Information dissemination in dynamic social networks enables fast and frequent access to social news. Thereinto, the coexistence of public and private information creates a hybrid dissemination dynamics process in social networks. However, most existing information dissemination models treat the hybrid information in isolation and fail to consider their interactions through shared nodes and temporal dependencies. Thus, we propose a novel hybrid information dissemination (HID) model that explicitly captures the interconnected dissemination mechanisms of both public and private information within dynamic social networks. Additionally, considering heterogeneity among individuals, we further design a decision-making algorithm for the proposed HID model, aiming at maximizing individuals’ initiative. Furthermore, we derive equilibrium points and analyze their stability for the proposed HID model. Numerous experiments are conducted, and results show that the proposed HID model can effectively describe the dissemination process of hybrid information.

UAV-enabled edge storage systems provide data storage services to users by deploying UAVs in areas lacking infrastructure coverage, overcoming delay limitations and improving Quality of Service (QoS). Most existing studies focus on storing replicas on UAVs to ensure low-latency data access. Nonetheless, replica-based strategies incur high storage cost, posing significant challenges for UAVs with limited storage resources. In this paper, we introduce erasure coding into the UAV-enabled edge storage system, aiming to reduce user data access latency while minimizing storage cost. However, the mobility of users and the non-fully-connected nature of the UAV network pose new challenges for the coupled decisions of data encoding, block placement, and access. In this paper, we propose a Mobility-Enhanced Hierarchical Deep Reinforcement Learning algorithm (ME-HDRL). Specifically, we design a trajectory prediction algorithm combining CNN and ConvLSTM to account for user mobility in decision-making. We further decompose the original problem into two subproblems: data encoding and placement, as well as block access. A hierarchical deep reinforcement learning algorithm involving multiple UAV agents and an edge agent is proposed to collaboratively learn optimal decisions. To improve the convergence of the algorithm, we design an invalid action filter to reduce the action space. Experimental results show that our approach outperforms existing rule-based and reinforcement learning-based algorithms in various scenarios, exhibiting significant convergence improvements and a substantial reduction in both storage cost and user data access latency.

With increasing computational demands and limited network resources in marine environments, efficient service caching and task offloading have become critical. In such environments, Autonomous Underwater Vehicles (AUVs) rely on Unmanned Surface Vehicles (USVs) as relays, forming a cross-domain network comprising underwater acoustic and above-water RF links. However, the heterogeneity in bandwidth, latency, and bit error rates introduces challenges for reachability analysis and delay estimation. This paper addresses the joint optimization of caching, task offloading, and resource allocation in a cross-domain marine network composed of offshore base stations, USVs, and AUVs. To tackle the inherent heterogeneity in network links and decision timescales, we formulate the problem as a two-time-scale Hierarchical Markov Decision Process (H-MDP) and propose a Two Time-Scale Deep Reinforcement Learning (T2S-DRL) approach that integrates a hybrid policy network and a lightweight structure-aware action masking mechanism. The large time-scale agent optimizes caching decisions, while the short time-scale agent focuses on offloading and resource allocation. Extensive simulations show that our approach significantly reduces task execution delay and energy consumption, validating its effectiveness.

Data collection and distribution through crowdsensing has become an emerging trend in smart city scenarios. By leveraging existing vehicle resources without deploying dedicated infrastructure, Vehicular CrowdSensing (VCS) provides low-cost and high-mobility data collection on road networks. Typically, the Crowdsensing Platform (CP) issues data collection tasks, recruits Sensing Vehicles (SVs) to complete tasks, and sells the collected data to Data Demanders (DDs). Here, the goal of CP is to maximize profits through data collection and sales, and the goal of DDs is to improve satisfaction by purchasing high-quality sensing data. It can be seen that both CP and DD hope that SVs can complete more sensing tasks at a limited cost (high efficiency) while ensuring the accuracy of data collection (high quality). However, due to individual rationality and selfishness, not all SVs are willing to complete the sensing task. Therefore, how to motivate SVs to complete sensing tasks with high quality and efficiency, while handling the relationship among CP, DDs, and SVs, is a problem that needs to be considered. To solve the above problems, this paper proposes a Reputation-based Hybrid Incentive Approach (RHIA), with the goal of maximizing the utility of CP, SVs, and DDs. Specifically, in order to improve the task completion quality of SVs, we introduce vehicle reputation to measure SVs. Then, we propose a one-to-one bargaining game between CP and each SV, and use the reputation value as the sequential basis of the game. Meanwhile, in order to improve the task completion efficiency of SVs, we also design a unique SV Trajectory Planning Algorithm (STPA). Further, in order to meet the needs of DDs, a one-to- multi Stackelberg game between CP and DDs is proposed. Here, the existence and uniqueness of Nash equilibrium is proved through backward induction. Finally, based on real-world datasets, the effectiveness of our proposed RHIA and STPA is verified. Our proposed method can ensure the long-term stability of the VCS system, which also improves the utility of participating individuals.

Collaborative agents, consisting of at least one human and one mobile robot agent working toward a common objective, are increasingly prevalent and effective in both social and industrial spheres, such as manufacturing. The inherent heterogeneity of these agents requires efficient and scalable Task Scheduling and Allocation (TSA) schemes that match individuals to tasks based on their abilities and meet specific temporal constraints, maximizing performance in less time. Existing works face challenges as exact methods rely on assumptions and deterministic models, which struggle to scale and infer time-varying, stochastic human task performance. While offline reinforcement learning shows promise, it is time-consuming and heavily dependent on training data that is often scarce in practical factory settings. To address these challenges, we formulate the TSA problem in mobile multi-agent teams as a temporal-constrained contextual decision-making process and propose the Bayesian Optimization-augmented Team coordination among Heterogeneous agents (BOTH), a novel scalable and training-free scheduling approach. The core idea is to use Gaussian Processes (GP) to iteratively infer agent dynamics in real-time, enabling the automatic derivation of a robust TSA solution that requires no prior data and adapts to varying problem sizes. We start by employing a heterogeneous graph-based encoder to extract representative context from the individual differences among team agents and tasks, considering strict temporal constraints. Following this, we propose a GP-driven Bayesian optimizer to intelligently explore and exploit optimal task assignments for each context, without making assumptions about the system. Experiments on synthetic and real datasets demonstrate that BOTH boosts accuracy and time efficiency compared to competing baselines, even within a few iterations.

Satellite-assisted Mobile Edge Computing (MEC) is a promising paradigm for enabling low-latency and high-efficiency computing in deep-sea and far-offshore marine environments. However, the inherent heterogeneity of three-layer marine networks—comprising satellites, Unmanned Surface Vehicles (USVs), and Autonomous Underwater Vehicles (AUVs)—introduces unique challenges. These include the coupling of underwater acoustic and above-water Radio Frequency (RF) communication links, the highly constrained computing and caching resources of edge devices, and the strong interdependence between service caching, vertical task offloading, and horizontal migration. Existing solutions often overlook these cross-layer dynamics and the spatio-temporal interactions among network nodes, leading to suboptimal task scheduling and degraded system utility. To address these challenges, we formulate a joint optimization problem that maximizes the Quality of Experience (QoE), with decision variables spanning caching placement, task migration, and offloading under resource constraints. Through rigorous theoretical analysis, we prove that the formulated problem is NP-hard, highlighting its inherent computational intractability. To overcome this, we propose an Attention-Enhanced Multi-Agent Reinforcement Learning algorithm (AE-MARL), which adopts a hybrid policy network to learn discrete decisions and continuous resource allocation. Furthermore, a lightweight attention module is integrated to infer the importance of partial observations and guide collaborative decision-making across agents. Extensive experiments and analysis under diverse system configurations demonstrate that AE-MARL consistently outperforms state-of-the-art baselines.

Federated Learning (FL) enhances data privacy for End Equipment Workers (EWs) by enabling the sharing of model parameters instead of raw data. However, energy constraints and individual self-interest may discourage EWs from participating or slow down training, ultimately affecting the performance of the global FL model. To address these challenges, we propose a three-stage Stackelberg game-based framework that leverages wireless power to incentivize participation while ensuring the successful completion of FL tasks. In this framework, the Base Station (BS) publishes FL task and seeks to obtain an improved global model at a reduced cost. EWs train local models, aiming to maximize their payments while minimizing energy consumption. Meanwhile, the Charging Service Provider (CSP) supplies energy to EWs via Wireless Power Transfer (WPT) during model training and uploading, charging appropriate fees for the service. We employ the backward induction method to analyze the proposed game problem, proving the existence of a unique Stackelberg equilibrium and Nash equilibrium. Furthermore, we propose the Trust Region Method (TRM) to solve the unit payment strategy problem of BS. Extensive simulations validate that our method consistently outperforms benchmark schemes, achieving higher average utility across a wide range of scenarios.

Connectivity restoration is essential for ensuring continuous operation in wireless sensor networks (WSNs). However, existing works lack enough network robustness when suffering from the secondary external damages. In this article, we propose a novel connectivity restoration scheme to address this problem. This scheme comprises three connectivity mechanisms regarding relay segment selection in different regions. The first one is a data traffic decentralization mechanism, which establishes more transmission paths near the sink for reliability improvement and traffic load balancing. The second one is a segment shape selection mechanism, in which the segments with high-reliability preferably become the relay segments for greater network robustness. The third one is a traffic load transfer mechanism, in which data traffic is transferred from a high-load segment to a low-load segment for balancing energy depletion of the network. The distinctive characteristics of this work are twofold: different regions perform diverse connectivity restoration approaches according to the demand diversity of different regions, and traffic load can be balanced from upstream regions rather than only from downstream regions. Extensive simulation experiments validate the effectiveness and advantages of our proposed scheme in terms of connection cost, network robustness, load balance degree, and network longevity.

Mobile Edge Computing (MEC) significantly enhances service response speeds and improves the Quality of Service (QoS) by facilitating the offloading of computation-intensive tasks from Mobile Users (MUs) to nearby Edge Servers (ESs). However, due to the inherent selfishness of involved entities, MUs may be unwilling to offload tasks without reasonable resource pricing, and ESs may lack motivation to provide computation resources without appropriate compensation. Furthermore, improving the utilization of ESs' computation resources and achieving efficient task scheduling remains a major challenge. To ad dress these issues, we introduce a profitable broker between ESs and MUs, and propose TORP, a Two-stage Stackelberg Game based Computation Offloading and Resource Pricing mechanism, to maximize the utility of each entity. Specifically, we model the interactions among three entities (i.e., the broker, MUs, and ESs) as a two-stage Stackelberg game, where the interactions between the broker and ESs is defined as Stage I, while the interactions between the broker and MUs is defined as Stage II. By using the backward induction method, we theoretically prove the Stackelberg Equilibrium (SE) for each stage of the two-stage Stackelberg, and the SE of the whole game. Then, recognizing that the optimization problem is a Mixed-Integer Nonlinear Programming (MINLP) problem, an Alternating Iteration-Based Resource Pricing and Task Offloading Algorithm (AIPOA) is proposed to obtain the optimal solution. Finally, we perform extensive simulations comparing TORP against multiple base lines. Experimental results show that TORP achieves substantial improvements, enhancing the utilities of three entities by about 2.00%-52.69% under different scenarios.

Federated Learning (FL) faces critical challenges in heterogeneous and resource-constrained environments, including device diversity, high communication overhead, and training delays. Therefore, we propose FedKDC, a federated learning framework that integrates knowledge distillation with data compression to jointly optimize server bandwidth, client computation resources, and compression ratios, thereby minimizing training latency. In particular, FedKDC employs a Generative Adversarial Network (GAN)-based generator to produce synthetic data for knowledge transfer across heterogeneous models without sharing raw data, mitigating privacy risks. Then, FedKDC uses a loss-driven adaptive compression mechanism to adjust the minimum compression threshold based on training stability, reducing communication volume while maintaining accuracy. In addition, we further discuss the problem of resource allocation under system constraints, and uses Particle Swarm Optimization (PSO) algorithm to solve it. Based on the three real world datasets (i.e., Fashion-MNIST, CIFAR-10, and CIFAR-100), the experimental results demonstrate that FedKDC reduces communication cost by up to 17% and training time by 8%. This shows that FedKDC is effective for large-scale heterogeneous FL deployment while maintaining the accuracy of the model.

The advent of 6G and the Metaverse has created a need for efficient real-time data processing and low-overhead communication. To address this challenge, we propose SemCom-MN, a semantic communication-enhanced Metaverse framework integrating an Edge Service Provider (ESP), Edge Sensing Units (ESUs), and Virtual Service Providers (VSPs). ESUs capture physical-world data, ESP manages semantic information, and VSPs create immersive virtual environments. To improve utility under heterogeneous information and computational requirements, we model semantic information trading as a three-stage Stackelberg game and prove the existence of a Nash equilibrium. Furthermore, to overcome high-dimensional dynamics and slow convergence in semantic trading, we develop a Diffusion Game Algorithm (DGA) combining strategic exploration with a game-theoretic denoising mechanism, achieving robust convergence. Simulation results show DGA increases system utility by 8.49%–33.94%.

Autonomous aerial vehicle (AAV) crowdsensing, as a complement to mobile crowdsensing, can provide ubiquitous sensing in extreme environments and has gathered significant attention in recent years. In this article, we investigate the issue of sensing data storage in AAV crowdsensing without edge assistance, where sensing data is stored locally in the AAVs. In this scenario, replication scheme is usually adopted to ensure data availability, and our objective is to find an optimal replica distribution scheme to maximize data availability while minimizing system energy consumption. Given the NP-hard nature of the optimization problem, traditional methods cannot achieve optimal solutions within limited timeframes. Therefore, we propose a centralized training and decentralized execution deep reinforcement learning (DRL) algorithm based on actor-critic, named “MUCRS-DRL.” Specifically, this method derives the optimal replica placement scheme based on AAV state information and data file information. Simulation results show that compared to the baseline methods, the proposed algorithm reduces data loss rate, time consumption, and energy consumption by up to 88%, 11%, and 11%, respectively.

Vehicle edge computing can effectively ensure the quality of experience for user vehicles (UVs), but road side units (RSUs) with limited resources may not be able to handle intensive tasks under high traffic conditions. In this case, worker vehicles (WVs) with idle resources can share resources to alleviate the pressure on RSUs. However, selfish WVs may be reluctant to share idle computation resources without any rewards. In addition, the optimization problems in previous research are relatively simple and cannot be applied to complex scenarios. To address the above challenges, we propose an incentive-driven partial offloading framework aiming to maximize social welfare. In particular, the computing service provider (CSP) managing RSUs first determines resource prices and offloading rates with UVs, while also determining contract terms with WVs. Then, it generates the optimal task scheduling strategy and notifies the UVs to offload tasks to the corresponding WVs. Considering that maximizing social welfare is a mixed-integer nonlinear programming (MINLP) problem, we design the hybrid proximal policy optimization (HPPO)-based task offloading and resource allocation algorithm (HORA) with a hybrid action space to directly solve the original problem. Finally, extensive simulation results show that HORA outperforms other baseline methods across various scenarios, and the contract terms meet the constraints of individual rationality (IR) and incentive compatibility (IC).

As a promising methodology of distributed Machine Learning (ML) paradigm, Federated Learning (FL) protects data privacy and reduces communication cost by aggregating model parameters rather than raw data. However, training superb FL models incurs a lot of energy consumption, which is a significant challenge for energy-limited Mobile Devices (MDs). To address this challenge, this paper proposes a Wireless Power Transfer (WPT)-aided FL framework, where MDs train local FL models for Base Station (BS) and get corresponding payoff, while Wireless Charge Provider (WCP) provides energy supplement for MDs and charges energy fees. Furthermore, we take into account the time-varying nature of MDs datasets, which affects their energy consumption and reward from BS. Then, we formulate the investigated problem to achieve joint optimization of WPT duration, computing resource allocation and the number of local iterations, with the goal of maximizing the total utility of all MDs throughout the whole FL process. The optimization problem is NP-hard and difficult to be solved by traditional optimization methods within limited timeframes. Therefore, we use Karush-Kuhn-Tucker (KKT) conditions and Lagrange dual method to analyze the problem, and propose a new Improved Lagrangian Subgradient Method (ILSM) as an efficient solution. Finally, extensive simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme under various scenarios, and the results show that the proposed ILSM significantly outperforms other benchmarks in terms of the total utility of all MDs.

This paper presents a fully automatic method, BladeView, for drone-based wind turbine blade inspection using an Unmanned Aerial Vehicle (UAV). With the need for highly efficient blade inspection coupled with the rapid increase of wind turbines, existing methods provide limited automation on wind turbine parameter estimation, full blade coverage, and safety control. We introduce an Automatic Parameter Calculation (APC) algorithm and an Automatic Flight System (AFS) in BladeView to compute wind turbine parameters and inspection paths, respectively. Leveraging triangulation and linear fitting integration techniques, the APC automatically calculates the wind turbine parameters and estimates the relative angle and position between a drone and the turbine. Furthermore, with dynamic path finding and B-spline optimization, the AFS plans a path covering 3 blades within specified flight corridors, in compliance with the turbine parameters obtained from APC. Thus, the proposed BladeView can properly ensure an inspection’s automation, coverage, safety, and smoothness. The efficiency and usability of BladeView are validated through 100,000 flight simulations in the Gazebo simulation environment and 9,239 field runs at various wind farms, including offshore, near-shore, deserts, mountainous areas, farmlands, and suburbs.Note to Practitioners—The proposed BladeView is distinguished in three aspects: (1) It automatically adapts wind turbines with varying geometric properties and physical locations relative to the take-off point. (2) It dramatically improves the quality of collected data with optimal UAV speed and flight corridors. (3) It thoroughly covers all three blades of a Horizontal-Axis Wind Turbine (HAWT), including regions where defects frequently occur. Thus, BladeView is more efficient and robust than existing UAV-based methods for blade inspection, with only around 25 minutes per HAWT. Moreover, it does not require experienced pilots to fly the UAV and manual interventions are rarely needed. Extensive simulation and real-world experiments demonstrate the efficiency and usability of BladeView in various on- and offshore wind farms.

Broadcast plays a significant role in the emergency message propagation in Vehicular Ad hoc Networks (VANETs). However, current broadcast relay strategies easily cause serious message loss and larger broadcast costs due to the poor environmental adaptability. In this paper, to handle the above problems, we propose an Adaptive Connectivity-Aware Relay (ACAR) strategy, which has two striking features: multiple network connectivity states and dynamic broadcast relay policies. We design four network connectivity states and their corresponding four broadcast relay policies. In disconnected networks, only the vehicle with the same movement direction as the message propagation direction acts as the relay, so as to relieve the message loss. Moreover, different broadcast periods are allocated for different applications, so as to reduce the broadcast costs. In connected networks, the link quality and transmission distance are adopted to select the relay in the sender-based relay pattern, so as to address the relay invalidation problem. Further, different candidate relays are assigned different broadcast priorities and different broadcast waiting time in the receiver-based relay pattern, so as to address the transmission conflict problem. Simulation results show that ACAR outperforms existing competing schemes in terms of end-to-end delay, broadcast success rate, and packet overhead.

Traffic signal control plans significantly impact transportation system efficiency by regulating traffic conditions at intersections. Adaptive traffic plans that can adjust to real-time road conditions are more effective as a result. Reinforcement learning succeeds at adapting strategies based on feedback derived from the environment, and is thus proficient in dealing with complex traffic scenarios that change dynamically. However, current RL methods rely on significant computational periods to obtain precise functioning mechanisms within the scenarios, posing barriers to their adoption for new scenarios. In addition to directly optimizing the RL model itself to enable fast learning from scratch, another idea is to make the model transferable or reusable with the learned experience. Given the diversity of migration scenarios, the underlying control algorithm should guarantee convergence and endeavor to be parameter-insensitive. From the above concern, we proposed MetaSignal, an efficient meta-reinforcement learning method for traffic signal control. Specifically, our approach utilizes the Fourier basis as the value function approximation in reinforcement learning, distinguishing it from methods like neural network approximation. This linear approximation offers advantages such as convergence facilitation, error bound achievement, and reduced parameter dependence. The meta-learning framework adopts a model-agnostic approach, enabling effective adaptation of the base model to the target scenario with limited training cost. Empirically, the proposed method shows promising and stable performance for traffic signal control through comprehensive comparison experiments in both synthetic and real-world traffic networks.

Recently, For-Hire Vehicles (FHVs) have emerged as major players in Vehicular CrowdSensing (VCS). However, the heterogeneity of tasks issued by Data Requesters (DRs) and the heterogeneity of sensors equipped on FHVs under different Vehicle Platforms (VPs) bring difficulties to task allocation and execution. It can be concluded that it is important to reasonably analyze the relationship among DRs, VPs, and FHVs, as well as to motivate VPs and FHVs to complete sensing tasks. Therefore, taking advantage of the real-time simulation and intelligent decision-making of Digital Twins (DT), this paper proposes a DT-drivenDouble-layerReverseAuctionMethod (DRAM). In the first layer, the reverse auction is established between each DR and VPs, and in the second layer, the reverse auction is established between each VP and FHVs. Meanwhile, we also introduce a sensing fairness index to ensure the sensing balance of different sub-regions and consider it in the DRAM process. Here, the idea of backward induction is used to solve the above problems, with the goal of minimizing the overhead of winning VP and the average overhead of all DRs. Finally, the effectiveness of the DRAM proposed in this paper is verified based on the real data set. Compared with the baseline method, DRAM can reduce the average overhead of DR by about 4%-25%. Meanwhile, in terms of sensing fairness, it can be improved by up to 55%.

Unlike traditional edge caching paradigms, similarity edge caching enables the retrieval of similar content from local caches to fulfill user requests, reducing reliance on remote data centers and improving system performance. Although several pioneering works have contributed to similarity edge caching, most focus on single-edge nodes and/or static environment settings, which are impractical for real-world applications. To address this gap, we investigate the similarity caching problem in dynamic cooperative edge networks, where a set of edge nodes cooperatively serve requests generated from arbitrary distributions with similar content over fluctuating transmission links. This presents a significant challenge, as it requires balancing content similarity with delivery latency over the transmission network and learning the environment in real-time to optimize caching policies. We frame this problem within an adversarial Multi-Armed Bandit framework to accommodate the continuously changing operational environment. To solve this, we propose an online learning-based approach named MABSCP, which dynamically updates caching policies based on real-time feedback to minimize the service cost of edge caching networks. To enhance implementation efficiency, we devise both an offline compact strategy construction method and an online Gibbs sampling method. Finally, trace-driven simulation results demonstrate that our proposed approach outperforms several existing methods in terms of system performance.

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) has received extensive attention because it provides resilient computation services for multiple Mobile Users (MUs). However, due to the increasing scale of offloaded tasks, the uncertain mobility of MUs, and the limited energy budget of UAV and MUs, it is extremely challenging to achieve satisfactory Quality-of-Service (QoS). Non-Orthogonal Multiple Access (NOMA), a promising technology to serve multiple MUs with limited communication resources, has great potential to be integrated with MEC. To this end, this paper proposes a QoS-oriented NOMA-enhanced UAV-MEC system, which aims to capture the potential gains of uplink NOMA and enable more MUs to benefit from edge computing servers in resource-constrained UAV-assisted MEC environments. This synergy reduces MUs' uplink energy consumption but poses new challenges in resource allocation and UAV trajectory design. To address these challenges, we define a new metric called System Overhead Ratio (SOR) to reflect the system's QoS, and then consider a joint optimization problem of resource allocation, transmission power control, and UAV trajectory design, with the goal of minimizing the SOR. Given the NP-hard nature of the optimization problem, we propose a Lyapunov and convex optimization-based Low-complexity Online Resource allocation and Trajectory optimization method (LORT) to solve it, and further analyze the convergence and complexity of LORT. Finally, extensive simulations show that the proposed method surpasses other benchmarks, reducing the SOR by approximately$10\%$-$25\%$under various scenarios.

By sharing model parameters instead of raw data to train machine models, Federated Learning (FL) can protect End equipment Workers (EWs)’ data privacy. However, due to energy constraints and selfishness, EWs may not be willing to participate or train slowly, which affects the performance of global FL model. To address these issues, we propose a three-stage Stackelberg game-based wireless powered FL framework to incentivize all players to participate in the system while ensuring the successful completion of FL tasks. Specifically, Base Station (BS) publishes the FL task and wants to obtain a better FL model at a lower cost. EWs train local FL models, and want to get more payment with less energy consumption. When EWs train and upload their local models, Charging Service Provider (CSP) transmits energy to them via Wireless Power Transfer (WPT) while charging fees. In order to obtain the optimal strategy for all participants, we analyze the proposed game problem using the backward induction method. Meanwhile, we prove that the unique Stackelberg equilibrium and Nash equilibrium can be obtained, and we obtain the approximate optimal solution of BS using the subgradient method. Finally, extensive simulations are conducted to evaluate the performance of the proposed method in different scenarios. The results show that the proposed method improves the utility of three parties by an average of 19.09% - 51.86% compared with the benchmark methods.

Vehicular Edge Computing (VEC) allows vehicles to offload their delay-sensitive tasks to nearby Road Side Units (RSUs) for processing, which improves network quality of service (QoS). However, the self-interested SDN controller is unwilling to ask RSUs to provide free computing resources for vehicles. At the same time, complicated dependencies between vehicular subtasks may cause non-ideal task delay and energy consumption. In order to solve these problems, this paper proposes a Stackelberg game-based Dependency-aware task Offloading and resource Pricing framework (SDOP). Specifically, we first model a vehicular edge network that partially offloads dependency-aware tasks. Then, we depict the interaction between the SDN controller and vehicles as a Stackelberg game, with the goal of maximizing the utility of both parties. Next, we present a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the problem. Finally, numerous simulations are performed, and the results show that compared with other baseline schemes, the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios.

This paper investigates the joint trajectory optimization and resource allocation problem in an UAV-Assisted NOMA-MEC System, aiming to minimize the system overhead. First, considering user mobility and Non-Orthogonal Multiple Access (NOMA) technology, we transform UAV trajectory optimization and resource allocation problem as a dynamic coverage location problem. Second, we design a Low-complexity Online Trajectory optimization and Resource allocation Scheme based on Lyapunov and convex optimization (LOTRS) with the goal of minimizing the system overhead. Simulation results show that compared with other benchmark schemes, the proposed LOTRS performs best in terms of system overhead in various scenarios.

This paper proposes a Stacklberg game-based Dependent task Offloading and resource Pricing framework (SDOP), where vehicles partially offload their dependent substaks to the SDN controller and pays corresponding fees. Firstly, we model the interaction between the SDN controller and vehicles as a Stackelberg game, where both parties wish to maximize their utility. Then, we employ the backward induction approach to analyze the investigated problem, and prove the existence and uniqueness of Nash and Stackelberg equilibrium. Next, we propose a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the considered problem. Finally, extensive simulation results show that the proposed GAPG outperforms other baseline schemes under various scenarios.

Federated learning (FL) enables the training of a global model using clients' local datasets, leveraging their computing resources for efficient machine learning while preserving user privacy. This paper explores FL in wireless networks, focusing on client selection and bandwidth allocation as key factors impacting latency, covert constraint and energy consumption. We propose the per-round energy drift plus cost (PEDPC) algorithm to address this optimization problem from an online perspective. The performance of the PEDPC algorithm is validated through simulations, evaluating latency and energy consumption under both IID and non-IID data distributions.

This paper investigates dynamic model partitioning and resource allocation in split federated learning, aiming to maximize the utility of clients and the Central Server (CS). We first model the interactions between the CS and clients as a Stackelberg game, where the CS acts as the leader to set payment and allocate computation resources, while clients as followers to determine model partitioning strategies. Then, we transform the problem into a bi-level optimization and propose a Nash-Equilibrium-based Stackelberg Algorithm (NESA) to solve it. Finally, the experimental results indicate that a Stackelberg equilibrium exists between the CS and clients, and NESA achieves higher utility and improves accuracy and convergence speed.

Computation offloading, resource allocation, and endurance issues in unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks have always been a research focus. UAV-aided MEC allows mobile users (MUs)' tasks to be offloaded to drones for processing in special scenarios, such as natural disasters or military attacks. However, as the number and size of offloaded tasks increase, a single UAV is difficult to meet all computational demands, result in the decline of QoS. To address this issue, this paper presents a collaborative computation offloading scheme where multiple UAVs can cooperate to handle massive tasks. Firstly, considering that battery-limited UAVs cannot complete all tasks and sustain flight without charging, we incorporate charging stations (CS) into multi-UAV-assisted MEC networks. Subsequently, we design a price-based incentive mechanism to maximize the total revenue obtained from UAVs' collaborative computation. Then, we formulate the joint optimization problem of computation offloading, resource allocation and charging scheduling as a Markov Decision Process (MDP), and propose a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to find optimal strategies. Finally, extensive simulations demonstrate that the proposed TD3 algorithm outperforms other benchmark methods, achieving the highest overall system utility under different scenarios.

Edge caching is a promising technology to reduce backhaul strain and content access delay in Internet of Vehicles (IoV). It precaches frequently used contents close to vehicles through intermediate roadside units. Previous edge caching works often assume that content popularity is known in advance or obeys simplified models. However, such assumptions are unrealistic, as content popularity varies with uncertain spatial-temporal traffic demands in IoVs. Federated learning (FL) enables vehicles to predict popular content with distributed training. It preserves the training data remain local, thereby addressing privacy concerns and communication resource shortages. This article investigates a mobility-aware edge caching strategy by exploiting asynchronous FL and deep reinforcement learning (DRL). We first implement a novel asynchronous FL framework for local updates and global aggregation of stacked autoencoder (SAE) models. Then, utilizing the latent features extracted by the trained SAE model, we adopt a hybrid filtering model for predicting and recommending popular content. Furthermore, we explore intelligent caching decisions after content prediction. Based on the formulated Markov decision process (MDP) problem, we propose a DRL-based solution, and adopt neural network-based parameter approximations for the curse of dimensionality in RL. Extensive simulations are conducted based on real-world data trajectory. Especially, our proposed method outperforms federated averaging, least recently used, and NoDRL, and the edge hit rate is improved by roughly 6%, 21%, and 15%, respectively, when the cache capacity reaches 350 MB.

Routing plays an essential role in ensuring normal and lasting operation of wireless body area networks (WBANs). However, existing routing schemes cause inefficient and unbalanced energy dissipation, which contributes to premature death of some nodes and high temperature within a small area of the body. In this article, we propose an adaptive time-varying routing (ATVR) protocol to address these issues. Unlike in conventional routing solutions, in our protocol a node may act as different roles (source node or relay node) and select different paths in disparate periods. This dynamic routing pattern helps to achieve a globally optimal routing solution. In ATVR, a node evaluation function and a path evaluation function are designed to reflect the node state and the path state respectively. Then, the path selection problem is transformed into a Hitchcock transportation problem, in which the nodes with worse node state act as source nodes (i.e., producers) and the nodes with better node state act as relay nodes (i.e., consumers). Then, this Hitchcock transportation problem is addressed by the AlphaBeta algorithm, in which the paths with less energy consumption and less path loss are selected to forward data. The experimental results show that our protocol has better performance in terms of energy consumption, network lifetime, and node temperature.

By utilizing on-board sensors and computing resources in intelligent vehicles, vehicular crowdsensing can collect a series of sensing data. Typically, sensing vehicles can be divided into opportunistic vehicles with fixed trajectories and participatory vehicles with changeable trajectories. Therefore, to complete sensing tasks more effectively, how to combine the advantages of the mobility characteristics of the two vehicles is a challenging problem. To solve this problem, this paper innovatively proposes a joint scheduling and incentive-driven two-stage hybrid sensing method. Specifically, the method is divided into two stages: opportunistic vehicle selection and participatory vehicle scheduling. In particular, both types of vehicles are managed through the Crowd Sensing Platform (CSP). For the first stage, this paper proposes a reverse auction-based incentive mechanism to select the lowest-cost set of vehicles to complete sensing tasks. This mechanism mainly consists of two steps: winning vehicle selection and reward payment. It is also verified that the proposed mechanism can ensure the individual rationality and truthfulness of opportunistic vehicles. For the second stage, based on the first-stage sensing results, this paper proposes a Soft Actor-Critic (SAC) based approach to scheduling participatory vehicle trajectories to complete sensing tasks. In addition, this paper also considers sensing fairness to ensure the balance of sensing task completion in different sub-regions. Through the two-stage hybrid sensing method, this paper aims to minimize the CSP overhead while ensuring sensing fairness. Finally, extensive evaluation results based on Roma taxi data sets demonstrate that the proposed method works effectively and outperforms other benchmark schemes in different working scenarios.

Federated learning (FL) is a distributed machine learning methodology that can achieve collaborative model training among clients without collecting their private training data. Despite the great benefits in privacy protection, FL still faces challenges like limited computation capabilities of clients (e.g., end devices) and significant communication overheads when applied to mobile edge-cloud networks. To address these issues, this paper proposes a novel three-layer FL framework with Parameter Selection and Pre-synchronization (PSPFL) to achieve fast and accurate model training in mobile edge-cloud networks. The basic idea of PSPFL is that clients can select partial model parameters for transmission. Then, base stations aggregate these model parameters cooperatively (i.e., pre-synchronization) and send the aggregated results to the server for global model update periodically. However, there is an intrinsic trade-off between parameter transmission overhead and model training loss. To strike a desirable balance between them, we investigate the optimal parameter pre-synchronization round and local training round under PSPFL. Specifically, we propose an Alternating Minimization (AM) algorithm to obtain the initial local training round and parameter pre-synchronization round. Moreover, we integrate Deep Q-network with AM (namely DQNAM) to explore and update the optimal solution. Finally, extensive simulations are conducted to evaluate the performance of the proposed method on commonly used datasets. The results show that the proposed method can reduce the sum of FL completion time and training loss by an average of 20.72% - 69.25% compared to benchmark methods.

Recently, in response to the low efficiency and high transmission latency of traditional centralized content delivery networks, especially in congested scenarios, edge caching has emerged as a promising method to bring content caching closer to the edge of the network. However, traditional content delivery methods might still lead to low utilization of cache resources. To tackle this challenge, this paper investigates a content recommendation-based edge caching method in multi-tier edge-cloud networks while considering content delivery and cache replacement decisions as well as bandwidth allocation strategies. First, we consider a multi-tier edge caching-enabled content delivery network architecture combined with a content recommendation system and formulate the optimization problem with the objective of minimizing long-term content delivery delay and maximizing cache hit rate. Second, considering time-varying system environments and uncertain content demands, we approximate the optimization process of content delivery and cache replacement for each agent as a Partially Observable Markov Decision Process (POMDP) and propose a single-agent Deep Deterministic Policy Gradient (DDPG)-based method. Subsequently, we extend the POMDP to a multi-agent scenario. To address the issue of agents converging to local optima and establish more personalized models, we propose a Federated Distributed DDPG-based method (FD3PG) to solve the corresponding problem in a multi-agent system. Finally, simulation results demonstrate that the proposed FD3PG achieves lower delivery delay and higher cache hit rate compared with other baselines in various scenarios. Specifically, compared with FADE, MADRL, and DDPG, FD3PG achieves a significant decrease in average delivery delay, approximately 10%, 11%, and 35% on the Synthetic dataset, and 12%, 14%, and 48% on the MovieLens Latest Small dataset, respectively.

The multi-hop broadcast is of crucial significance to emergency message dissemination in vehicular ad hoc networks (VANETs). However, current solutions focus on a single and fixed goal, which cannot satisfy the dynamic and diverse emergency message requirements. In this article, we propose an adaptive broadcast-relay selection scheme to fill up this gap. An adaptive control message is designed to reflect the dynamic and diverse emergency message requirements. An adaptive relay pattern switching mechanism is designed to accommodate such requirements based on the adaptive control message. Delay-sensitive messages are broadcasted by an adaptive sender-based relay pattern, in which the link quality is used to address the inherent transmission reliability problem. Delay-insensitive messages are broadcasted by an adaptive receiver-based relay pattern, in which the time interval of adjacent potential relays is set to address the inherent packet collision problem. The unique features of our solution are twofold. One is the stronger adaptivity due to the implementation of the dynamic relay patterns, and the other is the wider applications due to the satisfaction of multiple types of emergency messages. Extensive simulations demonstrate the advantages of our solution in terms of transmission delay, dissemination speed, and adaptability in different scenarios.

Covert communications can enhance users's privacy by hiding the existence of communication. In this paper, we analyze a covert cooperative cognitive radio (CCCR) networks, where a primary transmitter (PT) transmits information with the aid of one secondary transmitter (ST). In return, ST attempts to transmit private information by exploiting PT's spectrum in presence of an eavesdropper (Eve). Specifically, a full-duplex secondary receiver (SR) sends jamming signals to the Eve to cause uncertainty by varying the jamming power. Then, the closed-form expression of the minimal detection error probability at Eve, the approximate expression for the optimal transmit power as well as the corresponding covert rate can be obtained under the given constraint. Numerical results show that the jamming signals and noise uncertainty have a significant influence on Eve's minimum detection error probability. Moreover, it can be seen that under the same covert constraints, the joint impact of noise uncertainty and jamming power on Eve's detection error probability, covert rate and covert outage probability (COP) is remarkable when noise uncertainty is large or the self-interference cancellation coefficient is small.

This work proposes a novel three-layer federated learning (FL) framework with parameter selection and pre-synchronization (PSPFL) to achieve fast and accurate model training. The basic idea of PSPFL is that clients select partial model parameters for transmission and then base stations aggregate them cooperatively (i.e., pre-synchronization) and send the aggregated results to the server for global model update periodically. However, there is an intrinsic trade-off between parameter transmission overhead and model training loss. To strike a desirable balance between them, we investigate the optimal parameter pre-synchronization round and local training round under PSPFL. Specifically, we propose a Deep Q-Network (DQN)-based method to obtain the local training round and parameter pre-synchronization round. Finally, extensive experiments are conducted to evaluate the performance of the proposed method on commonly used datasets. The results show that the proposed method can reduce the sum of FL completion time and training loss by an average of 8.17%-18.82% compared to benchmarks.

Unmanned aerial vehicles (UAVs) are considered as a promising method to provide additional computation capability and wide coverage for mobile users (MUs), especially when MUs are not within the communication range of the infrastructure. In this article, a UAV-aided mobile-edge computing (MEC) network, including one UAV-MEC server, one BS-MEC server, and several MUs, is investigated for computation offloading, in which the edge service provider (ESP) manages two kinds of servers. It is considered that MUs have a large number of computation tasks to conduct, while the ESP has idle computational resources. MUs can choose to offload their tasks to the ESP to reduce their pressure and cost, and the ESP can make a profit by selling computational resources. The interaction among the ESP and MUs is modeled as a Stackelberg game, and both the ESP and MUs want to maximize their utility. The proposed game is analyzed by using the backward induction method, and it is proved that a unique Nash equilibrium can be achieved in the game. Then, a gradient-based dynamic iterative search algorithm (GDISA) is proposed to get the approximate optimal solution. Finally, the effectiveness of GDISA is verified by extensive simulations, and the results show that GDISA performs better than other benchmark methods under different scenarios.

With the continuous expansion of the power Internet of Things (IoT) and the rapid increase in the number of Smart Devices (SDs), the data generated by SDs has exponentially increased. The traditional cloud-based smart grid cannot meet the low latency and high reliability requirements of emerging applications. By moving computing, data, and services from the centralized cloud to Edge Servers (ESs), edge computing exhibits excellent performance in communication delay and traffic reduction. Simultaneously, service caching also shows attractive advantages in handling the surge in data traffic. In this paper, we consider the joint optimization of computing offloading and service caching in edge computing-based smart grid, and formulate the problem as a Mixed-Integer Non-Linear Program (MINLP), aiming to minimize the task cost of the system. The original problem is decomposed into an equivalent master problem and sub-problem, and a Collaborative Computing Offloading and Resource Allocation Method (CCORAM) is proposed to solve the optimization problem, which includes two low-complexity algorithms. Specifically, a gradient descent allocation algorithm is first proposed to determine the computing resource allocation strategy, and then a game theory-based algorithm is proposed to determine the computing strategy. Simulation results show that CCORAM with low time complexity is very close to the optimal method, and performs much better than other benchmark methods.

Edge computing is a commonly used paradigm for providing low-latency computation services by locally deploying computation and storage resources close to the user equipments (UEs). Since the computation resource demand of the offloaded tasks of a UE is naturally a random variable, it is possible that the real-time computation capacity demand of a resource-limited hosting virtual machine (VM) or edge computing server (ECS) is larger than its computation capacity, causing unexpected delay or delay-jitter to the services, which should be avoided if possible, for delay-sensitive applications. We consider an edge computing scenario wherein the transmission links are unmanageable and computation resource demands of VM servers are stochastic. We propose a novel Logistic function-based service reliability probability (SRP) estimation model without specifying the distributions of the resource demands. We study the average SRP maximization problem (ASRPMP) in a VM-based edge computing server (ECS) by jointly optimizing the service quality ratios (SQRs) and the computation resource allocations, and we propose an alternative optimization algorithm (AOA) by decomposing the problem into a resource allocation problem (RAP) and a service quality control problem (SQCP). Based on the derived analytical solutions of the two subproblems, we propose an effective and low-complexity heuristic AOA (HAOA) to solve the ASRPMP. The simulation results obtained from both synthetic Gaussian workload data and PlanetLab trace data demonstrate that, given the same target SQR or computation resource, the proposed method can achieve similar performance compared with the convex AOA (CAOA) method with much higher complexity, and can improve the reliability of the services compared with the baseline weighted allocation method (WAM) in both high and low SRP regimes.

Interference is a serious problem in Wireless Body Area Networks (WBANs) and heavily weakens system performance. In this paper, we propose an exchange-free resource scheduling scheme to overcome the interference of dynamic coexisting WBANs. For each data transmission period, we design a transmission channel/slot allocation scheme based on a Latin square, where each character denotes a specific combination of a channel and a time slot. For each data retransmission period, we design a retransmission time-slot selection scheme based on a hash function, in which the unique identity information of the collided node is used to calculate the retransmission slot. Compared with existing solutions, our work has two key advantages. First, all nodes can independently allocate and coordinate resources rather than exchange information with each other in traditional methods, and thus guaranteeing strong adaptability to the fast changes of WBANs. Second, the contention-free resource allocation pattern is implemented for both the data transmission period as well as the data retransmission period, and thus guaranteeing no intra-WBAN interference and extremely low probability of inter-WBAN interference. Our simulation results show that interferences can be well addressed based on the metrics of the packet loss rate, throughput, power dissipation, and data delivery delay.

This article proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM for the mobile Cloud-Edge computing. The basic idea is that the Cloud Service Center (CSC) recruits edge server owners to replace it to accommodate offloaded computation from nearby resource-constraint Mobile Devices (MDs). In RACORAM, the reverse auction is used to stimulate edge server owners to participate in the offloading process, and the reverse auction-based computation offloading and resource allocation problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem, aiming to minimize the cost of the CSC. The original problem is decomposed into an equivalent master problem and subproblem, and low-complexity algorithms are proposed to solve the related optimization problems. Specifically, a Constrained Gradient Descent Allocation Method (CGDAM) is first proposed to determine the computation resource allocation strategy, and then a Greedy Randomized Adaptive Search Procedure based Winning Bid Scheduling Method (GWBSM) is proposed to determine the computation offloading strategy. Meanwhile, the CSC's payment determination for the winning edge server owners is also presented. Simulations are conducted to evaluate the performance of RACORAM, and the results show that RACORAM is very close to the optimal method with significantly reduced computational complexity, and greatly outperforms the other baseline methods in terms of the CSC's cost under different scenarios.

With the rapid development of Internet-of-Things (IoT) and the explosive advance of deep learning, there is an urgent need to enable deep learning inference on IoT devices in Mobile Edge Computing (MEC). To address the computation limitation of IoT devices in processing complex Deep Neural Networks (DNNs), computation offloading is proposed as a promising approach. Recently, partial computation offloading is developed to dynamically adjust task assignment strategy in different channel conditions for better performance. In this paper, we take advantage of intrinsic DNN computation characteristics and propose a novel Fused-Layer-based (FL-based) DNN model parallelism method to accelerate inference. The key idea is that a DNN layer can be converted to several smaller layers in order to increase partial computation offloading flexibility, and thus further create the better computation offloading solution. However, there is a trade-off between computation offloading flexibility as well as model parallelism overhead. Then, we investigate the optimal DNN model parallelism and the corresponding scheduling and offloading strategies in partial computation offloading. In particular, we propose a Particle Swarm Optimization with Minimizing Waiting (PSOMW) method, which explores and updates the FL strategy, path scheduling strategy, and path offloading strategy to reduce time complexity and avoid invalid solutions. Finally, we validate the effectiveness of the proposed method in commonly used DNNs. The results show that the proposed method can reduce the DNN inference time by an average of 12.75 times compared to the legacy No FL (NFL) algorithm, and is very close to the optimal solution achieved by the Brute Force (BF) algorithm with the difference of less than 0.04%.

In the past few years, satellite communications have greatly affected our daily lives, and the integrated terrestrial-satellite network can combine the advantages of satellite and base stations (BSs) to provide wider coverage and lower cost. Because the resources of terrestrial-satellite network are limited, how to allocate resources of terrestrial-satellite network through effective methods has become a major challenge. This paper proposes a framework for resource allocation of terrestrial-satellite network based on non-orthogonal multiple access (NOMA). Then, a deployment method of local cache pools is given to achieve lower time delay and maximize energy efficiency in terrestrial-satellite network. In the proposed framework, we adopt a multi-agent deep deterministic policy gradient (MADDPG) method to obtain the maximum energy efficiency by user association, power control, and cache design. The MADDPG algorithm is divided into two stages, users and BSs are set as agents to complete the optimization problem in the framework. Finally, the simulation results show that the proposed method has better optimized performance compared with the traditional single-agent deep reinforcement learning algorithm and can efficiently solve the problems of resource allocation and cache design in the integrated terrestrial-satellite network.

Signal detectors are quite important to attain the diversity of doubly-dispersive wireless channels. Detectors based on message-passing (MP) of factor graphs have been regarded as the way to achieve the near-optimal performance for OTFS. In this paper, by deriving the pattern of the multipath vectorized channel matrix of the orthogonal time frequency space (OTFS) system, it is shown that short girth (i.e. girth-4) may exist in the Tanner graphs, which will degrade the performance of MP detectors, especially with high modulation orders. By introducing interleavers at the transmitter and receiver, the vectorized channel matrix turns out to be a sparse upper block Heisenberg matrix, whose structure is beneficial for the computation of matrix QR decomposition (QRD). Successive interference canceling (SIC) detectors based on QRD and sorted QRD are constructed to eliminate the cross-symbol interference and improve the reliability of the symbol-level channel. Simulation results show that for 4QAM, the QRD-based SIC detectors can achieve about 4dB gain at 10−2 over the non-SIC detectors, while the sorted QRD-based SIC detectors can bring an additional 2dB at 10−3, which is only 1dB gap from the MP. For 16QAM, the sorted SIC detectors show superior BER performance than the MP method, and for 64QAM, the MP detector reaches the error floor while SIC detectors show their excellent performance in all configurations.

Reconfigurable Intelligent Surface (RIS) has grown rapidly due to its performance improvement for wireless networks, and the integration of unmanned aerial vehicle (UAV) and RIS has obtained widespread attention. In this paper, the downlink of non-orthogonal multiple access (NOMA) UAV networks equipped with RIS is considered. The objective is to optimize the UAV trajectory with RIS phase shift to maximize the system capacity under the UAV energy consumption constraint. By deep reinforcement learning, a capacity maximization scheme under energy consumption constraints based on double deep Q-Network (DDQN) is proposed. The joint optimization of UAV trajectory with RIS phase shift design is achieved by DDQN algorithm. From the numerical results, the proposed optimization scheme can increase the system capacity of the RIS-UAV-assisted NOMA networks.

Edge caching is a promising approach to reduce duplicate content transmission in Internet-of-Vehicles (IoVs). Several Reinforcement Learning (RL) based edge caching methods have been proposed to improve the resource utilization and reduce the backhaul traffic load. However, they only obtain the local sub-optimal solution, as they neglect the influence from environments by other agents. This paper investigates the edge caching strategies with consideration of the content delivery and cache replacement by exploiting the distributed Multi-Agent Reinforcement Learning (MARL). A hierarchical edge caching architecture for IoVs is proposed and the corresponding problem is formulated with the goal to minimize the long-term content access cost in the system. Then, we extend the Markov Decision Process (MDP) in the single agent RL to the context of a multi-agent system, and tackle the corresponding combinatorial multi-armed bandit problem based on the framework of a stochastic game. Specifically, we firstly propose a Distributed MARL-based Edge caching method (DMRE), where each agent can adaptively learn its best behaviour in conjunction with other agents for intelligent caching. Meanwhile, we attempt to reduce the computation complexity of DMRE by parameter approximation, which legitimately simplifies the training targets. However, DMRE is enabled to represent and update the parameter by creating a lookup table, essentially a tabular-based method, which generally performs inefficiently in large-scale scenarios. To circumvent the issue and make more expressive parametric models, we incorporate the technical advantage of the Deep-$Q$Network into DMRE, and further develop a computationally efficient method (DeepDMRE) with neural network-based Nash equilibria approximation. Extensive simulations are conducted to verify the effectiveness of the proposed methods. Especially, DeepDMRE outperforms DMRE,$Q$-learning, LFU, and LRU, and the edge hit rate is improved by roughly 5%, 19%, 40%, and 35%, respectively, when the cache capacity reaches 1, 000 MB.

With the development of Internet of Things (IoT) and the advance of deep learning, there is an urgent need to enable deep learning inference on IoT devices. To address the computation limitation of IoT devices in processing complex Deep Neural Networks (DNNs), partial computation offloading is developed to dynamically adjust computation offloading assignment strategy in different channel conditions for better performance. In this paper, we take advantage of intrinsic DNN computation characteristics, and propose a novel Fused-Layer-based (FL-based) DNN model parallelism method to accelerate inference. The key idea is that a DNN layer can be converted to several smaller layers to increase partial computation offloading flexibility, and thus further create better computation offloading solution. However, there is a trade-off between parallelism computation offloading flexibility and model parallelism overhead. Then, we discuss the optimal DNN model parallelism and the corresponding scheduling and offloading strategies in partial computation offloading. In particular, we present a Minimizing Waiting (MW) method, which explores both the FL strategy, the path scheduling strategy, and the path offloading strategy to reduce time complexity. Finally, we validate the effectiveness of the proposed method in commonly used DNNs. The results show that the proposed method can reduce the DNN inference time by an average of 18.39 times compared with No FL (NFL) algorithm, and is very close to the optimal solution Brute Force (BF) with greatly reduced time complexity.

Covert communication is able to provide high-level security by protecting communication behavior. In this paper, we develop a covert cooperative cognitive radio (CCCR) network, where primary transmitter (PT) transmits information with the aid of multiple secondary transmitters (STs). In return, STs are able to transmit private information by exploiting PT's spectrum in presence of a powerful eavesdropper (Eve). Meanwhile, we propose a cognitive user scheduling scheme based on link information and maximum-minimum principle. Moreover, we derive Eve's expected detection error probability and evaluate the covert performance of the novel scheme. Numerical results show that joint impact of self-interference and jamming power of Eve can enable STs to achieve covert transmission. Furthermore, it can be found that the influence of the interference power on Eve's detection error probability and covert performance is significant when the self-interference cancellation coefficient is sufficient large.

This paper considers the joint optimization of computation offloading, service caching, and resource allocation in the Digital Twin Edge Network (DTEN), and formulates the problem as Mixed-Integer Non-Linear Programming (MINLP), whose goal is to minimize the long-term energy consumption of the system. To solve the optimization problem, a Deep Deterministic Policy Gradient (DDPG) based algorithm is proposed for determining the strategies of computation offloading, service caching, and resource allocation. Simulation results demonstrate that the proposed DDPG based algorithm can reduce the long-term energy consumption of the system greatly, and outperform other benchmark algorithms under different scenarios.

In recent years, great attention has been paid on Electric Vehicles (EVs) in terms of environmental pollution. Here, EVs can greatly reduce the environmental pollution, compared with traditional Internal Combustion Vehicles (ICVs). However, since EVs cannot be replenished fast like ICVs, the rigid deployment of charging infrastructure and its limited charging capability, leads to service congestion particularly due to a large number of EVs being parked with charging demand. Compared to CSs with rigid extension in location and charging facilitates, the Vehicle-to-Vehicle (V2V) charging service provides a spatial and temporal advance in flexibility, with potential to supplement with G2V charging mode, which can supplement or even replace the G2V Charging Mode. In this paper, we propose a hybrid V2V charging scheme, consisting of direct and asynchronous V2V charging modes, to achieve a great charging flexibility and alleviate the burden for grid load. Here, we estimate the Minimum Waiting Time (MWT) under each mode, as guidance to switch between modes and optimize charging service under each mode. Results show that our proposed hybrid V2V charging scheme outperforms literature works, in terms of average waiting time and number of full charged EVs.

In wireless sensor networks (WSNs), the imbalanced energy consumption in data transmission may cause energy holes around the sink, which dramatically shortens the network lifespan. Current solutions have two limitations: one is the inevitable traffic gathered around the sink, and the other is the overly ideal network model, e.g., the square region or circular area. In this article, we focus on strip-shaped networks and propose a novel data transmission scheme, which employs a few super nodes near the sink to take traffic load. Due to the high energy capacities and communication abilities, super nodes transfer a part of the data of the network and send it to the sink directly. The entire network is partitioned into multiple clusters, and super nodes are placed in a specific cluster. We discover that the greatest effect on the network lifetime is the energy consumption of two clusters: 1) the cluster nearest to the sink and 2) the upstream cluster nearest to the super nodes. To improve the system longevity, we make the two clusters have equal energy dissipation, and thus obtain the optimal location of super nodes. Some simulations are carried out to verify the reasonability of the results and exhibit the advantage of our scheme in terms of network lifetime.

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

Offloading computation tasks through cloud–edge collaboration has been a promising way to improve the Quality of Service (QoS) of applications. Usually, cloud server (CS) and edge server (ES) are selfish and rational and, therefore, it is imperative to develop incentive mechanisms, which can encourage idle ESs or the CS to participate in the task offloading process. In this article, we propose a computation offloading method based on the game theory, which is suitable for cloud–edge computing networks. It is considered that the CS has a lot of computation tasks to conduct, and ESs usually have idle computational resources. The CS can offload computation tasks to ESs with idle computational resources to reduce its own cost and pressure, and ESs can profit by selling their computational resources. The interaction between the CS and ESs is modeled as a Stackelberg game, and the proposed game is analyzed by using the backward induction method. It is proved that the game can achieve a unique Nash equilibrium. Then, a gradient-based iterative search algorithm (GISA) is proposed to obtain the optimal solution in order to maximize the utility of the CS and ESs. Finally, numerical simulation results show that our proposed method greatly outperforms other benchmark schemes under different scenarios, and can encourage ESs to trade their computational resources with the CS effectively.

In this paper, an uplink non-orthogonal multiple access (NOMA) terrestrial-satellite network is investigated, where the terrestrial base stations (BSs) communicate with satellite by backhaul link, and user equipments (UEs) share spectrum resource of access link. Firstly, a utility function which consists of the achieved terrestrial user rate and cross-tier interference caused by terrestrial BSs to satellite is design. Thus, the optimization problem can be modeled by maximizing the system utility function while satisfying the varying backhaul rate and UEs’ quality of service (QoS) constraints. The optimization problem is highly non-convex and can not be solved directly. Thus, we decouple the original problem into user association sub-problem, bandwidth assignment sub-problem, and power allocation sub-problem. In user association sub-problem, an enhanced-caching, preference relation, and swapping based algorithm is proposed, where the satellite UEs are selected by the channel coefficient ratio. The terrestrial UEs association considers the both caching state and backhaul link. Then we derive the closed-form expression of the bandwidth assignment. In power allocation sub-problem, we convert the non-convex term of the target function into the convex one by the Taylor expansion, and solve the transformed convex problem by an iterative power allocation algorithm. Finally, a three-stages iterative resource allocation algorithm by joint considering the three sub-problems is proposed. Simulation results are discussed to show the effectiveness of the proposed algorithms.

Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method.

In inhospitable environments, connectivity recovery plays a significant role in enabling normal data transmission in wireless sensor networks (WSNs). However, existing approaches lack thorough load-balancing and delay-control functions. In this article, we present a load-balanced connectivity recovery (LBCR) strategy to address these problems. This strategy consists of two relay-segment selection approaches. The first one is the delay-controlled connectivity mechanism, in which mobile relays and static relays are employed to connect isolated segments, and the path length of the two kinds of relays is adjusted based on the requirement of data delivery delay. The second one is the load-balanced connectivity mechanism, in which both the intra-segment and the inter-segment traffic distribution are evaluated to balance the traffic load. For intra-segment load equilibrium, we use the A-Star algorithm to calculate the number of disjoint paths of a relay segment, which helps to evaluate the load sharing ability from a micro perspective. For inter-segment load equilibrium, we compare the traffic load of different paths, which helps to evaluate the load sharing ability from a macroscopic angle. Extensive simulations demonstrate the effectiveness and advantage of our strategy in terms of connectivity cost, data collection delay, and system lifespan.

Medium access control (MAC) plays a crucial role in ensuring proper operation in vehicular ad hoc networks (VANETs). However, existing solutions cannot meet the strict requirements of high throughput and low latency in high-density scenarios. In this paper, we propose a novel MAC protocol to meet such demands for VANETs. The roadside unit (RSU) pattern and the dual-transceiver manner are adopted to assign channel resources for all packets. The striking features of our approach are twofold: the time slot allocation based on global vehicle information and the consideration of different delay requirements of different applications. First, we design a time slot exchange mechanism, which avoids any transmission conflict between two adjacent RSUs, to reduce channel resource waste. Then, we design a packet weight allocation mechanism, by which the packets of any network edge area are assigned higher priorities to further reduce channel resource waste. Moreover, we devise a packet quality evaluation mechanism, by which our multi-objective problem is transformed into a single-objective problem. In addition, we devise a greedy branch-and-bound algorithm to address the multiple-knapsack problem, which is transformed into a single-knapsack problem. Extensive simulation results show the advantages of our approach in terms of throughput and latency.

In this paper, an uplink non-orthogonal multiple access (NOMA) satellite-terrestrial network is investigated, where the terrestrial base stations (BSs) can simultaneously communicate with the satellite by backhaul, and user equipments (UEs) share fronthaul spectrum resource to communicate. The communication of satellite UEs is influenced by crosstier interference caused by terrestrial cellular UEs. Thus, a utility function which consists of system achieved rate and crosstier interference is build. And we aim to maximize the utility function while satisfying the constraints of the varying backhaul rate and quality of service (QoS) of UEs. The optimization problem is decomposed into AP-UE association, bandwidth assignment, and power allocation sub-problems, and solved by proposed matching algorithm and successive convex approximation (SCA) method, respectively. The simulation results show the effectiveness of the proposed algorithm.

In this paper, we propose a computation offloading method based on the game theory, which is suitable for cloud-edge computing networks. We consider that the Cloud Server (CS) can offload the computation tasks to wireless Access Points (APs) associated with Edge Servers (ESs) to accelerate processing. ESs can gain benefits through computation offloading, while the CS can reduce its cost and computing pressure. We model the interaction between the CS and ESs as a Stackelberg game, and use the backward induction method to analyze the proposed game. We prove that the game can achieve a unique Nash equilibrium. Then, we propose a Gradient-based Iterative Search Algorithm (GISA) to maximize the utility of the CS and ESs. Finally, numerical simulation results show that our proposed method greatly outperforms other benchmark schemes under different scenarios, and can encourage ESs to trade their computation resources with the CS effectively.

This paper jointly considers the optimization of multi-user computing offloading and service caching in Mobile Edge Computing (MEC), and formulates the problem as a Mixed-Integer Non-Linear Program (MINLP), aiming to minimize the task cost of the system. The original problem is decomposed into an equivalent master problem and sub-problem, and a Collaborative Computing Offloading and Resource Allocation Method (CCORAM) is proposed to solve the optimization problem, which includes two low-complexity algorithms. Simulation results show that CCORAM with low time complexity is very close to the optimal method, and performs much better than other benchmark methods.

Offloading cellular traffic via Device-to-Device communication (or D2D offloading) has been proved to be an effective way to ease the traffic burden of cellular networks. However, mobile nodes may not be willing to take part in D2D offloading without proper financial incentives since the data offloading process will incur a lot of resource consumption. Therefore, it is imminent to exploit effective incentive mechanisms to motivate nodes to participate in D2D offloading. Furthermore, the design of the content caching strategy is also crucial to the performance of D2D offloading. In this paper, considering these issues, a novel Incentive-driven and Deep Q Network (DQN) based Method, named IDQNM is proposed, in which the reverse auction is employed as the incentive mechanism. Then, the incentive-driven D2D offloading and content caching process is modeled as Integer Non-Linear Programming (INLP), aiming to maximize the saving cost of the Content Service Provider (CSP). To solve the optimization problem, the content caching method based on a Deep Reinforcement Learning (DRL) algorithm, named DQN is proposed to get the approximate optimal solution, and a standard Vickrey-Clarke-Groves (VCG)-based payment rule is proposed to compensate for mobile nodes' cost. Extensive real trace-driven simulation results demonstrate that the proposed IDQNM greatly outperforms other baseline methods in terms of the CSP's saving cost and the offloading rate in different scenarios.

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.

Mobile charging can provide stable and reliable energy replenishment for wireless rechargeable sensor network (WRSN). However, relatively low charging utility exists in existing solutions. In this paper, we present a utility-based collaborative charging (UBCC) strategy to maximize the charging utility of mobile chargers (MCs) in large-scale WRSNs. Charging MCs and server MCs are employed to jointly achieve our goal by three aspects. First, a path merging scheme is designed to save the traveling paths of MCs. Unlike existing studies with entirely diverse movement trajectories of MCs, the same traveling path is assigned to both the departure charging MCs and the return MCs, which serve different charging areas. Second, an idle-difference alleviating scheme is devised to improve the utilization rate of MCs. Different from current solutions with a large difference of working hours of MCs, each charging MC is assigned the equal charging tasks, resulting in synchronous charging and simultaneous energy replenishment of MCs. Third, an energy-waste averting scheme is designed to maximize the energy utilization of MCs. The energy of each MC is just exhausted until the MC completes its charging tasks and traveling roles. Extensive simulation results demonstrate the advantages of UBCC in the charging cost and charging utility.

Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBANs). However, current solutions still cannot satisfy the stringent requirements of low power and low delay for emergency data reporting. In this paper, we propose an energy-efficient and emergency-aware MAC (EEEA-MAC) protocol for meeting such a rigorous demand. First, we design a node-different channel access scheme, in which source nodes use the CSMA/CA pattern while relay nodes adopt the hybrid CSMA/CA-TDMA pattern. Second, we devise an emergency-first time-slot allocation scheme, in which channel sensing is performed and the emergency data is handled by relay nodes according to different cases. EEEA-MAC has two striking features. One is that, source nodes adopt the CSMA/CA scheme instead of the conventional CSMA/CA-TDMA scheme, ensuring the requirement because there are almost no collisions and no confirmation messages in this scheme. The other is that, relay nodes use a sensing-based emergency data handling mechanism instead of the traditional empty-slot occupying mechanism, further guaranteeing the requirement owing to the immediate handling of emergency data and the short time of channel sensing. Extensive simulations demonstrate the advantages of EEEA-MAC in terms of energy dissipation and latency.

In this preliminary study, we consider the server allocation problem for edge computing system deployment. Our goal is to minimize the average turnaround time of application requests/tasks, generated by all mobile devices/users in a geographical region. We consider two approaches for edge cloud deployment: the flat deployment, where all edge clouds co-locate with the base stations, and the hierarchical deployment, where edge clouds can also co-locate with other system components besides the base stations. In the flat deployment, we demonstrate that the allocation of edge cloud servers should be balanced across all the base stations, if the application request arrival rates at the base stations are equal to each other. We also show that the hierarchical deployment approach has great potentials in minimizing the system's average turnaround time. We conduct various simulation studies using the CloudSim Plus platform to verify our theoretical results. The collective findings trough theoretical analysis and simulation results will provide useful guidance in practical edge computing system deployment.

Mobile Edge Computing (MEC) has emerged as a promising computing paradigm in 5G networks, which can empower User Equipments (UEs) with computation and energy resources offered by migrating workloads from the UEs to the MEC servers. Although the issues of computation offloading and resource allocation in MEC have been studied with different optimization objectives, they mainly investigate quasi-static system environments, without considering the different resource requirements and time-varying system conditions in a dynamic system. In this paper, we exploit a multi-user MEC system, and investigate the task execution scheme for dynamic joint optimization of offloading decision and resource assignment. Our objective is to minimize the energy consumption of all UEs, with considering the delay constraint as well as the dynamic resource requirements of heterogeneous computation tasks. Accordingly, we formulate the problem as a mixed integer non-linear programming problem (MINLP), and propose a value iteration based Reinforcement Learning (RL) approach, named Q-Learning, to obtain the optimal policy of computation offloading and resource allocation. Simulation results demonstrate that the proposed approach can significantly decrease UEs' energy consumption in different scenarios, compared with other baseline methods.

Offloading cellular traffic through Opportunistic Mobile Networks (OMNs) is an effective way to relieve the burden of cellular networks. Providing data offloading services requires a lot of resources, and nodes in OMNs are selfish and rational, they are not willing to provide data offloading services for others without any compensation. Therefore, it is urgent to design an incentive mechanism to stimulate mobile nodes to participate in data offloading process. In this paper, we propose a Reverse Auction-based Incentive Mechanism to stimulate mobile nodes in OMNs to provide data offloading services, and take the cache management into consideration. We model the incentive-driven data offloading process as a non-linear integer programming problem, then a Greedy Helper Selection Method (GHSM) and a Caching Replacement Scheme (CRS) are proposed to solve the problem. In addition, we also propose an innovative payment rule based on the Vickrey-Clarke-groves (VCG) model to ensure the individual rationality and authenticity of the proposed algorithm. Trace-driven simulation results show that the proposed algorithm can reduce the cost of Content Service Provider (CSP) significantly in different scenarios.

Edge caching has been emerged as a promising solution to alleviate the redundant traffic and the content access latency in the future Internet of Vehicles (IoVs). Several Reinforcement Learning (RL) based edge caching methods have been proposed to improve the cache utilization and reduce the backhaul traffic load. However, they can only obtain the local sub-optimal solution, as they neglect the influence of environment by other agents. In this paper, we investigate the edge caching strategy with consideration of the content delivery and cache replacement by exploiting the distributed Multi-Agent Reinforcement Learning (MARL). We first propose a hierarchical edge caching architecture for IoVs and formulate the corresponding problem with the objective to minimize the long-term cost of content delivery in the system. Then, we extend the Markov Decision Process (MDP) in the single agent RL to the multi-agent system, and propose a distributed MARL based edge caching algorithm to tackle the optimization problem. Finally, extensive simulations are conducted to evaluate the performance of the proposed distributed MARL based edge caching method. The simulation results show that the proposed MARL based edge caching method significantly outperforms other benchmark methods in terms of the total content access cost, edge hit rate and average delay. Especially, our proposed method greatly reduces an average of 32% total content access cost compared with the conventional RL based edge caching methods.

Offloading cellular traffic through WiFi Access Points (APs) has been a promising way to relieve the overload of cellular networks. However, data offloading process consumes a lot of resources (e.g., energy, bandwidth, etc.). Given that the owners of APs are rational and selfish, they will not participate in the data offloading process without receiving the proper reward. Hence, there is an urgent need to develop an effective incentive mechanism to stimulate APs to take part in the data offloading process. This paper proposes a novel Delay-constraint and Reverse Auction-based Incentive Mechanism, named DRAIM. In DRAIM, we model the reverse auction-based incentive problem as a nonlinear integer problem from the business perspective, aiming to maximize the revenue of the Mobile Network Operator (MNO), and jointly consider the delay constraint of different applications in the optimization problem. Then, two low-complexity methods: Greedy Winner Selection Method (GWSM), and Dynamic Programming Winner Selection Method (DPWSM) are proposed to solve the optimization problem. Furthermore, an innovative standard Vickrey-Clarke-Groves scheme-based payment rule is proposed to guarantee the individual rationality and truthfulness properties of DPWSM. At last, extensive simulation results show that the proposed DPWSM is superior to the proposed GWSM and the Random Winner Selection Method in terms of the MNO’s utility and traffic load under different scenarios.

Device-to-Device (D2D) communication has become a promising candidate in future cellular networks to improve spectrum efficiency and energy efficiency, while reducing the latency. As the capacity of D2D user equipments (DUEs) increases, it makes DUEs caching possible, and it can offload traffic from macro base stations, perform computation-intensive and latency-critical tasks. In this paper, in-band communication is considered, and the Poisson cluster process is utilized to model and analyze the clustered D2D networks under cluster-centric caching strategy. Firstly, we use the Thomas cluster process to model cellular user equipments (CUEs) and DUEs, and give a deployment scheme of clustered D2D caching networks. Secondly, the aggregated interference of the typical D2D receiver is analyzed in the clustered D2D networks. Then the Laplace transform of the aggregated interference is analyzed, and the expressions of coverage probability, average achievable rate and cache hit probability of the typical D2D receiver are deduced. The simulation results show that we can adjust the path loss exponent, densities of DUEs and CUEs, transmitting power of CUEs, mean of simultaneously active transmitters in each cluster and Zipf exponent to improve the performance of clustered D2D caching networks.

Offloading cellular traffic through Opportunistic Mobile Networks, also known as opportunistic offloading has been proposed as a promising way to relieve the overload of cellular networks. The efficiency of such opportunistic offloading is highly determined by the selection of initial seeds. With considering both the freshness of the content and the cost of transmission from the cellular network to the initial seeds, this paper defines a novel freshness-aware seed selection optimization problem to find both the optimal number of initial seeds and the maximum overall content utility. To solve the optimization problem, the optimal strategy is first analyzed, and then two seed selection methods: the greedy seed selection method and the decay-based seed selection method are proposed to find the optimal number of initial seeds to maximize the overall content utility. The greedy seed selection method iteratively selects nodes with the maximum Freshness Centrality value as initial seeds. To further improve the performance, the decay-based seed selection method selects initial seeds who are far apart and important in theirs local structure. Extensive real trace-driven simulations are conducted to evaluate the performance of our proposed seed selection methods. The results show that as expected the proposed decay-based seed selection method is superior to the proposed greedy seed selection method and the random seed selection method, not only in the Infocom 06 trace, but also in the MIT Reality trace.

HTTP video streaming over the wireless mobile network is challenging because the wireless mobile networking environment usually suffers fluctuation in available bandwidth and mobile users usually keep moving and playing streaming video simultaneously. To deal with the issue, a Quality Of Experience (QoE)-oriented adaptive HTTP video streaming control method based on MPEG-DASH over the wireless mobile networking environment was proposed in this paper. In order to get better QoE, the proposed method considers both temporal and geo concerns for estimating the available bandwidth in the future and the proposed adaptive streaming control scheme considers buffer level, video quality of the most recently downloaded segment and the estimated bandwidth to decide the video quality for the next downloaded video segment. The proposed method has been implemented in the Android system for the client side and the Linux system for the server side. The experiments shown that the proposed method can improve initial delay time, suspended time, suspended times, bitrate difference per segment, and average bitrate difference considering suspending.

Offloading traffic through Opportunistic Mobile Networks, also known as opportunistic offloading is a promising way to relieve the overload of cellular networks. The efficiency of such opportunistic offloading is highly determined by the selection of initial seeds. With considering both the freshness of the content and the cost of transmission from the cellular network to the initial seeds, this paper defines a novel freshness-aware seed selection optimization problem to find K initial seeds to maximize the overall content utility. To solve the optimization problem, we propose two seed selection methods: the greedy seed selection method and the decay-based seed selection method. The greedy seed selection method iteratively selects nodes with the maximum Freshness Centrality value as initial seeds. To further improve the performance, the decay-based seed selection method selects initial seeds who are far apart and important in theirs local structure. Extensive real trace-driven simulations are conducted to evaluate the performance of our proposed seed selection methods. The results show that as expected the proposed decay-based seed selection method is superior to the proposed greedy seed selection method and the random seed selection method.

One important issue in the study of Mobile Social Networks (MSNs) is to measure the centrality (importance) of nodes in networks. However, when measuring the centrality metrics in a certain time interval, the current studies in MSNs focus on analyzing static aggregation networks that do not change over time. Actually, network topology in MSNs is changing very rapidly, which is driven by natural social behavior of people. Therefore, it will not be accurate if the static aggregation network graph is used to measure centrality metrics in a period of time. In this paper, to solve this problem, we first introduce a time-ordered aggregation model, which reduces a dynamic network to a series of time-ordered networks. Then, we propose three particular time-ordered aggregation methods to measure the centrality of nodes in a certain period under two widely used centrality metrics, namely Betweenness centrality and Degree centrality. Finally, extensive trace-driven simulations are conducted to evaluate the performance of different aggregation methods. The results show that the time-ordered aggregation methods can measure the Betweenness and Degree centrality in a time interval more accurately than the Static Aggregation Method, and the Exponential Time-ordered Aggregation Method performs much better than other aggregation methods. Therefore, we recommend to use the Exponential Time-ordered Aggregation Method to measure centrality metrics in a certain time interval.

This paper explores the correlation among three kinds of social relationships: face-to-face social relationship, online social relationship, and self-report social relationship. An experiment was carried out to collect users' three-dimensional social data: real-world mobile trace data, virtual-world online social data, and self-report social data. By analyzing network structure, we find that friendship in online social networks can better describe self-report friendship compared to friendship created by frequent physical encounters. Several supervised classifiers with the combination of features extracted from mobile trace data and online social data are used to predict the self-report social relationship under different social strengths. Results show that the proposed model can correctly predict more than 80% friends under strongest social tie strength. What is more, we define social popularity according to social relationships self-reported by users. By comparing social popularity with online and offline social behaviors, we find diversity in weekend is a good measure to describe social popularity.

Recently, the content-based publish/subscribe (pub/sub) paradigm has been gaining popularity in opportunistic mobile networks (OppNets) for its flexibility and adaptability. Since nodes in OppNets are controlled by humans, they often behave selfishly. Therefore, stimulating nodes in selfish OppNets to collect, store, and share contents efficiently is one of the key challenges. Meanwhile, guaranteeing the freshness of contents is also a big problem for content dissemination in OppNets. In this paper, in order to solve these problems, we propose an incentive-driven and freshness-aware pub/sub Content Dissemination scheme, called ConDis, for selfish OppNets. In ConDis, the Tit-For-Tat (TFT) scheme is employed to deal with selfish behaviors of nodes in OppNets. Moreover, a novel content exchange protocol is proposed when nodes are in contact. Specifically, during each contact, the exchange order is determined by the content utility, which represents the usefulness of a content for a certain node, and the objective of nodes is to maximize the utility of the content inventory stored in their buffer. Extensive realistic trace-driven simulation results show that ConDis is superior to other existing schemes in terms of total freshness value, total delivered contents, and total transmission cost.

In this paper, we develop a smart phone-based driving behavior evaluation system, named Join Driving, which helps drivers notice how aggressive their driving behaviors are and be aware of the riding comfort level of passengers. The proposed evaluation system is made of two parts: driving events detection and evaluation part and riding comfort level evaluation part. In driving events detection and evaluation part, the proposed system, Join Driving, first presents a model to detect drivers' driving events, based on the data collected from the acceleration, orientation and GPS sensors in smart phones. Then, based on the detected drivers' driving events, Join Driving implements a novel scoring mechanism to quantitatively evaluate how aggressive these driving events are. In riding comfort level evaluation part, the proposed system gives the specific scores to rate passengers' riding comfort level based on ISO 2631. Finally, several practical experiments are conducted to evaluate the effectiveness of the proposed scoring system.

In Opportunistic Mobile Networks (OppNets), a large amount of energy is consumed by idle listening, instead of infrequent data exchange. This makes energy saving a challenging and fundamental problem in OppNets, since nodes are typically battery-powered. Asynchronous duty-cycle operation is a promising approach for energy saving in OppNets, however, if its working schedule is not effectively designed, it may also cause significant network performance degradation. Therefore, it is pressing to design an energy-efficient working schedule for duty-cycle OppNets. In this paper, we first analyze the contact process in duty-cycle OppNets, then propose an adaptive working schedule for duty-cycle OppNets. The proposed schedule uses the past recorded contact histories to predict the future contact information, so as to adaptively configure the working schedule of each node in the network. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed schedule. The results show that our proposed adaptive working schedule is superior to the random working schedule in terms of delivery ratio and delivery delay.

In Opportunistic Mobile Networks (OppNets), data is opportunistically exchanged between nodes who encounter each other. In order to enable such data exchanges, nodes in the network have to probe their environment continually, so as to discover neighbor nodes. This can be an extremely energy-consuming process. If nodes probe very frequently, they will consume a lot of energy, and might be energy inefficient. On the other hand, infrequent contact probing might cause nodes to miss many of their contacts, and thus opportunities to exchange data are lost. Therefore, there exists a trade-off between energy efficiency and the contact opportunities in OppNets. In this paper, in order to investigate this trade-off, we first propose a model to quantify the detecting probability in OppNets, using the Random WayPoint (RWP) model. Then, extensive simulations are conducted to validate the correctness of our proposed model. Finally, based on the proposed model, we analyze the trade-off between energy efficiency and the total number of effective contacts under different situations. Our results show that the good trade-off points are obviously different when the speed of nodes is different. Moreover, the detecting probability increases as the speed of nodes decreases, while the total number of effective contacts increases as the speed of nodes increases.

Recently, the content-based publish/subscribe (pub/sub) paradigm is gaining popularity in opportunistic mobile networks (OppNets) for its flexibility and adaptability. Since nodes in OppNets is controlled by humans, they often behave selfishly with an aim to maximize their own revenues without considering the performance of others. Therefore, stimulating nodes in OppNets to collect, store, and share content efficiently is one of the key challenges under this scheme. Meanwhile, guaranteeing the freshness of content is also a big problem for content dissemination in OppNets. In this paper, in order to solve these problems, we propose an incentive-driven and freshness-aware pub/sub content dissemination scheme, called ConDis (Content Dissemination), for selfish OppNets. In ConDis, the Tit-For-Tat (TFT) scheme is employed to deal with selfish behaviors of nodes in OppNets. ConDis also implements a novel content exchange protocol when nodes are in contact. Specifically, during each contact, the exchange order is determined by the content utility, which is calculated by the direct subscribed value and the indirect subscribed value, and the objective of nodes is to maximize the utility of the content inventory stored in their buffer. Extensive realistic trace-driven simulation results show that ConDis is superior to other existing schemes in terms of total freshness value, total delivered contents, and total transmission cost.

Recently, content-based publish/subscribe (pub/sub) services have become a significant research field in opportunistic mobile networks (OppNets). Pub/sub is an asynchronous messaging paradigm, in which content transmissions are guided by the interest. Since selfish behavior is common in reality, nodes often behave selfishly with an aim to maximize their own utilities without considering performance of other nodes. Therefore, how to encourage nodes to collect, store and share network content efficiently is one of the key challenges under this paradigm. In this paper, we propose an incentive-based pub/sub scheme, called ConSub, for OppNets. In ConSub, Tit-For-Tat (TFT) mechanism is employed to deal with selfish behavior. ConSub also implements a content exchange protocol between two interacting node, thus encouraging them to play as businessmen and carry contents to satisfy each other's interest. Specifically, the exchange order is determined by the content utility, which is calculated by contact probability and cooperation level between the current node and its neighbors subscribing to the interest. Extensive realistic trace-driven simulation results show that ConSub is superior to existing schemes in terms of delivered packets and transmission hops with reasonable transmission cost.

In Opportunistic Mobile Networks (OppNets), a large amount of energy is consumed by idle listening, instead of infrequent data exchanging. This makes energy saving become a serious problem in OppNets, as nodes are typically with limited energy supplies. Duty-cycle operation is a promising approach for energy saving in OppNets, however, it may cause the degradation of network connectivity. Therefore, there exists a tradeoff relationship between energy saving and network connectivity in duty-cycle OppNets. In this paper, we propose a model to quantify the contact probability (a metric of connectivity) in duty-cycle OppNets based on the realistic mobility trace. Then the tradeoff between energy saving and the contact probability under different situations is derived and analyzed. Our results show that the duty-cycled nodes can guarantee an energy saving of around 50% without impacting the contact probability if the period meets a certain condition, and higher energy saving can be achieved at the cost of reducing the contact probability. Finally, realistic trace-driven simulations are performed to validate the correctness of our results.