Uncrewed aerial vehicle (UAV) networks are increasingly exposed to widespread and various network attacks due to their fully distributed nature and the limited defensive capabilities of individual devices. Existing defense strategies rely on network connectivity and UAV status information, which overlook information of network traffic. Malicious traffic detection offers a promising solution to achieve fine-grained attack detection. However, the dynamic nature and complexity of UAV networks limit the effectiveness of traditional traffic detection methods. Current approaches either fail to fully exploit the raw characteristics of traffic or do not consider the timeliness requirements of UAV networks. To address these challenges, we propose a novel twin graph encoder neural network, which can extract features of raw traffic bytes for efficient traffic detection. First, we propose a decoupled architecture for model training and inference to enable efficient detection of malicious traffic in UAV networks. Second, we propose a novel modeling method that models traffic as the co-occurrence graph and word frequency graph based on raw bytes. Then, we propose TGE-ETD, a Twin Graph Encoder for Encrypted Traffic Detection. TGE-ETD consists of a set of twin graph encoders that effectively extract intrinsic traffic features from graphs constructed from raw bytes. In addition, TGE-ETD employs a global attention pooling mechanism to effectively distinguish the feature contributions of different bytes. Finally, we conducted extensive experiments on a real UAV traffic dataset and four real-world network traffic datasets. TGE-ETD achieved an improvement of 1%-20% over the baseline methods by reducing the number of parameters by 20 times. Tested on multiple UAV hardware devices, TGE-ETD can achieve millisecond-level traffic detection.

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$64.17\%$.

With the rapid advancement of unmanned aerial vehicle (UAV) technology, UAV cell free massive multiple-input multiple-output (CF-mMIMO) systems demonstrate significant potential for enhancing wireless communication performance. However, optimizing energy efficiency (EE) to meet the growing demands of communication has become a critical research challenge. This paper explores resource allocation in the UAV CF-mMIMO uplink under ultra reliable and low latency communication (URLLC) constraints to improve EE. We first conduct a systematic analysis in the system model to identify key factors impacting EE. Accounting for these factors, an optimization problem is then formulated. To solve this problem, we employ a self-supervised deep neural network (SSDNN) composed of two submodules: One is to extract channel characteristics of all users equipment (UEs), and the other is to optimally allocate power for both UAVs and ground user equipments (GUEs) via a resource allocation layer. Numerical results show that our proposed method achieves higher EE compared to approaches that do not consider GUE interference.

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.

Watermarking for video playback authorization faces the classic challenge of balancing imperceptibility and robustness, while also maintaining resilience against statistical attacks. This paper introduces a novel scheme that integrates hyperchaos, a human visual system (HVS) model, and asymmetric modulation to address these challenges. First, a four-dimensional hyperchaotic system is constructed to achieve triple dynamic randomization of the watermark information, embedding locations, and embedding strength, thereby enhancing security. Guided by an HVS-based just noticeable distortion (JND) model, a spatio-temporally adaptive embedding strength is then derived, maximizing robustness under strict imperceptibility constraints. Furthermore, a blind extraction mechanism using coefficient-relation modulation is designed, inherently improving resilience against common video processing and malicious attacks. Collectively, these strategies unify imperceptibility, robustness, and security. The experimental results confirm the algorithm’s superior performance against benchmarks. It exhibits stronger resistance to statistical analysis, with a mean Kullback-Leibler (KL) divergence of only 0.003, and enhanced watermark robustness, shown by a 61.8% improvement in normalized correlation (NC). For imperceptibility, it achieves a gain in the peak signal-to-noise ratio (PSNR) over 2.2 dB.

As more users seek generative AI (GAI) models to enhance work efficiency, GAI and Model-as-a-Service will drive transformative changes and upgrades across all industries. However, when users utilize GAI models provided by the service provider, they cannot be certain that the model’s quality matches the provider’s claims. Considering the need to protect intellectual property, the service provider will not disclose model details for user verification. To this end, we take the Internet of Vehicles as research background, proposing a zero knowledge model proof architecture based on UAVs. We also introduce a multiagent reinforcement learning algorithm to optimize the verification process. In specific, we first propose a verification scheme for the key operations of generative adversarial networks based on noninteractive zero knowledge proof. The zero knowledge proof architecture ensures that model parameters cannot be stolen during the verification process. After that, we propose an Age of Verification (AoV) metric to ensure the timeliness and freshness of zero knowledge proof. We also construct a tradeoff optimization problem between the energy consumption of UAV as a verifier and the AoV of edge servers as service providers, and transform the problem based on Lyapunov optimization theory. Following that, we propose an enhanced multiagent proximal policy optimization algorithm to enable the collaborative verification of edge servers by multiple UAVs. The algorithm simulation results demonstrate that the reward value of our proposed algorithm is over 10% higher than that of the standard algorithm, with a faster and more stable overall convergence speed. Additionally, the zero knowledge proof performance test results indicate that the verification delay in our proposed architecture is less than 500 ms during the verification phase, meeting practical requirements.

Embodied Artificial Intelligence (AI) bridges the cyberspace and the physical space, driving advancements in autonomous systems like theVehicularEmbodiedAINETwork (VEANET). VEANET integrates advanced AI capabilities into vehicular systems to enhance autonomous operations and decision-making. Embodied agents, such as Autonomous Vehicles (AVs), are autonomous entities that can perceive their environment and take actions to achieve specific goals, actively interacting with the physical world. Embodied Agent Twins (EATs) are digital models of these embodied agents, with various Embodied Agent AI Twins (EAATs) for intelligent applications in cyberspace. In VEANETs, EAATs act as in-vehicle AI assistants to perform diverse tasks supporting autonomous driving using generative AI models. Due to limited onboard computational resources, AVs offload EAATs to nearby RoadSide Units (RSUs). However, the mobility of AVs and limited RSU coverage necessitates dynamic migrations of EAATs, posing challenges in selecting suitable RSUs under information asymmetry. To address this, we construct a multi-dimensional contract theoretical model between AVs and alternative RSUs. Considering that AVs may exhibit irrational behavior, we utilize prospect theory instead of expected utility theory to model the actual utilities of AVs. Finally, we employ a Generative Diffusion Model (GDM)-based algorithm to identify the optimal contract designs, thus enhancing the efficiency of EAAT migrations. Numerical results demonstrate the superior efficiency of the proposed GDM-based scheme in facilitating EAAT migrations compared with traditional deep reinforcement learning methods.

Generative Artificial Intelligenge (GAI) is revolutionizing the world with its unprecedented content creation ability. Large Language Model (LLM) is one of its most embraced branches. However, due to LLM’s substantial size and resource-intensive nature, it is cloud-hosted, raising concerns about privacy, usage limitations, and latency. In this paper, we propose to utilize ubiquitous distributed wireless edge computing resources for real-time LLM inference. Specifically, we introduce a novel LLM edge inference framework, incorporating batching and model quantization to ensure high throughput inference on resource-limited edge devices. Then, based on the architecture of transformer decoder-based LLMs, we formulate an edge inference optimization problem which is NP-hard, considering batch scheduling and joint allocation of communication and computation resources. The solution is the optimal throughput under edge resource constraints and heterogeneous user requirements on latency and accuracy. To solve this NP-hard problem, we develop an OT-GAH (Optimal Tree-search with Generalized Assignment Heuristics) algorithm with reasonable complexity and$\frac {1}{2}$-approximation ratio. We first design the OT algorithm with online tree-pruning for single-edge-node multi-user case, which navigates the inference request selection within the tree structure to miximize throughput. We then consider the multi-edge-node case and propose the GAH algorithm, which recrusively invokes the OT in each node’s inference scheduling iteration. Simulation results demonstrate the superiority of OT-GAH batching over other benchmarks, revealing an over 45% time complexity reduction compared to brute-force searching.

In recent years, there has been a proliferation of Edge Intelligence (EI) services, especially within Internet of Vehicles (IoV) scenarios, accompanied by a growing demand for multi-modal content generation. In response, Generative Artificial Intelligence (GAI) has emerged as a promising solution, equipping EI to produce diverse Artificial Intelligence-Generated Content (AIGC) for ubiquitous edge services. However, existing cloud-based GAI capabilities, which are mostly provided via the web and the Internet, introduce unacceptable latency overhead and heightened security risks for vehicular services. To address the above shortcomings and the lack of endogenous mechanisms for applying GAI to IoV scenarios, in this paper, we propose a layered vehicular GAI framework that seamlessly integrates GAI and EI. Within this framework, we devise a distributed collaborative inference mechanism between Road-Side Units (RSUs) and vehicles. Furthermore, we formulate the shared and local inference splitting problem, a pivotal challenge influencing both GAI service latency and content-generation capability. To tackle this issue, we introduce a backward induction-based algorithm, which enables the system can make splitting decisions using a simple threshold-based policy. Simulation results underscore the remarkable performance of the proposed system and vehicular collaborative inference mechanism, promising to facilitate diverse content generation within vehicular networks.

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 utilization of emergent Generative Artificial Intelligence (GAl) within the realm of Internet of Vehicles (loV) can augment edge intelligence, thereby catering to the diverse content-generation needs of novel in-vehicle services. Nonethe-less, existing cloud-centric GAl paradigms are not inherently suitable for wireless vehicular networks, primarily due to their extensive computing requirements, lack of specificity, and spatial detachment from end users. To cope with these challenges, we introduce an innovative Generative 10 V (g 10 V)architecture that employs a collaborative fine-tuning mechanism for pre-trained GAl models. The mechanism is mainly orchestrated collaboratively by Road-Side Units (RSUs) and vehicles within a Federated Learning (FL) paradigm. Here, we take text-to-image diffusion models as typical examples to show the co-fine-tuning workflow in detail, aiming to utilize edge traffic data to realize rapid, customized, and lightweight GAl in the resource-limited 10 V scenario. Thereafter, we formulate the problem of edge communication and computation resource allocation during RSU-vehicle co-fine-tuning, which is pivotal for optimizing time and energy consumption within this process. To address the challenge, we deploy a Self-adaptive Harmony Search (SHS)-based resource allocation strategy. Experiments based on Stable Diffusion vl-4 model validate the excellent performance in image generating and the time and energy consumption during co-fine-tuning in resource-limited and fast-changing 10 V scenarios.

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.

Generative diffusion models (GDMs) have emerged as potent tools for generating high-quality, creative content across various media, including audio, images, videos, and 3-D models. Their application in artificial intelligence-generated content (AIGC) marks a pivotal advancement in the evolution from the Internet of Things (IoT) to the Artificial Intelligence of Things (AIoT). Considering the inherent multiple-access nature of AIoT, training GDMs via federated learning and deploying them collaboratively is paramount. However, such approaches introduce considerable security risks and energy consumption challenges. To address these issues, we propose a comprehensive architecture for GDMs, encompassing both training and sampling stages. This architecture, termed secure and sustainable diffusion (SS-Diff), aims to thwart trigger-based security threats, such as backdoor attacks and trojan attacks, while simultaneously reducing energy consumption in multiple-access AIoT. The SS-Diff architecture incorporates a dynamic quantization mechanism within the training phase, significantly reducing communication overhead and thereby improving both spectrum and energy efficiency. During the sampling stage, a detection-based defense strategy is employed to identify and negate trigger inputs associated with malicious attacks. Through extensive simulations, we evaluate the performance of the SS-Diff architecture. The results demonstrate that the SS-Diff can effectively train GDMs and eliminate the impact of the attacks, compared with existing schemes.

Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys. To maintain uninterrupted metaverse experiences, VTs must be migrated among edge servers following the movements of vehicles. This can raise concerns about privacy breaches during the dynamic communications among vehicular edge metaverses. To address these concerns and safeguard location privacy, pseudonyms as temporary identifiers can be leveraged by both VMUs and VTs to realize anonymous communications in the physical space and virtual spaces. However, existing pseudonym management methods fall short in meeting the extensive pseudonym demands in vehicular edge metaverses, thus dramatically diminishing the performance of privacy preservation. To this end, we present a cross-metaverse empowered dual pseudonym management framework. We utilize cross-chain technology to enhance management efficiency and data security for pseudonyms. Furthermore, we propose a metric to assess the privacy level and employ a Multi-Agent Deep Reinforcement Learning (MADRL) approach to obtain an optimal pseudonym generating strategy. Numerical results demonstrate that our proposed schemes are high-efficiency and cost-effective, showcasing their promising applications in vehicular edge metaverses.

The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physical and virtual spaces, transforming drone interaction and virtual exploration. UAV Twins (UTs), as the digital twins of UAVs that revolutionize UAV applications by making them more immersive, realistic, and informative, are deployed and updated on ground base stations, e.g., RoadSide Units (RSUs), to offer metaverse services for UAV Metaverse Users (UMUs). Due to the dynamic mobility of UAVs and limited communication coverages of RSUs, it is essential to perform real-time UT migration to ensure seamless immersive experiences for UMUs. However, selecting appropriate RSUs and optimizing the required bandwidth is challenging for achieving reliable and efficient UT migration. To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses. Specifically, we formulate a multi-leader multifollower Stackelberg model considering a new immersion metric of UMUs in the utilities of UAVs. Then, we design a Tiny Multi-Agent Deep Reinforcement Learning (Tiny MADRL) algorithm to obtain the tiny networks representing the optimal game solution. Specifically, the actor-critic network leverages the pruning techniques to reduce the number of network parameters and achieve model size and computation reduction, allowing for efficient implementation of Tiny MADRL. Numerical results demonstrate that our proposed schemes have better performance than traditional schemes.

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.

Lightweight training and distributed tiny data storage in local model will lead to the severe challenge of convergence for tiny federated learning (FL). Achieving fast convergence in tiny FL is crucial for many emerging applications in Internet of Unmanned Aerial Vehicles (IUAVs) networks. Excessive information exchange between UAVs and IoT devices could lead to security risks and data breaches, while insufficient information can slow down the learning process and negatively system performance experience due to significant computational and communication constraints in tiny FL hardware system. This paper proposes a trusting, low latency, and energy-efficient tiny wireless FL framework with blockchain (TBWFL) for IUAV systems. We develop a quantifiable model to determine the trustworthiness of IoT devices in IUAV networks. This model incorporates the time spent in communication, computation, and block production with a decay function in each round of FL at the UAVs. Then it combines the trust information from different UAVs, considering their credibility of trust recommendation. We formulate the TBWFL as an optimization problem that balances trustworthiness, learning speed, and energy consumption for IoT devices with diverse computing and energy capabilities. We decompose the complex optimization problem into three sub-problems for improved local accuracy, fast learning, trust verification, and energy efficiency of IoT devices. Our extensive experiments show that TBWFL offers higher trustworthiness, faster convergence, and lower energy consumption than the existing state-of-the-art FL scheme.

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.

In heterogeneous networks (HetNets), although deploying numerous small base stations (SBSs) can effectively enhance spectral efficiency (SE), it is difficult to achieve seamless coverage due to their fixed locations. To handle this issue, we propose a HetNet structure assisted by unmanned aerial vehicles (UAVs), where the high mobility and flexible deployment of UAVs are leveraged. However, interference is inevitable in the proposed UAV-assisted HetNet, thus we design a comprehensive interference management (IM) scheme, selectively adopting successive interference cancellation (SIC) algorithm, space-time interference alignment (STIA) and interference steering (IS) according to the location of users and interference types. The numerical results verify that with SIC-STIA-IS scheme, the proposed UAV-assisted HetNet is advantageous in degrees of freedom (DoF) and sum rate.

In this paper, the performance of a multiple-in-single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system assisted by simultaneous trans-mitting and reflecting reconfigurable intelligent surface (STAR-RIS) under fading channel is studied. Firstly, the joint BS active beamforming and STAR-RIS passive beamforming are discussed. Based on that and using the Gamma approximation method, the statistical characteristics of the equivalent cascaded channels for BS-IR and BS-ER assisted by STAR-RIS are analyzed and derived, including the first-order and second-order moments, as well as the distribution function (CDF) and probability density function (PDF). Furthermore, we define and derive the rate outage probability of IR and power outage probability of ER and their approximate expressions at high SNR. Finally, the theoretical analysis results are verified by numerical simulations, and it is confirmed that the number of STAR-RIS units has positive effects on improving the system outage performance.

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 popularization of smart Internet of Things (IoT) devices has facilitated the development of fog/edge computing. However, these infrastructure-based service paradigms may fail to complete tasks successfully due to computation and communication overload, or damage in challenging scenarios such as disasters or traffic jams. Noticing that a crowd of devices with considerable idle resources could be available, we investigate the problems of addressing the computation and communication unavailability with peer assistance in this work. To this end, we propose a dispersed service framework for resource-exhausted scenarios that adaptively offloads users’ data to available network computation points. However, the users may not be able to achieve the offloading due to geographical hindrances. Consequently, the relay is introduced as a bridge for data offloading between the users and the network computation points. Furthermore, a game-based incentive-driven offloading mechanism is designed by analyzing and balancing the cost and gain factors of three main entities (users, relays, and network computation points). Considering the interactions among the entities, a two-level Stackelberg game is established for efficiently allocating potential computation resource, as well as balancing the utility conflicts due to the data offloading. Given the hierarchical interaction structure, the upper level game involves network computation points as followers and the relay as a leader, while the lower level game includes the relay as a follower and users as leaders. Moreover, to facilitate applicability in large-scale scenarios with multiple relays, we decompose multiple relays into multiple single relay problems using a tripartite matching strategy that assigns appropriate relays to users and network computation points. The simulation results demonstrate the effectiveness of the proposed game-based incentive-driven mechanism and show that it outperforms the baselines in terms of the overall utilities of the involved entities and the average energy consumption of users.

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design.

Intelligent reflecting surface (IRS) is considered as an effective technology to enhance the performance of wireless communication systems. In this paper, the robust optimization design of the IRS-assisted wireless multi-group multicast MISO system with statistical CSI errors is investigated. Two optimization problems, namely max-min fairness problem and QoS problem, are discussed separately. In order to deal with the non-convex imperfect CSI constraint, the Bernstein-type inequality is utilized to transform the outage probability constraint into a second-order cone (SOC) constraint and linear inequalities. Furthermore, two efficient algorithms based on alternating optimization (AD) are proposed to solve the reformulated problems, respectively. In particular, the semi-definite relaxation (SDR) technique is applied to optimize the transmit beamforming and IRS reflection coefficients. The numerical simulation results indicate that by deploying IRS and utilizing the proposed algorithms, the system performance can be improved significantly. However, the gain of introducing IRS in the system heavily depends on the bound of the CSI error.

In fifth generation networks (5G), beamforming technique is widely used to obtain higher system capacity, but it cannot eliminate inter-user interference (IUI) of networks due to excessive number of users. To handle this problem, interference alignment (IA) schemes attract great attention as they can effectively restrain IUI. However, the existing IA schemes cannot achieve antenna adaptation and the obtained degree of freedom (DoF) may be not optimal. In this paper, a novel antenna adaptation based interference elimination and regeneration (AA-IER) scheme is proposed for cooperative networks, where a relay with hybrid antenna array structure is adopted to assist the communication. The proposed transmission process is completed in two phases, including interference elimination phase (IEP) and interference regeneration phase (IRP). For the former, the IUI is eliminated and the redundant symbols are erased so that the received signal of multiple users can be decoded simultaneously. For the latter, the redundant symbols of all users are regenerated where the space resources are fully utilized. The simulation results show that AA-IER scheme obtains higher DoF than that of three benchmark schemes. Meanwhile, it requires fewer antennas of relay than HAA-CIE-RIA scheme.

Leveraging on the wealth of data and advancements in Artificial Intelligence, smart cities have demonstrated their great potential in providing solutions to challenges that the urban population faces today. However, as the urban population becomes more privacy sensitive and with the introduction of stringent privacy regulations, the differential-private FL (DPFL) is a promising technology that can enable privacy-preserving collaborative model training. In this paper, we consider an FL network of model owners and data owners with heterogeneous privacy budgets and preferences respectively. In exchange for their participation in the training, the model owner offers a reward pool that is shared among the data owners that take part in the FL training. In turn, the FL worker with heterogeneous privacy preferences may select the model owner to contribute its parameters to. To model the dynamic and strategic behaviour of the workers in the process of model owner selection, we propose an evolutionary game approach. Then, we conduct simulations to validate the evolutionary equilibrium, as well as provide the sensitivity analyses of the model.

With the vehicle-to-grid and computing capabilities, a parked electric vehicle (EV) has a dual role, namely being an energy prosumer as well as a computing node for accommodating computation-offloading services. This dual-role feature of EVs yields a new computing paradigm named Electric Vehicle Edge Computing (EVEC). To ease the implementation of EVEC, we propose a fine-grained EV management approach to jointly provide parking guidance for EVs and control their charging/discharging power in parking lots. We formulate a bilevel optimization problem where the top-level problem optimizes the matching between EVs and parking lots from the perspective of computation offloading, and the bottom-level problem optimizes the control of EV charging/discharging power from the view of power networks. We transform the bilevel optimization problem into a single-level form, which is a nonconvex mixed-integer nonlinear programming problem, and we further tackle it by linearization techniques. Finally, we provide numerical results to demonstrate the efficiency and effectiveness of our approach.

Abstract The design of dynamic wireless networks assisted by reconfigurable intelligent surfaces (RIS) mounted on aerial platforms (RIS‐APs) is conceived, where the connection status among users and RIS‐APs are selected according to the average channel quality dynamically and timely. Taking into account the time‐varying selection status and the mobility of users, we construct a long‐term dynamic process. The goal is to minimize the time‐averaged power consumption under the requirements of the time‐averaged minimum rate for users as well as the constraint of the maximum transmit power for the base station (BS), via jointly optimizing the active beamforming at the BS and passive beamforming at RIS‐APs. With the aid of Lyapunov concept‐based drift‐plus‐penalty (DPP) algorithm, the long‐term optimization problem is transformed into short‐term sub‐problems related to each other at each frame. Subsequently, the fractional programming method based on Lagrangian dual theory is applied to derive the solutions for active‐passive beamforming in a closed form. Finally, simulation results validate the convergence and effectiveness of the proposed algorithm.

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.

Advances in communications and networking technologies are driving the computing paradigm toward the end-edge-cloud collaborative architecture to leverage ubiquitous data and resources. Opposite to centralized intelligence, Hierarchical Federated Learning (HFL) relieves overwhelmed communication overhead and enjoys the advantages of high bandwidth as well as abundant computing resources while retaining privacy-preserving benefits of Federated Learning (FL). It is difficult to balance system overhead and model performance in the HFL framework, while it could be solved by introducing an incentive mechanism. Although the incentive mechanism can alleviate the above anxiety by compensating relevant participants, some limitations (multi-dimensional properties, incomplete information and unreliable participants) will significantly degrade the performance and efficiency of the designed mechanism. To address the challenges caused by the above limitations, we propose InFEDge, a blockchain-based incentive mechanism in the HFL. The InFEDge considers 1) multi-dimensional individual properties to model system participants and proves the uniqueness of Nash equilibrium with the closed-form solution. Meanwhile, 2) we transform the problem under incomplete information into a contract game where we obtain the optimal solution. Moreover, 3) we also leverage the blockchain to provide economic incentives, prevent unreliable participants’ disturbance and further ensure data privacy by implementing the mechanism in the smart contract to offer a credible, faster, and transparent resource trading system. Experimental evaluations on a proof-of-concept testbed along with real traces demonstrate the superiority of our mechanism. Further, our method solves a real-world user allocation problem for future communications and networking.

The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT). In maritime IoT, the spatio-temporal vessel trajectories, collected from the hybrid satellite-terrestrial automatic identification system (AIS) base stations, are of considerable importance for promoting traffic situation awareness and vessel traffic services, etc. To guarantee traffic safety and efficiency, it is essential to robustly and accurately predict the AIS-based vessel trajectories (i.e., the future positions of vessels) in maritime IoT. In this work, we propose a spatio-temporal multigraph convolutional network (STMGCN)-based trajectory prediction framework using the mobile edge computing (MEC) paradigm. Our STMGCN is mainly composed of three different graphs, which are, respectively, reconstructed according to the social force, the time to closest point of approach, and the size of surrounding vessels. These three graphs are then jointly embedded into the prediction framework by introducing the spatio-temporal multigraph convolutional layer. To further enhance the prediction performance, the self-attention temporal convolutional layer is proposed to further optimize STMGCN with fewer parameters. Owing to the high interpretability and powerful learning ability, STMGCN is able to achieve superior prediction performance in terms of both accuracy and robustness. The reliable prediction results are potentially beneficial for traffic safety management and intelligent vehicle navigation in MEC-enabled maritime IoT.

With the rapid development of cloud manufacturing, industrial production with edge computing as the core architecture has been greatly developed. However, edge devices often suffer from abnormalities and failures in industrial production. Therefore, detecting these abnormal situations timely and accurately is crucial for cloud manufacturing. As such, a straightforward solution is that the edge device uploads the data to the cloud for anomaly detection. However, Industry 4.0 puts forward higher requirements for data privacy and security so that it is unrealistic to upload data from edge devices directly to the cloud. Considering the abovementioned severe challenges, this article customizes a weakly supervised edge computing anomaly detection framework, i.e., federated learning-based transformer framework (FedAnomaly), to deal with the anomaly detection problem in cloud manufacturing. Specifically, we introduce federated learning (FL) framework that allows edge devices to train an anomaly detection model in collaboration with the cloud without compromising privacy. To boost the privacy performance of the framework, we add differential privacy noise to the uploaded features. To further improve the ability of edge devices to extract abnormal features, we use the transformer to extract the feature representation of abnormal data. In this context, we design a novel collaborative learning protocol to promote efficient collaboration between FL and transformer. Furthermore, extensive case studies on four benchmark datasets verify the effectiveness of the proposed framework. To the best of our knowledge, this is the first time integrating FL and transformer to deal with anomaly detection problems in cloud manufacturing.

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.

The effective combination of physical layer security communication and intelligent reflecting surface (IRS) technology has recently attracted extensive attention to improve the system security. Unlike existing works that mostly focus on single-carrier systems, we consider an IRS-assisted multi-carrier MIMO wireless physical layer security communication system, which consists of a legitimate transmitter, a legitimate receiver, an IRS node and an eavesdropper. With the aim of maximizing the sum secrecy rate, the precoding matrix and IRS reflecting coefficient matrix were jointly optimized under the constraints on the budget of the transmit power and unit modulus of IRS reflecting coefficients. An alternate optimization (AO) based inexact block coordinate descent (IBCD) algorithm was proposed to tackle the non-convexity of the formulated problem, where the Lagrange multiplier method and complex circle manifold (CCM) method were adopted to solve the subproblems and then closed-form solutions were obtained at each iteration. Finally, the simulation results validate the effectiveness of the proposed beamforming schemes.

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.

Blockchain technologies allow the Internet of Things (IoT) to build trust among various interest parties. For the resource-limited IoT devices, offloading computation-intensive tasks (blockchain verification and mining tasks, and data process tasks) to edge servers for execution is considered as a promising solution in mobile-edge computing. However, conventional methods (such as linear programming or game theory) for the computation offloading problem cannot achieve long-term performance while the existing deep reinforcement learning (DRL)-based algorithms suffer from slow convergence, lack of robustness, and unstable performance. In this article, we propose a multiagent DRL framework to achieve long-term performance for cooperative computation offloading, in which a scatter network is adopted to improve its stability and league learning is introduced for agents to explore the environment collaboratively for fast convergence and robustness. First, we study the nonorthogonal multiple access-enabled cooperative computation offloading problem and formulate the joint problem as a Markov decision process by considering both the blockchain mining tasks and data processing tasks. Second, to avoid useless exploration and unstable performance, we initially train an intelligent agent represented by scatter networks using conventional expert strategies. Third, in order to enhance the performance, we subsequently establish a hierarchical league where agents collaborate with others to explore the environment. Finally, our experimental results demonstrate that our algorithm could perform better in terms of reducing energy cost and delay cost, and shortening almost 60% of the training time compared with the state-of-the-art approaches.

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.

Due to air quality significantly affects human health, it is becoming increasingly important to accurately and timely predict the air quality index (AQI). To this end, this article proposes a new federated learning (FL)-based aerial-ground air quality sensing framework for fine-grained 3-D air quality monitoring and forecasting. Specifically, in the air, this framework leverages a lightweight Dense-MobileNet model to achieve energy-efficient end-to-end learning from haze features of haze images taken by unmanned aerial vehicles (UAVs) for predicting AQI scale distribution. Furthermore, the FL framework not only allows various organizations or institutions to collaboratively learn a well-trained global model to monitor AQI without compromising privacy but also expands the scope of UAV swarms monitoring. For ground sensing systems, we propose a graph convolutional neural network-based long short-term memory (GC-LSTM) model to achieve accurate, real time, and future AQI inference. The GC-LSTM model utilizes the topological structure of the ground monitoring station to capture the spatiotemporal correlation of historical observation data, which helps the aerial-ground sensing system to achieve accurate AQI inference. Through extensive case studies on a real-world data set, numerical results show that the proposed framework can achieve accurate and energy-efficient AQI sensing without compromising the privacy of raw data.

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%.

Vehicle taillight detection is essential to analyze and predict driver intention in collision avoidance systems. In this article, we propose an end-to-end framework that locates the rear brake and turn signals from video stream in real-time. The system adopts the fast YOLOv3-tiny as the backbone model and three improvements have been made to increase the detection accuracy on taillight semantics, i.e., additional output layer for multi-scale detection, spatial pyramid pooling (SPP) module for richer deep features, and focal loss for alleviation of class imbalance and hard sample classification. Experimental results demonstrate that the integration of multi-scale features as well as hard examples mining greatly contributes to the turn light detection. The detection accuracy is significantly increased by 7.36%, 32.04% and 21.65% (absolute gain) for brake, left-turn and right-turn signals, respectively. In addition, we construct the taillight detection dataset, with brake and turn signals are specified with bounding boxes, which may help nourishing the development of this realm.

The mobile crowdsensing paradigm facilitates a broad range of emerging sensing applications by leveraging ubiquitous mobile users to cooperatively perform certain sensing tasks with their smart devices. As this paradigm involves data collection from users, the issue of designing rewards to incentivize users is fundamentally important to ensure participation in crowdsensing. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in healthcare-based crowdsensing services, the fun of tracking daily nutrition information for a certain user can be promoted by comparing her nutritional information with that contributed and shared by her socially-connected friends. To be more general and practical, we study the incentive mechanisms 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 mechanisms. With this focus, 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 prove the existence and uniqueness of the Stackelberg equilibrium. We conduct extensive simulations to investigate game equilibrium properties, and the real-world dataset is applied to evaluate and demonstrate the performance effectiveness of the proposed game model.

Mobile crowdsensing has shown great potential in addressing large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive a satisfactory reward. In this paper, to effectively and efficiently recruit a sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. The numerical results corroborate the fact that the network effects significantly stimulate a higher mobile participation level and greater revenue for the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider achieve greater revenue gain.

Mobile Crowdsensing has shown a great potential to address large-scale problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs will participate in a Crowdsensing platform if they can receive satisfactory reward. In this paper, in order to effectively and efficiently recruit sufficient MUs, i.e., participants, we investigate an optimal reward mechanism of the monopoly Crowdsensing Service Provider (CSP). We model the rewarding and participating as a two-stage game, and analyze the MUs' participation level and the CSP's optimal reward mechanism using backward induction. At the same time, the reward is designed taking the underlying social network effects amid the mobile social network into account, for motivating the participants. Namely, one MU will obtain additional benefits from information contributed or shared by local neighbours in social networks. We derive the analytical expressions for the discriminatory reward as well as uniform reward with complete information, and approximations of reward incentive with incomplete information. Performance evaluation reveals that the network effects tremendously stimulate higher mobile participation level and greater revenue of the CSP. In addition, the discriminatory reward enables the CSP to extract greater surplus from this Crowdsensing service market.

As a popular approach to implementing Visible Light Communication (VLC) on commercial-off-the-shelf devices, LED-Camera VLC has attracted substantial attention recently. While such systems initially used reflected light as the communication media, direct light becomes the dominant media for the purpose of combating interference. Nonetheless, the data rate achievable by direct light LED-Camera VLC systems has hit its bottleneck: the dimension of the transmitters. In order to further improve the performance, we revisit the reflected light approach and we innovate in converting the potentially destructive interferences into collaborative transmissions. Essentially, our ReflexCode system codes information by superposing light emissions from multiple transmitters. It combines traditional amplitude demodulation with slope detection to "decode" the grayscale modulated signal, and it tunes decoding thresholds dynamically depending on the spatial symbol distribution. In addition, ReflexCode re-engineers the balanced codes to avoid flicker from individual transmitters. We implement ReflexCode as two prototypes and demonstrate that it can achieve a throughput up to 3.2kb/s at a distance of 3m.

As a popular approach to implementing Visible Light Communication (VLC) on commercial-off-the-shelf devices, LED-Camera VLC has attracted substantial attention recently. While such systems initially used reflected light as the communication media, direct light becomes the dominant media for the purpose of combating interference. Nonetheless, the data rate achievable by direct light LED-Camera VLC systems has hit its bottleneck: the dimension of the transmitters. In this demo, we revisit the reflected light approach and propose a novel modulation mechanism, ReflexCode, which converts the potentially destructive interferences into collaborative transmissions. Essentially, our ReflexCode system codes information by superposing light emissions from multiple transmitters. It combines traditional amplitude demodulation with slope detection to "decode" the grayscale modulated signal, and it tunes decoding thresholds dynamically depending on the spatial symbol distribution. In addition, ReflexCode re-engineers the balanced codes to avoid flicker from individual transmitters. We implement ReflexCode as a prototype and demonstrate that it can achieve a promising throughput.