In this paper, we establish a two-timescale framework for the joint optimization for the content placement and content delivery problem in satellite-terrestrial edge computing networks (STECN). Our goal is to optimize content placement to improve network performance while ensuring diverse quality of service (QoS) for content delivery. We decouple the problem into two timescales to balance real-time responsiveness and long-term efficiency. Specifically, considering frequent content placement incurs huge traffic cost, we optimize the content placement in order to reduce resource expenses in large timescales. The optimization problem is formulated as an integer linear programming (ILP) problem to improve both traffic efficiency and cache resource utilization. We leverage a heuristic atom search optimization (ASO) approach to address the problem, which yields an optimal strategy with low computational complexity. In small timescales, we model content delivery as a Markov decision process (MDP) to minimize content delivery delays at small timescales while maintaining smooth network traffic. A deep reinforcement learning (DRL) framework is used for policy learning to dynamically adapt to varying network conditions. By considering the correlation between the small and large timescale optimization, we propose a hierarchical solution to jointly address both issues. Finally, extensive simulations confirm the effectiveness and superiority of the proposed scheme.

As an emerging computing paradigm, Computing Power Networks (CPNs) are dedicated to coordinating and managing network resources and computing resources to achieve interconnectivity in computing power perception. Efficient collaborative computing of massive data can be achieved through the scheduling function of CPNs. However, existing scheduling research mainly focuses on selecting network links and computing nodes, lacking consideration for task execution after scheduling, which may degrade the Quality of Service (QoS), leading to widespread failures and significant losses. To address this issue, we design a priority-aware preemptive task scheduling (P2TS) strategy for CPNs to jointly optimize task scheduling and execution in terms of success rate, average processing delay, and load balancing. Specifically, at the execution level, we propose a priority-aware preemptive mechanism (P2M) to optimize post-scheduling task execution. Then, at the scheduling level, we apply deep reinforcement learning (DRL) to optimize the scheduling process supporting the P2M in CPNs. A series of simulations are conducted to demonstrate the superiority of our strategy.

This paper introduces ProxyLLM, a novel framework designed to enhance the tool utilization capabilities of Large Language Models (LLMs) by leveraging an ensemble of smaller, specialized proxy models. Specifically, instead of invoking tools directly, ProxyLLM delegates tasks to these proxy models, each of which is responsible for a distinct domain and equipped with a curated set of relevant tools. Meanwhile, ProxyLLM employs a two-step knowledge transfer mechanism, utilizing data generated by the LLM for knowledge distillation and LLM-guided Deep Reinforcement Learning (DRL) to enhance the decision-making abilities of the proxy models. During the data-driven knowledge distillation process, the introduction of rationales ensures that proxy models maintain a comprehensive understanding of tasks, thereby improving the learning effectiveness. In the DRL learning process, LLM guidance is separately integrated into both the actor and critic learning phases. This ensures consistency in strategy and uniformity in evaluating the action space, which enhances both the efficiency and effectiveness of the learning process. Extensive experiments, including real-world applications such as network service generation in a Computing Power Network (CPN) system, demonstrate that ProxyLLM significantly outperforms existing methods in terms of task accuracy and tool invocation efficiency. The proposed framework offers a promising solution for constructing generalizable, large-scale intelligent agents capable of effectively leveraging diverse tools to solve complex, cross-domain problems.

Digital Twin (DT) technology is a key enabler of the Industrial Internet of Things (IIoT), facilitating predictive control, fault detection, and simulation through high-fidelity virtual replicas of physical assets. To alleviate the significant computational burden on the Cloud Server (CS) of centralized DT services, existing solutions commonly deploy DT modules on edge servers (ESs). However, deploying each DT module at the edge requires intensive resources to support frequent data updates, processing, and analysis; thus, unrestricted module deployment can pose substantial sustainability challenges, particularly when renewable energy availability is limited. To tackle this challenge, we propose a green DT-IIoT architecture and formulate a DT module placement problem that jointly minimizes DT service freshness and carbon emissions, subject to constraints on data synchronization accuracy, computing resources, and storage capacity. Among these, DT service freshness, which indicates real-time responsiveness, and data synchronization accuracy, which reflects the reliability of real-time input data, are two critical metrics affecting DT service quality. Furthermore, we propose a Dueling Double Deep Q-Network (D3QN) based placement algorithm (DDMP), which achieves high performance with a relatively simple structure that is well-suited for rapidly evolving IIoT scenarios. Simulation results demonstrate that our proposed approach enhances DT service freshness while reducing carbon emissions compared with baseline methods.

The exponential increase in connected user devices poses scalability challenges for centralized cloud computing. Mobile Edge Computing (MEC) and Fog Computing alleviate latency by deploying computation and storage resources closer to end-users. However, due to the resource limitations, heterogeneity, and dispersed nature of edge servers, there is a need to jointly optimize service caching and application placement strategies to enhance service quality. Given the widespread use of containerized services at the edge, we propose a distributed caching scheme that allows all edge nodes to cache services at the granularity of container image layers. This collaborative caching approach reduces the real-time latency, bandwidth consumption, and caching costs associated with retrieving and initializing applications. Additionally, to address the variability in application popularity across different edge regions, we model application popularity using a Zipf distribution and construct a multi-slot joint optimization model for caching and deployment decisions based on deployment cost, application startup time, and average delay. We then propose a two-stage optimization method to solve this model, demonstrating through comparison with centralized and P2P models the effectiveness of the proposed approach.

In recent years, the rapid development of the industrial Internet of things (IIoT) has brought new innovation opportunities to the manufacturing industry, but it also faces some major challenges. Currently, the IIoT systems often lack flexibility in sensing capabilities and have rigid communication architectures, resulting in insufficient coordination between different control tasks. In addition, the disconnect between sensing, communication, and computing further limits the system's ability to achieve optimal control, and the lack of intelligent data processing in the system also increases the control cost. In response to these challenges, this paper proposes an intelligent control framework, CISCC, which combines industrial edge computing technology with artificial intelligence (AI) models to achieve a deep integration of sensing, communication, and computing resources in IIoT systems, aiming to jointly optimize the configuration of these resources to improve the overall system performance and reduce control costs. Simulation results show that the CISCC framework can effectively handle resource allocation problems in IIoT systems, thereby better supporting the development of smart manufacturing applications.

In this paper, we propose an innovative architecture for sharing intelligence in low earth orbit (LEO) satellite edge computing networks. Specifically, we adopt the sharing of intel-ligence to satellites via ground stations to improve the response speed of satellites in processing intelligent services. Considering the burden of frequent transmission of intelligent models over unstable ground-satellite links, the satellites share intelligence with each other in the coalition, which greatly reduces the service response delay. In addition, considering the poor generalization of pre-trained intelligent models transmitted by ground stations, we design a model aggregation scheme with differentiated weights. Each coalition appoints a coalition center satellite, tasked with aggregating models and re-sharing them to individual satellites, thereby enhancing model performance. Then, we propose two low-time complexity algorithms to solve the above problems. Finally, the effectiveness and superiority of the proposed schemes are verified through extensive simulations.

With the rise of the computing power network (CPN), which integrate edge computing, cloud computing, and network infrastructure, replicated computing services are increasingly distributed to meet user demands for location-independent, reliable, and low latency services. Service anycast forwarding coordinates distributed service instances by binding them to a unified identifier and dynamically routing requests to the optimal instance. However, challenges such as varying user demand distribution, network complexity, and service instance heterogeneity complicate balanced service forwarding. To address these, we propose an SDN-based service anycast forwarding mechanism for CPN (SA-CPN). In the data plane, a cyclic forwarding queue efficiently maps weighted strategies and selects instances for each service request, improving policy performance. In the control plane, an optimal transport model balances network and computation latency based on service instance capabilities. We further design an optimal transport-based service anycast forwarding algorithm (OTSAF) using Sinkhorn iterations. Our implementation of SA-CPN in a real system shows that OTSAF consistently outperforms four baseline methods across various performance metrics.

With the continuous advancement of network technologies and hardware devices, computation-intensive and latency-sensitive tasks have emerged worldwide, requiring networks to provide extensive coverage, low latency, and robust computing capabilities. Leveraging the global coverage of LowEarth Orbit (LEO) satellites and the flexible resource invocation capabilities of the Computing Power Network (CPN), we propose a Satellite-Terrestrial Computing Power Network (ST-CPN) framework that integrates both strengths. In this framework, tasks can be offloaded to satellites closer to users for processing, ensuring high-quality services anytime and anywhere. However, due to the dynamic nature of the network and the limited resources of individual nodes, efficiently executing complex dependent tasks presents significant challenges. Therefore, we investigate the dependent task offloading problem in the dynamic ST-CPN environment. Considering dynamic changes of topology and available resources caused by satellite mobility, we propose a Time-Space-Varying Resource Graph (TSVRG) to capture the status of the communication, storage, and computation resources. On this basis, given that individual nodes struggle to process dependent tasks, we offload multiple subtasks of a task to different nodes for collaborative processing. In this paper, we model the task as a Directed Acyclic Graph (DAG) and transform the offloading problem into a mapping problem from the DAG to TSVRG. We then introduce a Delay Predictionbased Graph Mapping Algorithm (DPGMA) to address this problem. Simulation results indicate that our scheme achieves better performance than the benchmark schemes.

Existing video-based human pose estimation (HPE) methods commonly rely on large networks to localize body joints across all frames, achieving remarkable accuracy but imposing high memory and computational demands that limit their applications on resource-constrained devices. Moreover, most models lack the capability to accommodate dynamic changes in available resources, which can negatively impact the performance of parallel tasks. To address these issues, this article proposes a novel yet effective framework for efficient and adaptive HPE on resource-constrained devices. Specifically, the proposed approach adopts the knowledge distillation (KD) strategy to train a light-weight pose estimator network, which is capable of executing rapidly with low computational cost. To further increase the overall efficiency, it exploits the temporal coherence between successive video frames and explicitly propagates body joints from previous frames rather than naively extracting them using a pose estimator. Furthermore, a prediction-based mechanism is adopted to facilitate adaptive key-frame selection, dynamically determining the optimal number of keyframes, thus enhancing the overall efficiency and adaptability. Experiments on Penn Action, Sub-JHMDB, and real-world systems demonstrate that the proposed method achieves comparative accuracy, superior efficiency, and robust flexibility in dynamic scenarios.

Federated learning (FL) has emerged as a pivotal approach for edge-based distributed machine learning, yet it faces significant challenges due to the constrained capacities and heterogeneity of edge devices, including non-IID data distribution, communication constraints, and learning inefficiencies. Furthermore, a one-fits-all global model often fails to perform optimally across diverse participating devices. In this paper, we present NestFL, an efficient FL framework for edge computing that can jointly improve the training efficiency and achieve personalization. Specifically, NestFL innovates by incorporating distributed model pruning, creating a hierarchy of structured-sparse subnetworks tailored to the unique resource profiles of client devices. These subnetworks are integrated into a nested global model, ensuring parameter sharing without increasing the parameter space, thereby significantly reducing computational and communication burdens. Meanwhile, it implements a cross-training mechanism, allowing clients to train on a broader dataset and maintain consistent decision boundaries. Furthermore, a weighted aggregation mechanism is designed to improve training performance and maximally preserve personalization. Experimental results in different applications demonstrate the superiority of NestFL over the baseline approaches in terms of model accuracy, convergence speed, and personalization preservation.

This paper presents AdaHPE, an innovative and efficient framework for human pose estimation (HPE) designed specifically for edge computing devices with constrained and fluctuating resources. AdaHPE redefines the conventional HPE workflow by converting the resource-demanding pose regression into a sequence of computationally feasible pose propagation tasks. The framework incorporates a memory-augmented LSTM network with a global memory repository, allowing AdaHPE to adaptively choose keyframes based on real-time data and the device’s resource status, thereby optimizing the trade-off between accuracy and computational efficiency. A reinforcement learning component is further integrated to intelligently adjust the ratio of keyframes used, enhancing the framework’s adaptability. Utilizing policy gradient algorithms, AdaHPE is optimized to maximize a reward function that encourages both accurate and resource-efficient pose estimations, while respecting a given keyframe constraint. Extensive experiments on benchmarks including Penn Action, Sub-JHMDB, NTU RGB+D 120, and real-world datasets demonstrate that AdaHPE can significantly reduce computational overhead compared to per-frame HPE models while preserving high accuracy and robustness under varying resource limitations. Moreover, the seamless compatibility of our approach with various off-the-shelf HPE models highlights its versatility and potential for broad applications.

Industrial Cyber-Physical Systems (CPS) have made significant strides in recent years, driving the future of manufacturing. However, for further advancement in Cloud-Fog Automation (CFA), several challenges remain: rigid sensor sampling, inflexible communication configurations, insufficient coordination between cloud and fog resources, and a lack of integration between sensing, communication, and computing for effective control. To address these issues, this article presents SeCo4, an intelligent control framework for the co-design of sensing, communication, and computing in industrial CPS. The SeCo4 optimization problem is analyzed and divided into two sub-problems: a multi-controller cloud resource competition problem, formulated with a combinatorial auction to enable multi-controller competition for additional cloud resources and improve control performance; and a joint resource optimization problem for sensing, communication, and computing, modeled using a Mixed Integer Programming (MIP) problem to minimize control costs. Given the interdependence of these sub-problems, a hierarchical solution based on the online matching mechanism and the heuristic approach is developed to iteratively find the optimal solution. Finally, extensive simulations demonstrate the effectiveness and superiority of the proposed approach.

Decentralized federated learning (DFL) is a promising technique to enable distributed machine learning over edge nodes without relying on a centralized parameter server. However, existing DFL network topologies, such as fully connected, partially connected, or lower-tier hierarchical topology often struggle to effectively address the unique challenges presented by edge networks, including edge heterogeneity, communication resource constraint, and data Non-IID. In order to tackle these challenges, we propose a data-aware clustering algorithm, called FRACTAL, to construct a multi-tier hierarchical topology in a bottomup manner taking into consideration both data distribution and communication efficiency for DFL. We theoretically explore the quantitative relationship between the convergence bound of multi-tier FL and the data distribution among each-tier servers. To further improve communication efficiency and address edge heterogeneity, we deploy a time-sharing communication scheduling algorithm within each fractal unit (the basic structure in FRACTAL consisting of multiple nodes and an aggregator), called magic mirror method (MMM), to determine the optimal order of model distributing and uploading for nodes. We conduct extensive experiments on the classical models and datasets to evaluate the performance of FRACTAL, and the results show that FRACTAL can significantly accelerate the DFL model training by 48.6%- 72.3% compared with the state-of-the-art solutions.

Recently, Computing Force Networks (CFNs) have emerged to deeply integrate and flexibly schedule multi-layer, multi-domain, distributed, and heterogeneous computing force resources. CFNs build a resources trading platform between consumers and providers, facilitating efficient resource sharing. Therefore, resources trading is an important issue but it faces some challenges. Firstly, because all kinds of large-scale and small-scale resource providers are distributed in a wide area and the number of consumers is larger compared with edge/cloud computing scenarios, the credibility of consumers and providers is hard to guarantee. Secondly, due to market monopolies by large resource providers, fixed pricing strategies, and information asymmetry, both consumers and providers exhibit a low willingness to engage in resources trading. To solve these challenges, the paper proposes an incentive mechanism for trust-driven resources trading to guarantee trusted and efficient resources trading. We first design a trust guarantee scheme based on reputation evaluation, blockchain, and trust threshold setting. Then, the proposed incentive scheme can dynamically adjust prices and enable the platform to provide appropriate rewards based on providers’ classified types and contributions. We formulate an optimization problem aiming at maximizing the trading platform’s utility and obtaining an optimal contract based on individual rationality and incentive compatible constraints. Simulation results verify the feasibility and effectiveness of our scheme, highlighting its potential to reshape the future of computing resource management, increase overall economic efficiency, and foster innovation and competitiveness in the digital economy.

The Industrial Internet of Things (IIoT) has become a critical technology to accelerate the process of digital and intelligent transformation of industries. As the cooperative relationship between smart devices in IIoT becomes more complex, obtaining deterministic responses of IIoT periodic time-critical computing tasks becomes a crucial and nontrivial problem. However, few current works in cloud/edge/fog computing focus on this problem. This paper is a pioneer in exploring deterministic scheduling and network structural optimization problems for IIoT periodic time-critical computing tasks. We first formulate the two problems and derive theorems to help quickly identify computation and network resource sharing conflicts. Based on this, we propose a deterministic scheduling algorithm,IIoTBroker, which realizes a deterministic response for each IIoT task by optimizing the fine-grained computation and network resources, and a network optimization algorithm,IIoTDeployer, which provides a cost-effective structural upgrade solution for existing IIoT networks. Our methods are illustrated to be cost-friendly, scalable, and deterministic response guaranteed with low computation cost from our simulation results.

Recently, the advancement of information technologies have accelerated the generation of big data, necessitating substantial computing power. This has spurred the development of Computing Power Networks (CPNs), which can overcome the limitations of computing power isolation. However, CPNs consume significant energy and produce large carbon emissions during big data processing. Therefore, an energy-efficient task scheduling scheme, coupled with the utilization of renewable energy, appears to be particularly necessary. Nevertheless, the interplay between computing power and networks, and the spatiotemporal variations in green CPNs pose a challenge to designing the task scheduling scheme. In this paper, we propose a transferable spatiotemporal task scheduling scheme with a triple selection of CPN nodes, routing paths, and forwarding time of tasks. The scheme can overcome the dynamics of green CPNs, and jointly optimize the energy consumption and carbon emissions with ensuring delay constraints and long-term load balancing. Then, we present a task scheduling algorithm based on improved nondominated sorting genetic algorithm-II (NSGA-II) to solve the problem, and numerical results demonstrate that our scheme is effective in reducing the overall energy consumption and carbon emissions of CPNs.

This paper establishes a dual-timescale framework for joint resource slicing and task scheduling in satellite edge computing (SEC) networks. Specifically, to capture network dynamics and task stochasticity at small timescales, we formulate the task scheduling problem as a Markov decision process (MDP) to minimize task delay, network energy consumption, and packet loss. We design a deep reinforcement learning-assisted task scheduling (DRTS) algorithm inspired by the soft actor-critic (SAC) algorithm to learn the scheduling policy. Task processing performance is affected by communication and computing re-sources allocated to respective resource slices. Thus, considering that frequent resource slicing has a significant management over-head, we further optimize resource slices on a larger timescale. To obtain a policy with low complexity, we propose a greedy-based heuristic algorithm. A hierarchical solution is constructed to find the optimal solution due to the correlation between the two timescale problems. Finally, to validate the effectiveness and superiority of the proposed scheme, extensive simulations are performed.

Service Customized Networking (SCN) is emerging as an escalating technological trend to address the personalized requirements of services, which are plagued in traditional “best-effort” transmission networks. Organically coordinating heterogeneous domains in Wide Area Networks (WANs) is essential for establishing end-to-end customized service delivery. However, the peer-to-peer centralized communication mode between Au-tonomous Domains (ADs) hinders their connectivity and makes it challenging to support diverse intra-domain routing protocols for the desired Quality of Service (QoS). To achieve customized service scheduling for predictable QoS in WANs, we propose a contract theory-based incentive mechanism. In specific, we first select trusted ADs with high service qualities by computing their reputations through a subjective logic model. The Service Provider (SP) decomposes the overall QoS requirements by domains logically from a global perspective. These decomposed QoS metrics will splice differentiated service capabilities from ADs to obtain the expected end-to-end connection. To address information asymmetry between the SP and ADs, we formulate contribution-reward contract items and devise an optimization problem of maximizing the whole system utility. The optimal contract problem is solved through constraints of individual rationality and incentive compatibility. Simulation results indi-cate the feasibility and effectiveness of our scheme on service customization and economic benefits.

In recent years, there has been a proliferation of Edge Intelligence (EI) services, especially within Internet of Vehicles (IoV) scenarios, accompanied by a growing demand for multi-modal content generation. In response, Generative Artificial Intelligence (GAI) has emerged as a promising solution, equipping EI to produce diverse Artificial Intelligence-Generated Content (AIGC) for ubiquitous edge services. However, existing cloud-based GAI capabilities, which are mostly provided via the web and the Internet, introduce unacceptable latency overhead and heightened security risks for vehicular services. To address the above shortcomings and the lack of endogenous mechanisms for applying GAI to IoV scenarios, in this paper, we propose a layered vehicular GAI framework that seamlessly integrates GAI and EI. Within this framework, we devise a distributed collaborative inference mechanism between Road-Side Units (RSUs) and vehicles. Furthermore, we formulate the shared and local inference splitting problem, a pivotal challenge influencing both GAI service latency and content-generation capability. To tackle this issue, we introduce a backward induction-based algorithm, which enables the system can make splitting decisions using a simple threshold-based policy. Simulation results underscore the remarkable performance of the proposed system and vehicular collaborative inference mechanism, promising to facilitate diverse content generation within vehicular networks.

In the era of data-driven intelligent Internet, efficient utilization of computing power is paramount. Yet, current cloud-edge collaboration architectures face challenges with computing power isolation, adversely affecting efficiency and user experience. Computing Power Networks (CPNs) leverage networks with cloud-native applications to connect and manage resources, offering a blueprint for a collaborative computing ecosystem. In the CPN, scheduling stands as a pivotal function. However, conventional scheduling often neglects the interplay between computing and networks. To rectify this, we present a collaborative task scheduling system in CPNs that simultaneously contemplates the selections of computing nodes and network links. Aiming to maintain a balanced load for both computing and network resources, we formulate the scheduling challenge as a Constrained Markov Decision Process (CMDP). This approach focuses on optimizing both execution delay and success rate of computing tasks with load balancing constraints in CPNs. To facilitate the resolution of the CMDP, we introduce a Lyapunov-optimized Deep Reinforcement Learning (DRL) algorithm, which reconfigures the long-term constraint into immediate optimization. We provide numerical results to demonstrate the effectiveness of our suggested policy and algorithm.

The utilization of emergent Generative Artificial Intelligence (GAl) within the realm of Internet of Vehicles (loV) can augment edge intelligence, thereby catering to the diverse content-generation needs of novel in-vehicle services. Nonethe-less, existing cloud-centric GAl paradigms are not inherently suitable for wireless vehicular networks, primarily due to their extensive computing requirements, lack of specificity, and spatial detachment from end users. To cope with these challenges, we introduce an innovative Generative 10 V (g 10 V)architecture that employs a collaborative fine-tuning mechanism for pre-trained GAl models. The mechanism is mainly orchestrated collaboratively by Road-Side Units (RSUs) and vehicles within a Federated Learning (FL) paradigm. Here, we take text-to-image diffusion models as typical examples to show the co-fine-tuning workflow in detail, aiming to utilize edge traffic data to realize rapid, customized, and lightweight GAl in the resource-limited 10 V scenario. Thereafter, we formulate the problem of edge communication and computation resource allocation during RSU-vehicle co-fine-tuning, which is pivotal for optimizing time and energy consumption within this process. To address the challenge, we deploy a Self-adaptive Harmony Search (SHS)-based resource allocation strategy. Experiments based on Stable Diffusion vl-4 model validate the excellent performance in image generating and the time and energy consumption during co-fine-tuning in resource-limited and fast-changing 10 V scenarios.

With the continuous development of network technology and hardware devices, panoramic live cast is promising and widely used in various industries. To meet massive heterogeneous viewer demands, live cast video streams must be transcoded into multiple versions and then transmitted to viewers. By offloading transcoding workloads to network nodes closer to broadcasters and viewers, computing power networks (CPN) have been considered an effective means to provide viewers with a higher quality of experience (QoE). In this paper, we study the joint video transmission and transcoding resource allocation in the CPN-based panoramic livecast system. Considering the versatility and simplicity, we offer a multi-layer network model to capture the stochastic characteristics of transmission and transcoding processes and transform the joint resource allocation problem into a broader shortest path problem (SPP). As the scale of networks expands, the SPP may become intractable on classical computers. This paper explores the viability of solving the SPP on quantum computers by utilizing quantum resources such as superposition and entanglement, then proposes a shortest path algorithm based on the quantum approximate optimization algorithm (QAOA-SPP) for jointly optimizing the latency and overhead of transmission and transcoding. Simulation results indicate that our algorithm can achieve better performance than the benchmark schemes under reasonable parameter settings.

Multiedge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, and more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multiedge computing system particularly complicated. This article first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Second, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS is proposed. CoRaiS embeds the real-time states of the multiedge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that the CoRaiS can make a high-quality scheduling decision in real-time, and can be generalized to other multiedge computing system, regardless of the system scales. Characteristic validation also demonstrates that the CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling.

The fiercely competitive business environment and increasingly personalized customization needs are driving the digital transformation and upgrading of the manufacturing industry. IIoT intelligence, which can provide innovative and efficient solutions for various aspects of the manufacturing value chain, illuminates the path of transformation for the manufacturing industry. It’s time to provide a systematic vision of IIoT intelligence. However, existing surveys often focus on specific areas of IIoT intelligence, leading researchers and readers to have biases in their understanding of IIoT intelligence, that is, believing that research in one direction is the most important for the development of IIoT intelligence, while ignoring contributions from other directions. Therefore, this paper provides a comprehensive overview of IIoT intelligence. We first conduct an in-depth analysis of the inevitability of manufacturing transformation and study the successful experiences from the practices of Chinese enterprises. Then we give our definition of IIoT intelligence and demonstrate the value of IIoT intelligence for industries in fucntions, operations, deployments, and application. Afterwards, we propose a hierarchical development architecture for IIoT intelligence, which consists of five layers. The practical values of technical upgrades at each layer are illustrated by a close look on lighthouse factories. Following that, we identify seven kinds of technologies that accelerate the transformation of manufacturing, and clarify their contributions. The ethical implications and environmental impacts of adopting IIoT intelligence in manufacturing are analyzed as well. Finally, we explore the open challenges and development trends from four aspects to inspire future researches.

In this paper, we establish a two-timescale framework for the joint service deployment and task scheduling problem in satellite edge computing networks.We aim to optimize the computing performance of networks with diverse quality-of-service (QoS) guarantees for computing tasks. Specifically, to capture the small-timescale network dynamics and task randomness, we formulate the task scheduling problem as a constrained Markov decision process (CMDP) to minimize the energy consumption, load imbalance and packet loss of networks while ensuring the long-term delay. The Lyapunov technique is employed to deal with the delay constraints. A soft actor-critic (SAC)-based deep reinforcement learning (DRL) framework is designed to learn the stationary scheduling policy. We further explore the significant impact of deploying diverse services on the performance of task scheduling in satellite edge computing. Considering that frequent deployment of services will incur huge deployment overhead, we optimize the service deployment on a larger timescale. The optimization problem is modeled as an integer programming problem to improve the service capability of networks and reduce service deployment costs. A heuristic-based atomic orbital search (AOS) approach is proposed to obtain the superior policy with low complexity. Due to the correlation between the problems of two timescales, a hierarchical solution is constructed to iteratively find the excellent solution. Finally, extensive simulations are conducted to validate the effectiveness and superiority of the proposed scheme.

“Read-write-own” based Web3 has been proposed as a promising user-centric Internet to open the new generation of the World Wide Web, where Web3 users can independently manage data and derive value from creating content without relying on intermediaries. Connected and autonomous vehicles (CAVs) in Web3 can trade models in a self-controlled and decentralized credible way, which is a fundamentally and principally innovation based on novel architecture. Effectively implementing such paradigms involves proper model trading strategies. However, reinforcement learning (RL)-based strategies face challenges of poor generalization ability, low feasibility, and the exploration-exploitation dilemma. It is also difficult to define an explicit and appropriate reward function. Therefore, in this paper, we propose an intelligence-based reinforcement learning (IRL) approach for CAVs in Web3. We present a framework to enable model transactions between CAVs. Also, we provide a decentralized identifier (DID)-based identity management system for resource description and data verification to access Web3, followed by the mechanism and supporting smart contracts. Furthermore, we formulate the model trading issue as an active inference to form higher-level cognition about the environment without rewards. Then we use IRL to solve it. And we use “intelligence”, a high-level indicator, to quantify the efficiency of such cognition. It can evaluate the difference between the predicted state and the real state in policy exploration. The proposed scheme shows good generalization and can auto-balance exploration and exploitation, simultaneously achieving outperforming performance on the model trading issue with no rewards. In simulations, the performance of the proposed scheme is compared with existing methods.

In this paper, we optimize network computational performance and ensure diverse quality of service (QoS) for tasks by developing a dual-timescale joint optimization framework for satellite-terrestrial integrated edge computing networks (STECN). In our architecture, STECN can handle intelligent tasks for the Internet of remote things (IoRT) devices based on multiple configured applications deployed. Specifically, we formulate task scheduling as a Markov decision process (MDP) to minimize network energy consumption and task processing delay at small timescales. A deep reinforcement learning (DRL) framework is designed for policy learning. Recognizing the impact of resource slicing on task scheduling in STECN and the deployment overhead from frequent changes, we further optimize resource slicing at larger timescales. To enhance network service capability under dynamic demand, we establish a resource slice gap index, characterizing the difference between actual resources and service demand. By a heuristic-based artificial electric field (AEF) approach, we obtain an optimal strategy with low complexity. Considering the correlation between two timescales, the optimal solution is found by iteratively constructing a hierarchical solution. In addition, to guarantee the global load balancing of the network, we introduce a self-attention mechanism, which allows the knowledge of other satellites to be taken into account when slicing the satellite resources. Finally, extensive simulations confirm the effectiveness and superiority of the proposed scheme.

Distributed clouds decentralize cloud resources, moving from a single high-level point in the network to multiple low-level points, allowing for the dynamic distribution of services across the “cloud-edge-end”. Nevertheless, the “best-effort” traditional networks suffer from unpredictable service quality and limited collaboration between services and networks. To address these shortcomings, we present a novel solution named “Network-as-a-Service (NaaS) Tickets,” inspired by traffic tickets in transportation systems to empower distributed clouds with customized service capabilities. Specifically, we first propose NaaS Tickets-enabled service-customized distributed clouds (NT-SCDC) to realize on-demand and service-oriented interconnection in a wide area. To establish a solid connection between services and networks, we introduce an auction-driven matching mechanism for NaaS Tickets. Then, the matching problem is formulated via an online framework MatOnline, which translates the long-term market problem into a series of one-shot auctions for NaaS Tickets. Based on the Vickrey-Clarke-Groves (VCG) mechanism, we develop MatVCG algorithm to handle one-shot matching problems, guaranteeing truthfulness, individual rationality, and social welfare. Moreover, we improve the performance of MatOnline to find the minimum feasible scale-down ratio with reduced budget expenditure. Experimental results demonstrate our algorithm achieves a stable competitive ratio on social welfare, effectively meeting customized demands

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%.

The growth of intelligent vehicular services, like augmented reality (AR) road simulation, underscores the need for rapid, multi-modal content generation. Generative artificial intelligence (GAI) models, known for their swift production of diverse artificial intelligence-generated content (AIGC), stand out as a prime solution. However, integrating cloud-centric GAI models into vehicular networks is fraught with challenges. Notably, to offer specialized generative edge intelligence (EI) and boost vehicular AIGC, GAI models need to tap into user data and utilize significant computation resources. Moreover, their deployment across vehicular networks is essential for proximity-based distributed inferences. Yet, edge devices are resource-limited, and data sharing can raise safety and privacy concerns. Addressing these challenges, this paper introduces GAI-IoV, an EI-enabled GAI framework facilitated through the cooperation between road-side units (RSUs) and vehicles. Subsequently, we propose the workflow for collaborative fine-tuning and distributed inference. On this basis, two pivotal vehicle-centric problems are then formulated: computation and communication resource allocation for federated fine-tuning (FFT) to optimize time and energy cost, and splitting strategy of shared and local inferences to optimize inference latency and content-generation capability. To solve these optimizations, we introduce a self-adaptive global best harmony search (SGHS) algorithm for resource allocation and a backward induction method for determining inference splitting strategy. Our experiments based on the Stable Diffusion v1-4 model vouch for a superior fine-tuning and inference capabilities of GAI-IoV. Furthermore, simulations underscore its resource utilization and distributed inference efficiency in dynamic vehicular scenarios.

Joint resource allocation in integrated networking, computing, and caching frameworks has attracted plenty of attention. In software-defined service-centric networking (SDSCN), we investigate an energy cost economical functionality embedding (FE) problem. The FE problem is a non-convex quadratically constrained quadratic programming (QCQP) problem which is difficult to obtain its global optimal solutions. In this paper, we propose a low-complexity high-performance algorithm for energy-economical FE design in large-scale SDSCN systems by leveraging the alternating direction method of multipliers (ADMM) together with successive convex approximation (SCA). In specific, the FE problem is first approximated as a sequence of convex subproblems via SCA. Each convex subproblem is then reformulated as a novel ADMM form to enable parallel computations and closed-form solutions. Numerical results show that our fast algorithm reduces the complexity by orders of magnitude and obtains favorable performances compared with state-of-the-art algorithms.

Recently, Computing Force Networks (CFN) is emerging to deeply integrate and flexibly schedule multi-layer, multi-domain, distributed, and heterogeneous computing force resources among the cloud, edge network, and end devices. In CFN, the market monopoly of large resource providers results in a lack of bargaining power for small-sized providers. Existing resources pricing strategies ignore dynamic market factors affecting prices. Moreover, there is information asymmetry between resource consumers and providers. These problems destroy the fairness of the trading market and damage the benefits of consumers and providers, making them have a low degree of willingness to participate in resources trading. Therefore, this paper proposes a contract theory-based incentive mechanism to solve the above problems and motivate resource consumers and providers to join CFN. The proposed scheme classifies resource commodities into different types and enables the trading platform to provide appropriate rewards based on commodities' types and contributions. More specifically, we formulate an optimization problem aiming at maximizing the trading platform's utility and obtain an optimal contract scheme based on the individual rationality and incentive compatible constraints. Simulation results verify the feasibility and effectiveness of our scheme.

As Artificial Intelligence (AI) has flourished in various industries in recent years, the evolutionary trend of endogenous network intelligence continues to accelerate. Connected intelligence, which aims to achieve a widely distributed and collaborative evolution of intelligence, has received much attention. Meanwhile, the emerging Computing Power Network (CPN) provides more robust computation and communication capabilities for intelligence training and intelligent application processing. In this context, the integration of CPN and connected intelligence becomes a potential solution to drive the digital and intelligent transformation of networks. In this paper, we propose a scheme to jointly consider task scheduling, routing, and intelligence capability improvement during the processing of smart applications represented by Digital Twin (DT) in the CPN-enabled connected intelligence systems. We formulate the problem of jointly optimizing the processing time consumption and training accuracy improvement. We solve the problem using a modified NSGA-II algorithm and numerical results show that our approach is effective in optimizing the overall average time consumption and improving the accuracy of the intelligence models distributed in the system during task processing.

Serverless edge computing is seen as a promising enabler to execute differentiated Industrial Internet of Things (IIoT) applications without managing the underlying servers and clusters. In IIoT serverless edge computing, IIoT workflow scheduling for cloud-edge collaborative processing is closely related to the service quality of users. However, serverless functions decomposed by IIoT applications are limited in their deployment at the edge due to the resource-constrained nature of edge infrastructures. In addition, the scheduling of complex IIoT applications supported by serverless computing is more challenging. Therefore, considering the limited function deployment and the complex dependencies of serverless workflows, we model the workflow application as directed acyclic graph and formulate the scheduling problem as a multiobjective optimization problem. A dueling double deep Q-network-based solution is proposed to make scheduling decisions under dynamically changing systems. Extensive simulation experiments are conducted to validate the superiority of the proposed scheme.

Edge intelligence is emerging as a new interdiscipline to push learning intelligence from remote centers to the edge of the network. However, with its widespread deployment, new challenges arise in terms of training efficiency and service of quality (QoS). Massive repetitive model training is ubiquitous due to the inevitable needs of users for the same types of data and training results. Additionally, a smaller volume of data samples will cause the over-fitting of models. To address these issues, driven by the Internet of intelligence, this paper proposes a distributed edge intelligence sharing scheme, which allows distributed edge nodes to quickly and economically improve learning performance by sharing their learned intelligence. Considering the time-varying edge network states including data collection states, computing and communication states, and node reputation states, the distributed intelligence sharing is formulated as a multi-agent Markov decision process (MDP). Then, a novel collective deep reinforcement learning (CDRL) algorithm is designed to obtain the optimal intelligence sharing policy, which consists of local soft actor-critic (SAC) learning at each edge node and collective learning between different edge nodes. Simulation results indicate our proposal outperforms the benchmark schemes in terms of learning efficiency and intelligence sharing efficiency.

Recently, there are significant advances in networking, computing, and caching (NCC). Nevertheless, few attempts have been made to explore the potential of the promising server-less computing paradigm in NCC integrated frameworks. In this paper, we consider a software-defined serverless service-centric networking (SD-SSCN) framework that not only dynamically orchestrates NCC by combining software-defined networking and service-centric networking technologies but also strongly focuses on the context of serverless computing. To achieve a cost-efficient green SD-SSCN system, we first formulate the joint resource allocation problem to minimize an overall average cost model. We derive this problem as a nonlinear integer programming problem, then we relax it as a quadratic programming problem and develop a primal-dual interior-point algorithm to find joint resource allocation solutions. Simulation results show that our proposed SD-SSCN framework significantly outperforms the traditional networks in terms of the average cost in the context of serverless computing.

In this paper, we present NestFL, a learning-efficient FL framework for edge computing, which can jointly improve the training efficiency and achieve personalization. Specifically, NestFL takes the runtime resources of the edge devices into consideration and assigns each device a sparse-structured subnetwork by progressively performing the structured pruning. During training, only the updates of these subnetworks are transmitted to the central server. Additionally, these generated subnetworks adopt a structure- and parameter-sharing mechanism, making themselves nested inside a multi-capacity global model. In doing so, the overall communication and computation costs can be significantly reduced, and each device can learn a personalized model without introducing extra parameters. Furthermore, a weighted aggregation mechanism is designed to improve the training performance and maximally preserve personalization.

In this paper, we design a task scheduling scheme for Internet of Things (IoT) workflow applications in serverless edge computing. Notice the fact that complex applications in traditional serverless computing are decomposed into several stateless, dependent functions, whose execution environments are pre-deployed at the resource-finite edge domain, we model the workflow application as Directed Acyclic Graph (DAG) by considering the distribution of edge resources and the deployment of serverless functions. We further formulate the scheduling problem as a multi-objective optimization problem to reduce the time consumption, energy consumption, and cost simultaneously. Then, considering the diversity of solution space and the fast convergence to optimal solutions, an improved hybrid algorithm that combines Particle Swarm Optimization and Genetic Algorithm (PSO–GA) is introduced and utilized to make the scheduling decision. Finally, extensive simulation experiments are conducted to validate the superiority of the proposed scheme.

By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications. Task scheduling is a critical issue in serverless edge computing as it significantly impacts the quality of user experience. In contrast to the centralized scheduling in the cloud center, serverless edge task scheduling is more challenging due to the heterogeneous and resource-constrained nature of edge resources. This article aims to study the distributed task scheduling for the IoT in serverless edge computing networks, in which heterogeneous serverless edge computing nodes are rational individuals with interests to optimize their own scheduling utility while the nodes only have access to local observations. The task scheduling competition process is formulated as a partially observable stochastic game (POSG) to enable serverless edge computing nodes to noncooperatively schedule tasks and allocate computing resources depending on their locally observed system state, which takes into account the associated task generation state, data queue state, communication channel state, and previous computing resource allocation state. To solve the proposed POSG and deal with the partial observability, a multiagent task scheduling algorithm based on the dueling double deep recurrent$Q$-network (D3RQN) method is developed to approximate the optimal task scheduling and resource allocation solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.

Driven by the growing popularity of mobile applications, such as the Internet of Things (IoT), fog computing has been envisioned as a promising approach to enhance the computation capability of mobile devices and reduce the energy consumption. In this article, we aim to investigate the dynamic computation offloading problem in the IoT fog system under the fast time-varying wireless channel conditions. Our work differs from the existing work, which is based on the assumption that the channel-state information can be perfectly obtained by the offloading agent (e.g., the IoT device). In reality, due to hardware limitation, short sensing time, and network connectivity issues in IoT fog systems, it is difficult for the IoT device to have the perfect knowledge of a dynamic channel environment. Therefore, in this article, we propose a partially observable offloading scheme to enable the IoT device to make the optimal offloading decision with imperfect channel-state information. The optimization problem is formulated as a partially observable Markov decision process (POMDP) formulation, with the objective of minimizing the IoT device's energy consumption while meeting its requirement on task processing delay. To find the optimal offloading solution, an offline algorithm based on the deep recurrent $Q$ -network (DRQN) is developed. Finally, extensive simulation experiments are performed to evaluate the effectiveness of the proposed offloading scheme.

On-path caching is a building block in Named Data Networking that helps eliminate redundant traffic. The performance of redundancy elimination depends on both Content Store (CS) and Pending Interest Table (PIT), i.e., CS caches content for future reuse, and PIT aggregates repetitive requests in a short period. However, contemporary proactive caching strategies only take account of CS while neglecting PIT. In this work, we integrate both PIT and CS into the proactive caching model, derive how to calculate aggregated request rate, and propose an algorithm to calculate the aggregated request rate across the tree topology. Then we formulate caching placement into optimization problems and solve them with a decomposition-based evolutionary algorithm. The simulation results show that the proposed scheme outperforms conventional solutions.

Recently, fog computing has emerged as a prospective technique to provide pervasive and agile computation services for Internet-of-Things (IoT) devices and support advanced applications. Introducing the energy harvesting (EH) technique into the fog computing system can extend the battery lifetime and provide a higher quality of experiences (QoE) for IoT devices. In the EH-enabled IoT fog system, computation offloading is an important issue and has attracted much attention. In most existing works, it is assumed that the IoT device is fully aware of the system state. However, in practical offloading problems, the IoT device may not be able to obtain accurate system state information, and only have a partial observation of the environment. Therefore, in this article, we investigate the decentralized partially observable offloading problem in the EH-enabled IoT fog system, in which multiple IoT devices cooperate to maximize the network performance while meeting their QoE requirements. We formulate the optimization problem as a decentralized partially observable Markov decision process (Dec-POMDP) in which each IoT device makes the task offloading decisions according to its local observation of the environment. The Lagrangian approach and the policy gradient method are adopted to find the optimal solution for the proposed problem. Due to the high complexity of solving the Dec-POMDP, a learning-based decentralized offloading algorithm with low complexity is presented to find the approximate optimal solution. Finally, extensive experimental evaluation and comparison are carried out to show the effectiveness of the proposed scheme.

Built-in caching in Named Data Networking (NDN) promises to provide efficient content delivery, where the dedicated on-path caching scheme is deployed to serve users' requests on the forwarding path. In this work, to utilize limited caching resources to achieve optimal performance, the caching placement decision is made by jointly considering the content popularity, underlying network topology, forwarding strategy and caching service mechanism in NDN. More specifically, we propose a service-aware caching model. In the model, we first define the Cache Service Matrix (CSM), which describes the position where each user's request is served for each piece of content. In order to make CSM comply with the caching placement, underlying topology, forwarding strategy, and on-path caching service mechanism, we propose an algorithm to calculate CSM under the preceding constraints. With CSM, the utility of caching placement could be derived correctly, and we formulate the optimal caching placement into optimization problems. Moreover, the differential grouping co-evolutionary (DG2-E) algorithm is adopted to decompose and solve the NP-hard optimization problems. Simulation results show the proposed scheme outperforms state of the art solutions in terms of inter-domain traffic reducing and request-response accelerating under arbitrary topologies.

Computation offloading has been considered as one of the key research issues in edge computing fields. In order to reduce the energy consumption of the mobile terminal, the energy efficiency issue of computation offloading has attracted a lot of attention from academia and industry. In this study, the authors propose an energy‐efficient computation offloading scheme in 5G cellular networks with edge computing and device‐to‐device (D2D) communications. They consider the computation offloading to fog computing devices via D2D communications and mobile edge computing (MEC) servers via cellular networks. And thus the computation task execution model can be composed of local execution, fog computing device execution and MEC server execution. Then, they formulate the computation offloading issue as stochastic optimisation problem, and use the Lyapunov optimisation technology framework to solve this problem. Finally, extensive simulation results are presented to illustrate the effectiveness of the proposed scheme.

The caching in fifth generation (5G) networks has been considered as a promising technique to reduce the duplicate traffic transmission and improve the users' quality of experience (QoE). Although many works have been done for caching in 5G networks, most of them focus on the content caching policy design to optimise the users' QoE, the issue to realise the energy efficiency of the whole network is not fully considered. Therefore, in this study, by introducing the hierarchical cooperative caching property, i.e. the core gateway and the base stations can cooperative cache the content, the authors study the problem of the hierarchical cooperative caching policy to realise the energy efficiency. They then formulate the hierarchical cooperative content placement problem as an integer programming problem to minimise the total energy consumption. Also, to reduce the computation complexity, the optimisation algorithm based on the idea of quantum‐inspired evolutionary is proposed, which has the fast convergence and approximate to the optimal solution. Finally, extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.

Caching in mobile networks can reduce the redundant data transmission and cope with the challenge of the explosive growth of mobile data traffic. It has been considered as a promising technology in 5G networks and has been attracting a lot of attention in recent years. Although many existing works have addressed the content placement problem or the cache optimisation problem, most of them do not consider the issue of hierarchical collaborative caching. Collaborative caching can further alleviate the traffic pressure and reduce the user‐perceived latency by reducing duplicate content transmission. Therefore, in this study, the authors consider a hierarchical collaborative caching framework with the cache deployment at the distributed gateway and mobile edge computing servers, and then design a novel caching strategy based on this framework. They formulate the hierarchical collaborative content placement problem as an optimisation problem to maximise the latency saving under the constraint of limited cache capacity. Since finding the optimal solution is an NP‐hard problem, they propose a genetic placement algorithm to find the near‐optimal solution to reduce the computation complexity. Numerical experiment results show that the proposed algorithms can significantly improve the performance compared with the reference algorithms.

Combining named data networking (NDN) and software-defined networking (SDN) has been considered as an important trend and attracted a lot of attention in recent years. Although much work has been carried out on the integration of NDN and SDN, the forwarding mechanism to solve the inherent problems caused by the flooding scheme and discard of interest packets in traditional NDN is not well considered. To fill this gap, by taking advantage of SDN, we design a novel forwarding mechanism in NDN architecture with distributed controllers, where routing decisions are made globally. Then we show how the forwarding mechanism is operated for interest and data packets. In addition, we propose a novel routing algorithm considering quality of service (QoS) applied in the proposed forwarding mechanism and carried out in controllers. We take both resource consumption and network load balancing into consideration and introduce a genetic algorithm (GA) to solve the QoS constrained routing problem using global network information. Simulation results are presented to demonstrate the performance of the proposed routing scheme.

Although some excellent works have been done on networking, caching, and computing, these three important areas have traditionally been addressed separately in the literature. In this paper, we describe the recent advances in jointing networking, caching, and computing and present a novel integrated framework: software-defined networking, caching, and computing (SD-NCC). SD-NCC enables dynamic orchestration of networking, caching, and computing resources to efficiently meet the requirements of different applications and improve the end-to-end system performance. Energy consumption is considered as an important factor when performing resource placement in this paper. Specifically, we study the joint caching, computing, and bandwidth resource allocation for SD-NCC and formulate it as an optimization problem. In addition, to reduce computational complexity and signaling overhead, we propose a distributed algorithm to solve the formulated problem, based on recent advances in alternating direction method of multipliers (ADMM), in which different network nodes only need to solve their own problems without exchange of caching/computing decisions with fast convergence rate. Simulation results show the effectiveness of our proposed framework and ADMM-based algorithm with different system parameters.

We propose a novel framework called Software Defined Networking, Caching and Computing (SD-NCC) which integrates networking, caching and computing in a systematic way to improve the end-to-end system performance. In SDNCC, the more in-network resources it utilizes, the less network usage it costs under the same service demands. However only minimizing the total network usage leads to bottlenecks in the network, making the network fragile to traffic bursts. In this paper, we study the joint networking, caching and computing resource allocation issue and formulate it as an optimization problem to make a trade off between minimizing network usage and balancing servers' load. In addition, taking into consideration the inaccurate measurement of network state information (NSI), we reformulate this problem under imperfect NSI. Because the joint allocation problems with imperfect NSI are large-scale combinational optimization problems, we propose a discrete stochastic approximation(DSA) algorithm to deal with it. Finally, simulations are conducted to demonstrate the effectiveness of proposed framework and algorithms. Simulation results show that SD-NCC can significantly improve the end-to-end performance by sharing the physical infrastructure and information resources. Besides, DSA algorithms can achieve near-optimal performance.

Named Data Networking (NDN) is a prominent fully- fledged Information-Centric Networking (ICN) architecture. NDN can help users to take advantage of multiple access networks in Heterogeneous Wireless Networks (HWNs) more efficiently than IP. In HWNs with NDN, which we call information-centric HWNs, jointly designing forwarding strategy and resource allocation has great potential to improve network performance, which is ignored in the literatures. To fill in this blank, we propose a jointly designed forwarding strategy and resource allocation algorithm called Dynamic Forwarding and Resource Allocation (DFRA) that can adapt variable wireless environment. We also establish the fundamental throughput limitations of information-centric HWNs and prove that DFRA is throughput-optimal. By the cooperation between forwarding strategy and resource allocation, DFRA enables users to utilize wireless communication resource in information-centric HWNs more efficiently. From simulation results, DFRA can provide larger network throughput, faster download speed and better fairness than forwarding strategy that doesn't explicitly cooperate with resource allocation.

With the ever-increasing demand for high quality video, mobile video transmission optimization over a limited wireless network capacity has attracted extensive attention. Scalable Video Coding (SVC) is a main solution to provide better Quality of Experience (QoE) by encoding each video into one mandatory base layer and several optional enhancement layers. Deployment of caching in wireless networks has been considered as another effective method to mitigate redundant data transmission over backhaul links and to reduce the end-to-end video transmission delay. Although some works have been done for layered video content over cellular networks with caching, most of them focus on video quality selection or video caching to optimize the users' QoE. The problem of energy- efficient content placement is largely ignored. To fill this gap, we focus on the problem of energy- efficient content placement for layered video content delivery over cellular networks in this paper. Our design objective is to maximize the energy cost savings. We formulate the energy- efficient content placement problem as a convex optimization problem. Then, by solving the optimization problem, we can obtain the optimal set of content placement parameters for the Mobile Network Operator (MNO) to design an optimal caching policy for layered video contents. Finally, simulation results are presented to show the performance of the proposed content placement scheme.

With the ever-increasing demand for high quality video, mobile video transmission optimization over limited wireless network capacity has been attracted extensive attention. HTTP Adaptive Bit Rate (ABR) streaming is a main solution to provide better Quality of Experience (QoE) by adapting multimedia content over wireless channels real-timely. Deployment of caching in wireless network has been considered as another effective method to mitigate redundant data transmission over backhaul links and to reduce the end-to-end video transmission delay. Although some works have been done for HTTP ABR streaming caching, they only consider the users' QoE. The problem of energy-efficient cache resource allocation is largely ignored. In this paper, we focus on the problem of optimal cache resource allocation for HTTP ABR streaming in cellular networks. Our design objective is to maximize both the users' QoE and energy cost saving. We formulate the content cache management problem as two sub-optimization problems. Then, by solving the two sub-optimization problems, we can obtain the optimal set of playback rates selected by users and the MNO's caching policy for each individual content. Finally, simulation results are presented to show the performance of the proposed cache resource allocation scheme.

Network slicing has been considered as one of the key technologies in the next generation mobile network (fifth generation – 5G), which can create virtual network and provide customised services on demand. Most of the current work on network slicing mainly focuses on virtualisation technology, especially in virtual resource allocation. However, caching as a significant approach to improve the content delivery and quality of experience for end‐users has not been well considered in network slicing. In this study, the authors consider in‐network caching combining with network slicing, and propose an efficient caching resource allocation scheme for network slicing in 5G core network. They first formulate the caching resource allocation issue as an integer linear programming model, and then propose a caching resource allocation scheme based on chemical reaction optimisation (CRO) algorithm, which can significantly improve the caching resource utilisation. The CRO algorithm is a population‐based optimisation metaheuristic, which has advantages in searching optimal solution and computation complexity. Finally, extensive simulation results are presented to illustrate the performance of the proposed scheme.

As a promising future network architecture, named data networking (NDN) has been widely considered as a very appropriate network protocol for the multihop wireless network (MWN). In named-data MWNs, congestion control is a critical issue. Independent optimization for congestion control may cause severe performance degradation if it can not cooperate well with protocols in other layers. Cross-layer congestion control is a potential method to enhance performance. There have been many cross-layer congestion control mechanisms for MWN with Internet Protocol (IP). However, these cross-layer mechanisms for MWNs with IP are not applicable to named-data MWNs because the communication characteristics of NDN are different from those of IP. In this paper, we study the joint congestion control, forwarding strategy, and link scheduling problem for named-data MWNs. The problem is modeled as a network utility maximization (NUM) problem. Based on the approximate subgradient algorithm, we propose an algorithm called ‘jointly optimized congestion control, forwarding strategy, and link scheduling (JOCFS)’ to solve the NUM problem distributively and iteratively. To the best of our knowledge, our proposal is the first cross-layer congestion control mechanism for named-dataMWNs. By comparison with the existing congestion control mechanism, JOCFS can achieve a better performance in terms of network throughput, fairness, and the pending interest table (PIT) size.

Recently, there are significant advances in the areas of networking, caching and computing. Nevertheless, these three important areas have traditionally been addressed separately in the existing research. In this paper, we present a novel framework that integrates networking, caching and computing in a systematic way and enables dynamic orchestration of these three resources to improve the end-to-end system performance and meet the requirements of different applications. Then, we consider the bandwidth, caching and computing resource allocation issue and formulate it as a joint caching/computing strategy and servers selection problem to minimize the combination cost of network usage and energy consumption in the framework. To minimize the combination cost of network usage and energy consumption in the framework, we formulate it as a joint caching/computing strategy and servers selection problem. In addition, we solve the joint caching/computing strategy and servers selection problem using an exhaustive-search algorithm. Simulation results show that our proposed framework significantly outperforms the traditional network without in-network caching/computing in terms of network usage and energy consumption.

Hypertext transfer protocol based (HTTP) adaptive streaming (HAS) of video over wireless networks has brings huge challenge for the mobile networks. Although some works have been done for video streaming delivery in heterogeneous cellular networks, most of them are focus on the video streaming scheduling or the caching strategy design. The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored. In this paper, the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied, we model the optimization problem as a mixed integer programming problem. To reduce the computational complexity, an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved. Then we use the many-to-one matching model to analyze the user association problem, and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed. Finally, extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.

Deployment of caching in wireless networks has been considered an effective method to cope with the challenge brought on by the explosive wireless traffic. Although some research has been conducted on caching in cellular networks, most of the previous works have focused on performance optimization for content caching. To the best of our knowledge, the problem of caching resource sharing for multiple service provider servers (SPSs) has been largely ignored. In this paper, by assuming that the caching capability is deployed in the base station of a radio access network, we consider the problem of caching resource sharing for multiple SPSs competing for the caching space. We formulate this problem as an oligopoly market model and use a dynamic non-cooperative game to obtain the optimal amount of caching space needed by the SPSs. In the dynamic game, the SPSs gradually and iteratively adjust their strategies based on their previous strategies and the information given by the base station. Then through rigorous mathematical analysis, the Nash equilibrium and stability condition of the dynamic game are proven. Finally, simulation results are presented to show the performance of the proposed dynamic caching resource allocation scheme.

Recent advances of loop interference cancellation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same band with high spectrum efficiency. Unlike the existing works, in this paper, we study the energy efficiency aspect of resource allocation in FDR systems. We consider a OFDMA cellular network, where a shared FDR is deployed at the intersection of three sectors in a cell. Firstly, the problem of energy-efficient joint bandwidth sharing and power allocation is formulated as a three-stage Stackelberg game. Secondly, the subgame perfect equilibrium for each stage is analyzed. Then, the interplays of the three-stage game are discussed and an iterative algorithm is proposed to obtain the Stackelberg equilibrium solution. At last, simulation results are presented to show the effectiveness of the proposed game.

In heterogeneous wireless networks with femtocells, the issue of interference between femtocells and macrocells should be carefully considered. Using cognitive radio for interference management is a promising technique in heterogeneous wireless networks with femtocells. In this paper, we study the energy efficiency aspect of spectrum sharing and resource allocation in heterogeneous wireless networks with femtocells and cognitive radios. We use the price of interference to model the interference between femtocells and macrocells. We formulate the problem of interference management and power allocation as a Stackelberg game. In addition, an iteration algorithm based on price updating is proposed to obtain the Stackelberg equilibrium solution to the resource allocation problem for energy efficiency. Simulation results are presented to show that the proposed scheme can improve energy efficiency significantly in heterogeneous wireless networks with femtocells and cognitive radios.

Both cognitive radio and femtocell have been considered as promising techniques in wireless networks. However, most of previous works are focused on spectrum sharing and interference avoidance, and the energy efficiency aspect is largely ignored. In this paper, we study the energy efficiency aspect of spectrum sharing and power allocation in heterogeneous cognitive radio networks with femtocells. To fully exploit the cognitive capability, we consider a wireless network architecture in which both the macrocell and the femtocell have the cognitive capability. We formulate the energy-efficient resource allocation problem in heterogeneous cognitive radio networks with femtocells as a Stackelberg game. A gradient based iteration algorithm is proposed to obtain the Stackelberg equilibrium solution to the energy-efficient resource allocation problem. Simulation results are presented to demonstrate the Stackelberg equilibrium is obtained by the proposed iteration algorithm and energy efficiency can be improved significantly in the proposed scheme.

Cognitive radio networks with cooperative communications can effectively improve spectrum efficiency and data rate. Under this network scenario, there are three possible transmission modes: direct transmission, multi-hop transmission and cooperative communication. To optimise the system performance, three issues should be carefully considered: whether or not and which relay is needed, which channel is selected and which transmission mode is used. Therefore in this study, the authors solve these three problems jointly to optimise the outage capacity for cognitive radio networks with cooperative communications. Particularly, they emphasise on the imperfect channel sensing situation. Under these setup and the constraints, the authors formulate the problem of relay determination, transmission channel and corresponding transmission mode selection to maximise the outage capacity as a discrete optimisation problem. Then a discrete stochastic optimisation algorithm is proposed to maximise the outage capacity to adaptively determine relay and select the optimal transmission channel and transmission mode. The proposed algorithm has fast convergence rate and low computation complexity. Moreover, the time-varying radio environment scenario is also considered, and they show that the proposed algorithm has good tracking capability for time-varying radio environment. Finally, simulation results are presented to demonstrate the performance of proposed scheme.

Both cognitive radio and femtocell have been considered as promising techniques in wireless networks. However, most of previous works are focused on spectrum sharing and interference avoidance, and the energy efficiency aspect is largely ignored. In this paper, we study the energy efficiency aspect of spectrum sharing and power allocation in heterogeneous cognitive radio networks with femtocells. To fully exploit the cognitive capability, we consider a wireless network architecture in which both the macrocell and the femtocell have the cognitive capability. We formulate the energy-efficient resource allocation problem in heterogeneous cognitive radio networks with femtocells as a Stackelberg game. A gradient based iteration algorithm is proposed to obtain the Stackelberg equilibrium solution to the energy-efficient resource allocation problem. Simulation results are presented to demonstrate the Stackelberg equilibrium is obtained by the proposed iteration algorithm and energy efficiency can be improved significantly in the proposed scheme.

In this paper, we study the problem of mutual interference cancellation among secondary users (SUs) and interference control to primary users (PUs) in spectrum sharing underlay cognitive radio networks (CRNs). Multiple antennas are used at the secondary base station (SBS) to form multiple beams towards to individual SUs, and a set of SUs are selected to adapt to the beams. For the interference control to PUs, we study power allocation among SUs to guarantee the interference to PUs below a tolerable level while maximizing SUs' QoS. Based on these conditions, the problem of joint power allocation and beamforming with SUs selection is studied. Specifically, we emphasize on the condition of imperfect channel sensing. And we formulate the problem as a discrete stochastic optimization problem, then an efficient algorithm based on a discrete stochastic optimization method is proposed to solve the joint power allocation and beamforming with SUs selection problem. The proposed algorithm has fast convergence rate. Finally, simulation results are presented to demonstrate the performance of the proposed scheme.

Cognitive radio networks with cooperative transmission could effectively improve the spectrum efficiency and data rate. Under this network scenario, based on the sensing results and the channel quality on all channels, there are three possible transmission modes: direct transmission, multi-hop transmission and cooperative transmission. To optimize the system performance, three issues should be carefully considered: whether or not and which relay is needed, which channel is selected, and which transmission mode is used. Therefore, in this paper, we solve these three problems jointly to optimize the outage capacity for cognitive radio networks with cooperative transmission. Specifically, we emphasize on the imperfect channel sensing condition. We formulate the optimization problem as a discrete optimization problem. Then an efficient algorithm based on a discrete stochastic optimization algorithm is proposed to maximize the outage capacity to determine the relay and select the optimal channel and transmission mode. Simulation results are presented to demonstrate the performance of the proposed scheme.

In cognitive radio networks (CRNs), perfect knowledge of a dynamic radio environment is hard to know due to hardware limitation, short sensing time and network connectivity issues in CRNs. In this paper, we study the dynamic resource allocation problem for heterogeneous services in CRNs with imperfect channel sensing. We formulate the optimization problem as a mixed integer programming problem under constraints. Then we solve the optimal joint power and channel allocation problem using discrete stochastic optimization method. The proposed algorithm has low computation complexity and fast convergence to approximate to the optimal solution under imperfect channel sensing. Another advantage of this method is that it can track the changing radio environment to allocate the resources dynamically. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

In this paper, we take both the centralized and distributed architectures into account in heterogenous cognitive radio networks, and study the problem of the joint transmission time and power allocation. The problem is formulated as a dual optimization problem with the optimization objective to maximize the total capacity of the secondary users (SUs) with the constraint of fairness. We first optimize the joint transmission time and power allocation for centralized SUs and propose a corresponding resource allocation scheme in the time-frequency domain. Then for the heterogeneous case with both the centralized and distributed network architecture considered, we formulate the resource allocation problem as a cooperative game and propose an iterative power water-filling scheme to get to the Nash Equilibrium (NE). Based on the dual optimization, a dynamic optimal joint transmission time and power allocation scheme for heterogenous cognitive radio networks is proposed. Extensive simulation results are presented to illustrate the performance of the proposed scheme.

Resource allocation problem in cognitive radio networks (CRN) is one of the key issues to improve the efficiency of spectrum utilization. Most of previous work on resource allocation mainly concentrates on the secondary users (SUs) with only one type of service requirement, without considering the scenario with heterogenous services requirement. In this paper, we study the dynamic channel and power allocation for SUs supporting heterogenous services in CRN. Firstly we classify the SUs by service requirement, i.e., SUs with minimum rate guarantee and SUs with best-effort services. Then we introduce the minimum rate constraints and proportional fairness constraints for SUs respectively. Under this setup, we formulate the problem of dynamic channel and power allocation for SUs as a mixed integer programming problem. And the heuristic optimal algorithm and suboptimal algorithm are proposed to realize the dynamic channel and power allocation. Extensive simulation results are presented to demonstrate the performance of the proposed scheme.

In cognitive radio networks (CRN), underlay spectrum sharing allows secondary users (SUs) to utilize the spectrum on which the primary users (PUs) in primary radio networks (PRN) are working at the same time, without introducing intolerant interferences. In this paper, we study the joint transmission power and time allocation in CRN with underlay spectrum sharing technology. To maximize the total capacity of CRN and maintain the fairness for SUs, the system is modeled as an optimization problem with the constraints of interference and transmission power and time. Furthermore, we prove that the problem of joint optimization resource allocation under the constraints condition can be implemented independently from the time-frequency domain. Based on this study, we propose the optimal joint transmission power and time allocation method. Extensive simulation results show that the proposed optimal method can significantly improve the system capacity and maintain the fairness compared to the existing methods.