Vehicular edge computing is rapidly amplifying the need to process computation-intensive tasks generated in vehicular environments. Conventional centralized processing frameworks struggle to meet these low-latency demands due to network latency and bandwidth constraints. Federated learning (FL) addresses these challenges by retaining raw data on vehicle nodes (VNs). However, sustainable FL training in vehicular edge computing faces persistent obstacles: the instability of VNs (high entry/exit rates), resource heterogeneity, and the lack of effective incentives. Notably, while existing incentive mechanisms are extensively studied, they inadequately address two critical sustainability barriers: extreme data heterogeneity and the volatile energy costs of mobile VNs. To overcome these challenges, we propose an auction-based sustainable federated learning (ASFL) framework. In this framework, the edge server acts as both the FL task publisher and the auctioneer, while VNs serve as bidders. Each bid encapsulates critical attributes including data quality, computing capacity, and projected energy cost. The core objective of ASFL is to maximize long-term social welfare. Formulating this objective reveals an inherently nonconvex optimization problem. Through rigorous analysis, we derive an equivalent convex formulation. The systematic bidder selection process inherent in ASFL simultaneously mitigates data heterogeneity and promotes rational energy utilization across FL. We theoretically prove that the framework achieves incentive compatibility and individual rationality. Experimental results on MNIST and CIFAR-10 datasets demonstrate the effectiveness of the method in mitigating the impact of non-i.i.d. data and reducing energy consumption.

Deep neural network (DNN) model partitioning and pruning have proven to be effective methods for enhancing resource efficiency and reducing inference delay by strategically allocating DNN workloads across heterogeneous edge and cloud infrastructures. Nevertheless, the heterogeneous nature of resources complicates the deployment of DNN in mobile edge-cloud computing (MEC) networks. In this paper, we present an innovative framework for collaborative DNN inference in MEC networks by integrating fine-grained model partitioning and magnitude-based pruning. However, the joint model partitioning and pruning policy presents significant challenges due to the inherently coupled and mutually influential nature. To address it, we adopt Long Short-Term Memory (LSTM) networks as action generation controllers to generate discrete actions for model partitioning and pruning alternately. After that, we adopt the policy gradient algorithms to optimize the LSTM-generated actions with a moving average according to the Monte Carlo estimate. By directly optimizing the policy function, the proposed framework enhances the efficiency and stability of action space exploration, yielding faster convergence and improved inference performance. Experimental results on standard datasets indicate that the proposed framework outperforms state-of-the-art approaches, achieving an 8.247% increase in system reward and an average reduction of 27.313% in total delay within the considered MEC networks.

Federated learning (FL) that preserves privacy has appeared as a prospective paradigm in mobile edge computing networks. However, due to the system and data heterogeneity of mobile clients (MCs), group-based FL with a sampling mechanism is crucial for minimizing model training costs. To address these challenges, we investigate and formulate the problem of group-based FL with a sampling mechanism for reducing model training cost (i.e., latency and energy consumption), and propose a group-based FL with a cost-efficient sampling mechanism (GFLCSM) framework to address it. More precisely, before training, each MC locally pre-trains a model, estimates its data distribution from the classifier's gradient norms, and uploads it to the central server (CS) instead of raw data to preserve privacy. Using this information, the CS transforms vanilla FL into a group-based FL. During training, GFLCSM replaces the random sampling mechanism with a cost-efficient one. Moreover, to enhance robustness against network dynamics, we extend GFLCSM with a backup resampling mechanism, termed GFLCSM-E. Experimental results indicate that GFLCSM surpasses the baseline frameworks, reducing latency by 24.63% and energy consumption by 11.47% on average across two datasets, while GFLCSM-E maintains high performance even under client dropout. The source code address ishttps://github.com/kt4ngw/GFLCSM.

Deploying federated learning (FL) in mobile edge computing (MEC) networks has become a prevalent approach to distributed learning. However, the inherent heterogeneity in computing, transmission and data on edge devices (EDs) brings challenges in improving training efficiency and speed. Existing approaches primarily focus on increasing batch sizes or employing adaptive batching to expedite convergence, but often overlook the generalization ability of the model. In this paper, we propose an energy-efficient adaptive batching approach for FL in MEC networks, aiming at minimizing the energy consumption by balancing training efficiency and speed. Initially, we exploit the relationship between batch size and loss improvement while determining the optimal learning rate corresponding to the batch size and understanding the correlation among loss improvement, learning rate, and gradient noise scale (GNS). Then we dynamically adjust the batch size based on the GNS and propose a low-complexity approach for measuring GNS. Finally, we fine-tune the batch size by assessing gradient similarity on each ED to ensure an optimal level of gradient noise during training, thereby enhancing the model's generalization. Experiment results demonstrate the effectiveness of our approach with an approximate 50% and 20% reduction in energy consumption and time consumption compared with existing approaches.

With the rapid deployment of large language model (LLM) technology across various fields, numerous LLMs have been deployed on edge servers (ESs) to provide low-latency generative services for edge devices. However, as factual knowledge evolves, the massive number of parameters in LLMs poses significant challenges for updating LLMs on ESs. Existing studies employ federated fine-tuning to update LLMs, but these unconstrained updating approaches can lead to overfitting and knowledge forgetting, while also resulting in substantial overhead. Knowledge editing (KE), as a promising technology, ensures the injection of new knowledge while preserving existing knowledge by editing specific parameters. In this paper, we propose multi-ES collaborative KE for the first time and design the CoKE and pCoKE frameworks in edge-cloud scenarios. These frameworks enhance editing efficiency by extracting identical expressions of the same knowledge across different LLM parameters for collaborative editing across multiple ESs. Additionally, we incorporate a personalized selection module in pCoKE to provide domain-specific generative services on ESs. To further reduce editing latency, we design a binary search-based resource allocation algorithm to balance editing latency across ESs. Extensive experiments demonstrate that CoKE and pCoKE reduce editing latency by 73% while maintaining high editing quality. Moreover, pCoKE achieves an approximately 6% improvement in editing quality.

Federated learning (FL) enables model training on mobile clients (M Cs) while protecting data privacy by keeping the data local. However, the data and system heterogeneity among MCs can significantly undermine model performance, slow convergence, and increase energy consumption. To achieve green and efficient edge intelligence, we propose an energy consumption optimization problem under the FL framework for mobile edge computing networks in this paper. Our goal is to reduce the energy consumption of MCs and improve the FL model performance. Then we design a heterogeneity-aware client selection and gradient optimization (HCSGO) algorithm. Specifically, HCSGO selects MCs based on data, computation, and communication quality to mitigate the impact of heterogene-ity on model performance, while leveraging a residual gradient mechanism to optimize gradient aggregation and accelerate convergence. Experiment results demonstrate that the proposed algorithm achieves the lowest energy consumption and improves the model performance compared to the baselines.

End-edge collaborative inference refers to the fact that edge servers (ESs) and end devices (EDs) jointly participate in inference tasks, which can not only reduce communication latency and bandwidth consumption with the cloud but also protect user data privacy. However, existing collaborative inference methods do not fully consider the limited resources of EDs and ignore the latency and accuracy requirements of different inference tasks. In this paper, we design a DNN inference acceleration framework to balance inference latency and accuracy. Specifically, we first use a compression method based on deep reinforcement learning to determine the compression ratio and deeply compress the original model to reduce the complexity of the model. To reduce the cumulative error caused by compression, a knowledge distillation-based scheme is used to fine-tune the compressed model. Finally, the DNN model is partitioned and deployed on the ED and ES, respectively. Extensive experiments demonstrate the effectiveness of the framework in achieving lowlatency DNN inference on demand.

In mobile edge computing (MEC) networks, federated learning (FL) has emerged as the leading distributed framework for training a shared machine learning model, primarily benefiting from its ability to exchange the information of edge devices (EDs) while safeguarding their privacy. However, in MEC networks, the heterogeneity of communication, computation, and data can result in challenges such as stragglers and data imbalances, thereby impeding the training process of FL. To address these challenges, we propose a Semi-Asynchronous Federated Learning (Semi-AFL) framework with cluster-based scheduling. In Semi-AFL, the EDs can perform local training at their own pace using different stale global models to tackle the straggler effect. Considering the asynchronousity of Semi-AFL and data heterogeneity, we propose a cluster-based scheduling strategy that includes device clustering and device selection. Specifically, it performs clustering based on the label distribution and obtains device-to-cluster information. We further select devices based on clustering information as well as model staleness and contribution, aiming to reduce variance and bias and accelerate model convergence. Experiment results demonstrate the effectiveness of the proposed method in reducing the latency of FL.

The rapid evolution of Industrial Cyber-Physical Systems (ICPS) with cloud-fog automation calls for the deployment of Deep Neural Networks (DNNs) on edge devices to enable intelligent and autonomous decision-making. However, the resource constraints, heterogeneity, and dynamic nature of edge devices pose limitations to the efficient deployment of DNNs. Federated Learning-based Neural Architecture Search (FL-NAS) has been proposed to address these limitations, but achieving an effective balance between the generalized global model and personalized local models remains a non-trivial task due tosuboptimal aggregation of homogeneous neural blocks, knowledge waste of heterogeneous neural blocks, and high communication and energy overhead. In this paper, we proposeF²NAS, an energy-friendly federated neural architecture search framework tailored for ICPS. The fine-grained aggregation strategy adapts weights for each device during aggregation, enhancing the global and personalized local models. The bidirectional knowledge transfer mechanism leverages heterogeneous neural blocks, promoting knowledge sharing among local and global models. The adaptive communication strategy optimizes interactions between edge devices and the cloud server based on model performance, reducing energy costs while maintaining effective model collaboration. Extensive experiments demonstrate thatF²NASoutperforms baselines by up to 30.31% in accuracy on edge devices, 38.75% on the cloud server, and achieves a 65.2% reduction in energy consumption. When applied to the surface defect detection task in ICPS,F²NASsurpasses other baselines by up to 13.38% and 302.46% for edge devices and cloud servers, respectively, and reduces energy consumption by 44.8%.

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution; For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets.

With the proliferation of cloud services and the continuous growth in enterprises’ demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management.

Computation offloading is widely used in vehicular edge computing (VEC) networks to satisfy the computational intensity and latency sensitivity requirements. However, many existing offloading algorithms do not comprehensively consider the dynamically changing characteristics of heterogeneous tasks within a roadside unit (RSU), resulting in tasks timeout and being dropped. In this paper, we design a competitive and cooperative computation offloading (C3O) model to reduce task execution latency. Specifically, when intensive heterogeneous tasks are generated, these vehicles jointly compete for the computing resource of a RSU, or they can also offload tasks to the task vehicle (TaV) with free computing resource. Meanwhile, We analyze the latency model of local execution and offloading to RSU or TaV execution and formulate a sequential task offloading decision problem, NP-hard. To address it, we propose a multi-agent reinforcement learning algorithm based on C3O (MARC3O) to intelligently determine the computation offloading policy for each vehicle according to the state of VEC networks. Simulation results demonstrate that the proposed algorithm can significantly reduce task execution latency and improve task completion rates compared with baseline schemes.

With the rapid development of Internet of Things (loT) device performance, edge-cloud systems are generating vast volumes of complex data. Meanwhile, the distributed and multi-layer structure of edge-cloud systems pose significant challenges to scheduling decision-making and convergence of the algorithm. In this paper, from a vertical and horizontal perspective of edge-cloud systems, we propose a deep reinforcement learning (DRL) algorithm called CrossLearning. We apply a curiosity-driven multi-agent learning method horizontally to accelerate the convergence speed of the algorithm. We introduce an inter-layer decision refinement mechanism vertically to address the challenge of inaccurate decision-making. We also refine the service types and levels to efficiently match the various application needs of users in the big data era. Finally, we implement a prototype system on a network hardware system and conduct experiments using real datasets. The evaluation shows that, in comparison to baseline methods, CrossLearning demonstrates significant im-provements in terms of time efficiency and load balance, with a notable enhancement in algorithm convergence speed.

As a robust platform for mobile edge computing, 5G networks, while delivering high data rates and low latency, face a pressing concern with the escalating energy consumption of 5G Base Stations (BSs). To address this issue, we propose an algorithm called the Environmental Protection Prophet (EPP), based on the clustered and geographically inclined user request patterns. The EPP algorithm groups users according to their proximity to BSs and utilizes edge caching to reduce response times for user requests. This clustering strategy optimizes user allocation while minimizing BSs' energy consumption, all while meeting user Quality of Service (QoS) requirements. Simulation experiments illustrate the potential for energy savings and latency reduction, particularly in densely populated urban areas. The findings provide valuable insights for the design of energy-efficient 5G networks, concurrently addressing environmental concerns and meeting user performance expectations.

To address network congestion and data privacy concerns, federated learning (FL) that combines multiple clients and a parameter server has been widely used in mobile edge computing (MEC) networks to process the abundant data generated by mobile clients. However, the existing client sampling methods do not adequately consider the data heterogeneity and system heterogeneity. Parameter server selects inappropriate clients to participate in the FL training process. This inevitably leads to slower convergence of the global model and higher energy consumption. In this paper, we design a client sampling model with the goal of selecting suitable clients to improve the energy efficiency of FL in heterogeneous MEC networks. Then we propose an energy-efficient client sampling strategy by quantifying the communication capability, computation capability and data quality of clients. Based on the quantization results, clients are assigned with a corresponding sampled probability. Simulation results show that our proposed strategy can effectively accelerate the convergence of the global model and reduce the energy consumption compared with the baseline schemes.

The rapid growth of edge computing calls for fine-tuned deep neural network (DNN) deployment that emphasizes energy-efficient implementation, due to the resource constraints of edge devices. Traditional Federated Learning-based Neural Architecture Search (FL-based NAS) has been instrumental in the complexities of this deployment, particularly in addressing constraints posed by device heterogeneity, limited resources, and privacy preservation. However, it is hindered by issues such as suboptimal aggregation of homogeneous neural blocks, significant knowledge waste in disregarding heterogeneous neural blocks, and excessive communication energy consumption. This paper introduces F2NAS in green edge computing, a novel energy-efficient approach that addresses these limitations by ensuring flexible and energy-efficient model design and training for edge devices. Firstly, F2NAS introduces an innovative aggregation strategy that enhances the integration of homogeneous neural blocks by using inter-block distances to optimize weight allocation. Further, it employs a unique parameter extraction technique that recaptures valuable insights from previously overlooked heterogeneous neural blocks. Finally, F2NAS meticulously calibrates communication energy consumption by balancing loss function and model interaction, setting and refining an upper limit for model communication. Experimental results reveal F2NAS enhances model accuracy by 2.8% to 4.7%, simultaneously reducing the energy consumption by nearly 50% through optimizing the communication cost.

Federated learning, as a distributed machine learning paradigm, offers promising solutions for vehicular edge computing (VEC) networks. However, federated learning in VEC with classification tasks still faces two key challenges: i) Delayed data labeling hampers supervised training; ii) Dynamic vehicle behavior complicates training scheduling and model uploads to edge servers. In this paper, we propose a semi-asynchronous federated learning algorithm for VEC. Specifically, it utilizes knowledge distillation to generate soft labels from raw data for supervised training, and estimates model training and uploading time through trajectory prediction. We further logically group vehicles based on the characteristics of their dynamic behavior. We then employ synchronous aggregation within groups and asynchronous aggregation between groups to optimize model performance while reducing latency. Finally, we conduct separate comparative experiments for all components, demonstrating that each component possesses unique advantages. Experiment results show that the proposed algorithm outperforms existing schemes in terms of accuracy and latency. The code is available at: https://github.com/dyxcode/Semi-Asynchronous-Federated-Learning.

Mobile edge computing (MEC) as a potential technology can offload tasks from user devices (UDs) to network edges to alleviate network congestion and reduce task execution delay. However, computation offloading faces two challenges: 1) Poor wireless channel quality causes high transmission delay; 2) Computing tasks may be obtained by eavesdroppers (Eves) during task offloading. Therefore, we consider deploying intel-ligent reflecting surface (IRS) in MEC networks to increase data transmission rate and ensure data transmission security. This paper investigates the issue of joint computation offloading and resource allocation in a multi-IRS assisted MEC network. Our goal is to minimize task execution delay. To address this problem, we propose a multi-agent deep deterministic policy gradient algorithm to determine the optimal offloading strategy for each UD. Simulation results show that the proposed algorithm can significantly reduce task execution delay and ensure data transmission security.

Mobile edge-cloud computing utilizes the computing resources of edge devices and cloud servers to execute complex deep neural networks (DNNs) for collaborative inference. However, many existing collaborative inference methods do not fully consider the limited resources of edge devices, resulting in high inference latency. In this paper, we design an integrated computational framework that combines model partition and compression to reduce inference latency. Specifically, we partition a DNN model at the middle layer and deploy the previous layer on the edge device and the subsequent layer on the cloud server respectively. We propose a collaborative dual-agent reinforcement learning algorithm called CPCDRL to determine partition point and compression ratios. It enables adaptive adjustments of compression ratios based on various partition points, with the overarching goal of minimizing the inference latency across the entire DNN model. The proposed algorithm can significantly reduce computational latency while minimizing accuracy loss compared to the baseline schemes.

Model partitioning is a promising technique for improving the efficiency of distributed inference by executing partial deep neural network (DNN) models on edge servers (ESs) or Internet-of-Things (IoT) devices. However, due to heterogeneous resources of ESs and IoT devices in mobile edge computing (MEC) networks, it is non-trivial to guarantee the DNN inference speed to satisfy specific delay constraints. Meanwhile, many existing DNN models have a deep and complex architecture with numerous DNN blocks, which leads to a huge search space for fine-grained model partitioning. To address these challenges, we investigate distributed DNN inference with fine-grained model partitioning, with collaborations between ESs and IoT devices. We formulate the problem and propose a multi-task learning based asynchronous advantage actor-critic approach to find a competitive model partitioning policy that reduces DNN inference delay. Specifically, we combine the shared layers of actor-network and critic-network via soft parameter sharing, and expand the output layer into multiple branches to determine the model partitioning policy for each DNN block individually. Experiment results demonstrate that the proposed approach outperforms state-of-the-art approaches by reducing total inference delay, edge inference delay and local inference delay by an average of 4.76%, 10.04% and 8.03% in the considered MEC networks.

The popularity of microservices in industry has sparked much attention in the research community. Despite significant progress in microservice deployment for resource-intensive services and applications at the network edge, the intricate dependencies among microservices are often overlooked, and some studies underestimate the importance of system context extraction in deployment strategies. This paper addresses these issues by formulating the microservice deployment problem as a max-min problem, considering system cost and quality of service (QoS) jointly. We first study the attention-based microservice representation (AMR) method to achieve effective system context extraction. In this way, the contributions of different computing power providers (users, edge servers, or cloud servers) in the networks can be effectively paid attention to. Subsequently, we propose the attention-modified soft actor-critic (ASAC) algorithm to tackle the microservice deployment problem. ASAC leverages attention mechanisms to enhance decision-making and adapt to changing system dynamics. Our simulation results demonstrate ASAC's effectiveness, prioritizing average system cost and reward compared to the other state-of-the-art algorithms.

The development of deep learning and edge computing provides rapid detection capability for surface defects. However, components produced in actual industrial manufacturing environments often have tiny surface defects and training data for each specific defect type is limited. Meanwhile, network resources at the edge of industrial networks are difficult to guarantee. It is challenging to train a proper surface defect detection model for each specific surface defect type and provide a real-time surface defect detection service. To address the challenge, in this paper, we propose a real-time surface defect detection framework based on transfer learning with multi-access edge-cloud computing (MEC) networks. Furthermore, we improve the original YOLO-v5s framework by introducing the spatial and channel attention mechanism, and adding an additional detection head to enhance the detection ability on tiny surface defects. Evaluation results demonstrate that the proposed framework has superior performance in terms of improving detection accuracy and reducing detection delay in the considered MEC network.

Mobile edge-cloud computing networks can provide distributed, hierarchical, and fine-grained resources, and have become a major goal for future high-performance computing networks. The key is how to jointly optimize service caching and computation offloading. However, the joint service caching and computation offloading problem faces three significant challenges of dynamic tasks, heterogeneous resources, and coupled decisions. In this paper, we investigate the issue of joint service caching and computation offloading in mobile edge-cloud computing networks. Specifically, we formulate the optimization problem as minimizing the long-term average service latency, which is NP-hard. To solve the problem, we conduct in-depth theoretical analyses and decompose it into two sub-problems: service caching processing and computation offloading processing. We are the first to propose a novel hierarchical deep reinforcement learning algorithm to solve the formulated problem, where multiple edge agents and a cloud agent collaboratively determine the caching-action and offloading-action, respectively. The results obtained through trace-driven simulations reveal that the proposed framework outperforms several prevailing algorithms concerning the average service latency across diverse scenarios. In a complex real scenario, our framework achieves an approximately 33% convergence improvement and a remarkable 39% reduction in the average service latency when compared to reinforcement learning-based algorithms.

To break data silos and address the challenge of green communication, federated learning (FL) is widely used at network edges to train deep learning models in mobile edge computing (MEC) networks. However, many existing FL algorithms do not fully consider the dynamic environment, resulting in slower convergence of the model and larger training energy consumption. In this paper, we design a dynamic asynchronous federated learning (DAFL) model to improve the efficiency of FL in MEC networks. Specifically, we dynamically choose a certain number of mobile devices (MDs) by their arrival order to participate in the global aggregation at each epoch. Meanwhile, we analyze the energy consumption model of local update and upload update, and formulate the problem as a dynamic sequential decision problem to minimize the energy consumption, which is NP-hard. To address it, we propose an energy-efficient algorithm based on deep reinforcement learning named DDAFL, to intelligently determine the number of MDs participating in global aggregation according to the state of MEC networks at each epoch. Compared with baseline schemes, the proposed algorithm can significantly reduce energy consumption and accelerate model convergence.

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.

To support rapidly increasing services and applications from users, multi-tier computing is emerged as a promising system-level computing architecture by distributing computing/caching/communication/networking capabilities between cloud servers to users, especially deploying edge servers at network edges (e.g., base stations). However, due to heterogeneous content requests of users and a high-cost hit manner with direct hits, edge caching is still a most serious issue to be addressed. In this paper, we investigate the issue of recommendation-enabled edge caching in mobile two-tier (edge-cloud) computing networks. Particularly, we integrate recommender systems and edge caching to support both direct hits and soft hits and thus improve the resource utilization of edge servers. We model the factors affecting the user quality of experience as a comprehensive system cost and further formulate the problem as a multi-agent Markov decision process with the goal of minimizing the long-term average system cost. To address the formulated problem, we propose a decentralized recommendation-enabled edge caching framework that leverages a discrete multi-agent variant of soft actor-critic and federated learning. The proposed framework enables each edge server to learn its best policy locally and generate judicious decisions independently. Finally, trace-driven simulation results demonstrate that the proposed framework converges to a better caching policy and outperforms several existing algorithms on average system cost reduction.

With the explosive growth of mobile multimedia services and artificial intelligence applications involving automatic speech analysis (ASA), mobile devices are increasingly unable to handle these computation-intensive tasks generated by users due to the limited computing resource. Besides, the existing cloud computing paradigm is not capable of processing such real-time and delay-sensitive ASA tasks. In this paper, by leveraging mobile edge computing and deep learning (DL), we investigate task offloading for DL-empowered ASA in mobile edge-cloud computing networks to minimize the total time for processing ASA tasks, thereby providing an agile service response. Specifically, to accelerate the processing of ASA tasks, we decompose a convolutional neural network based encoder-decoder model and deploy the encoder at edge servers to extract the features of ASA tasks. Moreover, edge servers derive the user tolerance limit by using a linear regression model for further enhancing the quality of experience of users. Based on some certain network constraints (i.e., user association and edge servers’ storage/computing capacity), we propose a low-complexity and distributed offloading framework to solve the formulated complex problem. Evaluation results demonstrate the effectiveness of the proposed framework on reducing the total time and improving the satisfaction rate of users.

With the explosive increase in mobile data traffic and stringent quality-of-experience requirements of users, mobile edge caching is a promising paradigm to reduce delivery latency and network congestions by serving content requests locally. However, it is extremely challenging to conduct cache replacement when the cache is full and the future request pattern is unknown subject to enormous content volume but limited cache capacity at the network edge. In this paper, we propose a cache replacement algorithm based on the oracle approximation named OA-Cache in an end-to-end manner to maximize the cache hit rate. Specifically, we construct a complex model that uses a temporal convolutional network to capture the long and short dependencies between content requests. Then, an attention mechanism is adopted to find out the correlations between the requests in the sliding window and cached contents. Instead of training a policy to mimic Belady that evicts the content with the longest reuse distance, we cast the learning task into a classification model to distinguish unpopular contents from popular ones. Finally, we apply the knowledge distillation approach to assist in transferring knowledge from a large pre-trained complex network to a lightweight network to readily accommodate to the network edge scenario. To validate the effectiveness of OA-Cache, we conduct extensive experiments on real-world datasets. The evaluation results demonstrate that OA-Cache can achieve the superior performance compared to candidate algorithms.

Recently, we have observed an explosion in the intellectual capacity of user equipment, coupled by a meteoric rise in the need for very demanding services and applications. The majority of the work leverages edge computing technologies to accomplish the quick deployment of microservices, but disregards their inter-dependencies. In addition, while constructing the microservice deployment approach, several research disregard the significance of system context extraction. The microservice deployment issue (MSD) is stated as a max-min problem by concurrently evaluating the system cost and service quality. This research first analyzes an attention-based microservice representation approach for extracting system context. The attention-modified soft actor-critic method is proposed to the MSD issue. The simulation results reveal the ASAC algorithm's priorities in terms of average system cost and system reward.

Mobile-edge computing (MEC) has emerged as a promising computing paradigm in the 5G architecture, which can empower user equipments (UEs) with computation and energy resources offered by migrating workloads from UEs to the nearby MEC servers. Although the issues of computation offloading and resource allocation in MEC have been studied with different optimization objectives, they mainly focus on facilitating the performance in the quasistatic system, and seldomly consider time-varying system conditions in the time domain. In this article, we investigate the joint optimization of computation offloading and resource allocation in a dynamic multiuser MEC system. Our objective is to minimize the energy consumption of the entire MEC system, by considering the delay constraint as well as the uncertain resource requirements of heterogeneous computation tasks. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem, and propose a value iteration-based reinforcement learning (RL) method, named$Q$-Learning, to determine the joint policy of computation offloading and resource allocation. To avoid the curse of dimensionality, we further propose a double deep$Q$network (DDQN)-based method, which can efficiently approximate the value function of$Q$-learning. The simulation results demonstrate that the proposed methods significantly outperform other baseline methods in different scenarios, except the exhaustion method. Especially, the proposed DDQN-based method achieves very close performance with the exhaustion method, and can significantly reduce the average of 20%, 35%, and 53% energy consumption compared with offloading decision, local first method, and offloading first method, respectively, when the number of UEs is 5.

Network function virtualization (NFV) is a promising paradigm where network functions are migrated from dedicated hardware appliances onto software middleboxes to promote service agility and reduce management costs. Benefiting from the NFV, the service function chain (SFC) has emerged as a popular network service form. It allows network traffic to pass through a series of virtual network functions in a specific order required by the business logic to arrange a complex service. However, SFC deployment is facing new challenges in seeking a trade-off between pursuing the objective of high long-term average revenue and making decisions in an online manner. In this paper, we propose DRL-D, a deep reinforcement learning-based approach for the online SFC deployment problem to satisfy different demands of SFC requests within resource constraints of the underlying infrastructure. DRL-D aims to maximize the long-term average revenue by combining the strengths of the graph convolutional network in learning a comprehensive representation of network state and the temporal-difference learning in generating deployment solutions for the SFC requests on the fly. Then a heuristic algorithm and a new prioritized experience replay technique are integrated to optimize the DRL framework and reduce the time complexity. Experimental results demonstrate the superiority of our DRL-D approach when compared with other benchmarks in terms of the long-term average revenue, acceptance ratio, and revenue-to-cost ratio. Performance evaluation shows that DRL-D possesses good robustness under different scales of physical networks and achieves excellent deployment performance within acceptable runtime.

The rapid increase of data traffic has brought great challenges to the maintenance and optimization of 5G and beyond, and some smart critical infrastructures, e.g., small base stations (SBSs) in cellular cells, are facing serious security and failure threats, causing resiliency degradation concerns. Among special smart critical infrastructure failures, the sleeping cell failure is hard to address since no alarm is generally triggered. Sleeping cells can remain undetected for a long time and can severely affect the quality of service/quality of experience to users. To enhance the resiliency of the SBSs in sleeping cells, we design a mobile edge-cloud computing system and propose a semi-supervised learning-based framework to dynamically detect the sleeping cells. Particularly, we consider two indicators, recovery proportion and recovery speed, to measure the resiliency of the SBSs. Moreover, in the proposed scheme, experts’ optimization experience and each period’s detection results can be utilized to iteratively improve the performance. Then we adopt a dataset from real-world networks for performance evaluation. Trace-driven evaluation results demonstrate that the proposed scheme outperforms existing sleeping cell detection schemes, and can also reduce the communication and runtime costs and enhance the resiliency of the SBSs.

Network function virtualization (NFV) is a promising paradigm that network functions can be deployed on commodity servers instead of dedicated servers to enhance the resource utilization and reduce the management difficulty. Based on the NFV technology, a complex network service can be composed of a series of ordered virtual network functions, known as service function chain (SFC). In this context, how to efficiently place SFCs in acceptable running time to improve resource utilization and service quality while meeting the constraints of the physical network is a critical issue for infrastructure providers. In this paper, we propose a deep reinforcement learning-based approach called DRL-SFCP for adaptive SFC placement. DRL-SFCP maximizes the long-term average revenue by combining both the graph convolution network which extracts the features of the physical network and sequence-to-sequence model which captures the ordered information of the SFC request to generate placement strategies. It learns to make SFC placement decisions via observations of the corresponding performance of past decisions rather than a hypothetical environment. Extensive experimental results show that our DRL-SFCP can achieve 11.6% and 9.6% improvement in terms of the acceptance ratio and the long-term average revenue, compared with existing benchmarks.

Mobile edge computing (MEC) provides a new ecosystem that enables cloud computing capabilities at the edge of mobile networks, which is characterized by ultra-low latency and high bandwidth as well as real-time access to radio network information leveraged by applications. Nevertheless, various challenges, especially the decision-making issues for task offloading, are yet to be properly addressed. In this paper, leveraging the insight from the relative evaluation method, we propose a metric to quantify the benefit on users’ service experience enhancement by task offloading. Meanwhile, by comprehensively considering the energy cost, time cost and users’ service experience enhancement throughout the task offloading process, we formulate the task offloading decision-making problem as a two-dimensional knapsack loading problem to maximize the cost efficiency of task offloading. To solve the optimization problem more efficiently, we propose a suboptimal heuristic algorithm with polynomial-time complexity. Compared with four baseline algorithms, simulation results demonstrate the cost efficiency improvement of our proposed scheme.

With the rapid increase of data in mobile edge computing (MEC) networks, mobile devices (MDs) have been generating many computation-latency-sensitive tasks. As the MDs are limited by resources in terms of storage, computation, and bandwidth, part of tasks have to be offloaded to the edge of mobile networks or the remote cloud for more efficient processing. Hence, task offloading plays a vital role in this scene. Existing works about task offloading mainly aim at one-shot task offloading and rarely consider the dependencies of tasks. In this paper, we focus on minimizing the maximum delay of processing a series of tasks with dependencies in MEC networks, which supports device-to-device communications. Specifically, we consider task offloading under a hybrid scenario with a small base station (SBS) deployed with an edge server (ES) and several MDs which generate several tasks with dependencies. Then we model the tasks to a weighted directed acyclic graph (DAG) and formulate the optimization problem as minimizing the critical path of the weighted DAG. To tackle this NP-hard problem, we propose a heuristic scheme to iteratively optimize the delay of paths of the weighted DAG under the constraints of the ES. To evaluate the proposed scheme, we perform numerical experiments with different numbers of tasks. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of reducing the system delay and saving the energy consumption of the MDs.

Network function virtualization (NFV) simplies the coniguration and management of security services by migrating the network security functions from dedicated hardware devices to software middle-boxes that run on commodity servers. Under the paradigm of NFV, the service function chain (SFC) consisting of a series of ordered virtual network security functions is becoming a mainstream form to carry network security services. Allocating the underlying physical network resources to the demands of SFCs under given constraints over time is known as the SFC deployment problem. It is a crucial issue for infrastructure providers. However, SFC deployment is facing new challenges in trading off between pursuing the objective of a high revenue-to-cost ratio and making decisions in an online manner. In this paper, we investigate the use of reinforcement learning to guide online deployment decisions for SFC requests and propose a Policy network Assisted Monte Carlo Tree search approach named PACT to address the above challenge, aiming to maximize the average revenue-to-cost ratio. PACT combines the strengths of the policy network, which evaluates the placement potential of physical servers, and the Monte Carlo Tree Search, which is able to tackle problems with large state spaces. Extensive experimental results demonstrate that our PACT achieves the best performance and is superior to other algorithms by up to 30% and 23.8% on average revenue-to-cost ratio and acceptance rate, respectively.

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic.

Edge nodes (ENs) in Internet of Things commonly serve as gateways to cache sensing data while providing accessing services for data consumers. This paper considers multiple ENs that cache sensing data under the coordination of the cloud. Particularly, each EN can fetch content generated by sensors within its coverage, which can be uploaded to the cloud via fronthaul and then be delivered to other ENs beyond the communication range. However, sensing data are usually transient with time whereas frequent cache updates could lead to considerable energy consumption at sensors and fronthaul traffic loads. Therefore, we adopt Age of Information to evaluate data freshness and investigate intelligent caching policies to preserve data freshness while reducing cache update costs. Specifically, we model the cache update problem as a cooperative multi-agent Markov decision process with the goal of minimizing the long-term average weighted cost. To efficiently handle the exponentially large number of actions, we devise a novel reinforcement learning approach, which is a discrete multi-agent variant of soft actor-critic (SAC). Furthermore, we generalize the proposed approach into a decentralized control, where each EN can make decisions based on local observations only. Simulation results demonstrate the superior performance of the proposed SAC-based caching schemes.

As ubiquitous and personalized services are growing boomingly, an increasingly large amount of traffic is generated over the network by massive mobile devices. As a result, content caching is gradually extending to network edges to provide low-latency services, improve quality of service, and reduce redundant data traffic. Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests. In this article, we propose an evolving learning-based content caching policy, named PA-Cache in edge networks. It adaptively learns time-varying content popularity and determines which contents should be replaced when the cache is full. Unlike conventional deep neural networks (DNNs), which learn a fine-tuned but possibly outdated or biased prediction model using the entire training dataset with high computational complexity, PA-Cache weighs a large set of content features and trains the multi-layer recurrent neural network from shallow to deeper when more requests arrive over time. We extensively evaluate the performance of our proposed PA-Cache on real-world traces from a large online video-on-demand service provider. The results show that PA-Cache outperforms existing popular caching algorithms and approximates the optimal algorithm with only a 3.8% performance gap when the cache percentage is 1.0%. PA-Cache also significantly reduces the computational cost compared to conventional DNN-based approaches.

With the tremendous growth of mobile data traffic generated by various devices such as smartphones, smartpads and wearable devices, it is necessary for mobile network operators to introduce revolutionary networking techniques, thereby satisfying service requirements of mobile users. Recently, mobile edge computing (MEC) has been regarded as an effective technique to alleviate the traffic burden on backhaul networks. In this paper, we investigate the issue of mobility-aware content caching and user association for ultra-dense MEC networks by minimizing the system costs. The problem is formulated as a complex pure integer nonlinear programming, which is NP-hard. To address the original long-term optimization problem, we decompose it into a series of one-slot subproblems, and then optimize the short-term subproblem in two phases (i.e., content caching and user association). We further propose a mobility-aware online caching algorithm to achieve content caching, and a lazy re-association algorithm to determine user association based on matching theory. Trace-driven evaluation results demonstrate that the proposed framework has superior performance on reducing system costs.

Network function virtualization (NFV) has emerged as a promising paradigm for transforming network functions from dedicated hardware to software middleboxes, which can substantially improve service agility and reduce management cost. Benefiting from NFV, service function chains (SFCs) can be formulated through the orchestration of virtual network functions (VNFs). One of the most significant issues for infrastructure providers (InPs) is to determine how to deploy SFCs under the limited resources of underlying infrastructure in an online manner. In this paper, we propose a novel reinforcement learning-based approach named GCN-TD for online SFC deployment problem, aiming to maximize the long-term average revenue. GCN-TD combines the advantages of the graph convolutional network (GCN) which gives the comprehensive representations for network states and the temporal-difference (TD) learning which makes online deployment decisions for SFC requests. Experimental results demonstrate that GCN-TD outperforms other candidate algorithms in terms of the long-term average revenue and acceptance ratio.

In most Internet of Things (IoT) networks, edge nodes are commonly used as to relays to cache sensing data generated by IoT sensors as well as provide communication services for data consumers. However, a critical issue of IoT sensing is that data are usually transient, which necessitates temporal updates of caching content items while frequent cache updates could lead to considerable energy cost and challenge the lifetime of IoT sensors. To address this issue, we adopt the Age of Information (AoI) to quantity data freshness and propose an online cache update scheme to obtain an effective tradeoff between the average AoI and energy cost. Specifically, we first develop a characterization of transmission energy consumption at IoT sensors by incorporating a successful transmission condition. Then, we model cache updating as a Markov decision process to minimize average weighted cost with judicious definitions of state, action, and reward. Since user preference towards content items is usually unknown and often temporally evolving, we therefore develop a deep reinforcement learning (DRL) algorithm to enable intelligent cache updates. Through trial-and-error explorations, an effective caching policy can be learned without requiring exact knowledge of content popularity. Simulation results demonstrate the superiority of the proposed framework.

Explosively increasing multimedia services and applications, e.g., automatic speech recognition (ASR), have aggravated the burden on the cloud server in mobile networks. To address the challenge, mobile edge computing has emerged for partially alleviating the workload of the cloud server and enhancing the quality of service of mobile users. In this paper, we aim to employ the technique of edge-cloud computing to accelerate the processing of ASR tasks generated by users in mobile networks. Particularly, we deploy a convolutional neural network based encoder in each edge server to extract features of the audio data. Based on certain network constraints (i.e., user association and edge servers’ storage/computing capacity), we propose a low-complexity and distributed iterative greedy method to address the formulated nonlinear mixed-integer nonconvex optimization problem. Simulation results demonstrate the effectiveness of the proposed scheme on reducing the total delay in the network.

Sleeping cell problem refers to the degradation or unavailability of network services without triggered alarm, which is one of the most critical issues in current mobile networks. This problem is generally not detectable by the operators but only revealed after users’ complaints occur. Therefore, it leads to the degradations of network performance in the service provision in the long run. To address this problem, we introduce a cloud-based sleeping cell detection platform into radio access networks (RANs) to detect the sleeping cells and deal with them automatically. In the cloud RANs (C-RANs), we combine and improve different methods employed in the pioneering studies in this field, and creatively use labeled training data and ensemble learning method for improving the accuracy. Particularly, we utilize expert optimization experience for further improving the detection framework. To evaluate the proposed ensemble learning based sleeping cell detection framework, we use a time-series dataset of Key Performance Indicator (KPI) in a real-world network. Trace-driven evaluation results show that the proposed framework can achieve up to 14.38% and 20.50% improvements compared with two existing schemes, respectively.

Duplicated download has been a big problem that affects the users' quality of service/experience (QoS/QoE) of current mobile networks. Edge caching and Device-to-Device communication are two promising technologies to release the pressure of repeated traffic downloading from the cloud. There are many researches about the edge caching policy. However, these researches have some limitations in the real scenarios. Traditional methods are lacking the self-adaptive ability in the dynamic environment and privacy issues will occur in centralized learning methods. In this paper, based on the virtue of Deep Q-Network (DQN), we propose a weighted distributed DQN model (WDDQN) to solve the cache replacement problem. Our model enables collaboratively to learn a shared predictive model. Trace-driven simulation results show that our proposed model outperforms some classical and state-of-the-art schemes.

Recently, mobile edge computing has received widespread attention, which provides computing infrastructure via pushing cloud computing, network control, and storage to the network edges. To improve the resource utilization and Quality of Service, we investigate the issue of task offloading for End-EdgeCloud orchestrated computing in mobile networks. Particularly, we jointly optimize the server selection and resource allocation to minimize the weighted sum of the average cost. A cost minimization problem is formulated underjoint the constraints of cache resource and communication/computation resource of edge servers. The resultant problem is a Mixed-Integer Non-linear Programming, which is NP-hard. To tackle this problem, we decompose it into simpler subproblems for server selection and resource allocation, respectively. We propose a low-complexity hierarchical heuristic approach to achieve server selection, and a Cauchy-Schwards Inequality based closed-form approach to efficiently determine resource allocation. Finally, simulation results demonstrate the superior performance of the proposed scheme on reducing the weighted sum of the average cost in the network.

Traditional centralized token exchange (CEX) has been suffering from hacking due to the centralized management of users’ tokens. In contrast, decentralized token exchange (DEX) maintains users’ assets by smart contracts in a decentralized manner, but introduces additional overhead in terms of gas fee and transaction confirmation latency. Hybrid decentralized token exchange (HEX) has been proposed to combine the benefits of CEX and DEX. However, existing HEX is criticized for two issues. First, trading transactions are time-consuming and expensive for frequent token traders. Second, excessive simultaneous transactions might cause the pending transaction congestion in the Ethereum network. In this paper, we propose a payment channel based HEX, which extends existing solutions by adding a new payment channel layer to benefit frequent traders and alleviate the pending transaction congestion. Besides, we propose the very first gas-price vs. transaction-confirmation-latency function to guide Ethereum transaction issuers to choose an optimal gas price that minimizes the overall cost. Extensive simulations are conducted to compare the cost in the proposed HEX with that in the conventional HEX. The results demonstrate the effectiveness of our proposed mechanism in terms of reducing gas fees and transaction confirmation latency for frequent traders as well as the pending transaction congestion in Ethereum.

Edge caching is an emerging technology for addressing massive content access in mobile networks to support rapidly growing Internet-of-Things (IoT) services and applications. However, most current optimization-based methods lack a self-adaptive ability in dynamic environments. To tackle these challenges, current learning-based approaches are generally proposed in a centralized way. However, network resources may be overconsumed during the training and data transmission process. To address the complex and dynamic control issues, we propose a federated deep-reinforcement-learning-based cooperative edge caching (FADE) framework. FADE enables base stations (BSs) to cooperatively learn a shared predictive model by considering the first-round training parameters of the BSs as the initial input of the local training, and then uploads near-optimal local parameters to the BSs to participate in the next round of global training. Furthermore, we prove the expectation convergence of FADE. Trace-driven simulation results demonstrate the effectiveness of the proposed FADE framework on reducing the performance loss and average delay, offloading backhaul traffic, and improving the hit rate.

General traffic analysis based on deep packet inspection (DPI) techniques at switches cannot grasp the detailed knowledge of network applications going into internal switches, and the statistics-based reports of switches lack flow-level recognition of the traffic. Besides, DPI is generally expensive and has limited performance. Therefore, network-wise accurate flow-awareness by packet sampling is highly desirable for fine-grained quality of service guarantee, internal network management, traffic engineering, security analysis, and so on. In this paper, we propose a Spatial-Temporal Collaborative Sampling (STCS) framework in the flow-aware software-defined networks (SDNs). Particularly, considering the spatial-temporal factors and limits of network resources, the formulated STCS problem aims to maximize the network-wise sampling accuracy of flows including mice flows and elephant flows by characterizing both of the comprehensive influences of switches and the effects on sampling accuracy imposed by the collaborative strategy among switches in the spatial-temporal dimension. We propose a suboptimal approach to address the complex STCS problem in two steps: 1) Top-K switch selection based on the iterative comprehensive influence, and 2) sampling time slot allocation based on the local value maximization. Trace-driven evaluation results demonstrate the effectiveness of the proposed framework on improving the sampling accuracy and reducing redundant packets.

Motivated by complementing the ubiquitous wireless sensor networks (WSNs) and powerful cloud computing (CC), a lot of attention from both industry and academia has been drawn to Sensor-Cloud (SC). However, SC pricing is barely investigated. Towards pricing for SC, this paper 1) introduces five SC Pricing Models (SCPMs) first. Specifically, to charge a SC user, each SCPM considers one of the following factors respectively: i) the lease period of the SC user; ii) the required working time of SC; iii) the SC resources utilized by the SC user; iv) the volume of sensory data obtained by the SC user; v) the SC path that transmits sensory data from the WSN to the SC user. Further, this paper 2) performs analysis to discuss and exhibit the characteristics of the proposed SCPMs. With that, this paper 3) presents the case studies regarding the application of SCPMs. Eventually, this paper 4) conducts a review about the user behavior study. This paper aims to serve as a very favorable guidance for future research about pricing in SC.

Explosive growth of mobile data demand may impose a heavy traffic burden on fronthaul links of cloud-based small cell networks (C-SCNs), which deteriorates users' quality of service (QoS) and requires substantial power consumption. This paper proposes an efficient maximum distance separable (MDS) coded caching framework for a cache-enabled C-SCNs, aiming at reducing long-term power consumption while satisfying users' QoS requirements in short-term transmissions. To achieve this goal, the cache resource in small-cell base stations (SBSs) needs to be reasonably updated by taking into account users' content preferences, SBS collaboration, and characteristics of wireless links. Specifically, without assuming any prior knowledge of content popularity, we formulate a mixed timescale problem to jointly optimize cache updating, multicast beamformers in fronthaul and edge links, and SBS clustering. Nevertheless, this problem is anti-causal because an optimal cache updating policy depends on future content requests and channel state information. To handle it, by properly leveraging historical observations, we propose a two-stage updating scheme by using Frobenius-Norm penalty and inexact block coordinate descent method. Furthermore, we derive a learning-based design, which can obtain effective trade-off between accuracy and computational complexity. Simulation results demonstrate the effectiveness of the proposed two-stage framework.

The evolution of Internet of Things (IoT) devices and their adoption in new generation intelligent systems has generated a huge demand for wireless bandwidth. This bandwidth problem is further exacerbated by another characteristics of IoT applications, i.e., IoT devices are usually deployed in massive number, thus leading to an awkward scenario that many bandwidth-hungry devices are chasing after the very limited wireless bandwidth within a small geographic area. As such, cognitive radio has received much attention of the research community as an important means for addressing the bandwidth needs of IoT applications. When enabling IoT devices with cognitive functionalities including spectrum sensing, dynamic spectrum accessing, circumstantial perceiving, and self-learning, one will also need to fully study other critical issues such as standardization, privacy protection, and heterogeneous coexistence. In this article, we investigate the structural frameworks and potential applications of cognitive IoT. We further discuss the spectrum-based functionalities and heterogeneity for cognitive IoT. Security and privacy issues involved in cognitive IoT are also investigated. Finally, we present the key challenges and future direction of research on cognitive-radio-based IoT networks.

This paper considers content delivery of the cache-enabled small cell networks (C-SCNs), where users with the same request form a multicast group and are served by a cluster of small-cell base stations (SBSs) under the coordination of the central processor. The performance of such a coordination is severely limited by the fronthaul link, which may be saturated and degrade quality of service (QoS). To improve user QoS, we propose a latency driven scheme by jointly optimizing fronthaul bandwidth allocation, multicast beamforming, and BS clustering. Accordingly, with min-max fairness among multicast groups, a latency minimization problem is formulated under the constraints of fronthaul bandwidth and transmission power. The resultant problem is a mixed-integer nonlinear program, which is NP-hard. To address such a complex problem, a quadratic penalty-based algorithm is proposed by using a reformulation of binary constraint. Meanwhile, we present the necessary condition for an optimal solution, which shows that fronthaul bandwidth allocation is inherently adaptive to cached contents and patterns of BS cooperation. Finally, simulation results demonstrate that the proposed scheme can effectively reduce latency under different caching strategies.

Recent years have witnessed an exponential growth of mobile data traffic, which may lead to a serious traffic burn on the wireless networks and considerable power consumption. Network densification and edge caching are effective approaches to addressing these challenges. In this study, we investigate joint long-term cache allocation and short-term content delivery in cloud small cell networks (C-SCNs), where multiple small-cell BSs (SBSs) are connected to the central processor via fronthaul and can store popular contents so as to reduce the duplicated transmissions in networks. Accordingly, a long-term power minimization problem is formulated by jointly optimizing multicast beamforming, BS clustering, and cache allocation under quality of service (QoS) and storage constraints. The resultant mixed timescale design problem is an anticausal problem because the optimal cache allocation depends on the future file requests. To handle it, a two-stage optimization scheme is proposed by utilizing historical knowledge of users' requests and channel state information. Specifically, the online content delivery design is tackled with a penalty-based approach, and the periodic cache updating is optimized with a distributed alternating method. Simulation results indicate that the proposed scheme significantly outperforms conventional schemes and performs extremely close to a genie-aided lower bound in the low caching region.

To satisfy rapidly increasing multimedia service requests from mobile users, content caching at the network edges (e.g., base stations) has been regarded as a promising technique in future mobile networks. In this paper, by virtue of Deep Reinforcement Learning (DRL) with respect to solving complicated control problems, we propose a framework on Double Deep Q-Network for cooperative edge caching in mobile networks. Particularly, we aim at minimizing the long-term average content fetching delay of mobile users without requiring any priori knowledge of content popularity distribution. Trace-driven simulation results show that our proposed framework outperforms some existing caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU) and First-In First-Out (FIFO) caching strategies by 7%, 11% and 9% improvements, respectively. Besides, our proposed work is further shown that only average 4% performance loss exists compared to an omniscient oracle algorithm.

Caching at the edge of mobile networks can significantly offload network traffic while satisfying content requests from mobile users locally. The contents can be requested from the proximity users via Device-to-device (D2D) communications while proactive caching the popular content to local users. However, the assumptions that content popularity is equal to user preference in several existing studies, which are invalid and not rigorous due to the fact that content popularity is calculated by the statistic of user requests within a certain period while user preference reflects the probability of a content requested by the individual user. Motivated by this, in this paper, we study the edge caching optimization of hierarchical wireless networks. Our aiming is to maximize the size of content offload by D2D communications. In particular, the edge caching policy with D2D sharing model based on the analysis of user mobility and social relationship is derived. We first prove the problem is NP-hard and then formulate it as a Markov Decision Process (MDP) problem, finally a Q-learning based distributed content replacement strategy is proposed. The large-scale real trace based experiment results show the effectiveness of our proposed framework.

The explosive growth of content requests from mobile users is stretching the capability of current mobile networking technologies to satisfy users' demands with acceptable quality of service. An effective approach to address this challenge, which has not yet been thoroughly studied, is to offload network traffic by caching popular content at the edges (e.g., mobile devices and base stations) of mobile networks, thus reducing the massive duplication of content downloads. In this paper, we address the system modeling, large-scale optimization, and framework design of hierarchical edge caching in device-to-device aided mobile networks. In particular, taking into account the analysis of social behavior and preference of mobile users, heterogeneous cache sizes, and the derived system topology, we investigate the maximum capacity of the network infrastructure in terms of offloading network traffic, reducing system costs, and supporting content requests from mobile users locally. Our proposed framework has a low complexity and can be applied in practical engineering implementation. Trace-based simulation results demonstrate the effectiveness of the proposed framework.

This paper investigates joint user association (UA) and user scheduling (US) for load balancing over the downlink of a wireless heterogeneous network by formulating a network-wide utility maximization problem. In order to efficiently solve the problem, we first approximate the nonconvex throughput achieved with US to a concave function, and demonstrate that the gap for such an approximation approaches zero when the number of users is sufficiently large. Then, by exploiting a distributed convex optimization technique known as alternating direction method of multipliers, a joint UA and US algorithm, which can be implemented on each user's side and base station (BS)'s side separately, is proposed to obtain the single-BS association and resource allocation solutions. A remarkable feature of the proposed algorithm is that apart from load balancing, multiuser diversity is exploited in the association process to further improve system performance. We also extend the algorithm design to multi-BS association, whereby a user is associated with multiple BSs. The simulation results show the superior performance of the proposed algorithms and underscore the significant benefits of jointly exploiting multiuser diversity and load balancing.

The concept of smart grid gained tremendous attention among researchers and utility providers in recent years. How to establish a secure communication among smart meters, utility companies, and the service providers is a challenging issue. In this paper, we present a communication architecture for smart grids and propose a scheme to guarantee the security and privacy of data communications among smart meters, utility companies, and data repositories by employing decentralized attribute based encryption. The architecture is highly scalable, which employs an access control Linear Secret Sharing Scheme (LSSS) matrix to achieve a role‐based access control. The security analysis demonstrated that the scheme ensures security and privacy. The performance analysis shows that the scheme is efficient in terms of computational cost.

To address the challenge arising from mobile users' increasing demands for multimedia services, applying content caching in heterogeneous networks (HetNets) is regarded as an effective way to offload traffic and improve the capacity of mobile networks. In this paper, we aim at designing novel content caching strategies in HetNets to offload the network traffic and support users' requests locally. Specifically, based on some practical network constraints (i.e., patterns of user requests, link capacity and heterogeneous cache sizes) and the derived network topology, we propose a low-complexity and practicable distributed collaborative hierarchical caching framework by decomposing the formulated large-scale optimization problem into a series of simpler subproblems. Trace-based simulation results demonstrate the effectiveness of the proposed framework.

To deal with explosively growing demands for multimedia contents from mobile users, content caching in base stations has been considered as an effective solution to improve the network performance by, e.g., offloading network traffic and improving users' Quality of Service (QoS). Moreover, the proactive caching policy in a cache- enabled system needs to be optimized taking into account of content delivery by wireless transmissions. Thus, in this paper, we investigate and propose an efficient resource allocation scheme for min-rate guaranteed content delivery in the downlink multiuser cache-enabled orthogonal frequency division multiple access small cell networks (OFDMA-SCNs). Our aim is to maximize the weighted sum of data rates in an OFDMA-SCN based on the constraints of the caching method, users' QoS, subcarrier reuse and small base stations' transmit power. We employ the alternating direction multiplier method to decompose the formulated complex nonconvex optimization problem into a series of simpler subproblems for which optimal solutions can be easily obtained, and propose corresponding low-complexity methods to solve the subproblems and then the whole problem. Numerical results demonstrate the effectiveness of the proposed resource allocation scheme.

To deal with the explosive growth in multimedia service requests in mobile networks, caching contents at the cells (base stations) is regarded as an effective emerging technique to reduce the duplicated transmissions of content downloads, while heterogeneous networks (HetNets) are regarded as an effective technique to increase the network capacity. Yet, the combination of content caching and HetNets for future networks (i.e., 5G) is still not well explored. In this paper, we propose an efficient collaborative multi-tier caching framework in Het-Nets. In particular, based on patterns of user requests, link capacities, heterogenous cache sizes, and the derived system topology, we focus on exploring the maximum capacity of the network infrastructure so as to offload the network traffic and support users' content requests locally. Due to the NP-hardness of the complex multi-tier caching problem, we approximately decompose it into some subproblems that focus on the caching cooperation at different tiers by utilizing the derived system topology. Our proposed framework is low-complexity and distributed, and can be used for practical engineering implementation. Trace-based simulation results demonstrate the effectiveness of the proposed framework.

This paper investigates the joint user association (UA) and user scheduling (US) for load balancing in a wireless downlink heterogeneous network by formulating a network-wide utility maximization problem. In order to efficiently solve the problem, we first approximate the original non-convex throughput function to a concave function, and demonstrate that the gap for such approximation approaches zero when the number of users is sufficiently large. Then, a distributed algorithm is further proposed to obtain the UA and US solutions by exploiting the convex optimization technique known as alternating direction method of multipliers. A remarkable feature of the proposed algorithm is that apart from load balancing, multiuser diversity is exploited in the association time to further improve system performance. The simulation results show the superior performance of the proposed algorithm and underscore the significant benefits of jointly exploiting multiuser diversity and load balancing.

Due to explosive demands of multimedia services from mobile users, the growing network traffic load becomes a severe challenge for mobile network operators (MNOs). To address this problem, content caching is regarded as an effective emerging technique to reduce the duplicated transmissions of the content downloads demanded by mobile users, while heterogeneous networks (HetNets) are regarded as an effective technique to increase the network throughput. Thus, this paper focuses on content caching in HetNets to offload the weighted network traffic, in which we consider the problem of minimizing the weighted expected sum of traffic load of accessing the requested contents. By transforming the irregular problem into a binary integer linear programming problem, we propose a novel suboptimal heuristic algorithm with polynomial-time complexity to solve the problem, instead of using the existing optimal branch-and-bound method with exponential-time complexity. Numerical results demonstrate that our proposed content caching framework can reduce the weighted expected sum of traffic load significantly.

Due to environmental concerns of rising energy consumption caused by explosive growth in the demands of wireless multimedia services, energy efficiency has become an important consideration in the design of future wireless communication systems. In this paper, we investigate and propose an energy-efficient scheme of joint antenna-subcarrier-power allocation for min-rate guaranteed services in the downlink multiuser orthogonal frequency division multiplexing distributed antenna systems (OFDM-DASs) with limited backhaul capacity. Our aim is to maximize the energy efficiency in an OFDM-DAS based on the constraints of users' Quality of Service, subcarrier reuse, backhaul capacity, and remote antenna units' transmit power. By exploring the properties of the complex nonconvex energy efficiency optimization problem, we transform the problem into an equivalent problem based on fractional programming, and then, use the alternating direction multiplier method to decompose the problem into a series of simpler subproblems, where their optimal or suboptimal solutions can be easily achieved. We propose the corresponding low-complexity methods to solve the subproblems and, then, the whole problem. The numerical results demonstrate the effectiveness of the proposed low-complexity energy-efficient scheme and illustrate the fundamental tradeoff among energy consumption, spectral efficiency, and energy efficiency.

Incorporating ubiquitous wireless sensor networks (WSNs) and powerful cloud computing (CC), Sensor-Cloud (SC) is attracting growing attention from both academia and industry. However, pricing for SC is barely explored. In this paper, filling this gap, five SC pricing models (i.e., SCPM1, SCPM2, SCPM3, SCPM4 and SCPM5) are proposed first. Particularly, they charge a SC user, based on 1) the lease period of the user, 2) the required working time of SC, 3) the SC resources utilized by the user, 4) the volume of sensory data obtained by the user, 5) the SC path that transmits sensory data from the WSN to the user, respectively. Further, analysis is also presented to study and demonstrate the performance of the proposed SCPMs. We believe that the pricing designs and analysis performed in this work could be a very valuable guidance for future researches regarding pricing in SC.

Due to the exponentially increasing demands for multimedia services over recent years, the growing network traffic load becomes a severe concern for the mobile network operators (MNOs). However, the wireless link capacity, the radio access networks, and MNOs' backhaul networks cannot deal with the traffic load effectively. To solve this problem, content caching is regarded as an effective emerging technique to reduce the duplicated transmissions of the content downloads demanded by mobile users and improve users' quality of service (QoS). Therefore, in this paper, we mainly focus on the cooperative cell caching for future mobile networks, where each cell (e.g., base station) can cache popular contents for improving QoS especially on the overall delay performance of users. The task is formulated as a problem of minimizing the expected overall user delay of accessing the demanded contents. Instead of rewriting it as a non-linear and non-convex problem based on an approximate transformation, we convert it to a linear programming problem by using a novel equivalent transformation. To solve the problem, rather than using the existing branchand-bound (BNB) method, which suffers from exponential-time and exponential-space complexity, we propose a new distributed suboptimal algorithm, which has polynomial-time and linearspace complexity. Numerical evaluation results demonstrate that our proposed cooperative cell caching framework can reduce the expected overall delay significantly.

To solve the mobile traffic explosion problem, there have been many efforts to try to offload the mobile traffic from infrastructured cellular links to direct local short-range communications among users. In this paper, we propose a novel framework of traffic offloading by Tag-Assisted Social-Aware opportunistic sharing in mobile social networks, TASA, to offload traffic by device-to-device sharing. Based on the evaluation of the tags of users and contents, we select a subset of users who are likely to receive the same content as initial seeds depending on their spreading impacts in online SNSs and their mobility patterns in offline MSNs. Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct, LTE D2D) with each other. The observation from SNS activities reveals that individual users have distinct access patterns, which allows TASA to further exploit the user-dependent access delay between the content generation time and each users access time for traffic offloading purposes. We model and analyze the traffic offloading and content spreading among users by taking into account various options in linking SNS and MSN trace data. The trace-driven evaluation demonstrates that TASA can reduce up to 78.9% of the cellular traffic.

In this paper, we propose two opportunistic scheduling algorithms for uplink wiretap networks with multiple legitimate users (LUs) and eavesdroppers. Different from the existing works on scheduling algorithms with secrecy considerations, we focus on the practical scenario where LUs experience diverse path-loss to the base station (BS). Hence, both secrecy throughput and fairness among LUs are crucial design considerations. In the proposed scheduling algorithms, the feedback information generated by each LU is designed to reduce the probability that the LU is selected by the BS when the eavesdroppers overhear much information. It is proved that fairness among LUs can be achieved in arbitrary fading channels. In order to investigate the efficiency of our proposed scheduling algorithms, the normalized secrecy throughput, i.e., the secrecy throughput for a given LU normalized by the probability of it being selected, is analyzed and proved to achieve double-logarithmic growth when the number of LUs in the network increases to infinity.

The powerful data storage and data processing abilities of cloud computing (CC) and the ubiquitous data gathering capability of wireless sensor network (WSN) complement each other in CC-WSN integration, which is attracting growing interest from both academia and industry. However, job scheduling for CC integrated with WSN is a critical and unexplored topic. To fill this gap, this paper first analyzes the characteristics of job scheduling with respect to CC-WSN integration and then studies two traditional and popular job scheduling algorithms (i.e., Min-Min and Max-Min). Further, two novel job scheduling algorithms, namely priority-based two phase Min-Min (PTMM) and priority-based two phase Max-Min (PTAM), are proposed for CC integrated with WSN. Extensive experimental results show that PTMM and PTAM achieve shorter expected completion time than Min-Min and Max-Min, for CC integrated with WSN.

Distributed antenna systems (DASs) are promising for future wireless systems to provide high data transmission rates. So far, most works on resource allocation schemes for DASs have not considered the Quality of Services (QoS) and fairness of mobile users simultaneously. This paper investigates and presents an optimal max-min fair resource allocation scheme for min-rate guaranteed services in downlink multiuser DASs. By exploring the properties of the optimization problem, we transform it into an equivalent convex optimization problem. Then we propose an iterative allocation algorithm to solve the problem and get the optimal solution. Numerical results show that the proposed method realizes the max-min fair resource allocation for min-rate guaranteed services, in which the values of minimum transmission rates are maximized.

In this paper, we study the power allocation for both regenerative and non-regenerative relay transmission over Rayleigh fading channels in cognitive networks. Based on the analyses of features of cognitive networks, a relevant interference model is first built over Rayleigh fading channels. Then, we propose a combined power allocation strategy in order to minimize the outage probabilities in the cooperative communications. For regenerative system, we give a closed-form expression for the power allocation by taking into account the characteristics of the fading channels. For non-regenerative system, we utilize pattern search algorithm to solve the optimization problem since the objective function is complex and uneasy to be figured out directly. Numerical results show that the system performances with optimum power allocation outperform those with uniform power allocation whereas lower outage probabilities can be obtained.