized-training/decentralized-execution multi-agent policy that outputs a continuous offloading ratio while requiring only local observations at run time. Extensive simulations across diverse heterogeneity, workload burstiness, and link intermittency show that FE-CTDE consistently improves social welfare and fairness while reducing end-to-end latency/energy and sustaining highereffectiveconsensus throughput, outperforming strong baselines by up to22.23%. We further report protocol/learning overheads and provide reproducible implementation details.

Distributed Computing Continuum Systems (DCCS) unify cloud, fog, edge, and Internet of Things (IoT) into a single execution fabric. At scale, heterogeneity and bursty arrivals make it hard to meet per task deadlines. Prior approaches based on learning, optimization, market mechanisms, or class level slicing depend on global state or iterative coordination. Decisions lag arrivals, isolation is scoped to coarse classes rather than individual tasks, and the deadline violation ratio (DVR) rises. We present Task-level Adaptive Computing Slicing (TACS), a fine grained slicing paradigm that delivers task aligned resource governance through autonomous shard management. For each arriving task, TACS executes decentralized scheduling at the shard level to instantiate an ephemeral micro-slice governed by an autonomous shard formed exactly by the task's participants. Within the shard, participants apply closed form rules to make local resource allocation decisions. This design achieves strict task level isolation and precise, scalable matching between supply and demand without global coordination or model retraining. In simulations with 100 to 1000 heterogeneous nodes and a range of loads and heterogeneity levels, TACS maintains DVRs below 1% and provides steadier, higher throughput than advanced baselines. Under adverse conditions, representative baselines exceed 20% DVR and in several cases require retraining when device populations change.

The resource allocation problem that is posed by cloud services has long been a popular research topic. The existing auction mechanisms focus primarily on maximizing social welfare, but they often result in lower revenue for cloud service providers. The virtual valuation-based combinatorial auction (VVCA) mechanism can increase the revenue that is obtained by service providers while satisfying dominant strategy incentive compatibility (DSIC). In this study, we innovatively apply the VVCA mechanism to address a time-varying resource allocation problem that involves heterogeneous servers (HTs) in cloud services and effectively increase the revenue that is received by cloud service providers. We begin by transforming the HT problem into an integer programming model with time-varying and resource constraint features. Afterward, we provide the theoretical basis for using the VVCA mechanism to solve the aforementioned problem and provide the DSIC proof. On this basis, we design three progressively more effective mechanisms using the VVCA mechanism. (1) We develop a random mechanism$\rm {HT\_{V}VC{A^{m}}}$and prove that it has a logarithmic approximation ratio, thus offering a better lower bound guarantee than the existing approach does. (2) We propose a gradient-based optimization mechanism$\rm {HT\_{V}VC{A^ * }}$to approximate the optimal revenue. (3) We design an optimal revenue algorithm called HT_VVCANET on the basis of the transformer architecture that is used in deep learning; this algorithm achieves a good balance between execution efficiency and effectiveness. In the experiments, we implement these mechanisms, which significantly increase the revenue that is received by cloud service providers over that yielded by other benchmark mechanisms.

Mobile crowdsensing service (MCS) providers recruit users to complete data collection tasks by rewarding the users to obtain greater revenue. Therefore, maximizing revenue is a focus of the MCS provider. This article expresses this problem as a revenue maximization programming model with budget constraints and designs a reverse-auction mechanism based on the attention model to solve the task allocation and pricing problems. Specifically, we convert the programming model under multiple constraints into an augmented Lagrangian function, optimally solve it through a multilayer neural network on the basis of the attention interactive framework, and finally output the allocation and payment solution. Our design guarantees that the mechanism meets economic criteria such as truthfulness, individual rationality, and budget feasibility. Combining the revenue-optimal reverse-auction mechanism with deep learning provides a new approach to mechanism design. Compared with existing methods, our solution achieves very good results in terms of service provider revenue and generalization experiments.

Mobile crowdsensing service (MCS) providers recruit users to complete data collection tasks with an incentive mechanism. How to maximize the utility of service providers has long been a popular topic in MCS research. Applying the existing reverse auction mechanism to an MCS may result in excessively high payments, thereby reducing the utility of the MCS provider. The affine maximizer auction (AMA) mechanism increases the revenue of service providers and meets dominant-strategy incentive-compatible (DSIC) characteristics. However, the AMA mechanism is a forward auction mechanism and cannot be applied to MCSs. Inspired by the AMA mechanism, this paper innovatively proposes a reverse affine maximizer auction (RAMA) mechanism to solve the task allocation problem of MCSs, effectively improving the MCS provider utility. Specifically, we construct a RAMA theoretical model and prove that the mechanism satisfies DSIC characteristics. For the discrete MCS task allocation problem, we use the reverse virtual valuation combinatorial auction (RVVCA) mechanism, a subclass of RAMA, to design a random mechanism RVVCA$^{t}$and prove that the RVVCA$^{t}$has a logarithmic approximate ratio. For the differentiable MCS task allocation problem, we use the deep learning transformer framework to design RAMANet, which can fit an exponential number of allocation solutions and output the optimal allocation and payment. We experimentally compare the algorithms of the RAMA family we propose, which use affine maximization, with existing state-of-the-art algorithms, demonstrating that the proposed algorithms significantly improve MCS provider utility.

In contrast to traditional mechanism design, the posted pricing mechanism can quickly determine the winning user and ensure the revenue of the seller through a predetermined price. Additionally, the posted pricing mechanism inherently possesses economic properties such as truthfulness and individual rationality. These properties make it an ideal method for solving online task allocation problems for mobile crowdsensing services (MCSs). The challenge in posted pricing mechanism design is being able to find reasonable posted prices under complex MCS task constraints. This paper presents an innovative posted pricing mechanism to solve a general point of interest (POI)-based online MCS task allocation problem. We transform the problem into an integer programming model with the goal of maximizing the total utility of the system while satisfying various constraints. We prove that under any user arrival order, there must exist a posted price structure that can ensure that the total utility of the system is approximately optimal, with an approximation ratio of$1/(d+1)$in the worst case. With the support of theoretical analysis, the posted price calculation can be completed using only a simple gradient descent algorithm. Compared with existing methods, our solution achieves very good results in terms of total utility and the task completion ratio, indicating that it can effectively improve the efficiency and service quality of MCSs.

Mobile crowdsensing services are divided into two categories: opportunistic and participatory. In opportunistic mobile crowdsensing services, users do not need to specify the crowdsensing tasks to be completed. Compared with participatory crowdsensing services, the application scope is wider and more user-friendly. In participatory crowdsensing, the service provider assumes that the user can successfully complete the data collection task. However, such an approach cannot work in an opportunistic crowdsensing service because in opportunistic crowdsensing, the user’s execution of the task is uncertain, which brings great challenges to the quality of the crowdsensing service. This article is based on the assumption of the user coverage probability model and transforms the opportunistic mobile crowdsensing value maximization problem into an ordered submodularity value function model with budget constraints. This model is also good at representing participatory crowdsourcing problems. To the best of our knowledge, this is the first study to apply the ordered submodularity feature to a mobile crowdsensing service. Furthermore, we combine the properties of ordered submodular and auction models and propose an ordered submodularity-proportional share mechanism (O-PSM) to solve the allocation and payment problems in opportunistic mobile crowdsensing services. Specifically, in the allocation stage, the winning users are selected based on the proportional share threshold, and in the payment stage, the payment price for the winning users is designed based on critical value theory. We prove that the mechanism satisfies the economic characteristics of individual rationality, truthfulness, and budget feasibility. In the experimental section, the mechanism design based on ordered submodularity is shown to enable the service provider to obtain a higher value and a lower payment.

Digital twin (DT) technology uses Internet of Things (IoT) devices to collect real-world data and build a virtual world in the DT cloud. However, many IoT devices collect data in harsh natural environments, and these data cannot be transmitted through fixed base stations. Thus, many DT services adopt dynamic data transmission methods, such as transmission through unmanned aerial vehicle base stations (UAV-BSs). However, UAV-BS approaches have many communication constraints, such as limitations on the transmission bandwidth, data throughput, and number of channels. In addition, when integrating a large amount of data submitted by IoT devices, DT service providers need a corresponding mechanism to select the most valuable device data, which can be described by a winner decision problem with the goal of maximizing utility. In this paper, we consider the problem of maximizing the utility of a DT model under UAV-BS network transmission, transform it into a mixed integer programming model with communication and computing constraints, and adopt a reverse auction mechanism to solve it. Specifically, we design an optimal reverse auction mechanism based on optimal allocation and Vickrey–Clarke–Groves (VCG) theory. Additionally, a reverse auction mechanism with polynomial execution time is designed based on monotonic allocation, network maximum flow and critical value theory. These two mechanisms are proven to satisfy individual rationality and truthfulness. Experimental results indicate the favorable performance of the designed mechanisms.

The performance of vector graphics render has always been one of the key elements in mobile devices and the most important step to improve the performance is to enhance the efficiency of polygon fill algorithms. In this paper, we proposed a new and more efficient polygon fill algorithm based on the scan line algorithm and Grid Fill Algorithm (GFA). First, we elaborated the GFA through solid fill. Second, we described the techniques for implementing antialiasing and self-intersection polygon fill with GFA. Then, we discussed the implementation of GFA based on the gradient fill. Generally, compared to other fill algorithms, GFA has better performance and achieves faster fill speed, which is specifically consistent with the inherent characteristics of mobile devices. Experimental results show that better fill effects can be achieved by using GFA.