As the maritime industry evolves, applications such as real-time navigation, ocean monitoring, and emergency rescue increasingly require reliable communication. They also demand efficient computation offloading to support AI-driven services. However, terrestrial networks offer sparse coverage and unstable links in open-sea environments, severely constraining both connectivity and the execution of computation-intensive tasks. Although low Earth orbit (LEO) satellites extend coverage over oceans, their frequent handovers and high operating costs hinder stable, low-latency communication and efficient computation offloading. Unmanned aerial vehicle-based relays can enhance connectivity, but their limited endurance and environmental vulnerability hinder large-scale deployment. In contrast, high-altitude platform stations (HAPS) offer quasi-stationary positioning, broad coverage, and long operational duration, making them promising intermediaries between LEO satellites and maritime users. Building on this motivation, we design a space–air–ground–sea integrated network architecture in which HAPS function as relay nodes. We model the multi-layer task offloading and resource allocation as a partially observable Markov decision process to capture the uncertainty and dynamics of maritime environments. To solve it, we adopt multi-agent proximal policy optimization, which enables centralized training with decentralized execution. Furthermore, we incorporate an exact potential game mechanism into the reward design to enhance agent coordination and ensure alignment with system-wide objectives. Simulation results show that our method outperforms three representative baselines; under maximum task load, it reduces average latency, energy consumption, and overall cost by 8.7%, 2.0%, and 18.6%, respectively, verifying its effectiveness for computation-intensive maritime services.

The widespread adoption of 5G technology has significantly enhanced the interconnectivity and automation of factory equipment, increasing the complexity of device coordination. Equipment failures or performance degradation can result not only in individual device downtime but also in cascading disruptions across the entire production line, potentially leading to factory-wide shutdowns and decreased production efficiency. Ensuring high equipment reliability—particularly fault tolerance under extreme conditions—is therefore critical to the stable operation of a 5G fully connected factory. To address this challenge, this paper proposes a proactive fault tolerance (FT) framework that enhances system reliability through parallel redundancy strategies. The framework begins by evaluating system reliability based on device responses in the most recent service cycle. If the computed FT value falls below the threshold specified by the Service Level Agreement (SLA), the framework is triggered. Devices are then ranked according to their historical performance and their criticality in the upcoming production cycle. A “1+N” redundancy allocation strategy is applied, and the resulting Integer Nonlinear Programming (INLP) problem is solved using a Redundancy Allocation Algorithm based on Transformer-enhanced Deep Reinforcement Learning (RAA-TDRL). To validate the proposed framework, we conduct simulations using a real-world dataset to emulate performance fluctuations. Experimental results show that the proposed key equipment identification method and RAAT-DRL algorithm achieve superior performance and reduced time complexity compared to baseline approaches.

Functional brain connectivity networks capture complex relationships and temporal evolution between brain regions, which have become increasingly important for diagnosing neurological disorders. However, existing methods, which are primarily based on vector or graph representations, struggle to adequately characterize the intricate spatio-temporal topological architecture of functional brain networks. Additionally, they predominantly rely on data-driven paradigms and lack priors pertaining to cross-windows network interactions. To address these issues, we propose a spatio-temporal hypergraph attention network framework for brain network analysis. Specifically, we first propose a temporal attention network architecture embedded with temporal similarity-driven prior knowledge, which effectively extracts long-range dependency information from fMRI by combining multi-head self-attention mechanisms and cross-window temporal prior knowledge. Second, we design a hierarchical hypergraph generation module that fuses local and global brain topological information to achieve multi-scale modeling of high-order spatio-temporal structures. Additionally, the spatial attention network, developed based on transformer architecture, employs hypergraph message passing mechanisms to effectively construct multi-level spatial interaction relationships between brain regions. Finally, a multi-layer perceptron (MLP) is adopted for classification. Experiments on the ADNI and PD datasets demonstrate that our method outperforms several state-of-the-art approaches in diagnostic performance and provides discriminative graph features for relevant brain disease diagnosis.

The growing adoption of AI-Generated Content (AIGC) has made large-scale processing of multiple Generative AI (GAI) training jobs a key strategy for improving cost-efficiency in computing clusters. However, the distributed nature of GAI models, together with inherent network bottlenecks, imposes significant challenges on system performance. Moreover, differences in training purposes, variations in model sizes, and asynchronous lifecycles create a dynamic environment. As a result, the coexistence of multiple GAI training jobs in a computing cluster exacerbates problems such as resource misallocation, fragmentation, and network contention, leading to low resource utilization and inefficient training performance. These motivate us to explore an efficient resource scheduling approach for completing multiple GAI training jobs. Accordingly, we introduce an intrinsic topology-aware scheduling framework designed to ensure flexible scheduling and efficient distributed training of GAI models. To address the trade-off between the number of concurrent jobs and the communication contention they generate, we formulate a multi-objective optimization problem with two objectives: maximizing the utility of GAI jobs and minimizing communication bandwidth. We then propose the Diffusion Model-based AI-Generated Resources Scheduling (DARS) algorithm, designed to capture dynamic, high-dimensional environments and generate optimal resource scheduling decisions. DARS employs a denoising diffusion process to iteratively refine noisy resource allocations into optimized scheduling decisions. Subsequently, we replace the policy network of Deep Reinforcement Learning (DRL) with DARS to address environmental uncertainty and enhance efficiency. Finally, the simulation results confirm that the proposed algorithm outperforms existing approaches.

Advances in communication technology, coupled with the growing global demand for network connectivity, have established low Earth orbit (LEO) satellite networks as a critical complement to terrestrial networks. However, the inherent high latency, dynamic topology, and bandwidth limitations of LEO satellite networks, along with the inefficiencies of centralized routing strategies, pose significant challenges to the effectiveness of conventional terrestrial routing protocols. Traditional routing methods, which rely on static, rule-based approaches, lack the flexibility required to adapt to dynamic network conditions, underscoring the necessity for more adaptive packet routing strategies within satellite network protocols. To address these challenges, we propose a packet routing optimization algorithm that integrates cooperative multi-agent proximal policy optimization (MAPPO) with the M/M/1/K queuing theory model. Specifically, we develop a multi-attribute graph model for dynamic satellite networks that incorporates both communication delay and energy consumption metrics. For each satellite node, we establish an M/M/1/K queuing model that accounts for the queuing waiting time at each node. We formulate the packet routing optimization problem, aiming to minimize delay and energy consumption, as a partially observable Markov decision process and apply the multi-agent proximal policy optimization algorithm to refine the routing policy. Extensive simulations on real satellite network topologies demonstrate that the proposed algorithm significantly outperforms existing methods, achieving higher cumulative rewards while reducing both latency and energy consumption.

Low Earth orbit (LEO) satellite networks enhance terrestrial connectivity by providing global coverage and low-latency communication. However, their highly dynamic topology, time-varying propagation delays, and constrained bandwidth severely limit the efficiency of conventional centralized routing, underscoring the necessity for adaptive and distributed strategies that can operate effectively under partial observability. Multi-agent reinforcement learning (MARL) offers a promising foundation for such strategies by enabling decentralized, context-aware decision-making based on local information. Nevertheless, existing MARL-based routing approaches often struggle to maintain accurate congestion awareness, reconcile conflicting objectives, and ensure stable convergence in large-scale LEO constellations. To address these challenges, we present POMAP, a packet routing framework that integrates Pareto optimization with multi-agent proximal policy optimization (MAPPO) to achieve efficient and stable trade-offs across multiple key performance metrics. Specifically, we propose a dynamic multi-attribute graph model for LEO satellite networks that simultaneously captures communication delay and energy consumption. Within this framework, each satellite node is represented as a G/G/1/K queue equipped with active queue management and scheduled using weighted priority queueing, thereby enabling precise characterization and control of packet queueing behavior. We formulate the packet routing problem as a partially observable Markov decision process that jointly minimizes delay, energy consumption, and packet loss rate, and apply MAPPO to optimize the resulting policy. Extensive simulations on realistic satellite network topologies demonstrate that the proposed method achieves better convergence stability, improved Pareto front coverage, and enhanced overall network performance compared with state-of-the-art baselines.

As an advanced form of IoT technology, integrated sensing and communication (ISAC) deeply integrates communication and perception, enhancing the performance and application range of IoT. At the same time, the space-air-ground integrated network (SAGIN) provides a wider and more efficient connection and information processing support for both. However, the highly dynamic and time-varying characteristics of SAGIN lead to more frequent vertical handovers among heterogeneous wireless networks, which seriously affects the continuity and reliability of services. This motivates us to explore an effective vertical handover method in SAGIN to guarantee the quality of network service. The issue is a typical complex and high-dimensional problem with its online and dynamic characteristics, which provides a particularly favorable scenario for the adaptability of the diffusion model (DM). Accordingly, we propose a novel vertical handover decision algorithm with the aid of DM. First, we innovate a novel vertical handover analytical model that describes handover jitter, load difference, and handover robustness. Then we formulate it as a multiobjective optimization problem. Next, inspired by Generative AI (GAI), we propose a DM-based GAI-empowered handover decision (DGHD) algorithm to capture the time-varying and high-dimensional environments and generate optimal vertical handover decisions. Subsequently, the policy network of multiagent proximal policy optimization (MAPPO) is replaced with the proposed DGHD for addressing environmental uncertainty and enhancing efficiency. Finally, the simulations exhibit that our proposed algorithm outperforms existing algorithms.

With the prosperity of AI-generated content (AIGC), multitenant training in AI task clusters has become prevalent. To improve resource utilization, multiple tenants will coexist on the same server, while malicious tenants may exploit side-channel to pose significant co-resident eavesdropping risks. Due to the extensive attack surface, distributed training tasks are particularly vulnerable to model parameters and data leakage when subjected to the same level of security protection as inference tasks. Moreover, traditional security mechanisms, reliant on static encryption or isolation, suffer from high overhead, passive defense and poor scalability, failing to address the dynamic resilience requirements of AI clusters. However, the research on proactive resilience enhancement in AI clusters is almost blank. To fill this gap, we devise a grouping task migration mechanism (GTMM), which jointly considers adaptive server grouping and proactive task migration. Specifically, we first employ an adaptive server grouping algorithm to classify servers, offering customized protection based on tenants’ security requirements. Then, we formulate the task scheduling process as a multiobjective optimization problem for making a tradeoff between security, power consumption, and load balance. Next, we propose a deep reinforcement learning-based task migration algorithm to separate tenants that have completed co-residency for mitigating co-resident threats. Lastly, the simulation experiments demonstrate that GTMM’s security outperforms the baselines with only an affordable performance degradation.

This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learning (DRL) model to solve the entire multiobjective optimization problem. The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL, thereby mitigating the limitations of both approaches. Firstly, we design an MOVRPTW model to balance the minimization of travel cost and the maximization of customer satisfaction. Subsequently, we present a novel DRL framework that incorporates a transformer-based policy network. This network is composed of an encoder module, a weight embedding module where the weights of the objective functions are incorporated, and a decoder module. NSGA-II is then utilized to optimize the solutions generated by WADRL. Finally, extensive experimental results demonstrate that our method outperforms the existing and traditional methods. Due to the numerous constraints in VRPTW, generating initial solutions of the NSGA-II algorithm can be time-consuming. However, using solutions generated by the WADRL as initial solutions for NSGA-II significantly reduces the time required for generating initial solutions. Meanwhile, the NSGA-II algorithm can enhance the quality of solutions generated by WADRL, resulting in solutions with better scalability. Notably, the weight-aware strategy significantly reduces the training time of DRL while achieving better results, enabling a single DRL model to solve the entire multiobjective optimization problem.

With the rapid development of emerging services such as cellular vehicle-to-everything and immersive video service, network connections have further evolved from tangible physical connections to intangible virtual connections such as content, services, and computing resources, and the application scenarios have become more abundant. The mobile ultra-service, which is characterized by ultra-low latency, ultra-high reliability, and ubiquitous mobility, is becoming one of the most representative traffic types. However, the existing mobile network architecture has not evolved sufficiently to meet the specific requirements of these mobile ultra-services, the mobility anchors introduce unnecessary node and link latency, leaving space for further optimization. A compact network architecture (ComArch) is proposed in this paper for ultralow-latency services with ubiquitous mobility. ComArch is designed with a mapping control plane and a generalized forwarding plane to collaboratively implement packet forwarding in mobile scenarios. The generalized forwarding plane handles packet forwarding, while the mapping control plane manages terminals’ identifier and locator mapping entries. The node latency introduced by mobility anchors is eliminated, and an efficient routing scheme is proposed to find the optimal mandatory nodes in the forwarding path, thereby reducing unnecessary link latency. Experimental results show that ComArch can effectively reduce end-to-end delay while saving resources.

Low Earth orbit satellite networks (LSNs) are envisioned as key enablers of 6 G by offering ubiquitous, low-latency connectivity. Their mesh topology enables multipath differential routing, which improves bandwidth utilization and reduces transmission delay. However, the growing demand for data and the dynamic, self-organizing nature of LSNs pose significant challenges for joint multipath routing and traffic scheduling under strict latency and energy constraints. To address these challenges, this paper proposes a multipath routing optimization (MRO) and traffic scheduling method tailored for multipath differential routing. Specifically, a dynamic multi-attribute graph model is developed to precisely capture the dynamic properties of LSNs. Building on this model, a MRO algorithm, integrated with a Stackelberg game framework, is introduced. The MRO algorithm employs a decomposition-based approach to identify multiple optimal paths that minimize delay and energy consumption, while the Stackelberg game framework ensures efficient traffic distribution across these paths. Numerical results demonstrate that the proposed approach significantly outperforms existing baseline methods, achieving cumulative reward improvements of 26.77% to 43.8% across four real-world network topologies and exhibiting better Pareto front coverage. Furthermore, by leveraging the rapid convergence properties of the Stackelberg game model, the proposed method enhances network throughput by 12% to 43% and reduces transmission time by 14% to 49%.

The Service Function Chain (SFC) leverages Network Function Virtualization (NFV) and Software-Defined Networking (SDN) for flexible deployment, creating customized service chains tailored to specific applications. As NFV and SDN technologies play crucial roles in the SFC implementation, any security risk that arises in an NFV/SDN network can potentially pose a threat to SFC. Thus, SFC becomes vulnerable to network security attacks. To address this, intrinsic security technologies, including moving target defense and mimic defense, offer proactive protection against both known and unknown threats. It is expected to break through traditional security protection mechanisms such as “enhanced”, “plug-in” and “passive” defense. This paper proposes an intrinsic dynamic defense architecture to equip SFC with active defense capabilities, shifting from passive reactive mechanism based on prior knowledge to an active defense against various attacks. The architecture comprises two models and five modules, including a sub-pool partitioning algorithm that enhances heterogeneity across sub-pools by splitting the heterogeneous replica pool into several sub-pools among replica VNFs. To meet Quality of Service (QoS) requirements like latency, cost, and security, we formulate a multi-objective optimization problem with three objectives: latency, cost, and defense success rate. Following that, we propose a dynamic Deep Reinforcement Learning (DRL)-based deployment algorithm. This algorithm selects appropriate VNFs based on heterogeneity and historical information, improving SFC and VNF security against external attacks. Extensive experiments validate that our architecture significantly enhances network security, provided that this improvement comes at the expense of limited cost and latency.

In the 6G era, user demand for low-latency, cost-effective extreme services such as extended reality (XR) and holographic communications has significantly increased. Multi-domain networks, known for their vast capacity and coverage, are essential in fulfilling the growing demand for high-performance services. Despite their potential, these networks face challenges with domain isolation, requiring a software defined network (SDN) controller for inter-domain communication. Network function virtualization (NFV) enhances flexibility of service delivery with customizable service function chain (SFC), yet prior research falls short in delivering low-latency, cost-efficient services in multi-domain NFV networks alongside an unreasonable assumption that software on physical nodes can support the execution of all virtualization network functions (VNFs). In this paper, we study the problem of SFC deployment with VNF-dependent software migration (SD-VDSM) in multi-domain networks. Particularly, we first formulate the problem by setting an objective to minimize the end-to-end communication delay and the associated costs of service provisioning, while simultaneously ensuring load balancing across multi-domain networks. However, complexity of the issue escalates to an intractable level due to the intertwined nature of SFC deployment strategies and VNF-dependent software migration tactics, which mutually influence each other intricately. To tackle this issue, we propose an innovative heuristic algorithm, designated as the Joint SFC Deployment with VNF-Dependent Software Migration Algorithm (JSD-VDSMA). Comprising three fundamental steps, this algorithm is crafted to adeptly resolve the complexities of service provisioning across multi-domain networks. A suite of rigorous experimental assessments is detailed, demonstrating the capability of our proposed JSD-VDSMA. Through these comparative analyses, we demonstrate its effectiveness not only to increase the service acceptance rate but also to diminish both the end-to-end communication delay and resource utilization costs in comparison to its counterparts.

The development of immersive video service and large-scale cluster computing technology further expand the potential application scope of time-sensitive networks (TSN). In the delivery network for these emerging services, the Ultra-Service Flow (USF), which is characterized by ultra-high bandwidth and deterministic latency has become the most representative traffic type. Therefore, the route scheduling for hybrid deployment of Regular Service Flow (RSF) and USF has become an unavoidable issue within a deterministic domain. To resolve this issue, a multi-objective optimization model for joint routing of hybrid service flows is studied in this paper. Subsequently, an effective algorithm is designed to discover feasible routing solutions using a deep reinforcement learning approach with a transformer framework, followed by optimization utilizing NSGA-II. The simulation results indicate that, our proposed algorithm exhibits superior overall performance and enhanced generalization capabilities. It effectively reduces the overall latency of RSF by 10.526% and the path blocking degree of USF by 14.10256%, while significantly increasing the available bandwidth rate by 14.286%.

The introduction of network function virtualization (NFV) leads to service function chain (SFC) deployment problems, promoting the idea of composing network services as virtualized network functions (VNFs). Meanwhile, the rapid development of edge computing, artificial intelligence and big data has led to a surge in data volume and explosive growth in computing and forwarding demands. As such, a traditional central processing unit (CPU)-based data forwarding mode in the NFV network appears to be a bottleneck, and a CPU-only computing framework can no longer meet the forwarding needs of diverse business scenarios and services. The data processing unit (DPU)-based architecture allows better forwarding performance to be achieved more cost-effectively, largely alleviating the computing pressure of the CPU and reducing the node forwarding delay. Therefore, in this paper, a heterogeneous CPU+DPU architecture is investigated to solve the SFC deployment problem. To handle diverse service needs, we establish a multi-objective SFC deployment scheme to optimize the service latency, deployment cost and service acceptance rate. Because extreme services require better real-time performance, DPUs are adopted for fast processing according to the requirement of service requests. To address the unacceptable delay in sequential mode, a parallel strategy is proposed to process SFCs. To solve the multi-objective SFC deployment problem, a deep reinforcement learning (DRL)-based heterogeneous algorithm that includes multiple subalgorithms is designed, named parallelizable, shared and horizontally scaled service function chain deployment (PSHD), which uses diverse processing algorithms to deploy SFCs and break the delay bottleneck in NFV-based networks.The performance of PSHD is evaluated through extensive experiments. PSHD is found to be time-efficient, and it achieves a higher request acceptance rate and 37.73% and 34.26% lower latencies than state-of-the-art methods.

Deep Neural Network (DNN)-based video analytics significantly improves recognition accuracy in computer vision applications. Deploying DNN models at edge nodes, closer to end users, reduces inference delay and minimizes bandwidth costs. However, these resource-constrained edge nodes may experience substantial delays under heavy workloads, leading to imbalanced workload distribution. While previous efforts focused on optimizing hierarchical device-edge-cloud architectures or centralized clusters for video analytics, we propose addressing these challenges through collaborative distributed and autonomous edge nodes. Despite the intricate control involved, we introduce EdgeVision, a Multiagent Reinforcement Learning (MARL)-based framework for collaborative video analytics on distributed edges. EdgeVision enables edge nodes to autonomously learn policies for video preprocessing, model selection, and request dispatching. Our approach utilizes an actor-critic-based MARL algorithm enhanced with an attention mechanism to learn optimal policies. To validate EdgeVision, we construct a multi-edge testbed and conduct experiments with real-world datasets. Results demonstrate a performance enhancement of 33.6% to 86.4% compared to baseline methods.

Satellite-terrestrial integrated networks (STNs) are considered a promising architecture for 6G networks due to their ability to provide ubiquitous, high-capacity coverage on a global scale by combining satellite and terrestrial network infrastructures. However, the complex network architecture, time-varying topology, and frequent inter-satellite connection handovers present significant challenges for developing efficient routing and service continuity in STNs. To overcome these challenges, we propose Dyna-STN, a dynamic discrete topology-oriented wide-area routing mechanism in this paper. Dyna-STN utilizes a dynamic discrete topology model to characterize the time-varying topology of satellite networks. Furthermore, a hierarchical framework is established within the management plane to implement Dyna-STN, which comprises a dynamic discrete topology management plane and a routing management plane. A virtual overlay network composed of fixed virtual nodes shields the dynamics of satellite networks, while the open shortest path first protocol (OSPF) is deployed within the virtual overlay network to exchange routing reachability information among virtual nodes. Additionally, Dyna-STN performs dynamic binding and service migration between different satellite entities at specific time slots, thereby maintaining the continuity of virtual node services. Extensive numerical results demonstrate that Dyna-STN outperforms several baseline schemes in terms of routing protocol performance, packet forwarding performance, and service continuity. Furthermore, Dyna-STN maintains stable performance as the network scale increases and supports reliable data transmission among terminal devices in STNs.

In this paper, a novel two-timescale resource management framework for mobile edge computing (MEC) is constructed. Unlike existing studies, for providing seamless and cost-efficient MEC services, this work aims to strike the balance between service migration and task rerouting for mobile devices (MDs) whenever handovers occur (i.e., switching access from one edge server to another). Considering the network dynamics (e.g., randomness of MDs’ task generations and time-varying channel conditions) and the asynchronization of different management decisions with different triggers, we formulate an online optimization problem for jointly determining: 1) large-timescale decisions, including which edge server should be selected to access, and whether service migration or task rerouting should be chosen for each MD in each large time frame; and 2) small-time scale decisions, including how computing and communication resources should be allocated among MDs with task offloading requests in each small time slot. Then, we propose an online algorithm based on the improved Lyapunov method, together with an iterative algorithm integrating randomized rounding and Lagrange dual techniques, which solves the problem to asymptotic optimum in terms of the long-term average service delay. Theoretical analyses and simulations evaluate the performance of the proposed solution and show its superiority over counterparts.

With the development of mobile services such as autonomous driving and the industrial internet, ultralow latency and pervasive mobility have become key characteristics of the intelligent interconnections among people, machines, and things. As a prevailing mobile network architecture, the network function virtualization (NFV)-based edge-cloud architecture brings the computing and memory resources closer to the end user, significantly reducing service delays and supporting more efficient mobility management. However, the geographically distributed nature of the edge-cloud architecture and the quality of service (QoS) requirements of latency-sensitive services in extreme mobile scenarios make service function chain (SFC) deployment more challenging. In this paper, we investigate a mobility-aware SFC deployment scheme with service migration in an NFV-based edge-cloud system. To properly cope with the mobility pattern of mobile services, a multistage decision-making problem is formulated, aiming to jointly minimize the long-term deployment and migration costs and the average end-to-end service latency while simultaneously satisfying various QoS constraints for services and the physical resource constraints of the edge-cloud system. Then, to address the formulated problem, a deep reinforcement learning (DRL)-based online SFC deployment algorithm is proposed that can automatically detect variations in the widely distributed edge-cloud environment and generate online deployment solutions without human intervention to implement adaptive and fast service provision and also support mobile service migration. Extensive experimental results demonstrate our proposed scheme surpasses its competitors in terms of end-to-end latency and migration cost, with average reductions of 6.26% and 18.77%, respectively, while improving the average service acceptance rate by 19.19%.

For practical industrial wireless sensor networks (IWSNs), the system freshness of a specific task is usually related to multiple and multitype sensing data. However, most existing research on freshness metrics, such as Age of Information (AoI) or Age of Processing (AoP), only considers a single-package setting with a single type of data. To fill this gap, we propose the Age of Task-oriented Information (AoTI) for measuring the freshness of industrial tasks in IWSNs. It measures the time elapsed of the latest analyzed results before arriving at the receiver since the generation of any type of sampling data belonging to one certain task. Furthermore, we aim to minimize the long-term AoTI for IWSNs applications by jointly optimizing access modes and sampling frequencies for all sensors. By first formulating the problem as a Mixed Integer Nonlinear Program-ming problem, we then transform it to a constrained Markov Decision Process (CMDP) and relax it as an un-constrained MDP using Lagrangian method. Finally, we develop a Learning-based Access mode selection and Sampling frequency Control (LASC) algorithm and verify its superiority through simulations.

As conservative intersection management, signalized intersection has a significant bottleneck in improving traffic efficiency when it comes to connected autonomous vehicles (CAVs). In this paper, to make the intersection management more fine-grained, a decentralized conflict-free coordination scheme is tailed for CAVs at intersections without traffic signals. First, the problem of multiple vehicles navigation through an unsignaled intersection is formulated as a Partially Observable Stochastic Game (POSG). Second, we propose a cooperative multi-agent proximal optimization algorithm (CMAPPO) to make driving-decision for each CAV agent and achieve collaboration in a distributed manner. Finally, simulations are carried out on SUMO to evaluate the proposed method. The results show that the CMAPPO has significant effectiveness in solving the multi-vehicle coordination at intersections.

In this paper, a joint optimization of sensor activation and mobile charging scheduling for industrial wireless rechargeable sensor networks (IWRSNs) is studied. In the considered model, an optimal sensor set is selected to collaboratively execute a bundle of heterogeneous tasks of production-line monitoring, meeting the quality-of-monitoring (QoM) of each individual task. There is a mobile charger vehicle (MCV) which is scheduled for recharging sensors before their charging deadlines (i.e., the time instant of running out of their energy). Our goal is to jointly optimize the sensor activation and MCV scheduling for minimizing the energy consumption of the entire IWRSN, subjected to tasks’ QoM requirements, sensor charging deadlines and the energy capacity of the MCV. Unfortunately, solving this problem is non-trivial, because it involves solving two tightly coupled NP-hard problems. To address this issue, we design an efficient algorithm integrating deep reinforcement learning and marginal product based approximation algorithm. Simulations are conducted to evaluate the performance of the proposed solution and demonstrate its superiority over counterparts.

This paper investigates the content distribution in a hotspot area in which multiple cache-enabled unmarried aerial vehicles (UAVs) are deployed to offload part of the data traffic in a heavy-crowded cellular network. We formulate an optimization problem which minimizes the sum content acquisition delay of all users by designing the multiuser association and cache placement jointly with UAV transmission power and trajectory over a given flight duration. The non-convexity of the formulated problem and the uncertainty of the dynamic environment make it difficult and impractical to solve using traditional optimization methods. Thus we model our problem as a partially observable stochastic game where the macro base station (MBS) and UAVs act as agents and interact with the environment to receive distinctive observations. To guide exploration, we propose a new exploration criterion that gives each UAV agent an intrinsic reward when it explores beyond the boundary of explored regions (BeBold). Then we propose a Dual-Clip Proximal Policy Optimization (DC-PPO) algorithm to solve our problem. Extensive numerical results demonstrate that the proposed algorithm is superior than the PPO-based algorithm and the DC-PPO-based algorithm without exploration criterion.

In Internet of Electric Vehicles (IoEV), mobile charging stations (MCSs) can be deployed to complement fixed charging stations. Currently, the strategy of MCSs is to move towards the EVs with insufficient energy (IEVs) only after being requested, which is not efficient. However, similar to online car-hailing services, more IEVs could be charged and the charging expenses could be reduced if idle MCSs can actively move towards the potential charging positions. In this paper, the problem of placements of idle MCSs in an IoEV is investigated in order to enhance the proportion of charged IEVs and reduce the charging expenses of IEVs. To this end, we propose a Federated Learning based Placement Decision Method of Idle MCSs (FL-PDMIM) to help the idle MCSs to predict the future charging positions, by exploiting the historical routes of MCSs which contain rich information regarding the charging demand of IEVs. In the proposed framework, the historical routes are trained locally by each MCS, and then the local model parameters and charging records are periodically uploaded to an edge server for a global parameter aggregation. Then, idle MCSs decide their placements according to the predicted charging positions (potential charging positions). The training time can be largely shortened, because the distributed learning on each MCS is executed in parallel. Extensive simulations and comparisons demonstrate the performance superiority of FL-PDMIM. Specifically, with the proposed federated learning-based predictions, the waiting time of IEVs to be served can be significantly shortened, and FL-PDMIM enhances the proportion of charged IEVs and reduces the charging expenses of IEVs effectively.

In this paper, the computation resource configuration for vehicular edge computing is investigated in this study. Dissimilar to a large portion of the current literature, we center around the problem of determining the optimal edge computing resource allocation to vehicles with computation offloading requests for maximizing the long-term management profit of the network operator (i.e., the road-side unit of the vehicular network) under the randomness of vehicular traffics and task processing. A multi-type management framework is used to characterize the heterogeneities among different vehicles in terms of their edge computing quality-of-service (QoS) requirements. A novel fluid model is proposed that facilitates the formulation of the corresponding resource optimization problem by taking into account the system’s steady state characteristics with dynamic evolutions. In addition, rather than considering fixed QoS requirements in long-run, we explore the impact of the resulted service quality on the QoS requirements determined by vehicles. The QoS requirements of each vehicle is allowed to change adaptively according to the service quality fed back by the system. Based on this, we propose a simple but efficient approach, called threshold-based computation resource configuration scheme (TCRCS). The proposed solution’s performance is assessed by theoretical analysis and simulations, which show that it outperforms competitors.

In this paper, a novel management framework for fog computing with strategic computing speed control at fog nodes (FNs) is studied. In the considered model, mobile users declare requests of offloading resource-hungry computation tasks that are dynamically collected at a dedicated edge server (ES). Upon receiving these requests, the ES can decide to either self-process or delegate some workloads to third-party FNs for maximizing the overall management profit. Unlike the existing work, this paper takes into account strategic behaviors of FNs in computing speed control, i.e., each FN can strategically allocate its computing resource to maximize its utility, which consists of the benefit gained from executing offloaded tasks and the cost incurred by dissatisfied (delayed) service to its own subscribed tasks. To jointly address the long-term system performance and FNs’ strategic interactions, a scheduling mechanism integrating a noncooperative game and a queueing model is formulated. We then investigate two delegation reward settings, i.e., constant and utility-dependent delegation prices, and propose efficient adaptive algorithms to determine the optimal workload distribution at the ES and the computing speed equilibrium among FNs. Both theoretical analyses and simulations are conducted to evaluate the performance of the proposed solutions and demonstrate their superiorities over counterparts.

In recent years, numerous studies have adopted rs-fMRI to construct dynamic functional connectivity networks (DFCNs) and applied them to the diagnosis of brain diseases, such as epilepsy and schizophrenia. Compared with the static brain networks, the DFCNs have a natural advantage in reflecting the process of brain activity due to the time information contained in it. However, most of the current methods for constructing DFCNs fail to aggregate the brain topology structure and temporal variation of the functional architecture associated with brain regions, and often ignore the inherent multi-dimensional feature representation of DFCNs for classification. In order to address these issues, we propose a novel DFCNs construction and representation method and apply it to brain disease diagnosis. Specifically, we fuse the blood oxygen level dependent (BOLD) signal and interactions between brain regions to distinguish the brain topology within each time domain and across different time domains, by embedding block structure in the adjacency matrix. After that, a sparse tensor decomposition method with sparse local structure preserving regularization is developed to extract DFCNs features from a multi-dimensional perspective. Finally, the kernel discriminant analysis is employed to provide the decision result. We validate the proposed method on epilepsy and schizophrenia identification tasks, respectively. The experimental results show that the proposed method outperforms several state-of-the-art methods in the diagnosis of brain diseases.

Mobile charging services (MCSs) have been developed as a supplement charging method for electric vehicles (EVs), wherein energy replenishment is provided by mobile charging vehicles (MCVs). An MCV has an internal storage system employed to replenish the energy of a certain number of EVs. Charging scheduling of MCV is one of the key issues on the Internet of EVs for providing efficient and convenient charging services, which requires determining the charging sequence and the amount of energy when serving multiple EVs by one MCV. In this paper, a multi-objective MCV scheduling problem is investigated. By optimizing the charging sequence and the actual amount of energy being charged, the proposed framework aims to minimize the EV waiting time while simultaneously to maximize the charging benefits of all EVs. To solve the multi-objective optimization problem (MOP), a deep reinforcement learning (DRL) based framework is further explored. The MOP is first decomposed into a set of subproblems. Each subproblem is modelled as a neural network, wherein an actor-critic algorithm and a modified pointer network are adopted to solve each subproblem. Pareto optimal solutions can be directly obtained through the trained models. The experimental results demonstrate that the proposed method can efficiently and effectively solve the MCV scheduling problem and outperform NSGA-II and MOEA/D in terms of solution convergence, solution diversity, and computing time. In addition, the trained model can be applied to newly encountered problems without retraining.

Radio environment monitoring is crucial for many network engineering applications. Integrated with mobile crowdsourcing (MCS), radio map can be updated by mobile users in a low-cost manner. However, the crowdsourced measurement data may get quite sparse, and contain noises and errors. Therefore, how to efficiently infer missing data under low-quality measurements is critical in crowdsourced radio map construction. Existing inference methods like matrix completion require certain strict conditions, e.g. missing at completely random (MACR), which is impractical in the city-scale sensing. To address these issues, we propose a deep learning scheme based on adversarial auto-encoder (AAE) to handle measurements with large missing regions and complicated loss patterns. Specifically, this scheme applies variational auto-encoder (VAE) to infer missing data, and further utilizes the adversarial nets to play a min-max game with the VAE to improve recovery quality. Comprehensive experiments on three real datasets show that the proposed scheme can outperform state-of-the-art methods under large missing rates and low-quality measurements.

With the exponential growth of data traffic, the use of caching and device-to-device (D2D) communication has been recognized as an effective approach for mitigating the backhaul bottleneck in unmanned aerial vehicle (UAV)-assisted networks. In this article, we investigate the issue of secure transmission in a cache-enabled UAV-relaying network with D2D communications in the presence of an eavesdropper. Specifically, both UAVs and D2D users are equipped with cache memory, which can prestore some popular content to collaboratively serve users. Considering the fairness among users, we formulate an optimization problem to maximize the minimum secrecy rate among users, by jointly optimizing the user association and UAV scheduling, transmission power, and UAV trajectory over a finite period. The joint design problem is a nonconvex mixed-integer programming problem. To efficiently solve this problem, we propose an alternating iterative algorithm based on the block alternating descent and successive convex approximation methods. Specifically, the user association and UAV scheduling, UAV trajectory, and transmission power are optimized alternately in each iteration, and the convergence of the algorithm is proven. Extensive numerical results show that the proposed joint design scheme significantly outperforms other benchmark schemes in terms of the secrecy rate.

Electric vehicles (EVs) are being introduced by different manufacturers, thanks to their environment-friendly perspective to alleviate CO2pollution. In this paper, the proposed EV charging management scheme enables pre-empted charging service for heterogeneous EVs (depends on different charging capabilities, brands, etc.). Particularly, the anticipated EVs' charging reservations information, including their arrival time and expected charging time at charging stations (CSs), are brought for planning CS-selection (where to charge). Along with applying ubiquitous cellular network communication to deliver (delay tolerant) EVs' charging reservations, we further study the feasibility of applying opportunistic vehicle-to-vehicle (V2V) communication with delay/disruption tolerant networking (DTN) nature, due primarily to its flexibility and cost-efficiency in vehicular ad hoc networks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V-based charging reservation is promisingly cost-efficient in terms of communication overhead, while achieving a comparable charging performance to apply cellular network communication.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is an appealing concept, where a fixed-wing UAV equipped with computing resources is used to help local resource-limited user devices (UDs) compute their tasks. In this paper, each UD has separable computing tasks to complete, which can be divided into two parts: one portion is processed locally and the other part is offloaded to the UAV. The UAV moves around above UDs and provides computing service in an orthogonal frequency division multiple access (OFDMA) manner. This paper aims to minimize the weighted sum energy consumption of the UAV and UDs by jointly optimizing resource allocation and UAV trajectory. The resulted optimization problem is nonconvex and challenging to solve directly. With that in mind, we develop an iterative algorithm for solving this problem based on the block coordinate descent method, which iteratively optimizes resource allocation variables and UAV trajectory variables till convergence. Simulation results show significant energy saving of our proposed solution compared to the benchmarks.

In social and economic fields, the peer effect and its influence gradually attract public attention. In this paper, we explore the interactions between a load-serving entity and a group of households in a smart grid community and put forward a peer effect-based demand response (PEDR) scheme applying dynamic pricing. A two-stage Stackelberg game based framework is established in which the electricity price and consumption decisions are derived adopting backward induction. We obtain the closed-form solution of the game (i.e., the equilibrium) in each stage and prove its existence and uniqueness. Simulation results indicate that the PEDR scheme shows superiority in energy consumption and peak to average ratio (PAR) compared with the baseline scheme without considering peer effects. Additionally, we study the impacts of social network structure of users and show that by setting the central node to be a frugal consumer in star topology structure, the performance of PEDR can be further improved. Such evaluations, as we believe, shall provide useful insights for energy providers to devise rational demand response policies.

With the increasing popularity of car sharing, a large amount of vehicle data has been generated which has great potential values for various applications (e.g., analyzing user habits for more economic benefits). These valuable data can be traded among owners and buyers on a data trading platform. Traditionally, data is traded in a centralized market which requires data exchange by trustworthy authorities. In this work, to address the potential unreliable issues (e.g., data loss and leakage), we design a consortium blockchain-based data trading framework to create a P2P trading market and enhance the security of data trading. We classify the data into five types to distinguish data with different values. Specifically, we investigate the pricing issue in the proposed car-sharing data market, which consists of data owner, service provider and data buyer. The data owner gives the pricing strategy of original data, and then the service provider processes the raw data and provides hierarchical quality of data with different data accuracy and privacy levels to the buyer who determines the data purchase strategy. Based on the interactions among these three parties, we formulate the problem as a three-layer Stackelberg game. Backward induction is applied to analyze the solution of the problem, and we conduct theoretical analysis to show the existence of Stackelberg game equilibrium. Numerical results evaluate the performance of our system under different settings.

With the exponential growth of data traffic, the use of caching and device-to-device (D2D) communications has been regarded as an efficient approach for alleviating the backhaul congestion in unmanned aerial vehicle (UAV) assisted networks. In this paper, we investigate the security issue of a cache-enabled UAV-relaying network with D2D communications in the presence of eavesdropper. Specifically, a UAV and multiple D2D users are equipped with cache memory, which can pre-store some popular contents to cooperatively provide content transfer services for users. To achieve secure and fair transmission, an optimization problem is formulated with the aim of maximizing the minimum secrecy rate among receivers, by jointly optimizing the cache placement and UAV flight trajectory in a finite flight period. The joint design problem is a non-convex mixed-integer programming problem. To facilitate solving this problem, we propose an alternating iterative algorithm based on the block alternating descend and successive convex approximation methods. Numerical results show that the joint design scheme significantly outperforms other benchmark schemes in terms of the secrecy rate.

In a PoW-based blockchain network, miners participate in a block-discovery racing game for financial incentives. As the total computing power becomes overwhelming, miners join in the mining pool which combines the scattered computing power to win a stable profit. Miners in the same mining pool work as a team and once they successfully mine a valid block, mining pool plays a role in distributing the payoff to miners according to its reward sharing mechanism. Specifically, two main reward sharing strategies: Pay-Per-Share (PPS) and Pay-Per-Last-NShare (PPLNS) are considered. In the mining system model, a miner can choose to join a pool and adapt the selection for improving the expected reward. And we formulate the dynamic pool selection problem as an evolutionary game. We consider the required hash rate, network delay and reward sharing strategy as the main factors which affect the choice of miners. Evolutionary stable equilibrium (ESS) is considered to be the solution, and we conduct theoretical analysis on the existence and stability of the ESS for a case of two mining pools. A low complexity distributed algorithm is proposed for miners to reach the equilibrium. Numerical results show the evolution of miners and demonstrate the theoretical findings of our study.

In this paper, a novel computation offloading framework for edge computing is proposed. Unlike existing studies, this work considers that for offloading those computation-intensive tasks, mobile users are allowed to intentionally defer the declarations of their offloading requests and reserve some time for local pre-processing. By doing so, the offloading cost (including edge service charge and transmission cost) may be reduced because of less edge service demand, while the delay cost may increase due to later report. To strike the balance, each mobile user can strategically and selfishly determine a best timing of when to declare its offloading request (or the time-length of its local preprocessing). To characterize the resulted strategic interactions, a computation offloading game built upon a queueing model with strategic queue timing is formulated. Theoretical analyses and simulations evaluate the performance of the proposed equilibrium solution and demonstrate its superiority over counterparts.

With the exponential growth of data traffic, caching is regarded as a promising solution to combine with unmanned aerial vehicle (UAV)-assisted networks, which can offload cellular traffic and improve the system performance. Moreover, the cache capacity at user side can be leveraged, e.g., through local data storage or device-to-device (D2D) sharing. In this paper, we focus on the performance analysis and trajectory optimization of cache-enabled UAV-assisted networks with underlaid D2D communications. We consider both static and dynamic UAV deployments. For static UAV deployment, we first formulate an optimization problem to design the cache placement in order to maximize the cache hit probability. Then, the successful transfer probability (STP) and sum-rate are analyzed by using stochastic geometry, and their closed-form expressions are derived. For dynamic UAV deployment, the UAV moves over the cell and stops at several path points to serve users. To shorten the time required for the UAV to cover all users, a spiral algorithm is proposed to optimize the UAV trajectory, aiming at minimizing the number of UAV path points. Moreover, since at different locations, the UAV communication will incur different interference on D2D users, we derive the outage probability for the D2D users. Simulation results show the significant performance gain of our proposed probabilistic cache placement over existing strategies. For a given user density, we show that the optimal values for the UAV height which lead to the maximum UAV-STP and sum-rate exist.

It is well known that unmanned aerial vehicles (UAVs) can help terrestrial base stations (BSs) offload data traffic from crowded areas to improve coverage and boost throughput. However, the limited backhaul capacity cannot cope with the ever-increasing data demands, for which caching is introduced to relieve the backhaul bottleneck. In this paper, we focus on a multi-UAV assisted wireless network, and target to fully utilize the benefits of wireless caching and UAV mobility for multiuser content delivery. By taking into account the limited storage, our goal is to maximize the minimum throughput among UAV-served users by jointly optimizing cache placement, UAV trajectory, and transmission power in a finite period. The resultant problem is a mixed-integer non-convex optimization problem. To facilitate solving this problem, an alternating iterative algorithm is proposed by adopting the block alternating descent and successive convex approximation methods. Specifically, this problem is split into three subproblems, namely cache placement optimization, trajectory optimization, and power allocation optimization. Then these subproblems are solved alternately in an iterative manner. We show that the proposed algorithm can converge to the set of stationary solutions of this problem. Besides, we further analyze the computational complexity of this algorithm. Numerical results show that great throughput enhancement is achieved by applying our proposed joint design in comparison with other benchmarks without trajectory design and power control.

As a decentralized ledger to record all transaction information, blockchain can be applied to address the security and privacy issues in mobile application system. We term the blockchain applied to mobile applications as mobile blockchain. The mining process in mobile blockchain requires high computing capacity and energy which could overwhelm that mobile devices can offer. In this case, mobile edge computing servers (MESs) can be involved to offer computation services to miners in mobile blockchain. Note that the resources of MESs are also limited, MESs could further request resources from the cloud computing server (CCS). Accordingly, in this paper, both mobile edge computing and cloud computing are considered to support the mobile blockchain applications which makes the problem a hierarchical one. Naturally, the issue of hierarchical resource allocation arises. And a hierarchical combinatorial auction model is proposed to solve this problem, based on which an efficient and truthful framework is provided. Specifically, we formulate winner determination problems (WDPs) for mobile edge computing service providers and cloud computing service provider, and computationally tractable algorithms to address both problems are proposed. Finally, numerical analysis shows the effectiveness of the proposed scheme.

User association is indispensable for the operation of wireless network and has critical impacts on system performance. For most existing work, user associations are typically coupled, which require a user equipment (UE) to associate with the same base station (BS) in uplink (UL) and downlink (DL). However, wireless networks are becoming heterogeneous and densifying, which generates intrinsic distinctions (transmission power, data traffic and backhaul capacity etc.) between UL and DL. Accordingly, coupled association may no longer be optimal. In this work, we explore decoupled user association in full-duplex ultra-dense networks (UDNs), which allows a UE to associate with different BSs in UL and DL respectively. Furthermore, to fully exploit the benefits of UDNs, multiple association, referring to associating a UE with multiple BSs, is jointly adopted in UL and DL. Considering the dynamic and complicated association process, an evolutionary game (EG) is formulated, where UEs are players, and their strategies are association selections in UL/DL. Particularly, evolutionary equilibrium is viewed as the stable solution to the formulated problem. Moreover, an EG-based algorithm with low complexity is proposed for decoupled multiple association. Numerical results validate the convergence of the proposed algorithm for strategy adoption. Besides, the impacts of information exchange delay and learning rate are investigated for providing a better association decision.

Wireless virtualization has become a key concept in future cellular networks which can provide multiple virtualized wireless networks for different mobile virtual network operators (MVNOs) over the same physical infrastructure. Resource allocation problem is a main challenge for wireless virtualization for which auction approaches have been widely used. However, for most existing auction-based allocation schemes, the objective is to maximize the social welfare (i.e., the sum of all valuations of winning bidders) due to its simplicity. While in reality, MVNOs are more interested in maximizing their own revenues. However, the revenue-maximization auction problem is much more complex since the price is unknown before calculation. In this paper, we give a first attempt for designing a revenueoptimal auction mechanism for resource allocation in wireless virtualization. Considering the complexity in revenue maximization, we apply the deep learning techniques. Specifically, we construct a multi-layer feed-forward neural network based on the analysis of optimal auction design. The neural network adopts users' bids as the input and the allocation rule and conditional payment rule for the users as the output. The training set of this neural network is the users' valuation profiles. The proposed auction mechanism possesses several satisfactory properties, e.g., individual rationality and incentive compatibility. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

In recent years, unmanned aerial vehicles (UAVs) have attracted extensive interests from both academia and industry due to the potential wide applications with universal applicable nature of the deployment. However, currently the bottleneck for UAVs is the limited carried energy resources (e.g. oil box, battery), especially for electric-driven UAVs. For a system consisting of multiple UAVs using batteries, its stability depends on each UAV. Therefore, the lifetime of each UAV is expected to be extended. In this paper, we propose the concept of RF charging aerially for the UAV fleet. Specifically, in order to ensure the stability of the system, wireless charging is considered for enhancing the lifetime of each UAV. However, it may be unbalanced. Accordingly, the issue of charging scheduling arises. The problem is formulated as a Q-Learning problem in this paper. Agent constantly explores and optimizes its scheduling policy. Finally, it can adapt to different UAV distribution situations. We take the energy levels of UAVs as input, which is easy for implementation. We have compared with two other algorithms (RSA and LESA) and compared with the case of no-charging. The results show that comparing with no-charging, the stability of the system can be improved by up to 78%. Compared with RSA and LESA, system stability is increased by up to 30%-40%. In addition, our method is more flexible and applicable to fleet than other ways (such as return to base station, landing to power line, ground laser, etc) to supplement energy.

User association is a crucial factor that affects the performance of wireless networks. In current cellular networks, user association is typically coupled, which means an user equipment (UE) must associate with the same base station (BS) in uplink (UL) and downlink (DL). For single-tier wireless networks, such mechanism is simple and effective. However, in heterogeneous ultra-dense networks (UDNs), there are distinct differences in transmission power, data traffic and channel quality etc., for which coupled association could restrict the performance of system. To cope with it, the concept of decoupled UL-DL (DUDe) association has been introduced recently, which enables a UE to associate with different BSs in UL and DL. In this paper, we investigate decoupled UL-DL user association in UDNs. Considering the existence of asymmetric information (i.e., channel gains and intercell interferences), which can be seen as the private information for UE, we propose a contract-theoretic user association approach. Particularly, we model the decoupled association process as a monopoly labor market, where BSs act as employers and offer contracts to employees (i.e., UEs). The contract items cover the available associated bandwidths, transmitted powers and corresponding prices. Then BS broadcasts these drafted contract information, and UE selects to sign the optimal contract by considering her own demands. Numerical results show significant superiorities of DUDe than coupled UL-DL association in perspective of nodes utilities and social surplus, and compared with the existing user association methods, contract-theoretic approach shows a certain improvement in performance.

The opportunistic mobile sensor network has been extensively applied in various public safety applications such as the fire rescue and earthquake rescue, since it can provide a surveillance range with an inexpensive cost and avoid the dangers of humans staying in risk zones. However, due to some environmental events such as building structure damage, airflow push, and fire explosions, the sensor nodes sprinkled into the fire-rescue scenario may be kept moving. Thus, the contacts between nodes become momentary, and the data packets cannot be forwarded along stable communication paths. To this end, the opportunistic forwarding manner is adopted in the fire-rescue scenario to enable the data packets to be transferred to the rescue control center (RCC) through some discrete hops. The contributions of this paper are threefold. First, the nodes in the fire-rescue scenario are carefully investigated and classified into four types: small-range mobile nodes (SRNs), large-range mobile nodes (LRNs), firefighter nodes (FNs), and robot nodes (RNs). Second, we formulate the data forwarding problem, and the optimal proportions of SRNs, LRNs, and FNs in data holders are mathematically analyzed to obtain the maximum delivery ratio. Third, a data forwarding approach for fire-rescue scenario (DFAFR) is proposed. In DFAFR, the optimal proportions of SRNs, LRNs, and FNs in data holders are maintained as far as possible through selecting different types of data holder candidates, and then the new data holders are determined from these data holder candidates and the adjacent RNs on basis of their expected delivery delay. Finally, the performance of DFAFR is analyzed through simulations of the fire-rescue scenario, and the results indicate that DFAFR can enhance the delivery ratio and shorten the delivery delay while the forwarding overhead is restricted.

The opportunistic mobile sensor network has been extensively used in various public safety applications such as the fire-rescue scenario, since it can provide a surveillance range with an inexpensive cost and avoid the dangers of staying in the risk zones to humans. However, the sensor nodes thrown by firefighters in the fire-rescue scenario are easy to move away from current positions due to many environmental factors such as the building structure damages, airflow push or even some explosions. Consequently, the contacts between nodes become scarce and momentary, thereby making the gathered data packets difficult to be forwarded along stable communication paths. Firstly, the mobility patterns of nodes in the fire-rescue scenario are classified into three types: small-range mobile nodes, large-range mobile nodes and firefighter nodes. Then, the optimal proportions of different types of nodes in the data holders are specially investigated mathematically to maximize the delivery ratio. Thus, a data forwarding approach for fire-rescue scenario (DFAFR) is proposed. In DFAFR, each data holder forwards the held data packets to neighbouring nodes independently, and the optimal proportions of data holders are maintained approximatively. Finally, the performance of DFAFR is analyzed through simulation experiments that produce preferable results in the fire-rescue scenario, indicating that DFAFR can improve the delivery ratio and shorten the delivery delay, so that the fire behavior can be reported and processed timely.

The new trends in network denisification, heterogeneity, and the introduction of new techniques (e.g., full-duplex) introduce new challenges for user association. For most existing user association schemes, the uplink (UL) and downlink (DL) access are coupled. That is, a user equipment (UE) is associated with the same BS for UL and DL transmissions. However, in ultra-dense heterogeneous networks (UDNs), due to the large disparities among base stations in different tiers and among uplink and downlink, the coupled UL-DL user association will limit the system performance. In this paper, we propose a novel concept of decoupled multiple association for user association in UDNs, which allows a UE to be associated with multiple base stations (BSs) for UL and DL in a decoupled manner. Furthermore, the context information of UEs is considered when making association decisions. Specifically, a decoupled multiple association matching game is formulated and a context-aware swap matching algorithm is proposed. The proposed scheme could attain higher data rates and could satisfy the quality of service (QoS) requirements of different UEs. Additionally, it could overcome the back-haul limitation of individual BSs. We compare the proposed scheme with three other association schemes, and the simulation results show significant performance gains of our proposed scheme in UDNs.

In this paper, we investigate the profit maximization problem for the mobile virtual network operator in cognitive radio networks considering the uncertain property of users' spectrum demand. In order to achieve more revenues while simultaneously satisfying the needs of users, the cognitive mobile virtual network operator chooses to dynamically sense the idle spectrum in the licensed band which is more economic, and at the same time leases the spectrum from the spectrum owner which guarantees more stable spectrum resources. However, the fluctuant spectrum demand of users imposes unprecedented challenges on the decision making process. To deal with the uncertain features of the users' demand, a flexible distribution uncertainty model is developed. Particularly, a reference distribution is introduced based on historical data and then a uncertainty set is defined to confine the spectrum demand. The uncertainty model developed allows the actual users' spectrum requirement to fluctuate around the reference distribution. Chance constraint approximations and robust optimization approaches are developed to transform and then solve the optimization problem. Simulation results based on the real-world traces evaluate the performance of the proposed scheme and investigate the parameter impacts on the system utilities. Our research may also help shed some insights on the investment policy making for the mobile virtual network operator.

Integrating ambient backscatter communications into RF-powered cognitive radio networks has been shown to be a promising method for achieving energy and spectrum efficient communications, which is very attractive for low-power or no-power communications. In such scenarios, a secondary user (SU) can operate in either transmission mode or backscatter mode. Specifically, an SU can directly transmit data if sufficient energy has been harvested (i.e., transmission mode). Or an SU can backscatter ambient signals to transmit data (i.e., backscatter mode). In this paper, for investigating the performance of such systems, we apply stochastic geometry to analyze coverage probability and achievable rates for both primary and secondary users considering both communication modes. Analytical tractable expressions are obtained. Extensive simulations are performed and the numerical results show the validity of our analysis. Furthermore, the results indicate that the performance of secondary systems can be improved with the integration of both communication modes with only limited impact on the performance of primary systems.

Compressed sensing for wireless sensor networks has attracted a lot of research attention in the last decade for its advantages in energy saving, robustness, and so on. Nevertheless, existing solutions mostly focus on the data compression performance while neglecting the energy efficiency. In this paper, we first present the joint resource allocation problem formulation based on compressed sensing. Then a distributed algorithm to compute the sampling rate and routes utilizing local network status is proposed. We conduct extensive experiments based on meteorological wireless sensor networks to verify the merit of our mechanism; it is shown that the proposed mechanism is able to achieve very high efficiency in terms of network lifetime and sensing quality compared with existing approaches.

Caching popular contents at base stations (BSs) has been regarded as an effective approach to alleviate the backhaul load and to improve the quality of service. To meet the explosive data traffic demand and to save energy consumption, energy efficiency (EE) has become an extremely important performance index for the 5th generation (5G) cellular networks. In general, there are two ways for improving the EE for caching, that is, improving the cache‐hit rate and optimizing the cache size. In this work, we investigate the energy efficient caching problem in backhaul‐aware cellular networks jointly considering these two approaches. Note that most existing works are based on the assumption that the content catalog and popularity are static. However, in practice, content popularity is dynamic. To timely estimate the dynamic content popularity, we propose a method based on shot noise model (SNM). Then we propose a distributed caching policy to improve the cache‐hit rate in such a dynamic environment. Furthermore, we analyze the tradeoff between energy efficiency and cache capacity for which an optimization is formulated. We prove its convexity and derive a closed‐form optimal cache capacity for maximizing the EE. Simulation results validate the proposed scheme and show that EE can be improved with appropriate choice of cache capacity.

Integrating ambient backscatter communications into RF‐powered cognitive radio networks has been shown to be a promising method for achieving energy and spectrum efficient communications, which is very attractive for low‐power or no‐power communications. In such scenarios, a secondary user (SU) can operate in either transmission mode or backscatter mode. Specifically, an SU can directly transmit data if sufficient energy has been harvested (i.e., transmission mode). Or an SU can backscatter ambient signals to transmit data (i.e., backscatter mode). In this paper, we investigate the performance of such systems. Specifically, channel inversion power control and an energy store‐and‐reuse mechanism for secondary users are adopted for efficient use of harvested energy. We apply stochastic geometry to analyze coverage probability and achievable rates for both primary and secondary users considering both communication modes. Analytical tractable expressions are obtained. Extensive simulations are performed and the numerical results show the validity of our analysis. Furthermore, the results indicate that the performance of secondary systems can be improved with the integration of both communication modes with only limited impact on the performance of primary systems.

Self-organizing networks (SON) aim to offer high quality services while reducing both capital expenditure (CAPEX) and operational expenditure (OPEX). SON consists of three main functions: self- configuration, self-optimization, and self-healing. Comparing with self-configuration and self- optimization, there exits only few studies on self- healing. However, it plays an important role in maintaining network operation. Note that self- healing mainly includes fault detection, fault diagnosis, and fault compensation. In this paper, we focus on fault diagnosis and propose an ensemble learning based fault diagnosis system for a self- organizing cellular network. Specifically, in the proposed ensemble learning framework, the base learner is strengthened in each iteration and the final diagnosis result is obtained from the combination of all base classifications. Moreover, traditional classification algorithms are designed considering the premise of balanced data set. However, the classification accuracy of minority classes is not satisfactory. To deal with imbalanced training data sets, we applied the synthetic minority over- sampling technique (SMOTE) in the proposed system, which could also alleviate the difficulties caused by insufficient fault data. Simulation results show that the proposed system can achieve a high diagnosis accuracy, which can be further improved with the increase of training samples. In addition, the diagnosis accuracy of minority fault classes can be significantly improved with the application of SMOTE.

In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and energy storages. In such systems, the uncertain renewable energy will impose unprecedented scheduling challenges. To cope with the fluctuate nature of the renewable energy, an uncertainty model based on renewable energies' moment statistics is developed. Specifically, we obtain the mean vector and second-order moment matrix according to predictions and field measurements and then define uncertainty set to confine the renewable energy generation. The uncertainty model allows the renewable energy generation distributions to fluctuate within the uncertainty set. We develop chance constraint approximations and robust optimization approaches based on a Chebyshev inequality framework to firstly transform and then solve the scheduling problem. Numerical results based on real-world data traces evaluate the performance bounds of the proposed scheduling scheme. It is shown that the temporal-correlation information of the renewable energy within a proper time span can effectively reduce the conservativeness of the solution. Moreover, detailed studies on the impacts of different factors on the proposed scheme provide some interesting insights which shall be useful for the policy making for the future microgrids.

Wireless virtualization which enables resource sharing among different mobile virtual network operators (MVNOs) has become an important enabling technique in 5G cellular networks for increasing resource utilization and lowering the cost per bit. A main challenge for virtualization is efficient resource allocation while keeping isolation among different parties. In this paper, we consider a multi-dimensional resource market among multiple MVNOs and users. A combinatorial double auction (CDA) model is proposed, based on which a truthful and efficient resource allocation framework is provided. Specifically, for maximizing the social welfare, a winner determination problem (WDP) is formulated considering different QoS requirements of users, and a computationally tractable algorithm is proposed to solve the WDP. Also, a pricing scheme is designed such that several desirable properties (e.g., incentive compatibility, individual rationality, and budget balance) can be achieved in the proposed CDA framework. Numerical results show the effectiveness of the proposed scheme.

Charging management for Electric Vehicles (EVs) on-the-move (moving on the road with certain trip destinations) is becoming important, concerning the increasing popularity of EVs in urban city. However, the limited battery volume of EV certainly influences its driver's experience. This is mainly because the EV needed for intermediate charging during trip, may experience a long service waiting time at Charging Station (CS). In this paper, we focus on CS-selection decision making to manage EVs' charging plans, aiming to minimize drivers' trip duration through intermediate charging at CSs. The anticipated EVs' charging reservations including their arrival time and expected charging time at CSs, are brought for charging management, in addition to taking the local status of CSs into account. Compared to applying traditionally applying cellular network communication to report EVs' charging reservations, we alternatively study the feasibility of applying Vehicle-to-Vehicle (V2V) communication with Delay/Disruption Tolerant Networking (DTN) nature, due primarily to its flexibility and cost-efficiency in Vehicular Ad hoc NETworks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V for reservation reporting is promisingly cost-efficient in terms of communication overhead for reservation making, while achieving a comparable performance in terms of charging waiting time and total trip duration.

Virtualization has been seen as one of the main evolution trends in future cellular networks which enables the decoupling of infrastructure from the services it provides. In this case, the roles of infrastructure providers (InPs) and mobile virtual network operators (MVNOs) can be logically separated and the resources of a base station owned by an InP can be transparently shared by multiple MVNOs, while each MVNO virtually owns the entire BS. Naturally, the issue of resource allocation arises. Specifically, the InP is required to abstract the physical resources into isolated slices for each MVNO who then allocates the resources within the slice to its subscribed users. In this paper, we aim to address this two-level hierarchical resource allocation problem while satisfying the requirements of efficient resource allocation, strict inter-slice isolation, and the ability of intra-slice customization. To this end, we propose a hierarchical combinatorial auction model, based on which a truthful and efficient resource allocation framework is provided. And we show by an illustrative example how the proposed model can be applied for wireless virtualization. Specifically, winner determination problems (WDPs) are formulated for the InP and MVNOs, and computationally tractable algorithms are proposed for solving these WDPs. Also, pricing schemes are proposed for ensuring the incentive compatibility. Note that the proposed model can be generalized for the virtualization of resources with more dimensions (e.g., power, antennas, etc.).

Opportunistic underwater sensor networks (OUSNs) are developed for a set of underwater applications, including underwater creatures tracking and tactical surveillance. However, the storage capacity of nodes is sometimes insufficient, especially compared to a wealth of data messages which are generated rapidly in some emergency response applications. Therefore, the network throughput should be taken as one of the primary objectives of message dissemination. To this end, the strategies for message storing, disseminating and discarding are investigated, and a Message Dissemination Approach for Storage-Limited (MDA-SL) OUSNs is proposed hereby. In MDA-SL, the messages are preferred to be disseminated to the nodes with higher speed or larger residual storage. In addition, the newer messages are inclined to be discarded when their holders' storage is full. Furthermore, through simulation analysis, the performance of MDA-SL is proved excellent, which indicates that MDA-SL achieves a satisfactory throughput with the propagation delay being restricted according to application requirements.