Unmanned sanitation vehicles (USVs) promise cleaner cities, yet efficiently coordinating multiple USVs in large urban areas remains challenging due to constraints such as limited waste capacity and battery life. In this demo, we present MRTC, a multi-robot task cooperation system. First, Dynamic Task Assignment employs an Actor-Critic policy within a Markov decision framework to allocate cleaning tasks and decide the required number of USVs. Second, Single-USV Path Planning refines each route via a fast two-layer iterative search. Over an eight-month real-world deployment in three urban testbeds, our MRTC system markedly improved cleaning efficiency while lowering operating costs. Operating over a combined 10,775 km of routes per month, the system achieved average monthly savings of 20,575 kWh of energy and 2,744 labour hours. A demonstration video is available at https://llq978.github.io/Demo/.

Automatic Modulation Classification (AMC) is essential for wireless communication systems, particularly in resource-constrained environments such as vehicular networks. However, deploying Deep Neural Networks (DNNs) on receivers is challenging due to computational and energy constraints. To address this, we propose the first joint integration of early exit and structured pruning for AMC, optimizing computational efficiency without significantly compromising accuracy. Through theoretical analysis, we demonstrate the unique interactions between these techniques and the non-intuitive impact of pruning on early-exit models. Our experiments evaluate the impact of various pruning criteria, pruning percentages, and early-exit thresholds. Our results reveal that the effectiveness of different pruning criteria in early-exit AMC models is highly context-dependent, with no universally optimal strategy, and that increasing pruning levels and early-exit thresholds intensifies the trade-off between computational efficiency and classification accuracy.

Abstract Improving energy efficiency by adjusting the structure of energy consumption types is of great significance for reducing carbon emissions in the short term. The present paper constructs new data envelopment analysis models for evaluating energy utilization under different structural conditions and calculating potential emissions reductions. We conducted empirical research on 30 provinces in China from 2003 to 2019—a time frame that coincides with the instituting of China's “coal‐to‐gas” program. Our results show that technological progress is the main way for China to reduce carbon emissions and that it is possible to reduce the total amount of carbon emissions by 35%. Additionally, optimizing the energy consumption structure following the coal‐to‐gas program guidelines could reduce the country's carbon emissions by a further 25%. Finally, this paper provides specific policy recommendations based on the efficiency analysis results to guide each province in reducing carbon emissions under the conditions of energy demand growth.

In this article, we investigate infrastructure-assisted environment sensing in satellite-terrestrial vehicular networks (STVN) for connected autonomous vehicles (CAVs), where satellites and roadside units (RSUs) cooperate to provide CAVs with fresh sensing data. To support satellite- and RSU-assisted environment sensing for CAVs, we formulate a long-term resource scheduling problem in STVN to satisfy sensing data freshness requirements with efficient resource usage. To deal with the challenges posed by the dynamic network environment as well as stringent data freshness requirements, we propose a cooperative satellite-terrestrial resource scheduling (CSTRS) scheme. CSTRS is a model-data co-driven approach that can jointly optimize the sensing interval and resource allocation in STVN. Specifically, benefiting from the multicast feature of the low Earth orbit satellite, coalition game, and particle swarm optimization-based algorithms are designed to partition CAVs into groups and optimize sensing intervals in large timescales. Then, a reinforcement learning-based algorithm is developed to make real-time computing and communication resource allocation decisions based on the CAV partition. Simulation results demonstrate that the proposed scheme outperforms benchmark methods in terms of resource usage and reliability performance.

Mobile contact exhibits user co-traveling events within the same transportation tool, which is crucial for resident profiling, face-to-face interaction detection, etc. In this paper, we investigate urban user mobile contact detection with cellular signaling traces, which is cost-efficient to enable large-scale detection. Specifically, we develop a data collection platform to collect substantial user signaling traces, covering different types of road scenarios within a city. With the collected traces, we perform systematic data analysis to reveal several technical challenges, which are sparsity of signaling trajectory, remote base station noise, and fuzzy matching difficulties. To address challenges, we propose a mobile contact detection method namedMoCo. InMoCoframework, we first conduct data denoising to remove the noise from remote base stations. Then, we devise a spatio-temporal filter to eliminate unlikely mobile contact traces in both spatial and temporal domains, reducing the computational overhead. Finally, we design a detection network that integrates the submodules of data alignment, feature encoder, spatio-temporal representation learner, and user mobile contact detector. Extensive evaluation results demonstrate the superiority ofMoCoin comparison with state-of-the-art baselines. Robust experiments show thatMoCocan work efficiently in different transportation modes and urban densities.

Resource slicing in low Earth orbit satellite networks (LSN) is essential to support diversified services. In this paper, we investigate a resource slicing problem in LSN to reserve resources in satellites to achieve efficient resource provisioning. To address the challenges of non-stationary service demands, inaccurate prediction, and satellite mobility, we propose an adaptive digital twin (DT)-assisted resource slicing scheme for robust and adaptive resource management in LSN. Specifically, a slice DT, being able to capture the service demand prediction uncertainty through collected service demand data, is constructed to enhance the robustness of resource slicing decisions for dynamic service demands. In addition, the constructed DT can emulate resource slicing decisions for evaluating their performance, enabling adaptive slicing decision updates to efficiently reserve resources in LSN. Simulation results demonstrate that the proposed scheme outperforms benchmark methods, achieving low service demand violations with efficient resource consumption.

In this paper, we investigate video streaming for satellite broadcasting applications in satellite terrestrial integrated networks, employing the low complexity enhancement video coding scheme. Considering the inherent limitations of satellite broadcasting, we design a collaborative video streaming method where the base layer (BL) is transmitted via satellite links, while terrestrial links are utilized for transmitting the enhancement layer (EL). This design seamlessly integrates with existing satellite broadcasting schemes since the BL can utilize any standard video codec, while the EL significantly enhances video quality when received via terrestrial links. To accommodate dynamic network conditions, we formulate a video segment delivery problem aimed at maximizing the overall user satisfaction by determining the optimal number of video segments with ELs downloaded for each broadcasting channel. To solve the problem, we propose a group knapsack-based EL segment downloading (GKELD) algorithm, which transforms the user satisfaction maximization problem into a group knapsack problem and then solves it by devising a dynamic programming-based approach. Simulation results demonstrate that our proposed algorithm significantly outperforms benchmark methods, achieving higher overall audience satisfaction under limited network resources.

Satellite-terrestrial integrated networks (STIN) are envisioned as a promising architecture for ubiquitous network connections to support diversified services. In this paper, we pro-pose a novel resource slicing scheme with cross-cell coordination in STIN to satisfy distinct service delay requirements and efficient resource usage. To address the challenges posed by spatiotemporal dynamics in service demands and satellite mobility, we formulate the resource slicing problem into a long-term optimization problem and propose a distributed resource slicing (DRS) scheme for scalable and flexible resource management across different cells. Specifically, a hybrid data-model co-driven approach is developed, including an asynchronous multi-agent reinforcement learning- based algorithm to determine the optimal satellite set serving each cell and a distributed optimization-based algorithm to make the resource reservation decisions for each slice. Simulation results demonstrate that the proposed scheme outperforms benchmark methods in terms of resource usage and delay performance.

The low Earth orbit (LEO) satellite-assisted communications are envisioned as a prospective solution in next-generation networks to provide reliable, flexible, cost-effective, and globally seamless services. In this paper, we investigate satellite-terrestrial vehicular network (STVN) supporting connected autonomous vehicle (CAV) applications anytime and anywhere. We first establish a model for the LEO satellite-CAV communication system with different satellite orbital parameters. Then the LEO satellite-CAV communication performance in terms of service availability, outage probability, and system throughput is analyzed when considering practical satellite constellations. Furthermore, the impact of different terrestrial infrastructure deployment strategies on the STVN performance is investigated. Extensive numerical results are provided to validate our theoretical analysis and demonstrate the improvement of CAV network performance thanks to LEO satellites in the STVN.

Mobile edge computing is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers. However, within small-cell networks, the user mobilities can result in uneven spatio-temporal loads, which have not been well studied by considering adaptive load balancing, thus limiting the system performance. Motivated by the data analytics and observations on a real-world user association dataset in a large-scale WiFi system, in this paper, we investigate the mobility-aware online task offloading problem with adaptive load balancing to minimize the total computation costs. However, the problem is intractable directly without prior knowledge of future user mobility behaviors and spatio-temporal computation loads of edge servers. To tackle this challenge, we transform and decompose the original task offloading optimization problem into two sub-problems, i.e., task offloading control (ToC) and server grouping (SeG). Then, we devise an online control scheme, namedMOTO(i.e.,Mobility-awareOnlineTaskOffloading), which consists of two components, i.e., Long Short Term Memory based algorithm and Dueling Double DQN based algorithm, to efficiently solve theToCandSeGsub-problems, respectively. Extensive trace-driven experiments are carried out and the results demonstrate the effectiveness ofMOTOin reducing computational costs of mobile devices and achieving load balancing when compared to the state-of-the-art benchmarks.

In emergency scenarios, strong mobility and serious interference cause unstable transmission of on-site information such as close-up photos and high resolution videos, which requires a robust temporary communication network. In this paper, we focus on a UAV-assisted wireless cooperative communication and coded caching network, where emergency command vehicles and a UAV serve as content providers (CPs) to cache and transmit coded fragments or complete files for rescuers regarded as content requesters (CRs). The delivery success probability and content hit ratio are theoretically derived by incorporating the physical connectivity and social relationship between CPs and CRs. Aiming at maximizing the overall content hit ratio, we propose a multiagent two-timescale deep reinforcement learning (MA2T-DRL) algorithm to jointly optimize the transmission power and caching strategies for CPs. Specifically, we develop a two tier deep-Q networks (DQNs) framework integrating a slow-timescale DQN (ST-DQN) and a fast-timescale DQN (FT-DQN) for caching decision-making and power decision-making respectively, and then the QMIX framework is leveraged to aggregate all the outputs from local ST-DQNs. Considering the cooperative characteristics of coded caching, we further propose a novel clustering method for CPs such that CPs in the same cluster have the same willingness to serve CRs, and each cluster is regarded as the agent for training which further reduces the aggregation scale of the mixing network. Simulation results show that the proposed MA2T-DRL algorithm is efficient in model training, and presents the advantages in performance and complexity compared with the single-agent centralized training and the multiagent independent distributed training.

In distributed IoT data systems, full-size data collection is impractical due to the energy constraints and large system scales. Our previous work has investigated the advantages of integrating matrix sampling and inference for compact distributed IoT data collection, to minimize the data collection cost while guaranteeing the data benefits. This paper further advances the technology by boosting fast and accurate inference for those distributed IoT data systems that are sensitive to computation time, training stability, and inference accuracy. Particularly, we proposeCODE$^{+}$+, i.e.,Compact Distributed IOTData CollEction Plus, which features a cluster-based sampling module and a Convolutional Neural Network (CNN)-Transformer Autoencoders-based inference module, to reduce cost and guarantee the data benefits. The sampling component employs a cluster-based matrix sampling approach, in which data clustering is first conducted and then a two-step sampling is performed in accordance with the number of clusters and clustering errors. The inference component integrates a CNN-Transformer Autoencoders-based matrix inference model to estimate the full-size spatio-temporal data matrix, which consists of a CNN-Transformer encoder that extracts the underlying features from the sampled data matrix and a lightweight decoder that maps the learned latent features back to the original full-size data matrix. We implementCODE$^{+}$+under three operational large-scale IoT systems and one synthetic Gaussian distribution dataset, and extensive experiments are provided to demonstrate its efficiency and robustness. With a 20% sampling ratio,CODE$^{+}$+achieves an average data reconstruction accuracy of 94% across four datasets, outperforming our previous version of 87% and state-of-the-art baseline of 71%.

This paper investigates the age of information (AoI) and energy efficiency of wireless powered industrial Internet of Everything (IIoE) network, where multiple low-power IIoE devices (IIoEDs) are wirelessly charged by a hybrid access point (HAP) to transmit their sensing information to the control nodes. To enhance the system’s information timeliness with high energy efficiency, we define a novel performance metric, i.e., age-energy efficiency (AEE), which depicts the achievable AoI gain per unit energy consumption. Then, an optimization problem is formulated to maximize the system long-term AEE by jointly optimizing the IIoEDs scheduling and the HAP’s transmit power. Due to the non-convexity of the formulated problem and the intractable challenges with discrete binary variables, we first model the problem as a two-stage discrete-time Markov decision process (MDP) with carefully designed state spaces, action spaces, and reward functions. We then propose a deep reinforcement learning (DRL)-based approach to find the effective scheduling strategy and transmit power. To improve the accuracy of the learned policy, we design a dual-layer deep Q-network (DLDQN) algorithm with fast convergence. Simulation results show that our proposed DLDQN algorithm can improve the AEE by at least 25% when the number of IIoEDs exceeds 50 compared with benchmarks. Moreover, with the proposed DLDQN algorithm, the system long-term AEE can be improved with the increase of the number of IIoEDs.

The increasing network traffic and dynamic user connections have posed challenges for cellular operators in reducing operating costs while ensuring the quality of service (QoS) for users. Cloud radio access network (C-RAN) addresses these issues by separating baseband units (BBUs) and remote radio heads (RRHs), creating a centralized BBU pool. To optimize C-RAN performance, the key is to dynamically assigning RRHs to BBUs, which is challenging due to cost and QoS constraints. In this paper, we propose a data-driven RRH-BBU mapping scheme (KC-A3C) with deep reinforcement learning (DRL) to improve the performance of large-scale C-RANs. First, we analyze a dataset from a cellular operator containing approximately 26,652 active base stations and use the features of the dataset to construct an RRH popularity metric to cluster RRHs. Second, we model the RRH-BBU mapping as a Markov decision process and use the synchronous Advantage Actor-Critic (A3C) algorithm to find the optimal mapping scheme with the highest long-term gain in a dynamic environment, considering resource utilization, RRH migration, and BBU load balancing. Evaluations using real-world datasets show that our proposed scheme outperforms baseline methods.

In this paper, we investigate joint caching and computing resource reservation for supporting location-aware augmented reality (AR) applications in an edge-assisted two-tier radio access network. We aim at minimizing the caching and computing resource consumption while satisfying the AR service delay requirement. Specifically, to capture the spatio-temporal AR service dynamics, the resource consumption minimization problem is formulated as a long-term stochastic optimization problem. Due to the time-varying service demands and tightly coupled multi-resource reservation decisions, we propose a novel resource reservation algorithm based on the Lyapunov optimization technique to solve the problem. We first transform the original long-term problem into multiple one-shot optimization problems, each of which is then solved by our designed iterative algorithm in an online manner. Simulation results demonstrate that the proposed algorithm can significantly reduce the overall resource consumption compared to benchmark algorithms.

Mobile-edge computing (MEC) is a promising approach to support high-quality time-sensitive applications. With the increasing number of mobile devices, achieving efficient service migration management has become nontrivial in MEC. In addition, the service migration issue is difficult to be solved in real time due to user mobility and dynamic network conditions. In this article, we investigate the mobility-aware service migration problem in MEC by introducing a data-driven framework. First, service migration is formulated as an optimization problem for minimizing the long-term system delay that consists of computing, communication, and migration delays. Second, we propose a Mobility-aware Service Migration scheme, named MSM, consisting of three layers: 1) the data collection layer; 2) the association patterns analysis layer; and 3) the service migration layer. Specifically, we first collect users’ historical Wi-Fi traces to mine the association patterns. We then design a user management mechanism to reduce the complexity of decision making by using user association patterns. Finally, we formulate the service migration as a 2-D-Markov decision process and devise a deep reinforcement learning (DRL)-based algorithm to obtain service migration decisions in a large-scale MEC scenario. Extensive data-driven experiments are conducted to demonstrate the efficacy of MSM in reducing the system delay.

In this article, we propose an integrated satellite–terrestrial network (ISTN) architecture to support delay-sensitive task offloading for remote Internet of Things (IoT), in which satellite networks serve as a complement to terrestrial networks by providing additional communication resources, backhaul capacities, and seamless coverage. Under this architecture, we investigate how to jointly make offloading link selection and bandwidth allocation decisions for BSs and IoT users. Considering the differentiated decision-making time granularities, we formulate a two-timescale stochastic optimization problem to minimize the overall task offloading delay. To accommodate the two-timescale network dynamics and characterize state–action relations, we establish a hierarchical Markov decision process (H-MDP) framework with two separate agents tackling two-timescale network management decisions, and two evolved MDP-based subproblems are formulated accordingly. To efficiently solve the subproblems, we further develop a hybrid proximal policy optimization (H-PPO)-based algorithm. Specifically, a hybrid actor–critic architecture is designed to deal with the mixed discrete and continuous actions. In addition, an action mask layer and an action shaping function are designed to sample feasible task offloading decisions from the time-variant action set. Extensive simulation results have validated the superiority of the proposed ISTN architecture and the H-PPO-based algorithm, especially, in scenarios with scarce spectrum resources and heavy traffic loads.

Map matching is a fundamental component for location-based services (LBSs), such as vehicle mobility analysis, navigation services, traffic scheduling, etc. In this paper, we investigate federated learning augmented map matching based on heterogeneous cellular moving trajectories from different operator systems, the goal of which is to improve matching accuracy without violating the user privacy. First, we develop a data collection platform with one Android-based application, and conduct rigorous data collection campaigns. Second, we perform systematic data analytics to reveal the data-driven technical challenges, including the impact of sampling rate, high location error of cellular moving data, and poor heterogeneous matching performance. Third, we propose an augmented map matching model, named FL-AMM, i.e.,FederatedLearningAugmentedMapMatching, in which we i) adopt the vertical federated learning framework to achieve data collaboration and privacy protection for heterogeneous operators; ii) devise a data augmentation component to enhance the capability of representing the raw cellular data; and iii) design a map matching model to further learn the mapping function from cellular trajectory points to road segments. Finally, we conduct extensive data-driven experiments to corroborate the efficiency and robustness of the proposed FL-AMM.

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered sensor network (WPSN), where sensor nodes of multiple types can simultaneously harvest radio-frequency energy from the UAV and then transmit sensing data by using harvested energy. A joint distributed beamforming (DBF) and backscattering scheme is designed, in which the sensor nodes of one type can perform DBF while the sensor nodes of other types perform distributed backscattering (DBS) to improve the received signal strength. A sum-throughput maximization problem is formulated by jointly optimizing DBF phases, DBS phases, and time allocation (TA), subject to the received signal-to-noise ratio constraints. Since the formulated problem is difficult to be solved due to the tightly coupled optimizing variables, the problem is decoupled into a TA subproblem and a phase optimization subproblem, and then a two-step algorithm is proposed to solve them. Firstly, the closed-form solution for the TA subproblem is derived according to Karush-Kuhn-Tucker conditions. Secondly, based on iterative optimization and one-dimensional search methods, a centralized algorithm is proposed to obtain the optimal solution for the phase optimization subproblem. Moreover, a decentralized algorithm that obtains the suboptimal solution is proposed to reduce the computational complexity. Extensive simulation results validate the effectiveness of the proposed scheme on throughput enhancement.

In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE). Since PQoE models are user-specific and time-varying, existing schemes based on universal and time-invariant PQoE models may suffer from performance degradation. To address this issue, we first propose a DT-assisted PQoE model construction method to obtain accurate user-specific PQoE models. Specifically, user DTs (UDTs) are respectively constructed for individual users, which can acquire and utilize users' data to accurately tune PQoE model parameters in real time. Next, given the obtained PQoE models, we formulate a resource management problem to maximize the overall long-term PQoE by taking the dynamics of users' locations, video requests, and buffer statuses into account. To solve this problem, a deep reinforcement learning algorithm is developed to jointly determine segment version selection, and communication and computing resource allocation. Simulation results on the real-world dataset demonstrate that the proposed scheme can effectively enhance PQoE compared with benchmark schemes.

IoTs generally rely on resource-constrained devices to sense, relay, and collect data, which are highly vulnerable to Distributed-Denial-of-Services (DDOS) attacks on network throughput. In this paper, we propose a trust-based method to optimize the network throughput of IoTs under DDOS attacks. Specifically, with the assistance of a small number of dedicatedly deployed defense nodes as defenders, a network controller can first measure the behavior of other IoT nodes and categorize them into three types (i.e., innocent, selfish, and attack) through a well-designed trust evaluation model. Then, a Stackelberg game model is constructed accordingly, where defenders are leaders and other nodes are followers. We carefully define the utilities of the leaders and the followers in the game, and transform the optimization problem of the network throughput into the maximization problem of the defenders' utilities considering the utilities of the followers. We adopt the Dinkelbach Programming (DP)-based algorithm to solve the maximization problem such that a Stackelberg equilibrium can be reached with optimized network throughput. Extensive simulations are performed to demonstrate that the proposed defense method can significantly increase the IoT network throughput under different DDOS attack intensities.

Mobile edge computing (MEC) is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers for fast processing. In this paper, we investigate the computing task offloading in small-cell MEC systems. Considering the unevenly distributed mobile users, it is critical to balance the computing load among edge servers to better utilize the computing resources. To this end, we formulate a joint task offloading control and load balancing problem to minimize the average computational cost of users. The formulated problem is a mixed-integer nonlinear optimization problem and is intractable with system scale. To solve the problem in real time, we propose a reinforcement learning-based grouping and task offloading control (RLGTC) scheme. Specifically, we first decompose the problem into two sub-problems with the Tammer method, i.e., the task offloading control (ToC) and server grouping (SeG) sub-problems. Then, we devise two algorithms based on the Kalman Filter technique and reinforcement learning with Dueling Double DQN to solve them, respectively. Extensive data-driven experiments demonstrate the effectiveness of the RLGTC scheme in achieving load balancing and reducing UEs’ computational costs compared to the state-of-the-art benchmarks.

Mobile Edge Computing (MEC) is a promising paradigm to support high-quality time-sensitive applications. In this paper, we investigate the service migration (i.e., whether, when, and where to migrate the services) to seamlessly serve mobile users in small-cell MEC systems. The service migration is formulated as an optimization problem to minimize the long-term system average delay that consists of queuing, communication, and migration delays. Considering the dynamic user mobility and network conditions, the formulated problem is non-convex and difficult to solve in real time. To this end, we propose a Mobility-aware Service Migration scheme, named MSM, to make real-time decisions on service migrations by utilizing reinforcement learning (RL) approaches. Specifically, we first design a user classification mechanism based on users’ mobility patterns to reduce the complexity of decision-making. We then formulate the service migration as a Markov decision process and devise an RL-based framework to make service migration decisions in real time in the dynamic MEC environment. Extensive data-driven experiments demonstrate the efficacy of MSM in reducing the system average delay.

Space–air–ground-integrated networks (SAGINs) are deemed as a promising solution to support multifarious Internet of Vehicles (IoV) services with diversified Quality-of-Service (QoS) requirements in future communication networks. Network function virtualization (NFV) and software-defined networking (SDN) are two complementary and promising technologies to reduce the function provisioning cost and coordinate the heterogeneous physical resources in SAGIN. In this article, we investigate the online dynamic virtual network function (VNF) mapping and scheduling in SAGIN, considering the dynamicity of IoV services. The VNF live migration, VNF reinstantiation, and VNF rescheduling are enabled to increase the service acceptance ratio and service provider’s profits. Considering the heterogeneity of space, air, and ground nodes, we first model the migration cost and additional delay incurred by VNF live migration and reinstantiation. We then formulate the dynamic VNF mapping and scheduling jointly as a mixed-integer linear programming (MILP) problem with specified cost and delay models. We propose two Tabu search (TS)-based algorithms, i.e., TS-based VNF remapping and rescheduling (TS-MAPSCH) algorithm and TS-based pure VNF rescheduling (TS-PSCH) algorithm, to obtain suboptimal solutions to the MILP problem efficiently. Simulation results show that the proposed solution is very close to the optimum and that the proposed dynamic algorithms outperform existing works with respect to multiple performance metrics, including the service provider’s profit, service acceptance ratio, and QoS satisfaction level.

Real-time search is essential for accessing specific sensing data (SD) in vehicular networks to support safe, efficient, and intelligent road services. Considering the tremendous data volume, the SD search process should be carefully devised to avoid excessive retrieval and transmission delay. To alleviate the communication and computational burden for sensing devices and the cloud server, roadside edges are adopted to cache the SD in advance. Given a short lifetime of SD, the caching scheme is required to be efficient in facilitating both the search process and uplink/downlink transmission, which is quite challenging due to the coupling of resource allocation decisions. To guarantee the search efficacy and enhance the caching resource utilization, we propose a real-time search-driven caching (RSC) paradigm to enable the cooperation among storage-constrained edges. A hierarchical indexing framework is first introduced for cached data, based on which we then devise a search utility model to quantify the expected data freshness and response delay. With the objective of maximizing the long-term search reward, the RSC problem is formulated by jointly considering the search requests and utility model. A deep-reinforcement-learning-based caching (DRLC) method is proposed to solve the problem. Specifically, an action transition module is introduced to lower the computational complexity via reducing the selection space of caching actions. Extensive simulations are carried out based on the real trace data in Creteil, France, and results show that the intelligent DRLC method can improve the real-time search performance significantly comparing to the benchmark methods.

Digital twin (DT) has attracted a lot of attention from both industry and academia since it was proposed over a decade ago. A DT can be viewed as a virtual implementation of a real physical system (PS) and used as a representation of the PS for various applications. Despite the great potential of DTs in various fields, implementing DTs to obtain the desired functionality is not always straightforward. Specifically, accurate real-time synchronization between the features at a PS and its DT is essential for the DT to represent the PS. In this case, appropriate networking support is a key component to enable future DT development and applications. Currently, the research on DTs from a networking standpoint is still at an early stage, and only limited work has been done on DT implementation in practical systems. To fill this gap, this article investigates networking-related issues for DTs. Based on the existing literature, a feature-based method is provided for describing the desired properties and quality of DTs from the networking perspective. A stage-based implementation framework is presented for creating large-scale DTs for complex PSs by considering various networking constraints. Networking-related challenging issues and open research topics are discussed at the end.

In this paper, we propose a blockchain-based collaborative credential management scheme for anonymous authentication in space-air-ground integrated vehicular networks (SAGVN), namedSAG-BC. First, we build a consortium blockchain among service providers and design a distributed system setup (DSS) scheme to securely generate public parameters for issuing credentials. Second, we design a collaborative credential issuance (CCI) scheme to generate a succinct and easy-to-manage subscription credential. The credential can be used by users to access different access points in SAGVN efficiently without revealing true identities from the authentication messages. With co-designs of zero-knowledge proofs and succinct on-chain commitments,SAG-BCprovides efficient verifiability and incentives for credential management operations in SAGVN. By doing so, expensive on-chain storage and computational overheads are reduced in the DSS and CCI. Finally, we conduct a thorough security analysis to demonstrate thatSAG-BCachieves security and verifiability for credential management in SAGVN. We set up a real-world blockchain network and conduct extensive experiments to show the feasibility and efficiency ofSAG-BC.

In this paper, we study Space-Air-Ground integrated Vehicular Network (SAGVN), and propose an online control framework to dynamically slice the SAG spectrum resource for isolated vehicular services provisioning. In particular, at a given time slot, the system makes online decisions on the request admission and scheduling, UAV dispatching, and resource slicing for different services. To characterize the impact of those parameters, we construct a time-averaged queue stability criteria by taking queue backlogs of all services into consideration, and formulate a system revenue function which incorporates the time-averaged system throughput and UAV dispatching cost. The objective is to maximize the system revenue while stabilizing the time-averaged queue, which falls into the scope of Lyapunov optimization theory. By bounding the drift-plus-penalty, the original problem can be decoupled into four independent subproblems, each of which is readily solved. The merits of our control framework are three-fold: 1) the system is able to admit and process as many requests as possible (i.e., maximizing the time-averaged throughput); 2) the time-averaged UAV dispatching cost is minimized; and 3) service queues are stabilized in the long-term. Extensive simulations are carried out, and the results demonstrate that the control framework can effectively achieve the system revenue maximization and queueing stabilization. Moreover, it can balance the trade-off among system throughput, UAV dispatching cost, and queueing states via parameter tuning. Compared with the fixed slicing, our dynamic slicing can react to the vehicular environment rapidly and achieve an average 26% of throughput improvement.

In this paper, we propose a two-level adaptive resource allocation (TARA) framework to support vehicular safety message transmissions. In particular, three types of safety messages are considered in urban vehicular networks, i.e., event-triggered messages for urgent condition warnings, periodic messages for vehicular status notifications, and messages for environmental perception. Roadside units are deployed for network management, and thus messages can be transmitted through either vehicle-to-infrastructure or vehicle-to-vehicle connections. To satisfy the requirements of different message transmissions, TARA framework consists of a group-level resource reservation module and a vehicle-level resource allocation module. Particularly, the resource reservation module is designed to allocate resources to support different types of message transmissions for each vehicle group at the first level. To learn the implicit relationship between the resource demand and message transmission requests, a supervised learning model is devised in the resource reservation module, where to obtain the training data we further propose a sequential resource allocation (SRA) scheme. Based on historical network information, SRA scheme offline optimizes the allocation of sensing resources, i.e., choosing vehicles to provide perception data, and communication resources. With resources reserved for each group, the vehicle-level resource allocation module is then devised to distribute specific resources for each vehicle to satisfy the differential requirements in real-time. Extensive simulation results demonstrate the effectiveness of TARA framework in terms of the high packet delivery ratio and low latency for message transmissions, and the high quality of collective environmental perception.

In this paper, we propose an efficient and anonymous authentication protocol with a succinct multi-subscription credential (AnMsc) in Space-air-ground integrated vehicular networks (SAGVN). First, we adopt a subscription-based service model in SAGVN. Specifically, vehicular users (VEs) can subscribe to network services and conduct direct mutual authentication with subscribed access points (APs) to avoid message exchanges with VEs’ home network. Early application data can also be transmitted with authentication messages to improve communication efficiency. Second, we carefully tailor the design of the redactable signature and propose an efficient credential management mechanism in SAGVN. Multiple service subscriptions can be embedded into a succinct (constant-size) credential. With the credential, VEs can anonymously access any subscribed AP without revealing other subscription information. Thorough security analysis and comprehensive performance evaluation demonstrate that AnMsc can guarantee key-exchange security, VE anonymity, and service fairness while ensuring credential management and authentication efficiency.

The emergency of federated learning (FL) enables distributed data owners to collaboratively build a global model without sharing their raw data, which creates a new business chance for building data market. However, in practical FL scenarios, the hardware conditions and data resources of the participant clients can vary significantly, leading to different positive/negative effects on the FL performance, where the client selection problem becomes crucial. To this end, we proposeAUCTION, anAutomated and qUality-awareClient selecTIONframework for efficient FL, which can evaluate the learning quality of clients and select them automatically with quality-awareness for a given FL task within a limited budget. To designAUCTION, multiple factors such as data size, data quality, and learning budget that can affect the learning performance should be properly balanced. It is nontrivial since their impacts on the FL model are intricate and unquantifiable. Therefore,AUCTIONis designed to encode the client selection policy into a neural network and employ reinforcement learning to automatically learn client selection policies based on the observed client status and feedback rewards quantified by the federated learning performance. In particular, the policy network is built upon an encoder-decoder deep neural network with an attention mechanism, which can adapt to dynamic changes of the number of candidate clients and make sequential client selection actions to reduce the learning space significantly. Extensive experiments are carried out based on real-world datasets and well-known learning models to demonstrate the efficiency, robustness, and scalability ofAUCTION.

Information freshness is essential to industrial wireless networks (IWNs) and can be quantified by the age-of-information (AoI) metric. This paper addresses an AoI-aware spectrum sharing (AgeS) problem in IWNs, where multiple device-to-device (D2D) links opportunistically access the spectrum to satisfy their AoI constraints while maximizing primal links’ throughput. Particularly, we orchestrate the access of D2D links in a distributed manner. Since distributed scheduling results in incomplete observation, D2D links share the spectrum with uncertainty on the transmission environment. Therefore, we propose a distributed scheduling scheme, called D-age, to deal with the transmission uncertainty in the AgeS problem, where an adaptation of actor-critic method is adopted with AoI constraints tackled in the dual domain. To address the non-stationary environment and multi-agent credit assignment issue, cooperative multi-agent reinforcement learning (MARL) approach is developed, where multiple local actors are designed to guide D2D links to make real-time decisions via distributed scheduling policies, which are evaluated by an edge-assisted global critic with action-aware advantage functions. Integrated with graph attention networks (GATs), the critic selectively learns contextual information by assigning different importances to neighboring links, which enables the evaluation of scheduling policies in a scalable and computation-efficient manner. Theoretical guarantee of the time-averaged AoI constraints is provided and the effectiveness of D-age in terms of both AoI violation ratio and the capacity of primal links is demonstrated by simulation.

To support multifarious vehicular applications, content sharing among vehicles or between vehicles and infrastructures can enable efficient service provisioning. In this work, we investigate joint communication, caching, and computing (3C) resource allocation to support efficient content sharing between content providers (CPs) and content requesters (CRs) in vehicular networks. To tackle the high complexity of joint 3C resource allocation, we decouple the problem into a long-term content caching strategy to allocate caching resources plus a method for short-term CP-CR pairing and corresponding communication-computing resource allocation. Specifically, a popularity and social similarity (P-SS) based caching strategy is proposed by incorporating both physical and social information. As selfish CRs may refuse to reveal their quality of service (QoS) requirements to the CPs and lead to the information asymmetry, we adopt contract theory to allocate communication and computing resources for each potential CR-CP pair. We then propose a stable-matching based algorithm to match CPs and CRs for efficient content sharing. Simulation results verify that the proposed scheme can effectively solve the problem with low complexity.

To support the explosive content demands from multifarious services and applications, cache-enabled satellite-terrestrial integrated networks (STINs) are envisioned as a key enabler to reduce the content delivery delay and alleviate the backhaul pressure. In this paper, we investigate the joint optimization of cache placement and content delivery in the STIN to minimize the long-term overall content delivery delay. Considering that cache placement and content delivery are interrelated and affected by network dynamics in terms of satellite movement and random content requests, the joint optimization problem is formulated as a sequential decision making problem by leveraging a Markov decision process. We propose a hierarchical deep Q learning (HDQL) algorithm by leveraging two independent deep neural networks to learn the cache placement and content delivery policies with small action space and low time complexity. Simulation results demonstrate that the proposed HDQL algorithm outperforms the benchmark algorithms in terms of content delivery delay in the STINs.

To realize secure communication and prevent eaves-droppers from detecting the existence of communication activities, covert communication has attracted substantial research interests. In this paper, we propose a dynamic spectrum control (DSC)-assisted scheme to achieve covert and reliable data transmission. Specifically, by constructing time-frequency division channels, the proposed DSC-assisted scheme generates sequences with iterative and orthogonal transformations. Authorized users can orderly occupy different frequency slots in each time slot under the guidance of these sequences, thus achieving simultaneous data transmission without interfering with each other. Then, the covert performance of the proposed transmission scheme is analyzed to provide the closed-form expressions of covert transmission rate and the reliable transmission probability. Simulation results are provided to validate the accuracy of the theoretical analysis and demonstrate that the proposed scheme can achieve better covert and reliable transmission performances when compared with the existing scheme.

Space-ground integrated vehicular networks (SGIVNs) are envisioned as a promising architecture to support multifarious vehicular services with enhanced network flexibility and reliability. Access mode selection (AMS) is of capital importance in the SGIVN for the ingenious cooperation among different network segments to exploit their complementary advantages. In this paper, we investigate the AMS problem for vehicles in the SGIVN by taking distinct features of satellite networks (long propagation delay) and terrestrial networks (frequent handover) into account. In light of the high vehicle/satellite mobility and dynamic data packet arrivals, we formulate a stochastic integer programming problem of sequential AMS to maximize vehicles' long-term data rate. To cope with the time-varying network dynamics, we leverage a Markov decision process framework to model the evolution of vehicle states. For the special case with known stochastic model of data packet arrivals, we transform the problem into a linear programming problem that can be solved with low complexity. For the general case without the data packet arrival model, we propose a reinforcement learning-based algorithm to make adaptive AMS decisions to keep pace with network dynamics. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of data rate under different data packet arrival patterns and vehicle velocities.

To enhance driving safety and road intelligence for connected vehicles, the transmission of safety messages is critical in vehicular networks. In this paper, we focus on urban vehicular networks with deployed roadside units, and both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connections can be leveraged for message transmissions. We consider three types of safety messages: periodic messages for vehicular status notification, event-driven messages for urgent situation notification, and messages to achieve collective perception. To support different safety-related services, we develop a multi-dimensional resource allocation scheme to jointly optimize the sensing resource allocation (i.e., selecting vehicles as perception data providers), the V2I/V2V transmission mode selection, and the corresponding communication resource allocation. As the decisions on sensing resource allocation and wireless resource allocation are coupled, an iterative algorithm is proposed to solve the joint optimization problem by taking the differentiated service priorities into consideration. Extensive simulation results are presented to validate the effectiveness of the proposed resource allocation scheme.

In this paper, we investigate the joint cache placement and content delivery in satellite-terrestrial integrated cloud radio access networks. We consider the scenario where cache-enabled access points (APs), including multiple base stations (BSs) and one low orbit earth satellite, are connected to a central processor via backhaul links and cooperatively serve users via joint beamforming. To minimize the long-term power consumption, we formulate an optimization problem to jointly optimize the cache placement, AP clustering, and multicast beamforming, while satisfying the constraints on the transmission power, quality-of-service, and caching storage. Since the formulated problem has mixed timescales, it is decoupled into two sub-problems. For the short-term delivery sub-problem, we first reformulate it as an equivalent sparse multicast beamforming problem and approximate the non-convex objective function by a concave smooth function, and then solve it with a convex-concave procedure approach. For the long-term cache placement sub-problem, we propose an alternating based method to tackle it iteratively. Simulation results validate the advantages of our proposed method and show the impacts of different cache capacities and file numbers on the long-term power consumption.

As a promising architecture to provide ubiquitous and ultra-reliable network connectivity, space-air-ground integrated network (SAGIN) has attracted substantial research interests. In this paper, we propose a dynamic spectrum access control (DSAC) protocol for the SAGIN. Specifically, the proposed DSAC protocol uses sequences to represent the spectrum access decisions for authorized users at different time slots. Through iterative and orthogonal sequence transformation, the DSAC protocol can generate orthogonalized sequences to guarantee successful spectrum access for authorized users. In addition, the non-collision probability of the data packets accessing the shared spectrum under the guidance of DSAC protocol is analyzed, based on which a closed-form expression of the system throughput is further derived. Simulation results are provided to validate the accuracy of the theoretical analysis and demonstrate that the proposed protocol is effective in access control and throughput improvement in the SAGIN when compared with the existing random access protocol.

Real-time sensing data access is essential for vehicular networks to support safe, efficient, and intelligent road services. Considering the tremendous data volume, the sensing big data search process should be carefully devised to avoid excessive retrieval delay. Edge caching can effectively alleviate the traffic burden and shorten the data downloading route, where the sensing data has to be uploaded to the edge in advance. Given a short life-time of sensing data, the caching scheme is required to be efficient in facilitating both the search process and uplink/downlink transmission, which is challenging due to the coupling of resource allocation decisions. In this paper, an edge-cloud cooperative caching scheme is proposed. Specifically, to enable real-time data search, we first introduce a hierarchical indexing framework for cached data, based on which we then devise a search utility model to quantify the expected data freshness and response delay. Aiming at maximizing the search utility, a Caching-assisted Real-time Search (CRS) problem is formulated. Due to its NP-hardness, we devise a greedy-based algorithm to solve the CRS problem. Simulation results demonstrate that the proposed cooperative caching scheme can significantly improve the data freshness and cache hit ratio comparing to the benchmark schemes.

To support multifarious vehicular services with differentiated quality-of-service (QoS) requirements, space-air-ground integrated vehicular networks (SAGVNs) are envisioned as a promising solution to provide global network connectivity, enhance network flexibility, and improve network reliability. In this paper, we investigate cooperative content delivery in the SAGVN, where vehicular content requests can be simultaneously served by multiple access points (APs) in space, aerial, and terrestrial networks. In specific, a joint optimization problem of vehicle-to-AP association, bandwidth allocation, and content delivery ratio, referred to as the ABC problem, is formulated to minimize the overall content delivery delay while satisfying vehicular QoS requirements. To address the tightly-coupled optimization variables, we propose a load- and mobility-aware ABC (LMA-ABC) scheme to solve the joint optimization problem as follows. We first decompose the ABC problem to optimize the content delivery ratio. Then the impact of bandwidth allocation on the achievable delay performance is analyzed, and an effect of diminishing delay performance gain is revealed. Based on the analysis results, the LMA-ABC scheme is designed with the consideration of user fairness, load balancing, and vehicle mobility. Simulation results demonstrate that the proposed LMA-ABC scheme can significantly reduce the cooperative content delivery delay comparing to the benchmark schemes.

Drone cell (DC) is envisioned to enable the dynamic service provisioning for radio access networks (RANs), in response to the spatial and temporal unevenness of user traffic. In this article, we propose a hierarchical deep reinforcement learning (DRL)-based multi-DC trajectory planning and resource allocation (HDRLTPRA) scheme for high-mobility users. The objective is to maximize the accumulative network throughput while satisfying user fairness, DC power consumption, and DC-to-ground link quality constraints. To address the high uncertainties of the environment, we decouple the multi-DC TPRA problem into two hierarchical subproblems, i.e., the higher level global trajectory planning (GTP) subproblem and the lower level local TPRA (LTPRA) subproblem. First, the GTP subproblem is to address trajectory planning for multiple DCs in the RAN over a long time period. To solve the subproblem, we propose a multiagent DRL-based GTP (MARL-GTP) algorithm in which the nonstationary state space caused by the multi-DC environment is addressed by the multiagent fingerprint technique. Second, based on the GTP results, each DC solves the LTPRA subproblem independently to control the movement and transmit power allocation based on the real-time user traffic variations. A deep deterministic policy gradient (DEP)-based LTPRA (DEP-LTPRA) algorithm is then proposed to solve the LTPRA subproblem. With the two algorithms addressing both subproblems at different decision granularities, the multi-DC TPRA problem can be resolved by the HDRLTPRA scheme. Simulation results show that 40% network throughput improvement can be achieved by the proposed HDRLTPRA scheme over the nonlearning-based TPRA scheme.

Industrial wireless networks (IWNs) have attracted significant attention for providing time-critical delivery services, which can benefit from device-to-device (D2D) communication for low transmission delay. In this article, a distributed scheduling problem is investigated for D2D-enabled IWNs, where D2D links have various age-of-information (AoI) constraints for information freshness. This problem is formulated as a constrained optimization problem to optimize D2D packet delivery over limited spectrum resources, which is intractable since D2D users have no prior knowledge of the operating environment. To tackle this problem, in this article, an AoI-aware scheduling scheme is proposed based on primal-dual optimization and actor--critic reinforcement learning. In specific, multiple local actors for D2D devices learn AoI-aware scheduling policies to make on-site decisions with their stochastic AoI constraints addressed in the dual domain. An edge-based critic estimates the performance of all actors' decision-making policies from a global view, which can effectively address the nonstationary environment caused by concurrent learning of multiple local actors. Theoretical analysis on the convergence of learning is provided and simulation results demonstrate the effectiveness of the proposed scheme.

By connecting the maritime users to Internet, e.g., boats, ships, etc., it is possible to operate maritime sensing and informatics across seas and oceans. Such marine Internet of things (MIoT) is urging intelligent maritime applications, e.g., real-time vessel tracking, navigation safety, autonomous shipping, etc. Due to the bandwidth limitation of conventional marine channels, broadband communication is desired for these emerging applications. In this paper, we consider operating the TV white space (TVWS) spectrum in 700MHz to support the near-sea surface communication for MIoT terminals. To better utilize the TV channel capacity, we propose a proactive and efficient link adaptation (LA) scheme based on nonlinear autoregressive neural network (NARNN) time series prediction. Specifically, the historical signal samplings are used to predict the near-sea-surface channel link status for the next transmission slot, which is then used to select a proper modulation and coding scheme (MCS) for the next egress frame. We have conducted extensive simulations, and show that the average channel utility can achieve almost 85% of the optimal capacity. The proposed LA scheme can provide useful inspirations for applying data analytics to efficient and adaptive LA schemes for mobile Internet of things.

In this paper, we propose an online control frame-work to dynamically slice the network resource for isolated service provisioning in Space-Air-Ground integrated Vehicular Network (SAGVN). In particular, at a given time slot, the system makes online decisions on the request admission and scheduling, UAV dispatching, and resource slicing for different services. To characterize the impact of those parameters, we construct a time-averaged queue stability criteria by taking queue backlogs of all services into consideration, and formulate a system revenue function which incorporates the time-averaged system throughput and UAV dispatching cost. The objective is to maximize the system revenue while stabilizing the time-averaged queue, which can be achieved via the Lyapunov optimization theory. By bounding the drift-plus-penalty, the problem then can be decoupled into four independent subproblems, which are readily solved. The merits of our control framework are three-fold: 1) the system can admit and process as many requests as possible; 2) the time-averaged UAV dispatching cost is minimized; and 3) service queues can be stabilized over time. Extensive simulations are carried out, and the results demonstrate that the control framework can effectively achieve the system revenue maximization and queueing stabilization. Moreover, it can balance the trade-off among system throughput, UAV dispatching cost, and queueing states via parameter tuning.

In this article, we investigate the UAV-aided edge caching to assist terrestrial vehicular networks in delivering high-bandwidth content files. Aiming at maximizing the overall network throughput, we formulate a joint caching and trajectory optimization (JCTO) problem to make decisions on content placement, content delivery, and UAV trajectory simultaneously. As the decisions interact with each other and the UAV energy is limited, the formulated JCTO problem is intractable directly and timely. To this end, we propose a deep supervised learning scheme to enable intelligent edge for real-time decision-making in the highly dynamic vehicular networks. In specific, we first propose a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content placement strategy, we devise a time-based graph decomposition method to jointly optimize the content delivery and trajectory design, with which we then leverage the particle swarm optimization (PSO) algorithm to further optimize the content placement. We then design a deep supervised learning architecture of the convolutional neural network (CNN) to make fast decisions online. The network density and content request distribution with spatio-temporal dimensions are labeled as channeled images and input to the CNN-based model, and the results achieved by the CBTL algorithm are labeled as model outputs. With the CNN-based model, a function which maps the input network information to the output decision can be intelligently learnt to make timely inference and facilitate online decisions. We conduct extensive trace-driven experiments, and our results demonstrate both the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model.

To enable ever-increasing vehicular applications, heterogeneous vehicular networks (HetVNets) are recently emerged to provide enhanced and cost-effective wireless network access. Meanwhile, edge caching is imperative to future vehicular content delivery to reduce the delivery delay and alleviate the unprecedented backhaul pressure. This work investigates content caching in HetVNets where Wi-Fi roadside units (RSUs), TV white space (TVWS) stations, and cellular base stations are considered to cache contents and provide content delivery. Particularly, to characterize the intermittent network connection provided by Wi-Fi RSUs and TVWS stations, we establish an on-off model with service interruptions to describe the content delivery process. Content coding then is leveraged to resist the impact of unstable network connections with optimized coding parameters. By jointly considering file characteristics and network conditions, we minimize the average delivery delay by optimizing the content placement, which is formulated as an integer linear programming (ILP) problem. Adopting the idea of student admission model, the ILP problem is then transformed into a many-to-one matching problem and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with low complexity.

Ensuring network QoS for Internet of vehicles (IoVs) is crucial for safe and intelligent transportation system, while the vehicle density variation seems invincible for stationary base station (BS) networks. In this paper, we study IoV's downlink throughput guarantee, in which, in addition to the cellular BS resource, UAVs (equipped with WiFi interfaces) can be dynamically sent out to provide additional wireless connections. To cope with the dynamic IoV density, we propose an Online UAV Scheduling scheme, referred to as OUS, to online schedule and manage UAVs to guarantee seamless connections with reliable throughput performance. In OUS, we first use the complementary cumulative distribution function (CCDF) of IoV throughput to calculate the likelihood of a channel resource shortage. If a shortage condition is imminent and then minimal UAVs will be sent out to their optimal hovering positions. In particular, we revealed the marginal effect for the optimal hovering position acquisition, i.e., the further the UAV is away from the BS, the larger throughput gain can be achieved by the system. We conduct extensive simulations to evaluate the performance of our OUS scheme, and results demonstrate that it can well react to the throughput QoS demand by intelligently sending out minimal UAVs, and its hovering position acquisition method can fully utilize the efficacy of UAVs.

The flourishing vehicular applications require vehicles to download huge amount of Internet data, which consumes significant backhaul bandwidth and considerable time for data content delivery. Caching the popular data at network edge station can alleviate the congestion at backhaul network and reduce the data delivery delay. In this paper, we propose a dynamic edge caching policy for Heterogeneous Vehicular Network via Reinforcement Learning on adaptive traffic intensity and hot content popularity. We aim to enhance the download rate of vehicles adapting to dynamic vehicle velocity and hot file pool by caching the popular content on heterogeneous network edge stations. The proposed policy makes use of real time information and jointly considers download rate for each file and utility for edge stations to improve overall download performance. Simulation results show that our proposed policy can achieve better download rate than random and fixed caching policies.

To cope with the explosive vehicular content demands from various applications and services, it is imperative to cache the content files close to end users to reduce both the core network traffic load and the delivery delay. The heterogeneous vehicular networks provide multiple data pipes to access to the Internet for vehicles, and thus can be leveraged to cache the required content files on different access network edges and further improve the caching effectiveness. This paper focuses on the content delivery in heterogeneous vehicular networks that allow content caching in roadside WiFi APs, TV White Space Stations, and Cellular Base Stations. With the objective of minimizing the content delivery delay, a Student Admission matching- based content caching scheme is proposed by considering the file popularity, vehicle mobility and cache capacity of the edge infrastructure in heterogeneous vehicular networks, whereby a stable result is further obtained via the Gale-Shapley algorithm. Our simulation results demonstrate the advantages of the proposed caching scheme.

Offloading vehicular data traffic from cellular networks to roadside WiFi networks is a very interesting issue since it can not only alleviate the traffic congestion for cellular networks, but also reduce the communication cost for vehicle users. In this paper, we study the vehicle-to-vehicle (V2V) assisted WiFi offloading, where nearby vehicles that associate to different access points (APs) can use their idle WiFi resource to offload part of peer's data traffic. We also consider the Internet access delay introduced by the network detection, user authentication and network address assignment between the vehicle and the AP prior to actual data transmission, which has impact on the WiFi cell sojourn duration of the vehicle. The offloading efficiency, which is the traffic offloaded from cellular network, is analyzed by modeling an M/G/1/K queueing process under various conditions. The accuracy of our analysis is validated through the conducted simulation.

Coded caching systems can facilitate traffic offloading from base stations and content sharing between content providers and content requesters via device- to-device (D2D) communications. In particular, dividing content items into mul-tiple pieces not only can enable the possibility of decreasing transmission delay and energy consumption, but also can contribute to secrecy enhancement since potential social outcasts can decode the content item only if they can obtain enough pieces. In this work, the tradeoff problem between content sharing efficiency and secure transmission in coded caching systems is investigated. First, to maximize the caching hit ratio with secrecy outage constraints, a joint optimization problem considering coding parameters, content placement, and power allocation is formulated. Further, a lower bound on the caching hit ratio and a upper bound on the secrecy outage probability are derived to simplify the original optimization problem. Numerical results demonstrate the effectiveness of the proposed scheme.

It is envisioned that the Internet of Things (IoT) will provide promising opportunities to users, manufacturers, and service providers with a wide applicability in many fields. By employing social networking and device-to-device (D2D) communications in the IoT, the resulting social IoT can potentially provide services more effectively and efficiently. This paper focuses on content sharing among smart objects (devices) in the social IoT with D2D-based cooperative coded caching. Generally, complete content items or coded fragments are allowed to be delivered via multi-hop cooperative D2D communications. First, aiming at maximizing the overall success rate of multi-hop-based content sharing, the interplay between coding parameter optimization and wireless resource allocation is investigated by considering both physical and social characteristics. Moreover, a Roth and Vande Vate-based distributed scheme is proposed to solve the dynamic matching problem between the content helpers and content requesters. Numerical results demonstrate that the proposed scheme can achieve a good tradeoff between system performance and computational complexity.

This work investigates content sharing among smart objects (devices) via device-to-device (D2D) communications in the social Internet of Things (IoT) by exploiting distributed content coding schemes. Aiming at minimizing the overall transmission cost, the resource matching between content transmitters and content receivers is optimized by applying matching theory. Particularly, the dynamic distributed Roth and Vande Vate algorithm is employed to optimize the matching problem, achieving a lower computational complexity and a higher system stability. In addition, social characteristics among mobile users are also exploited in the optimization problem to further increase system reliability and robustness. Numerical results demonstrate the effectiveness of the proposed scheme.

Wireless caching using device-to-device (D2D) communications is a promising approach for reducing delivery delay and improving spectrum efficiency and energy efficiency. In addition to the physical link condition, social behaviors are also of great importance for effectiveness enhancement of the D2D-based wireless caching schemes. In this work, we focus on resource allocation including power and spectrum to improve the system efficiency in terms of jointly considered spectrum efficiency and energy efficiency, by leveraging both physical and social characteristics. Furthermore, a heuristic-based mixed integer nonlinear programming algorithm is proposed to solve the joint optimization problem, including the establishment of D2D links, spectrum allocation and power optimization. Numerical results demonstrate the effectiveness of the proposed scheme in terms of different pairing algorithms.

This work investigates the performance for D2D enabled wireless caching in distributed storage system by taking the typical erasure correcting code namely maximum distance separable (MDS) into consideration. Targeting on the minimization of the transmission cost for content sharing, the spectrum resource allocation is optimized based on their statistical channel state information (CSI). To evaluate the quality of D2D links, two cases are considered with different assumptions for whether those physical links are stable or not regarding to mobility impacted user behaviors. Numerical results demonstrate the effectiveness of the proposed scheme.

This work studies secrecy enhancement for socially enabled communications in cellular device-to-device (D2D) underlay through resource allocation. By analyzing the impact of social characteristics of those involved D2D users (DUs), proper selection of transmitters and friendly jammer nodes with appropriate power and spectrum allocation can facilitate secrecy transmission. First, we select D2D transmitter by characterizing the link to guarantee the desired success rate without downgrading the service requirement for impacted cellular users (CUs). Furthermore, jammers' trustiness based on their own social interaction is considered for secrecy enhancement. Numerical results verify the performance of our proposed scheme.

Distributed storage that leverages cellular device-to-device (D2D) underlay has attracted rising research interest due to its potential to offload cellular traffic, improve spectral efficiency and energy efficiency, and reduce transmission delay. This paper investigates the overall transmission cost minimization problem based on a content encoding strategy to download a new content item or repair a lost content item in D2D-based distributed storage systems while guaranteeing users' quality of service. In addition to the optimization of the coding parameters, the cost minimization problem also considers the distribution of content items, the selection of content helpers for each content requester, and the spectrum reuse for establishing D2D links in between. Formulating a hypergraph-based three-dimensional matching problem among content helpers, requesters, and cellular user resources, we present a local search based algorithm with low complexity for optimization. Numerical results demonstrate the performance and the effectiveness of our proposed approach.

This paper investigates the problem of resource and power allocation in device-to-device (D2D) underlays given a specific secrecy rate constraint. The objective is to optimize the pairing of D2D links with cellular user equipment (CUE) uplink channel resources, and to allocate their respective powers to combat against eavesdroppers for secrecy rate improvement. The proposed method first determines a set of candidate D2D links with the required signal-to-interference-plus- noise ratio level for each CUE to narrow the number of combinatorial sharing options. Afterwards, an optimization problem is formulated for maximizing the overall secrecy rate under user power constraints and minimum required secrecy rates. Finally, numerical results demonstrate the resulting performance.

Device-to-device (D2D) communications recently have attracted broad attention owing to its potential ability to improve spectrum and energy efficiency within the existing cellular infrastructure. Lacking sophisticated control, D2D user equipments (DUEs) themselves are not powerful enough to resist eavesdropping or fight against security attacks. This work investigates selection of jamming partners for D2D users to thwart reception by social outcasts in D2D overlay, by exploiting social relationship to improve secrecy rate. We aim to maximize the secrecy rate of worst case, eavesdropping by any outcast, through selecting jammer node while allocating transmit power for both source and jammer. We present a heuristic genetic algorithm based solution to evaluate the problem directly. In addition, we also propose approximated optimization solutions by considering power allocation of upper and lower bounds to simplify the problem, by leveraging the fractional programming (GFP) oriented Dinkelbach-type algorithm. Numerical results show that the proposed schemes can achieve better performance through finding an appropriate partner.