In the era driven by big data and algorithms, the efficient collaboration of pervasive computing power is crucial for rapidly meeting computing demands and enhancing resource utilization. However, current mainstream end-edge-cloud collaboration faces challenges of computing isolation, adversely affecting resource efficiency and user experience. The Computing Power Network (CPN) is a novel architecture designed to sense and collaborate ubiquitous computing resources through networks. Nevertheless, the expansion of its scope and the integration of networks complicate task scheduling. To address this, we design a collaborative scheduling system that considers the joint selection of computing nodes and network links, aiming to reduce delay, enhance reliability, and ensure long-term load balance. First, we propose a delay-prioritized reliable scheduling policy based on a dual-priority mechanism for forwarding and computing. Second, we define the scheduling problem as a Constrained Markov Decision Process (CMDP) and introduce Lyapunov optimization to transform constraints into instantaneous optimizations, achieving a long-term balanced load of computing and network resources. Lastly, we employ an enhanced Deep Reinforcement Learning (DRL) approach to solve the problem. Performance evaluation demonstrates that compared to standard DRL, the proposed algorithm effectively reduces delay and improves reliability while maintaining long-term load balance, resulting in an overall performance improvement of 54.7%.

With the widespread use of Internet of Things (IoT) technology, an enormous number of end devices that request various kinds of cloud services have been connected to the Internet. Multi-access edge computing (MEC) can reduce the service response time by selecting the required edge computing resources closer to the end device. However, MEC-based smart systems require heterogeneous and diverse services support. Taking unmanned aerial vehicle (UAV) last-mile delivery system as an example, there are two types of services required: delivery and computational services. The edge services in MEC environments are distributed and limited. Inefficient service selection plans will affect the quality of services of such smart systems. Therefore, how to design a suitable service selection strategy is a crucial issue for MEC-based smart systems. To address this issue, we propose a service selection framework and a holistic and hybrid service selection (<inline-formula><tex-math notation="LaTeX">$H^{2}S^{2}$</tex-math></inline-formula>) strategy for MEC-based UAV last-mile delivery systems in real-world UAV last-mile delivery scenarios. This framework considers three important characteristics of UAV delivery systems: diverse service requirements, service availability, and service mobility. The <inline-formula><tex-math notation="LaTeX">$H^{2}S^{2}$</tex-math></inline-formula> strategy focuses on selecting the optimal delivery and computational services and provides an integrated approach with a static service selection algorithm and a dynamic service re-selection algorithm. The <inline-formula><tex-math notation="LaTeX">$H^{2}S^{2}$</tex-math></inline-formula> strategy determines the optimal delivery and computational service selection plans with the lowest UAV energy consumption and shortest service response time. We assess the effectiveness and efficiency of the <inline-formula><tex-math notation="LaTeX">$H^{2}S^{2}$</tex-math></inline-formula> strategy through ablation studies and comparative analyses with diverse representative strategies. The experimental results show that the <inline-formula><tex-math notation="LaTeX">$H^{2}S^{2}$</tex-math></inline-formula> strategy improves the effectiveness and efficiency of the UAV delivery system by significantly reducing UAV's energy consumption and service response time.

Decentralized finance (DeFi) relies on crypto assets in blockchains to provide financial services. High volatility of crypto assets puts users at risk of financial loss. Options contracts address this issue by empowering a buyer to exchange his asset with that of a seller, which mitigates risks for both parties. Existing options contract protocols have the following two weaknesses: (i) The buyer have to lock his asset during the contract's lifespan, incurring heavy opportunity costs; (ii) Turing-completed smart contract (TCSC)/hash time lock contract (HTLC) is required to exchange assets, which restricts applicability as TCSC/HTLC is supported by a limited number of blockchains. In this paper, we propose UP-BLOC, a universal and practical blockchain-based options contract. We construct UP-BLOC using a buyer-pay-first design, and propose a blockchain-based secret storage mechanism to ensure the security of the assets involved. This allows the buyer to engage in an options contract without locking any asset and resulting opportunity costs, and thus is more practical than existing works. Besides, UP-BLOC achieves the exchange of assets using standard digital signatures instead of TCSC/HTLC. Hence, UP-BLOC is compatible with all blockchains and is universal. Security analysis and performance evaluation demonstrate that UP-BLOC is secure and efficient.

Mobility on-demand (MoD) systems widely use machine learning to estimate matching utilities of order-vehicle pairs to dispatch orders by bipartite matching. However, existing methods suffer from overestimation problems due to the complex interactions among order-vehicle pairs in the global bipartite graph, leading to low overall revenue and order completion rate. To fill this gap, we propose a multi-agent deep reinforcement learning (MADRL) based order dispatching method with bipartite splitting, named SplitMatch. The key idea is to split the global bipartite graph into multiple sub-bipartite graphs to overcome the overestimation problem. First, we propose a bipartite splitting theorem and prove that the optimal solution of global bipartite matching can be achieved by solving multiple sub-bipartite matching problems when certain conditions are met. Second, we design a spatial-temporal padding prediction algorithm to generate sub-bipartite graphs that satisfy this theorem, where the spatial-temporal feature of orders and vehicles is captured. Next, we propose a MADRL framework to learn the matching utility, where multi-objective, e.g., immediate revenue and quality of service (QoS), are taken into account to deal with varying action space. Finally, a series of simulations are conducted to verify the superiority of SplitMatch in terms of overall revenue and order completion rate.

As media streaming grows, service providers have to focus more on users’ quality of experience (QoE). Among the metrics that influence QoE, network-oriented and media-oriented factors have been the primary concern of most existing work. Apart from the two factors, however, another crucial aspect of QoE is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the user</i>. Though the analysis and modeling of users is a turning point in the transition from QoS to QoE, the impact of users’ memory and cognitive-psychological effects have not been fully explored. In this work, we analyze, validate and quantify the relationship between multiple cognitive effects and the QoE of users in media streaming. We propose PsyQoE, a new QoE assessment framework that uniquely focuses on user perceptions during media sessions. Besides providing real-time QoE assessments through leveraging machine learning, PsyQoE also predicts the overall QoE by taking into account user biases and long-term effects. Additionally, we have designed a further modified version to cater to the needs of hardware and energy-restricted scenarios. When compared with other existing methods, our approach demonstrates enhanced performance by boosting accuracy by 7% to 16%. It also provides better interpretability and broader applicability.

The advance of Digital Twin (DT) technology sheds light on seamless cyber-physical integration with the Industry 4.0 initiative. Through continuous synchronization with their physical objects, DTs can power inference service models for analysis, emulation, optimization, and prediction on physical objects. With the proliferation of DTs, Digital Twin Network (DTN) slicing is emerging as a new paradigm of service providers for differential quality of service provisioning, where each DTN is a virtual network that consists of a set of inference service models with source data from a group of DTs, and the inference service models provide users with differential quality of services. Mobile Edge Computing (MEC) as a new computing paradigm shifts the computing power towards the edge of core networks, which is appropriate for delay-sensitive inference services. In this paper we consider Age of Information (AoI)-aware inference service provisioning in an MEC network through DTN slicing requests, where the accuracy of inference services provided by each DTN slice is determined by the Expected Age of Information (EAoI) of its inference model. Specifically, we first introduce a novel AoI-aware inference service framework of DTN slicing requests. We then formulate the expected cost minimization problem by jointly placing DT and inference service model instances, and develop efficient algorithms for the problem, based on the proposed framework. We also consider dynamic DTN slicing request admissions where requests arrive one by one without the knowledge of future arrivals, for which we devise an online algorithm with a provable competitive ratio for dynamic request admissions, assuming that DTs of all objects have been placed already. Finally, we evaluate the performance of the proposed algorithms through simulations. Simulation results demonstrate that the proposed algorithms are promising, and the proposed online algorithm improves the number of admitted requests by more than 6% than its counterpart.

Cyber-physical systems (CPSs) are intricate systems integrating both physical and computational components. When these components fail due to malfunction or cyber-attack, potentially leading to significant damage or even collapse of the network topology. Cyber resilience, defined as the capability of a network to restore its function and structure after component failures, is crucial for ensuring that CPSs can sustain their operational capabilities in the face of complex disturbances. Recently, CPS resilience has garnered increasing attention, leading to the development of various resilience recovery methods. However, most existing studies address network and physical layer resilience in isolation, which hampers the ability to implement adaptive resilience recovery decisions across different systems. To overcome these limitations, we propose a multi-layered cascaded resilient recovery framework grounded in deep reinforcement learning. Initially, we synthesize the complex interactions between the information and physical layers in CPS resilience recovery from a global perspective, modeling the interrelations within CPSs. Subsequently, we introduce a hybrid resilient recovery strategy, encompassing both horizontal and vertical resilient recovery. The correlation matrix is used to partition the system into horizontal and vertical resilience slices. The resilient recovery strategy is subsequently modeled as an optimization problem using these slices. Following this, the Deep Recurrent Q-learning (DRQL) algorithm is introduced to implement the resilient recovery strategy in CPSs. While DRQL exhibits strong adaptability, it may lead to the sparse selection of critical samples, thereby hindering the learning process and convergence on essential experiences. To address this issue, we further develop the RR-DRQL algorithm, designed to identify the optimal CPS resilient recovery strategy. The RR-DRQL algorithm is rigorously proven to converge to the optimal solution through extensive theoretical analysis. Comprehensive experiments demonstrate that the RR-DRQL algorithm surpasses existing resilience recovery methods by 3.8%–25% regarding resilient policy recovery performance across realistic scenarios and various simulation platforms.

WebAssembly, or WASM for short, is a binary code format for a stack-based virtual machine, first published in 2018 and now becomes a main-steam technology for providing distributed serverless functions. Recently, the demand for privacy and trustless serverless functions has started to grow in cloud, edge, and grid computing, which poses a question for those serverless function providers: how they ensure trustworthy computation in safety-critical scenarios like financial systems, cybersecurity, private data handling, etc. To address this, we leverage the technology ZKSNARK (zero-knowledge Succinct Non-interactive Argument of Knowledge), a powerful proof system that allows efficient verification of the evaluation problem of statements, to give WASM runtime the ability to provide trustless computation service. More precisely, we present ZKWASM, a ZKSNARK backed virtual machine that emulates the execution of WASM bytecode and generates zero-knowledge-proofs for the emulation result. The proof generated by the ZKWASM virtual machine can then be used to convince an entity, with no leakage of confidential information, that the result of the emulation enforces the semantic specification of WASM.

A Dynamically Weighted Directed Network (DWDN) fundamentally illustrates the complex interactions among massive nodes from a big-data-oriented application, like the dynamic interactions among numerous terminals in a metropolitan network management system (MNMS). A High-Dimensional and Incomplete (HDI) tensor is able to flexibly quantize it, where lots of entries are missing primarily due to the impossibility in discovering the full interactions among numerous nodes. Such an HDI tensor can be effectively represented by a Latent Factorization of Tensors (LFT) model for extracting useful knowledge like potential links from it, while existing LFT models commonly adopt general regularization schemes without considering an HDI tensor's imbalanced known data, which impairs their generality. To address this issue, this paper develops an Fine-grained Regularized Nonnegative Latent factorization of tensors (FRNL) model based on two-fold ideas: a) innovatively proposing an Swish-p-based and fine-grained regularization scheme where the regularization effects acting on individual latent feature is proportional to its related instance count for precisely representing the imbalanced distribution of an HDI tensor's known data; b) implementing the self-adaptation of the model hyper-parameters via a fuzzy controller to achieve high practicability. The convergence ability of FRNL is justified theoretically. Experimental studies on eight DWDNs emerging from a real MNMS illustrate that compared with state-of-the-art LFT models, the proposed FRNL model obtains significantly higher learning accuracy and computational efficiency in representing a DWDN.

Truth and Privacy-preserving service are two key issues for data collection in Mobile Crowd Sensing (MCS). However, most of the existing data collection studies use CRH to calculate the truth value, which is difficult to guarantee the data accuracy. The proposed privacy-preserving service methods either neglect the truth-value computation or still adopt the CRH method. To solve the challenge, we propose a Reputation and Privacy-Preserving Service based Truth Data Collection (RPPS-TDC) scheme to achieve the privacy-preserving accurate data collection for MCS. RPPS-TDC scheme consists of two steps: worker trust calculation and reputation-based truth value calculation. Firstly, data requester performs the initial weights calculation on the received data using DBCRH method. Secondly, reputation center uses the initial weights to update the reputation, which is based on the trustworthy worker inference. Finally, the workers are reweighted according to their reputation. We select workers based on trust_MaxHeap, thus obtaining more accurate data. At the same time, we give workers payments based on their trust weights. All the above operations are completed under data encryption, ensuring that only the data requesters have access to the workers’ submitted data. The platform consists of mining nodes, and reputation center resides on the blockchain, thus achieving distributed computing which overcomes the shortcomings of the traditional MCS system. Extensive experiments demonstrate that RPPS-TDC is effective and outperforms previous strategies in two key performance metrics: data accuracy and compensation rationality allocation.