The explosion of cameras embedded in IoT devices—from mobile phones to autonomous vehicles—has positioned video analytics as a transformative AI tool across healthcare, smart cities, and beyond. Yet, the substantial computing and bandwidth demands of these applications outstrip what IoT devices alone can handle, particularly when low latency is required. Computing Power Networking (CPN) is an emerging solution that unifies cloud, edge, and device resources, enabling seamless, efficient task distribution for real-time analytics. While recent advances in cloud-edge frameworks show promise, current approaches often neglect the economic incentives that drive resource availability. To address this, we present a novel, privacy-enabled dual-layer Stackelberg game model that establishes a dynamic pricing strategy for video analytics in CPN. Our model introduces a two-stage negotiation: IoT devices contract with edge servers for computational and bandwidth resources, while edge servers may offload tasks to the cloud for enhanced service. Using game theory, we derive optimal pricing and offloading strategies under both complete and incomplete information, proving a Nash equilibrium. Comprehensive simulations validate our approach, showing improvements in resource efficiency, reduced latency, and incentivized resource-sharing across all CPN tiers. Specifically, our hybrid offloading strategy significantly reduces latency compared to edge-only and cloud-only computation models. For varying IoT device quantities, the average latency reduction across all scenarios is approximately 30.5%. This work provides an economically sustainable, privacy-conscious solution to the computational challenges of video analytics in an interconnected, resource-sharing ecosystem.

Vehicular edge computing (VEC) tackles the escalating computational demands of intelligent transportation systems by offloading tasks to nearby roadside units (RSUs) for processing. However, in the dynamic vehicular network environment, where vehicles are constantly moving, effective VEC demands a sophisticated approach to managing computing, caching, and communication resources. This involves coordinating resource allocation and data caching across multiple vehicles and RSUs while making complex decisions about task placement. In this paper, we present VECO, a Vehicular Edge Caching and Offloading framework powered by digital twins (DTs). VECO leverages DTs for real-time monitoring of network conditions and resource states, enabling predictive analysis and intelligent decision-making. The framework incorporates a Dynamic Task Caching and Computation Offloading (DT2C) mechanism to optimize data caching and adapt task offloading based on task characteristics and dynamic resource availability. Specifically, we develop a utility-based caching algorithm for RSUs and a novel task offloading strategy using a Proximal Policy Optimization-based deep reinforcement learning algorithm. Extensive experiments demonstrate that VECO, augmented by the DT2C mechanism, significantly outperforms baseline approaches, achieving faster learning convergence and a 21% reduction in total costs, including system latency and energy consumption.

Computing Power Networking (CPN) represents a transformative paradigm in distributed computing, harnessing the collective capabilities of edge servers dispersed across diverse geographical locations. CPN’s core strengths lie in its ability to accelerate data processing, diminish latency, and scale efficiently, rendering it particularly apt for real-time applications and the Internet of Things. When coupled with blockchain technology, CPN extends its potential by facilitating secure and transparent allocation and trading of computing resources, bolstering data integrity and reliability. However, current research at the intersection of CPN and blockchain primarily focuses on framework development and technology integration, often overlooking the challenge of delivering dependable computing services, especially in the presence of potentially unreliable nodes. To tackle this issue, we introduce a reputation-enhanced resource trading framework, designed to ensure equitable and trustworthy computing power transactions. We establish a decentralized reputation model, capable of accurately assessing node behavior over extended periods. Additionally, we present three optimization mechanisms for reputation updates, accounting for transaction history, quality of service, and transaction amount. Furthermore, our work introduces a reputation-enhanced consensus mechanism within the trading system, strategically employing incentives to motivate participants to deliver high-quality services, thereby increasing their rewards. Simultaneously, it effectively mitigates wealth inequality among resource providers of varying sizes. To validate our approach, we develop a prototype system and conduct performance evaluations, which affirm the superiority of our system in enhancing reputation and delivering robust economic features.

Outsourced cloud computing can be considered as an effective way to overcome the data island among users and relieve the pressure of limited resources. However, due to the concerns about trust in cloud servers, outsourcing the users’ data and model training task has considerable privacy disclosure risks. This article presents a PriKPM scheme by using additive secret sharing (ASS), so as to implement the privacy-preserving${k}$-prototype clustering for mixed data (i.e., including numerical and categorical attributes). In PriKPM, data samples are randomly split into two shares and delivered offline to two collaborative servers. We design a secure initialization method for determining the location and number of cluster centers. Then, both servers securely calculate the mixed distance between samples and cluster centers, and execute the samples partion and cluster updating operations. An efficient and secure comparison protocol is developed to offer flexibly the “less than or equal” and “equal” functions during the entire clustering process. Furthermore, theoretical analysis proves the effectiveness and security of PriKPM. Sufficient experiments demonstrate that PriKPM is computationally more efficient than existing secure clustering works. PriKPM can achieve the approximate accuracy of the plaintext${k}$-prototype clustering scheme.

Summary Collaborative cloud applications have become the dominant application mode in the big data era. These applications usually generate plenty of cooperative files, which share their ownerships with all collaborative participants. Data deduplication is a promising solution to improve the storage efficiency and save the user expenditure. However, it remains an open issue on how to securely prove the shared ownerships for the shared files and address the attacks on account of using data deduplication. To tackle the above issue, in this paper, we introduce a novel concept of the Proof of Shared oWnership (PoSW) and construct a secure multi‐server‐aided PoSW (ms‐PoSW) scheme for securing client‐side deduplication for the shared files, which is based on the convergent encryption, secret sharing, and bloom filter. In the ms‐PoSW scheme, we employ a sharing convergent key to avoid the single point of failure, introduce the secret sharing algorithm to implement the shared ownership, and construct a novel interaction protocol between the shared owners and the cloud server to prove the shared ownership. Furthermore, a hybrid PoSW scheme is constructed to address the secure proof of hybrid cloud architectures. Finally, security analysis and performance evaluation show the security and efficiency of the proposed schemes.

We are living in a world where massive end devices perform computing everywhere and everyday. However, these devices are constrained by the battery and computational resources. With the increasing number of intelligent applications (e.g., augmented reality and face recognition) that require much more computational power, they shift to perform computation offloading to the cloud, known as mobile cloud computing (MCC). Unfortunately, the cloud is usually far away from end devices, leading to a high latency as well as the bad quality of experience (QoE) for latency-sensitive applications. In this context, the emergence of edge computing is no coincidence. Edge computing extends the cloud to the edge of the network, close to end users, bringing ultra-low latency and high bandwidth. Consequently, there is a trend of computation offloading toward edge computing. In this paper, we provide a comprehensive perspective on this trend. First, we give an insight into the architecture refactoring in edge computing. Based on that insight, this paper reviews the state-of-the-art research on computation offloading in terms of application partitioning, task allocation, resource management, and distributed execution, with highlighting features for edge computing. Then, we illustrate some disruptive application scenarios that we envision as critical drivers for the flourish of edge computing, such as real-time video analytics, smart “things” (e.g., smart city and smart home), vehicle applications, and cloud gaming. Finally, we discuss the opportunities and future research directions.

Edge computing is an emerging technology which places computing at the edge of the network to provide an ultra-low latency. Computation offloading, a paradigm that migrates computing from mobile devices to remote servers, can now use the power of edge computing by offloading computation to cloudlets in edge-clouds. However, the task scheduling of computation offloading in edge-clouds faces a two-fold challenge. First, as cloudlets are geographically distributed, it is difficult for each cloudlet to perform load balancing without centralized control. Second, as tasks of computation offloading have a wide variety of types, to guarantee the user quality of experience (QoE) in terms of task types is challenging. In this paper, we present Petrel, a distributed and application-aware task scheduling framework for edge-clouds. Petrel implements a sample-based load balancing technology and further adopts adaptive scheduling policies according to task types. This application-aware scheduling not only provides QoE guarantee but also improves the overall scheduling performance. Trace-driven simulations show that Petrel achieves a significant improvement over existing scheduling strategies.

Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users’ data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user’s privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners’ dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

Cloud gaming is promising to provide high-quality game services by outsourcing game execution to cloud so that users can access games via thin clients (e.g., smartphones or tablets). However, existing cloud gaming systems su er from low GPU utilization in the virtualized environment. Moreover, GPU resources are scheduled in units of virtual machines (VMs) and this kind of coarse-grained scheduling at the VM-level fails to fully exploit GPU processing capacity. In this paper, we present ShareRender, a cloud gaming sys- tem that o oads graphics workloads within VMs directly to GPUs, bypassing GPU virtualization. For each game running in a VM, ShareRender starts a graphics wrapper to intercept frame rendering requests and assign them to render agents responsible for frame rendering on GPUs. Thanks to the exible workload assignment among multiple render agents, ShareRender enables ne-grained resource sharing at the frame-level to signi cantly improve GPU utilization. Further more, we design an online algorithm to determine workload assignment and migration of render agents, which considers the tradeo between minimizing the number of active server and low agent migration cost. We conduct experiments on real deployment and trace-driven simulations to evaluate the performance of ShareRender under di erent system settings. The results show that ShareRender outperforms the existing video-streaming-based cloud gaming system by over 4 times.

In cloud gaming systems, the game program runs at servers in the cloud, while clients access game services by sending input events to the servers and receiving game scenes via video streaming. In this paradigm, servers are responsible for all performance-intensive operations, and thus suffer from poor scalability. An alternative paradigm is called graphics streaming, in which graphics commands and data are offloaded to the clients for local rendering, thereby mitigating the server's burden and allowing more concurrent game sessions. Unfortunately, this approach is bandwidth-consuming, due to large amounts of graphic commands and geometry data. In this paper, we present LiveRender, an open-source gaming system that remedies the problem by implementing a suite of bandwidth optimization techniques including intraframe compression, interframe compression, and caching, establishing what we call compressed graphics streaming. Experiments results show that the new approach is able to reduce bandwidth consumption by 52%-73% compared to raw graphics streaming, with no perceptible difference in video quality and reduced response delay. Compared to the video streaming approach, LiveRender achieves a traffic reduction of 40%-90% with even improved video quality and substantially smaller response delay, while enabling higher concurrency at the server.

In cloud gaming systems, the game program runs at servers in the cloud, while clients access game services by sending input events to the servers and receiving game scenes via video streaming. In this paradigm, servers are responsible for all performance-intensive operations, and thus suffer from poor scalability. An alternative paradigm is called graphics streaming, in which graphics commands and data are offloaded to the clients for local rendering, thereby mitigating the server's burden and allowing more concurrent game sessions. Unfortunately, this approach is bandwidth consuming, due to large amounts of graphic commands and geometry data. In this paper, we present LiveRender, an open source gaming system that remedies the problem by implementing a suite of bandwidth optimization techniques including intra-frame compression, inter-frame compression, and caching, establishing what we call compressed graphics streaming. Experiments results show that the new approach is able to reduce bandwidth consumption by 52-73% compared to raw graphics streaming, with no perceptible difference in video quality and reduced response delay. Compared with the video streaming approach, LiveRender achieves a traffic reduction of 40-90% with even improved video quality and substantially smaller response delay, while enabling higher concurrency at the server.