Low-Altitude Economy Networks (LAENets) are emerging as a promising paradigm to support various low-altitude services through integrated air-ground infrastructure. To satisfy low-latency and high-computation demands, the integration of Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC) systems plays a vital role, which offloads computing tasks from terminal devices to nearby UAVs, enabling flexible and resilient service provisions for ground users. To promote the development of LAENets, it is significant to achieve low-carbon multi-UAV-assisted MEC networks. However, several challenges hinder this implementation, including the complexity of multi-dimensional UAV modeling and the difficulty of multi-objective coupled optimization. To this end, this paper proposes a novel Retrieval Augmented Generation (RAG)-based Large Language Model (LLM) agent framework for model formulation. Specifically, we develop HybridRAG by combining KeywordRAG, VectorRAG, and GraphRAG, empowering LLM agents to efficiently retrieve structural information from expert databases and generate more accurate optimization problems compared with traditional RAG-based LLM agents. After customizing carbon emission optimization problems for multi-UAV-assisted MEC networks, we propose a Double Regularization Diffusion-enhanced Soft Actor-Critic (R2DSAC) algorithm to solve the formulated multi-objective optimization problem. The R2DSAC algorithm incorporates diffusion entropy regularization and action entropy regularization to improve the performance of the diffusion policy. Furthermore, we dynamically mask unimportant neurons in the actor network to reduce the carbon emissions associated with model training. Simulation results demonstrate the reliability of the proposed HybridRAG-based LLM agent framework, which achieves a$6.6\%$improvement in F1 scores over traditional RAG, and validate the effectiveness of the R2DSAC algorithm, which outperforms the SAC algorithm by up to$64.17\%$.

Mobile metaverses are envisioned as a transformative digital ecosystem that delivers immersive, intelligent, and ubiquitous services through mobile devices. Driven by Large Language Models (LLMs) and Vision-Language Models (VLMs), Artificial Intelligence (AI) agents hold the potential to empower the creation, maintenance, and evolution of mobile metaverses, enabling seamless human-machine interaction and dynamic service adaptation. Currently, AI agents are primarily built upon cloud-based LLMs and VLMs. However, several challenges hinder their efficient deployment, including high service latency and a risk of sensitive data leakage during perception and processing. In this paper, we develop an edge-cloud collaboration-based federated AI agent construction framework in mobile metaverses. Specifically, Edge Servers (ESs), as agent infrastructures, first create agent modules in a distributed manner. The cloud server then integrates these modules into AI agents and deploys them at the edge, thereby enabling low-latency AI agent services for users. Considering that ESs may exhibit dynamic levels of willingness to participate in federated AI agent construction, we design a two-period dynamic contract model to continuously incentivize ESs to participate in agent module creation, effectively addressing the dynamic information asymmetry between the cloud server and ESs. Furthermore, we propose an Enhanced Diffusion Model-based Soft Actor-Critic (EDMSAC) algorithm to effectively generate optimal dynamic contracts. In the algorithm, we apply dynamic structured pruning to DM-based actor networks to enhance denoising efficiency and policy learning performance. Simulation results demonstrate that the EDMSAC algorithm outperforms the DMSAC algorithm by up to 23% in optimal dynamic contract generation.

Semantic Communication (SemCom), as a next-generation communication technology, promises to enhance message delivery efficiency while reducing network resource consumption. Despite progress in SemCom, research on SemCom security is still in its infancy. To bridge this gap, we propose a general covert SemCom framework for wireless networks, which introduces the application of covert communications aided by a friendly jammer, thereby reducing the risk of eavesdropping. Our approach transmits semantic information covertly, making it difficult for wardens to detect. Given the aim of maximizing covert SemCom performance, we formulate a power control problem in covert SemCom under energy constraints. Furthermore, we propose a learning-based approach based on the soft actor-critic algorithm, optimizing the power of the transmitter and the friendly jammer. Our numerical findings substantiate the efficacy of our proposed approach in bolstering covert SemCom performance.

Artificial intelligence-generated content (AIGC) is increasingly featuring a key to extract intent information from external instructions and generate required content in digital twin (DT)-enabled application scenarios. To construct DT contexts as the input of generative artificial intelligence (GenAI) algorithms, space–air–ground integrated networks (SAGINs) with hierarchical structures can facilitate object cloning from the physical world to a virtual space within vast geographical regions. In this work, we propose a novel DT synchronization framework residing in SAGINs to provide AIGC services. Autonomous aerial vehicles (AAVs) are in charge of gathering real-time information from the external environment and transmitting synchronization data to the core cloud via a communication relay, i.e., base station (BS) or satellite. In the proposed framework, we develop a resource allocation problem for DT synchronization, aiming to minimize the time-average energy costs of AAVs under the constraints on resource provision and long-term transmission queue stability. To address the complexity and dynamics of SAGINs, we first transform the original resource allocation problem into several deterministic problems based on the Lyapunov optimization. Then, a diffusion model-based resource allocation (DRA) algorithm is developed to solve the deterministic problem in each time slot, where a novel diffusion model is proposed to generate integer relay selection decisions with the aid of auxiliary gradients provided by conventional model-based optimization. Finally, we provide theoretical and simulation evaluations to demonstrate that the DRA algorithm can reduce energy consumption and improve resource utilization by comparing it with deep reinforcement learning (DRL) and heuristic algorithms.

The widespread application of AI-generated content (AIGC) services has driven demand for efficient computational resources, making effective task scheduling and computation offloading in edge computing (EC) environments a critical research topic. However, the high computational requirements and low latency demands of AIGC services, combined with the limitations of EC, present challenges for existing offloading methods, such as unstable decision making in dynamic task environments and resource overloading. Here, we propose a decentralized AIGC task offloading architecture within an IoT-assisted EC network to optimize the quality of AIGC services. In this architecture, we define a multiobjective joint optimization problem for AIGC task offloading, aiming to simultaneously optimize key performance metrics, such as task latency, energy efficiency, and load balancing. To address this problem, we introduce an improved proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm, named TOPPO. By incorporating a policy update step size constraint and a clipping mechanism, TOPPO significantly enhances the stability of the training process and reduces fluctuations during policy updates. Additionally, the algorithm integrates an LSTM model to improve its ability to handle temporal dependencies. Through continuous interaction between the model and the environment, the offloading strategy is iteratively updated to ensure that diverse AIGC tasks are efficiently executed on IoT devices or edge servers. Extensive simulations and performance evaluations demonstrate that the proposed method achieves significant improvements in task latency, energy consumption, and load management during AIGC task processing.

Semantic communication has recently garnered substantial attention due to its potential to alleviate bandwidth constraints and improve network capacity. Nonetheless, existing studies primarily concentrate on network architecture and overlook the communication performance analysis. Therefore, this paper seeks to derive semantic-oriented error probability. Specifically, we develop a novel text semantic communication framework that comprises distinct semantic and physical layers. In the semantic layer, we employ latent Dirichlet allocation (LDA) to extract text topics and evaluate the topic distribution. Given an expected transmission accuracy, we propose a dichotomy to determine the minimal number of topics. These acquired topics, along with their respective distributions, are defined as the text semantic features. In the physical layer, the semantic features are encoded into a binary sequence and modulated with conventional methods. The relationship between the semantic and physical layers is uncover by associating coding of the semantic features with the symbol error probability (SEP). Considering a scenario wherein base stations (BSs) following a specific Poisson point process (PPP), we derive the approximate SEP and semantic inference error probability (SIEP) for multiple coding strategies. Simulation results show that the proposed text semantic communication network enables effective text transmission and the derived error probability accurately reflect the performance of an actual communication system.

Rapid advancements in wireless communication have led to a dramatic upsurge in data volumes within mobile edge networks. These substantial data volumes offer opportunities for training Artificial Intelligence-Generated Content (AIGC) models to possess strong prediction and decision-making capabilities. AIGC represents an innovative approach that utilizes sophisticated generative AI algorithms to automatically generate diverse content based on user inputs. Leveraging mobile edge networks, mobile AIGC networks enable customized and real-time AIGC services for users by deploying AIGC models on edge devices. Nonetheless, several challenges hinder the provision of high-quality AIGC services, including issues related to the quality of sensing data for AIGC model training and the establishment of incentives for big data sharing from mobile devices to edge devices amidst information asymmetry. In this paper, we initially define a Quality of Data (QoD) metric based on the age of information to quantify the quality of sensing data. Subsequently, we propose a contract theoretic model aimed at motivating mobile devices for big data sharing. Furthermore, we employ a Proximal Policy Optimization (PPO) algorithm to determine the optimal contract. Numerical results demonstrate the efficacy and reliability of the proposed PPO-based contract model.

With the enhancement of health awareness and the development of artificial intelligent technology, healthcare recommender systems (HRS) play an increasingly important role in individual health management. Meanwhile, the widespread usage of large models has significantly improved the efficiency and accuracy in the analysis and utilization of medical data. In this paper, we comprehensively summarize the basic types of recommender systems as well as the new trends in utilizing large models. Then we introduce the recommendation applications in healthcare areas from six aspects, i.e., disease risk prediction, medication recommendation, medical resource recommendation, mental health support, health life management, and health education. At last, we explore some current issues and challenges within HRS, as well as the development of potential solutions and directions in the future.

Artificial Intelligence-Generated Content (AIGC) is a rapidly evolving field that utilizes advanced AI algorithms to generate content. Through integration with mobile edge networks, mobile AIGC networks have gained significant attention, which can provide real-time customized and personalized AIGC services and products. Since blockchains can facilitate decentralized and transparent data management, AIGC products can be securely managed by blockchain to avoid tampering and plagiarization. However, the evolution of blockchain-empowered mobile AIGC is still in its nascent phase, grappling with challenges such as improving information propagation efficiency to enable blockchain-empowered mobile AIGC. In this paper, we design a Graph Attention Network (GAT)-based information propagation optimization framework for blockchain-empowered mobile AIGC. We first innovatively apply age of information as a data-freshness metric to measure information propagation efficiency in public blockchains. Considering that GATs possess the excellent ability to process graph-structured data, we utilize the GAT to obtain the optimal information propagation trajectory. Numerical results demonstrate that the proposed scheme exhibits the most outstanding information propagation efficiency compared with traditional routing mechanisms.

Policy network parameter sharing is a commonly used technique in advanced deep multi-agent reinforcement learning (MARL) algorithms to improve learning efficiency by reducing the number of policy parameters and sharing experiences among agents. Nevertheless, agents that share the policy parameters tend to learn similar behaviors. To encourage multi-agent diversity, prior works typically maximize the mutual information between trajectories and agent identities using variational inference. However, this category of methods easily leads to inefficient exploration due to limited trajectory visitations. To resolve this limitation, inspired by the learning of pre-trained models, in this paper, we propose a novel Contrastive Trajectory Representation (CTR) method based on learning distinguishable trajectory representations to encourage multi-agent diversity. Specifically, CTR maps the trajectory of an agent into a latent trajectory representation space by an encoder and an autoregressive model. To achieve the distinguishability among trajectory representations of different agents, we introduce contrastive learning to maximize the mutual information between the trajectory representations and learnable identity representations of different agents. We implement CTR on top of QMIX and evaluate its performance in various cooperative multi-agent tasks. The empirical results demonstrate that our proposed CTR yields significant performance improvement over the state-of-the-art methods.

Semantic communication powered by artificial in-telligence is carried out vigorously to further improve communication efficiency. The knowledge base (KB), as a critical component of semantic communication systems, guides devices to do semantic coding/encoding. However, mismatched KBs hinder semantic alignment between the transceiver and the receiver, which brings severe semantic error. In this work, we design a semantic knowledge base synchronization (SKBS) framework based on federated knowledge distillation for KB establishment and dynamic evolution. In the SKBS, we use the mutual distil-lation mechanism to learn knowledge from heterogeneous local KBs. Meanwhile, the global KB is compressed to improve the synchronization efficiency. Moreover, a filtering method for KB parameters with noise is applied to mitigate the effects of noise for KB synchronization. The experiment results demonstrate that our proposed approach can assist in establishing a universal global KB and improve the accuracy of multi-user semantic communication while reducing the communication cost during KB synchronization.

Metaverse is an immersive, seamless, interactive, comprehensive virtual world, as well as a replication, extension, and transcendence of the real world. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is becoming a key technology for ubiquitous Metaverse services. To enhance network resource utilization, we introduce dual connectivity (DC) technologies in UAV-enabled MEC, which increases the time complexity associated with resource management. Considering the specific features of DC communication channels, we propose a UAV-assisted Metaverse rendering problem to enhance the Metaverse service experience and reduce the energy cost of edge devices. To solve the rendering problem with low complexity, we propose a diffusion model-based Metaverse rendering algorithm, where a novel diffusion model is used to generate integer rendering decisions with the aid of the gradient provided by the model-based Metaverse rendering problem. Moreover, with the given rendering decisions, the communication and computation resource allocation results are derived by the model-based optimization method. Finally, we conduct extensive simulation experiments based on real-world datasets. Comprehensive simulation results demonstrate that the diffusion model-based Metaverse rendering algorithm can reduce the Metaverse frame rendering time and improve user experience.

The advanced sixth-generation (6G) wireless network is considered as an indispensable part of the Metaverse, where a substantial volume of communication content is transmitted through multiple modalities, placing significant transmission loads on communication channels. In this paper, we propose a framework for multi-modal semantic communication using hashing-based semantic extraction approach to produce optimal binary signatures (hash codes). Instead of directly using coarse-grained feature fusion methods, we capture deep semantics in self-attention manner, achieving fine-grained multi-modal feature fusion thereby strengthening the representation ability of hash codes. To enhance adaptability in practical situations, we then design a modality-completion module to address missing modalities in data, accommodating scenarios with both single-modal and cross-modal data. We evaluate the proposed semantic extraction framework on two popular multi-modal datasets, comparing it with the latest hashing methods and then demonstrate the effectiveness in various channel conditions.

Semantic communication is an emerging paradigm for digital twin (DT) synchronization in unmanned aerial vehicle (UAV)-assisted edge computing environments, where machine learning (ML) models are deployed on edge servers and UAVs as semantic encoders and decoders to perform real-time synchronization. However, with limited system resources, additional computation workloads are still brought to all participants for semantic information extraction and recovery. In this work, we propose an optimized tiny ML-based DT synchronization framework to minimize the synchronization latency in UAV-assisted edge computing environments, considering time-average constraints on virtual energy deficit queue stability. Due to the coexistence of tiny ML-based semantic communications, a semantic extraction factor is introduced to formulate the DT synchronization problem as a time-average time minimization problem. By leveraging the Lyapunov optimization framework, the multi-stage DT synchronization problem is transformed into several per-slot resource allocation problems. To solve the per-slot optimization problem efficiently, a deep reinforcement learning-based synchronization (DRLS) algorithm is proposed, where an actor-critic structure is adopted to generate synchronization actions with low time complexity. Finally, we conduct simulation experiments to evaluate the performance of the proposed DRLS scheme. Numerical results demonstrate that our DRLS algorithm can reduce 8.23% of DT synchronization delay and 15.31% of synchronization data dropping rates on average by comparing it with the UAV-edge collaborative synchronization scheme without semantic communications. Besides, the DRLS algorithm can achieve up to 57.14% synchronization energy reduction compared with representative synchronization policies.

The fusion of the Internet of Things (IoT) with sixth-generation (6G) technology has significant potential to revolutionize the IoT landscape. With the ultrareliable and low-latency communication capabilities of 6G, 6G-IoT networks can transmit high-quality and diverse data to enhance edge learning. Artificial intelligence-generated content (AIGC) harnesses advanced artificial intelligence (AI) algorithms to automatically generate various types of content. The emergence of edge AIGC integrates with edge networks, facilitating real-time provision of customized AIGC services by deploying AIGC models on edge devices. However, the current practice of edge devices as AIGC service providers (ASPs) lacks incentives, hindering the sustainable provision of high-quality edge AIGC services amidst information asymmetry. In this article, we develop a user-centric incentive mechanism framework for edge AIGC services in 6G-IoT networks. Specifically, we first propose a contract theory model for incentivizing ASPs to provide AIGC services to clients. Recognizing the irrationality of clients toward personalized AIGC services, we utilize prospect theory (PT) to capture their subjective utility better. Furthermore, we adopt the diffusion-based soft actor-critic algorithm to generate the optimal contract design under PT, outperforming traditional deep reinforcement learning algorithms. Our numerical results demonstrate the effectiveness of the proposed scheme.

Stress tracing is an important research domain that supports many applications, such as health care and stress management; and its closest related works are derived from stress detection. However, these existing works cannot well address two important challenges facing stress detection. First, most of these studies involve asking the users to wear physiological sensors to detect their stress states, which has a negative impact on the user experience. Second, these studies have failed to effectively utilize the multimodal physiological signals, which results in less satisfactory detection results. This article formally defines the stress tracing problem, which emphasizes the continuous detection of human stress states. A novel deep stress tracing (DST) method, named DST, is presented. Note that, DST proposes tracing human stress based on the physiological signals collected by a noncontact ultrawideband radar, which is more friendly to users when collecting their physiological signals. In DST, a signal extraction module is carefully designed at first to robustly extract the multimodal physiological signals from the raw RF data of the radar, even in the presence of body movement. Afterward, a multimodal fusion module is proposed in DST to ensure that the extracted multimodal physiological signals can be effectively fused and utilized. Extensive experiments are conducted on the three real-world data sets, including one self-collected data set and two publicity data sets. Experimental results show that the proposed DST method significantly outperforms all the baselines in terms of tracing human stress states. On average, DST averagely provides a 6.31% increase in detection accuracy on all the data sets, compared with the best baselines.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely extended the border and capacity of artificial intelligence of things (AIoT) by providing a key element for enabling flexible distributed data inputs, computing capacity, and high mobility. To enhance data privacy for AIoT applications, federated learning (FL) is becoming a potential solution to perform training tasks locally on distributed IoT devices. However, with the limited onboard resources and battery capacity of each UAV node, optimization is required to achieve a large-scale and high-precision FL scheme. In this work, an optimized multi-UAV-assisted FL framework is designed, where regular IoT devices are in charge of performing training tasks, and multiple UAVs are leveraged to execute local and global aggregation tasks. An online resource allocation (ORA) algorithm is proposed to minimize the training latency by jointly deciding the selection decisions of clients and a global aggregation server. By leveraging the Lyapunov optimization technique, virtual energy queues are studied to depict the energy deficit. With the help of the actor-critic learning framework, a deep reinforcement learning (DRL) scheme is designed to improve per-round training performance. A deep neural network (DNN)-based actor module is designed to derive client selection decisions, and a critic module is proposed through a conventional optimization method to evaluate the obtained selection decisions. Moreover, a greedy scheme is developed to find the optimal global aggregation server. Finally, extensive simulation results demonstrate that the proposed ORA algorithm can achieve optimal training latency and energy consumption under various system settings.

Data augmentation is vital to the generalization ability and robustness of deep neural networks (DNNs) models. Existing augmentation methods for speaker verification manipulate the raw signal, which are time-consuming and the augmented samples lack diversity. In this paper, we present a novel difficulty-aware semantic augmentation (DASA) approach for speaker verification, which can generate diversified training samples in speaker embedding space with negligible extra computing cost. Firstly, we augment training samples by perturbing speaker embeddings along semantic directions, which are obtained from speaker-wise covariance matrices. Secondly, accurate covariance matrices are estimated from robust speaker embeddings during training, so we introduce difficulty-aware additive margin softmax (DAAM-Softmax) to obtain optimal speaker embeddings. Finally, we assume the number of augmented samples goes to infinity and derive a closed-form upper bound of the expected loss with DASA, which achieves compatibility and efficiency. Extensive experiments demonstrate the proposed approach can achieve a remarkable performance improvement. The best result achieves a 14.6% relative reduction in EER metric on CN-Celeb evaluation set.

For future Internet of Things (IoT) systems, data-driven and dynamic spectrum-sharing schemes can significantly improve the spectrum utilization and efficiency. However, conventional centralized architecture of such dynamic IoT spectrum-sharing systems is often considered to be nontransparent, costly, and vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-based dynamic spectrum-sharing scheme has been proposed and investigated in this work, which aims at enhancing the system by providing desirable features, such as decentralization, transparency, immutability, and auditability. By considering the privacy and transaction dynamics issues when blockchain is integrated into spectrum-sharing systems, a privacy-preserving double auction mechanism based on differential privacy is developed for incentivizing spectrum sharing, where the time-varying valuations of the spectrum resources are also taken into consideration. In the proposed auction, a winner determination problem (WDP) is formulated to decide the winning bidders and spectrum allocation. A deep reinforcement learning (DRL)-based method is then proposed for efficiently solving the WDP. The proposed auction mechanism can be integrated with smart contracts on blockchain platforms. Furthermore, the computation of the DRL-based method for solving the WDP is designed as part of the consensus mechanism in the blockchain. Theoretical analysis show that the proposed privacy-aware double auction mechanism satisfies the properties of differential privacy, individual rationality, and truthfulness. Finally, simulation results are provided to validate the performance of the spectrum-sharing approach.

The collaborative edge and cloud computing system has emerged as a promising solution to fulfill the unprecedented high requirements of 5G application scenarios. Due to vendor variations, it is often difficult to manage hardware facilities in such a collaborative system. Moreover, the amount of data generated and tasks requested by end devices are increasing exponentially, which introduces storage and computation bottlenecks. To address these issues, a novel systematic framework called software-defined edge and cloud computing (SD-ECC) is designed to manage the underlying physical resources of edge and cloud layers via software. SD-ECC is combined with an erasure-coded storage system, for which a task scheduling problem is formulated by considering data access and task processing steps. Then, a joint data access and task processing (JDATP) algorithm is proposed to minimize the task response time including data access latency and task processing latency. A practical SD-ECC platform is developed on OpenStack, OpenDaylight, and Kubernetes to conduct experiments with real-world datasets. The experimental results demonstrate that our proposed JDATP algorithm can reduce 20.87% of the task response time and increase 14.16% of the remaining storage space on average by comparing it with alternative schemes.

Device positioning has generally been recognized as an enabling technology for numerous vehicular applications in intelligent transportation systems (ITS). The downlink time difference of arrival (DL-TDOA) technique in cellular networks requires range information of geographically diverse base stations (BSs) to be measured by user equipment (UE) through the positioning reference signal (PRS). However, inter-cell interference from surrounding BSs can be particularly serious under poor network planning or dense deployments. This may lead to a relatively longer measurement time to locate the UE, causing an unacceptable location update rate to time-sensitive applications. In this case, PRS muting of certain wireless resources has been envisioned as a promising solution to increase the detectability of a weak BS. In this paper, to reduce UE measurement latency while ensuring high location accuracy, we propose a muting strategy managed by positioning functions that utilizes a combination of optimized pseudo-random sequences (CO-PRS) for multiple BSs to coordinate the muting of PRS resources. The original sequence is first truncated according to the muting period, and a modified greedy selection is performed to form a set of control sequences as the muting configurations (MC) with balance and concurrency constraints. Moreover, efficient information exchange can be achieved with the seeds used for regenerating the MC. Extensive simulations demonstrate that the proposed scheme outperforms the conventional random and ideal muting benchmarks in terms of measurement latency by about 30%, especially when dealing with severe near-far problems in cellular networks.

On behalf of the Program Committee of the 20th IEEE international conference on embedded and ubiquitous computing (EUC 2022), we would like to offer our great welcome to you to attend the conference in person in Wuhan, China, 28-30 October 2022, or virtually online.

In this paper, we present Multi-scale Feature Aggregation Conformer (MFA-Conformer), an easy-to-implement, simple but effective backbone for automatic speaker verification based on the Convolution-augmented Transformer (Conformer).The architecture of the MFA-Conformer is inspired by recent stateof-the-art models in speech recognition and speaker verification.Firstly, we introduce a convolution subsampling layer to decrease the computational cost of the model.Secondly, we adopt Conformer blocks which combine Transformers and convolution neural networks (CNNs) to capture global and local features effectively.Finally, the output feature maps from all Conformer blocks are concatenated to aggregate multi-scale representations before final pooling.We evaluate the MFA-Conformer on the widely used benchmarks.The best system obtains 0.64%, 1.29% and 1.63% EER on VoxCeleb1-O, SITW.Dev, and SITW.Eval set, respectively.MFA-Conformer significantly outperforms the popular ECAPA-TDNN systems in both recognition performance and inference speed.Last but not the least, the ablation studies clearly demonstrate that the combination of global and local feature learning can lead to robust and accurate speaker embedding extraction.We have also released the code 1 for future comparison.

Great concerns on the Internet of Things (IoT) security are raised as IoT becomes an emerging paradigm to achieve ubiquitous connectivity. This article studies low-complexity secure transceiver and feedback design with the assistance of a jammer for physical-layer security in multiantenna IoT systems. We consider the general setting where a legitimate multiantenna controller broadcasts confidential messages to multiple multiantenna IoT devices in the presence of a passive external multiantenna eavesdropper. Moreover, there is only quantized downlink channel state information (CSI) at the controller and the jammer through feedback channels. We introduce several secure transceivers for different system setups, all of which employ block-diagonal precoding at the controller and null-space beamforming at the jammer but are with the different receivers at each IoT device. Considering the practical setup of IoT and for the tractability of analysis, we study the secrecy performance of the transceiver with an arbitrarily selected receive matrix independent of the channels of all devices. We derive an approximate lower bound on the ergodic secrecy rate (ESR) of each devicewithoutassuming any asymptotes for system parameters. The obtained result can also be viewed as a lower bound on the ESR performance of any other transceivers. We also optimize this bound to find an adaptive feedback bit allocation to the two feedback channels of each legitimate device. Numerical results are shown to illustrate the obtained analytical ESR lower bound, the feedback bit allocation algorithm, and significant ESR performance gain that results from the proposed feedback bit allocation.

The edge-cloud computing and network slicing have emerged as promising solutions to fulfill the diversity of IoT applications enabled by 5G and beyond. However, edge-cloud computing systems are composed of various hardware facilities, leading to difficulties in hardware control and management. With network slicing, underlying resource sharing among multiple slice users is allowed, leading to potential attacks to the slice formulation processes and malicious usage of network slices that may result in inefficient resource utilization of the system. To address the aforementioned network slice security issue, we first propose a new systematic framework, named software-defined edge-cloud computing (SD-ECC), which applies standard software to control the hardware infrastructure regardless of vendor variations. With SD-ECC, resource slices are formulated by including storage and computational resources provided by edge and cloud servers. Then, we study an optimal slicing-based resource orchestration problem by considering slice-initiated attacks as possible adversaries, which includes both interslice and intraslice resource orchestrations. A secure slicing-based resource orchestration (SS-RO) algorithm is designed by minimizing the delay and resource utilization simultaneously to mitigate the impacts of the slice-initiated attacks, where the Benders decomposition is employed to obtain the interslice orchestration outcome, and a quadratic transformation method is applied to derive the intraslice orchestration solution. The experimental results demonstrate that the proposed SS-RO algorithm outperforms baseline schemes in terms of the ratio of accepted attacking tasks, energy consumption, and system throughput.

Large interactive displays are widely used in industrial scenarios to enhance ubiquitous and seamless human–machine interactions. However, few studies have paid attention to design implicit interaction that users can directly manipulate physical circumstance without touch or specific gesture. This article proposes ReflectU, a novel reflection-based approach that leverages mirror reflection for a natural and implicit interactive method for remote control, i.e., user will be able to directly interact with physical circumstance just via the reflection of their bare hands. We compare its performance with that of other two generally known devices: Wii Remoter and Microsoft Kinect. Moreover, performance metrics of ReflectU are evaluated in real-life scenarios and provide evidence in convincing performance in both the tasks requiring instant targeting and trajectory control. Furthermore, ReflectU is reported by users to be the most intuitive and satisfactory approach among all three candidates in user studies. Future industrial applications of the reflection-base mirror approach are discussed.

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is produced. One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm known as Federated Learning (FL), which enables data owners to conduct model training without having to transmit their raw data to third-party servers. However, the FL network is envisioned to involve thousands of heterogeneous distributed devices. As a result, communication inefficiency remains a key bottleneck. To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this article, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange for the data owners' participation, and the data owners are free to choose which cluster to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, each cluster head can choose to serve a model owner, whereas the model owners have to compete amongst each other for the services of the cluster heads. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head's services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing properties of the deep learning based auction.

Sequence-to-sequence (seq2seq) learning has greatly improved text-to-speech (TTS) synthesis performance, but effective implementation on resource-restricted devices remains challenging as seq2seq models are usually computationally expensive and memory intensive. To achieve fast inference speed and small model size while maintain high-quality speech, we propose FCL-taco2, a Fast, Controllable and Lightweight (FCL) TTS model based on Tacotron2. FCL-taco2 adopts a novel semi-autoregressive (SAR) mode for phoneme level based parallel mel-spectrograms generation conditioned on prosody features, leading to faster inference speed and higher prosody controllability than Tacotron2. Besides, knowledge distillation (KD) is leveraged to compress a relatively large FCL-taco2 model to its small version with minor loss of speech quality. Experimental results on English (EN) and Chinese (CN) datasets show that the small version of FCL-taco2 achieves comparable performance with Tacotron2 in terms of speech quality, while it has a 4.8× smaller footprint with 17.7× and 18.5× faster inference speeds on average for EN and CN experiments respectively. Besides, execution on mobile devices shows that the proposed model can achieve faster than real-time speech synthesis. Our code and audio samples are released1.

Automatic speaker verification (ASV) is a well developed technology for biometric identification, and has been ubiquitous implemented in security-critic applications, such as banking and access control.However, previous works have shown that ASV is under the radar of adversarial attacks, which are very similar to their original counterparts from human's perception, yet will manipulate the ASV render wrong prediction.Due to the very late emergence of adversarial attacks for ASV, effective countermeasures against them are limited.Given that the security of ASV is of high priority, in this work, we propose the idea of "voting for the right answer" to prevent risky decisions of ASV in blind spot areas, by employing random sampling and voting.Experimental results show that our proposed method improves the robustness against both the limited-knowledge attackers by pulling the adversarial samples out of the blind spots, and the sufficient-knowledge attackers by introducing randomness and increasing the attackers' budgets.

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains artificial intelligence (AI) models without revealing local datasets of the FL workers. One of the main challenges is the straggler effects where the significant computation delays are caused by the slow FL workers. As such, Coded Federated Learning (CFL), which leverages coding techniques to introduce redundant computations to the FL server, has been proposed to reduce the computation latency. In order to implement the coding schemes over the FL network, incentive mechanisms are important to allocate the resources of the FL workers and data owners efficiently in order to complete the CFL training tasks. In this paper, we consider a two-level incentive mechanism design problem. In the lower level, the data owners are allowed to support the FL training tasks of the FL workers by contributing their data. To model the dynamics of the selection of FL workers by the data owners, an evolutionary game is adopted to achieve an equilibrium solution. In the upper level, a deep learning based auction is proposed to model the competition among the model owners.

Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its large-scale implementation. Recently, hierarchical FL (HFL) has been proposed in which data owners, i.e., workers, can first transmit their updated model parameters to edge servers for intermediate aggregation. This reduces the instances of global communication and straggling workers. To enable efficient HFL, it is important to address the issues of edge association in the context of non-cooperative players, i.e., workers, edge servers, and model owner. However, the existing studies merely focus on static approaches and do not consider the dynamic interactions and bounded rationalities of the players. In this paper, we propose the edge association strategies of the workers to be modelled using an evolutionary game. Then, we provide numerical results to validate that our proposed framework captures the HFL system dynamics under varying sources of network heterogeneity.

As the paradigm of crowdsensing involves the data collection from users, the issue of designing reward to incentivize the users is fundamentally important to be addressed, thereby effectively enhancing the participation. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in crowdsensing-based healthcare services, the accuracy of diet recommendation for a certain user can be promoted by exploiting the nutritional information contributed and shared by the socially-connected friends of him/her taking similar types of food. To be more general and practical, we study the incentive schemes in presence of multiple crowdsensing service providers and multiple users. Understanding the behaviors of users and service providers in socially-aware crowdsensing is of paramount importance for incentive schemes. Considering this, we propose a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among service providers and users, where the social influence of users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, we theoretically validate the existence and uniqueness of the Stackelberg equilibrium. Simulations are conducted to evaluate game equilibrium properties, and the results are presented to assess and demonstrate the performance effectiveness of the proposed game model.

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming increasingly important. Furthermore, data collected by the edge device contain massive user's private data, which is challenging current detection approaches as user privacy has attracted more and more public concerns. With this focus, this article proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce an FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an attention mechanism-based convolutional neural network-long short-term memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based convolutional neural network units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of the long short-term memory unit in predicting time-series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top- k selection to improve communication efficiency. Extensive experimental studies on four real-world data sets demonstrate that our framework accurately and timely detects anomalies and also reduces the communication overhead by 50% compared to the FL framework that does not use the gradient compression scheme.

Future generation communication systems, such as 5G and 6G wireless systems, exploit the combined satellite-terrestrial communication infrastructures to extend network coverage and data throughput for data-driven applications. These ground-breaking techniques have promoted the rapid development of Internet of Things (IoT) in maritime industries. In maritime IoT applications, intelligent vessel traffic services can be guaranteed by collecting and analyzing high volume of spatial data flows from automatic identification system (AIS). This AIS system includes a highly integrated automatic equipment, including functionalities of core communication, tracking, and sensing. The increased utilization of shipboard AIS devices allows the collection of massive trajectory data. However, the received raw AIS data often suffers from undesirable outliers (i.e., poorly tracked timestamped points for vessel trajectories) during signal acquisition and analog-to-digital conversion. The degraded AIS data will bring negative effects on vessel traffic services (e.g., maritime traffic monitoring, intelligent maritime navigation, vessel collision avoidance, etc.) in maritime IoT scenarios. To improve the quality of vessel trajectory records from AIS networks, we propose to develop a two-phase data-driven machine learning framework for vessel trajectory reconstruction. In particular, a density-based clustering method is introduced in the first phase to automatically recognize the undesirable outliers. The second phase proposes a bidirectional long short-term memory (BLSTM)-based supervised learning technique to restore the timestamped points degraded by random outliers in vessel trajectories. Comprehensive experiments on simulated and realistic data sets have verified the dominance of our two-phase vessel reconstruction framework compared to other competing methods. It thus has the capacity of promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems.

Effective screening of COVID-19 cases has been becoming extremely important to mitigate and stop the quick spread of the disease during the current period of COVID-19 pandemic worldwide. In this article, we consider radiology examination of using chest X-ray images, which is among the effective screening approaches for COVID-19 case detection. Given deep learning is an effective tool and framework for image analysis, there have been lots of studies for COVID-19 case detection by training deep learning models with X-ray images. Although some of them report good prediction results, their proposed deep learning models might suffer from overfitting, high variance, and generalization errors caused by noise and a limited number of datasets. Considering ensemble learning can overcome the shortcomings of deep learning by making predictions with multiple models instead of a single model, we proposeEDL-COVID, an ensemble deep learning model employing deep learning and ensemble learning. The EDL-COVID model is generated by combining multiple snapshot models of COVID-Net, which has pioneered in an open-sourced COVID-19 case detection method with deep neural network processed chest X-ray images, by employing a proposed weighted averaging ensembling method that is aware of different sensitivities of deep learning models on different classes types. Experimental results show that EDL-COVID offers promising results for COVID-19 case detection with an accuracy of 95%, better than COVID-Net of 93.3%.

With a sponsored content scheme of mobile services, a content provider can encourage end users/subscribers to access its contents, e.g., with an advertisement, by paying part of the data price to the network operator. As a result, the content provider and end users are both actively engaged into the sponsored content ecosystem. As such, a key challenge is how to provide proper sponsorship given the content demand from the users and the service fee charged by the network operator. Furthermore, the information asymmetry between the content provider and users makes the sponsorship problem more challenging. In this paper, we propose a Stackelberg game-based framework to tackle this challenge. In the framework, the network operator, as the leader, determines the data price first, and the content provider as well as users, as the followers, make the decisions on sponsorship and content demand based on the data price, respectively. We model the interaction between the content provider and the users as a contract game in the presence of asymmetric information. In the contract game, the content provider designs a contract that contains its sponsorship strategies toward all types of users. We then derive the necessary and sufficient conditions of feasible contracts and obtain an optimal contract to maximize the profit of the content provider. Taking into account the optimal contract of contract game, we also investigate the optimal pricing of the network operator through backward induction. We prove that the Stackelberg equilibrium is unique under a mild condition and present the numerical results to illustrate some important properties of the equilibrium.

A sponsored content policy enables a content provider (CP) to pay a network service provider (SP), and thereby mobile users (MUs) can access contents from the CP through network services from the SP with a lower charge. Thus, more users want to access the contents which potentially generates more profit gain to the CP. In this article, we study the interactions among three entities under the sponsored content policy, namely, the network SP, which is referred to as SP for brevity, the CP and MUs. We model the interactions as a hierarchical Stackelberg game, where the SP and the CP act as the leaders determining the pricing and sponsoring strategies, respectively, and the MUs act as the followers deciding on their content demand. The model incorporates the network effects in a social domain and congestion in a network domain which enables us to obtain insights from the sponsored content policy. In the model, we investigate the mutual interplay between the SP and the CP in three scenarios: 1) sequential competition, where the SP first optimizes its pricing strategy for maximizing its revenue, and then the CP optimizes its sponsoring strategy for maximizing its profit sequentially; 2) simultaneous competition, where the CP and the SP optimize their individual strategies separately and simultaneously; and 3) cooperation, where both providers jointly optimize their strategies with the purpose of maximizing their aggregate payoff. Through backward induction, we derive the unique Nash equilibrium among the MUs. Furthermore, the existence and uniqueness of the Stackelberg equilibrium under three proposed scenarios are validated analytically. Via extensive simulations, it is shown that the network effects significantly improve the utilities of MUs, the profit of the CP, and the revenue of the SP.

The Internet of Things (IoT) connects mobile and wireless devices, and enables the IoT service providers to deliver IoT services to the mobile users in various applications, e.g., transportation and communications. In this paper, the problem of IoT service delivery management is studied with the consideration of substitutability, complementarity, and externalities of delivering IoT services due to the diversity of different IoT components in mobile systems. The substitutable IoT services have similar functionalities to serve IoT users, and the IoT users can switch to buy service from any IoT service provider. The complementary IoT services have different functionalities to serve IoT users, and the IoT users may request a bundle of IoT services from multiple IoT service providers as their IoT services can be integrated. Externalities represent the situation in which IoT users in the same system can affect the utilities of each other due to the connections and interference among the IoT users, which leads to the presence of network effect and congestion effect. To analyze the impact of these factors on the performance of IoT systems, a multi-leader multi-follower Stackelberg game model is introduced. Therein, the IoT service providers and IoT users make their strategic decisions in terms of pricing and service requests, respectively, toward their individual objectives in a distributed manner. A closed-form equilibrium solution is derived analytically through backward induction.

Mobile data demand is increasing tremendously in wireless social networks, and thus an efficient pricing scheme for social-enabled services is urgently needed. Though static pricing is dominant in the actual data market, price intuitively ought to be dynamically changed to yield greater revenue. The critical question is how to design the optimal dynamic pricing scheme, with prospects for maximizing the expected long-term revenue. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile network operator in the social data market. In the market, the operator, i.e., the seller, individually offers each mobile user, i.e., the buyer, a certain price in multiple time periods sequentially and repeatedly. The proposed scheme exploits the network effects in the mobile users' behaviors that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Thereafter, we propose a modified sequential pricing policy in order to ensure social fairness among mobile users in terms of their individual utilities. To gain more insights, we further study a simultaneous dynamic pricing scheme in which the operator offers the data price simultaneously. We analytically demonstrate that the proposed dynamic pricing scheme can help the operator gain greater revenue and users achieve higher total utilities than those of the baseline static pricing scheme. We construct the social graph using Erdös-Rényi (ER) model and the real dataset based social network for performance evaluation. The numerical results corroborate that the dynamics of pricing schemes over static ones can significantly improve the revenue of the operator.

Per sponsored content policy, a content provider can pay the network operator on behalf of mobile users to lower the data usage fees so as to generate more advertising revenue. Under such a scheme, how to offer proper sponsorship to the users in response to varying data prices becomes an important issue. Furthermore, the information asymmetry between the content provider and users makes the problem more challenging. In this paper, we propose a Stackelberg game based framework to tackle this challenge. In the framework, the network operator determines the data price first as the leader of the game, and the content providers as well as users make the decisions based on the data price as the followers. Specifically, the decision making process of the followers with the presence of asymmetric information is formulated as a contract game. In the contract game, the content provider designs a contract that contains sponsoring strategies toward all types of the users. After obtaining the optimal contract that maximizes the profit of the content provider, we also derive the optimal pricing of the network operator through backward induction. The Stackelberg equilibrium is proved to be unique, and numerical results are presented for performance evaluation.

Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT), requiring increased decentralization and autonomous operation. To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. However, in complex and large-scale networks, the state and action spaces are usually large, and existing Tabular Reinforcement Learning technique is unable to find the optimal state- action policy quickly. In this paper, deep reinforcement learning is proposed to overcome the mentioned shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput. When benchmarked against advanced DQN techniques, our proposed DQN configuration offers performance speedup of up to 1.8× with good overall performance.

Benefit from the capabilities of providing decentralized tamper-proof ledgers and platforms for data-driven autonomous organization, open-access blockchains based on proof-of-work protocols have gained tremendous popularity. Yet, the proof-of-work based consensus protocols under threats, e.g., double-spending. In this paper, by adopting the cyber-insurance as an economic tool to neutralize cyber risks, we propose a novel approach of cyber risk management for blockchain-based service. The blockchain service market under our consideration is composed of four entities, i.e., the infrastructure provider, blockchain provider, cyber-insurer, and users. The blockchain provider purchases the computing resources, e.g., a cloud, from the infrastructure provider to maintain the blockchain consensus and then offers blockchain services to the users. The blockchain provider optimize its profit by strategizing its investment in the infrastructure in order to improve the security of the blockchain and the service price charged to the users. In the meantime, to prevent the potential damage incurred by the attacks and then fully secure the cyber-space, the blockchain provider purchases a cyber-insurance from the cyber-insurer. In return, the cyber- insurer adjusts the insurance premium according to the perceived risk level of the blockchain service and will pay the claim to the blockchain provider once attacks happen. Based on the rationality of the market entities, we model the interaction among the blockchain provider, users, and cyber-insurer as a two- stage Stackelberg game. Specifically, the blockchain provider and cyber-insurer lead to set their pricing/investment strategies in the upper level subgame, and then the users follow to determine their demand of the blockchain service in the lower level subgame. Specifically, we consider the scenario of double-spending attacks and provide a series of analytical results about the Stackelberg equilibrium in the market game.

With a sponsored content scheme in a wireless network, a sponsored content service provider can pay to a network operator on behalf of the mobile users/subscribers to lower down the network subscription fees at the reasonable cost in terms of receiving some amount of advertisements. As such, content providers, network operators and mobile users are all actively motivated to participate in the sponsored content ecosystem. Meanwhile, in 5G cellular networks, caching technique is employed to improve content service quality, which stores potentially popular contents on edge networks nodes to serve mobile users. In this work, we propose the joint sponsored and edge caching content service market model. We investigate an interplay between the sponsored content service provider and the edge caching content service provider under the non-cooperative game framework. Furthermore, a three-stage Stackelberg game is formulated to model the interactions among the network operator, content service provider, and mobile users. Sub-game perfect equilibrium in each stage is analyzed by backward induction. The existence of Stackelberg equilibrium is validated by employing the bilevel optimization programming. Based on the game properties, we propose a sub-gradient based iterative algorithm, which ensures to converge to the Stackelberg equilibrium.

The data sponsored scheme allows the content provider to cover parts of the cellular data costs for mobile users. Thus the content service becomes appealing to more users and potentially generates more profit gain to the content provider. In this paper, we consider a sponsored data market with a monopoly network service provider, a single content provider, and multiple users. In particular, we model the interactions of three entities as a two-stage Stackelberg game, where the service provider and content provider act as the leaders determining the pricing and sponsoring strategies, respectively, in the first stage, and the users act as the followers deciding on their data demand in the second stage. We investigate the mutual interaction of the service provider and content provider in two cases: (i) competitive case, where the content provider and service provider optimize their strategies separately and competitively, each aiming at maximizing the profit and revenue, respectively; and (ii) cooperative case, where the two providers jointly optimize their strategies, with the purpose of maximizing their aggregate profits. We analyze the sub-game perfect equilibrium in both cases. Via extensive simulations, we demonstrate that the network effects significantly improve the payoff of three entities in this market, i.e., utilities of users, the profit of content provider and the revenue of service provider. In addition, it is revealed that the cooperation between the two providers is the best choice for all three entities.

Internet of Things (IoT) technology enables various physical devices to collect, process and exchange information. Market oriented models become important for IoT systems to efficiently utilize information, as IoT network nodes operate in a highly distributed and autonomous manner. In this work, we propose a three-player game theoretic market model for IoT information trading, considering direct and indirect externalities among market participants. In the model, an IoT service provider collects and processes IoT information, and then delivers the processed information as IoT services to IoT users. Then, an IoT content vendor senses and generates raw information for the IoT service provider to collect, and receives rewards from the provider. Finally, an IoT user pays a fixed service fee to the IoT service provider to access the IoT services. To jointly derive the optimal market decisions of the three participants in the model, we employ a Stackelberg game approach. The equilibria are obtained as the closed form solutions of the game, with which the existence and uniqueness properties are proved. The analytical results show that the IoT service provider operates as an intermediary agent between the IoT content vendor and users, reducing the information trading complexity of both user and vendor sides.

With the advancement of wireless energy harvesting and transfer techniques, an Internet of Things (IoT) node equipped with a wireless charging facility can request and receive energy from wireless chargers deployed at different locations. This provides more opportunity for the mobile IoT node to replenish its battery and be able to operate without interruption due to shortage of energy supply. In this paper, we develop an optimal energy charging scheme for the mobile IoT node, considering the states of location, traffic generation, and energy storage. We formulate the problem of energy charging as a Markov decision process (MDP) to obtain the mobile IoT node’s optimal policy. The objective is to maximize the expected utility. Furthermore, we prove that the optimal policy of the proposed MDP has a threshold structure. The numerical results show the performances of the mobile IoT node under various scenarios and parameter setting. Furthermore, the proposed MDP-based wireless energy charging scheme outperforms conventional baseline schemes.

Sponsored content policy enables a content provider to pay a network operator, and thereby their users access contents from the content provider through network services from the network operator with lower charge. In this paper, we study the interaction among three entities under the sponsored content policy, namely, the network operator or service provider, the content provider and the end-users. We consider a hierarchical three-stage setting to formulate the game theoretic model to analyze the interaction. Using the game model, we derive the user content demand, optimal sponsoring of content provider, and pricing of service provider based on backward induction. The model incorporates the network effects in social domain and congestion in network domain which enables us to obtain insights from the sponsored content policy. We derive the closed-form solution, i.e., equilibrium, and prove its existence and uniqueness in each stage of the game. Additionally, we develop an iterative algorithm to obtain the Stackelberg equilibrium of the entire three-stage game. The simulation results indicate that the revenue, profit, and utility of the service provider, content provider, and end-users have been improved to a large extent under the sponsored content policy because of the network effects.

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

In cloud computing, cloud services can be allocated to users upon requests in an on-demand basis. Heterogeneous cloud service providers may join the cloud systems to serve various types of users. Cloud services can be complementary or substitutable. For the complementary services, users may request for a bundle of the services, e.g., CPU and storage, to gain higher benefit from requesting them alone. The substitutable services have similar functionalities to serve users, e.g., different cloud database services, obtaining one of them can replace another one. Furthermore, the users of the cloud systems also influence each other because of externalities, particularly, network effect and congestion effect. From the perspective of each user, the existence of other users may introduce positive or negative impacts on the user utility, in the case of network and congestion effects, respectively. In this work, the participants in the cloud systems are treated as social enabled rational individuals. We model the complementarity, substitutability and externalities in cloud services by employing a multiple-leader multiple- follower Stackelberg game approach, including a two-stage service transaction process where service providers and users make their transaction decisions in a distributed manner. The analytical expressions of equilibria, service pricing strategies, and service allocations are derived with numerical results. We also find in the numerical results that both collusive and competitive service pricing schemes may lead to the optimized provider and user performances simultaneously.

Publish-subscribe mobile social networks enable content providers to disseminate up-to-date contents to end users with the help of mobile content relays by opportunistic wireless contacts. Since content providers, relays and end users are independent and self-interest entities in the mobile social networks, the content relay has to take a content requesting/transferring action to achieve the lowest cost. In this paper, we propose and solve a Markov decision process (MDP) based scheme for the content relay to optimally take the actions to receive contents from content providers, and to transfer/forward contents to end users. The relay takes an action based on the observed content price, the number of end users of contents, as well as the queue length. The proposed MDP scheme aims to minimize an expected cost of the content relay. The numerical results show that the proposed MDP scheme significantly outperforms baseline schemes.

The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve performance. However, due to mobility and cloudlet capacity, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected cloudlet system, considering the users' local load and availability of cloudlets. We examine users' mobility patterns and cloudlets' admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.

Mobile social network (MSN) has been widely studied as a novel and effective way for communication in the current era of fast developing mobile devices, which enables the contents and services to be delivered opportunistically when mobile users contact. In this paper, we aim to optimize the content delivery from the content provider (CP) to the subscribers via relays in between. Tandem queueing model is applied to model the content delivery process, based on which absorbing Markov chain is used to derive the quality of the content delivery service received by the subscribers. The validity of the proposed queuing model has been verified by our simulation results. Observing that the content provider always has dominant control on the content delivery process and is pursuing the maximum profit by strategically designing the “rights” and “obligation” items for the subscribers, contract theory is adopted to reach an economically optimal solution. The numerical results verify the effectiveness of the contract-theoretic approach in maximizing the content provider's profit, and the capability to ensure the satisfaction of the heterogeneous subscribers with different quality of service (QoS) requirements.

The emergence of mobile cloud computing enables mobile users to dynamically offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve execution efficiency. However, due to mobility, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by a mobile user may fail (e.g., the user moves out of transmission range of cloudlets). In this paper, we model and develop an optimal offloading algorithm for the mobile user, considering the users' local load and availability of cloudlets. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with an objective to minimize the computation and offloading cost. The numerical results show that the proposed dynamic offloading algorithm outperforms conventional baseline schemes.