With the rapid advancement of the Internet of Things and 5G technologies, edge computing has emerged as a vital paradigm for supporting real-time processing and low latency applications. At the core of edge computing lies Virtual Network Embedding (VNE), whose resource allocation efficiency directly influences both service quality and revenue generation in edge environments. Recent research has identified reinforcement learning (RL) as a promising approach to enhance the VNE process. However, most existing RL-based methods overlook the potential of attention mechanisms, which can help agents better understand complex network topologies and thereby improve embedding performance. To address this gap, we propose a novel algorithm, D2ARL (Dual Dynamic Attention-driven Reinforcement Learning), which adaptively captures the dynamic characteristics of both physical and virtual networks through a dynamic attention mechanism. D2ARL is built on a sequence-to sequence (seq2seq) architecture, leveraging dynamic attention in the encoder to capture local features of the networks, and in the decoder to extract globally fused features. Experimental results show that D2ARL outperforms state-of-the-art methods across various network environments. It achieves higher overall benefits and demonstrates stable performance under diverse conditions. Notably, D2ARL improves long-term average revenue by 14.5% compared to the best-performing existing method.

Training neural radiance fields without pre-estimated camera poses remains challenging, particularly in the presence of complex illumination and large inter-frame motion. This work presents a self-calibrating radiance-field framework designed to address these difficulties through a unified optimization strategy. Reliable monocular depth priors from the Depth Anything V2 model are incorporated to provide stable geometric constraints, and multiview point-cloud alignment is enforced to prevent pose drift under wide baselines. Illumination-induced photometric inconsistencies are handled by a jointly optimized exposure-compensation module that separates transient brightness variations from the underlying scene radiance. To enhance the representational capacity beyond that of standard multilayer perceptrons, a Kolmogorov-Arnold Network is adopted as the radiance-field backbone, enabling improved recovery of fine geometric and appearance details. The complete pipeline is deployed on a cloud-native infrastructure that supports scalable optimization and interactive access through an LLM-assisted interface. Experiments on indoor and outdoor datasets demonstrate that the proposed method consistently improves pose accuracy and novel-view synthesis quality over existing self-calibrating approaches.

Metaverse as-a-Service (MaaS) enables <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meta</i>verse tenants to execute their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APP</i>lications (MetaAPP) by allocating Metaverse resources in the form of Metaverse service functions (MSF). Usually, each MSF is deployed in a virtual machine (VM) for better resiliency and security. However, these MSFs along with VMs and virtual machine monitors (VMM) running them will encounter software aging after prolonged continuous operation. Then, there is a decrease in MetaAPP dependability, namely, the dependability of the MSF chain (MSFC), consisting of MSFs allocated to MetaAPP. This paper aims to investigate the impact of both software aging and rejuvenation techniques on MetaAPP dependability in the scenarios, where both active components (MSF, VM and VMM) and their backup components are subject to software aging. We develop a hierarchical model to capture behaviors of aging, failure, and recovery by applying Semi-Markov process and reliability block diagram. Numerical analysis and simulation experiments are conducted to evaluate the approximation accuracy of the proposed model and dependability metrics. We then identify the key parameters for improving the MetaAPP/MSFC dependability through sensitivity analysis. The investigation is also made about the influence of various parameters on MetaAPP/MSFC dependability.

The Cloud-Edge collaborative computing enables the deployment of latency-sensitive and data-intensive applications closer to end users. However, it introduces significant challenges for microservice placement, due to resource heterogeneity, limited edge capacity, and the need to satisfy storage requirements using aggregated resources across multiple nodes. To address these issues, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Diktopos</b>, a topology-aware, two-stage scheduling framework that jointly optimizes microservice placement and distributed storage volume allocation in cloud-edge networks. The joint optimization problem is decomposed into two subproblems: (i) microservice placement and (ii) distributed volume allocation, with the objective of minimizing computation, communication, energy, and storage costs. At its core, Diktopos employs a low-complexity, rank-based heuristic that ensures scalable and accurate placement across heterogeneous edge nodes. Simulation results show that our method achieves near-optimal placement decisions (within 1.67% of the optimal solution), and converges up to 5× faster than state-of-the-art approaches in large-scale deployments. Real-world experiments in Kubernetes environments demonstrate up to 53% latency reduction compared to the default scheduler, and up to 23% improvement over other baselines, confirming Diktopos' effectiveness in dynamic, resource-constrained edge scenarios.

We consider the edge-vehicle computing system (EVCS), where the combination of edge computing and vehicle computing takes respective advantages to provide various services. We address the problem of computation offloading in EVSC, where the computing tasks and the sensing tasks with limited budgets are offloaded to edge servers and vehicles. The resource-sharing model is proposed, where sensing resources of one vehicle are shared by multiple tasks. We consider the vehicle hierarchy, where vehicles with different equipment accuracy are classified into different hierarchies. A sensing task has different values and different demands for different hierarchies. A budget-feasible mechanism based on the clock auction is proposed. We show our proposed mechanism is strategy-proof and group strategy-proof, this drives the system into an equilibrium. In addition, the proposed mechanism achieves individual rationality, budget balance, and consumer sovereignty. The proposed mechanism consists of two algorithms that are based on the idea of dominant resource and iteration to improve resource utilization and reduce costs. Furthermore, the approximate ratios of the two allocation algorithms are analyzed. Experimental results demonstrate that the proposed mechanism achieves the near-optimal value and brings higher utility for participants.

Cloud computing is a distributed infrastructure that centralizes server resources on a platform in order to provide services over the internet. Traditional public-key encryption protects data confidentiality in cloud computing, while functional encryption provides a more fine-grained decryption method, which only reveals a function of the encrypted data. However, functional encryption in cloud computing faces the problem of key sharing. In order to trace malicious users who share keys with others, traceable FE-IP (TFE-IP) schemes were proposed where the key generation center (KGC) knows users’ identities and binds them with different secret keys. Nevertheless, existing schemes fail to protect the privacy of users’ identities. The fundamental challenge to construct a privacy-preserving TFE-IP scheme is that KGC needs to bind a key with a user's identity without knowing the identity. To balance privacy and accountability in cloud computing, we propose the concept of privacy-preserving traceable functional encryption for inner product (PPTFE-IP) and give a concrete construction which offers the features: (1) To prevent key sharing, both a user's identity and a vector are bound together in the key; (2) The KGC and a user execute a two-party secure computing protocol to generate a key without the former knowing anything about the latter's identity; (3) Each user can ensure the integrity and correctness of his/her key through verification; (4) The inner product of the two vectors embedded in a ciphertext and in his/her key can be calculated by an authorized user; (5) Only the tracer can trace the identity embedded in a key. We formally reduce the security of the proposed PPTFE-IP to well-known complexity assumptions, and conduct an implementation to evaluate its efficiency. The novelty of our scheme is to protect the user's privacy and provide traceability if required.

This work addresses the problem of ensuring service availability, trust, and profitability in sensor-cloud architecture designed to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sensors-as-a-Service</i> (Se-aaS) using IoT generated data. Due to the requirement of geographically distributed wireless sensor networks for Se-aaS, it is not always possible for a single Sensor-cloud Service Provider (SCSP) to meet the end-users requirements. To address this problem, we propose a federated sensor-cloud architecture involving multiple SCSPs for provisioning high-quality Se-aaS. Moreover, for ensuring trust in such a distributed architecture, we propose the use of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consortium blockchain</i> to keep track of the activities of each SCSP and to automate several functionalities through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Smart Contracts</i>. Additionally, to ensure profitability and end-user satisfaction, we propose a composite scheme, named BRAIN, comprising of two parts. First, we define <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">miner's score</i> to select an optimal subset of SCSPs as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">miners</i> periodically. Second, we propose a modified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple-leaders-multiple-followers Stackelberg game</i>-theoretic approach to decide the association of an optimal subset of SCSPs to each service. Thereafter, we evaluate the performance of BRAIN by comparing with three existing benchmark schemes through simulations. Simulation results depict that BRAIN outperforms existing schemes in terms of profits and resource consumption of SCSPs, and price charged from end-users.