Satellite-enabled multi-access edge computing (MEC) networks have emerged as a promising solution for low-latency data processing in areas lacking infrastructure. However, these satellite MEC networks face significant security vulnerabilities and high communication latency due to the open-air interface and large-scale data transmission. To address these challenges, we propose a secrecy-aware adaptive federated learning (AFL) approach for a satellite MEC network. In this network, terrestrial devices perform local model training using their own data and periodically transmit updated model parameters to a satellite server in the presence of an eavesdropper. To secure both model uploading and downloading, idle devices act as friendly jammers, transmitting jamming signals to disrupt eavesdropping attempts. Our goal is to minimize the overall federated learning latency by jointly optimizing the number of quantization bits, the transmit power of MEC devices, the satellite’s transmit power, and the jammer selection strategy. To solve this problem, we first propose an AFL framework that minimizes the model uploading size while ensuring the required model accuracy. Building on this, the problem is divided into two subproblems of model uploading and model downloading, which are solved using a successive convex approximation (SCA)-based algorithm. Additionally, to improve secrecy performance, we introduce a low-complexity jammer selection strategy that significantly enhances the secrecy rate for both model uploading and downloading. Simulation results demonstrate that the proposed scheme significantly outperforms baseline methods in terms of AFL convergence, secrecy performance, and overall latency.

Leveraging their mobility and feasibility, Unmanned Aerial Vehicles (UAVs) present a promising solution for assisting covert communications to mitigate the risk of eavesdropping. However, existing studies mainly rely on passive optimization, where the UAV adjusts its transmit parameters according to the channel state, without actively balancing covertness constraints and average system throughput. To solve the above challenge, we propose for the first time a UAV relay-assisted covert communication framework with a buffer. Specifically, we derive the optimal detection threshold for the eavesdropper with mobility and uncertain locations, and obtain a closed-form solution for the lowest detection error probability. To solve the formulated average system throughput maximization problem, we transform the covertness constraint into a tractable analytical form, and obtain the optimal transmit power for both the UAV relay and the friendly UAV jammer. Then, through a rigorous theoretical analysis of upper and lower bounds on average system throughput, we prove the existence of optimal UAV trajectories. Finally, optimal transmission and reception decisions of the UAV relay are derived under covertness and buffer size constraints. Numerical results and theoretical analysis demonstrate the effectiveness of the proposed scheme in terms of average system throughput and covert performance.

Enabled by 6G wireless technologies, Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) create a new dimension for optimizing performance in Uncrewed Aerial Vehicle (UAV) communications through fullspace signal coverage. However, existing research on STAR-RIS-assisted UAV secure communications still faces critical challenges, including the reliance on ideal Channel State Information (CSI) assumptions, amplitude optimization complexity under mode switching protocols, and limited scalability to meet multi-user communication demands. To address these challenges, we propose a robust and secure STAR-RIS-assisted UAV communication approach for a multi-user and multi-eavesdropper scenario. By jointly optimizing user scheduling, transmitting and reflecting coefficients of STAR-RIS, transmit power and flight trajectory of UAV, we aim to maximize the average worst-case achievable secrecy rate. To tackle the non-convexity and coupled decision variables of the formulated problem, we propose an alternating optimization framework, with a Lagrange multiplier method for power allocation, a deterministic model reformulated via S-procedure for CSI uncertainty quantification and robust handling, and a penalty-based double-loop iterative algorithm forcing the phase-shift matrix toward a rank-one solution. Finally, theoretical analysis and simulation results validate the superior secrecy performance of the proposed algorithm over other representative algorithms.

This paper proposes a novel robust covert integrated sensing and communication (RC-ISAC) system, where mobile Warden tracking is leveraged to assist covert communication design. We focus on a typically overlooked yet highly threatening Warden-blocked scenario, in which temporary tracking loss prevents timely updates of covert communication strategy. To overcome this challenge, the reconfigurable intelligent surface (RIS) is introduced to establish a controllable sensing link that bypasses the obstacle. Furthermore, a robust extended Kalman filtering (R-EKF) strategy with a sensing-failure fallback mechanism is developed to achieve reliable Warden tracking, where sensing failures are detected and promptly addressed through re-scanning of the Warden. In addition, a high-capacity covert optimization (HCO) scheme is proposed to improve the covert transmission performance and maintain reliable Warden tracking, which is achieved by the joint design of ISAC sensing-communication beamforming and RIS passive beamforming. Simulation results demonstrate that the proposed RC-ISAC system achieves superior robustness and covert transmission performance compared with the no-RIS baseline and the element-wise optimization baseline.

Low Earth Orbit (LEO) satellite networks are expected to become a key component of Sixth Generation (6G) communication networks, to relieve the communication burden on ground networks. Driven by the rapid advancement of communication technologies and intelligent applications, dense traffic flow in the Internet of Vehicles (IoV) inevitably leads to a surge in task generation and an increased demand for network resources. The requirement for low latency further intensifies this challenge, making it difficult to rely solely on ground network resources to process tasks efficiently and promptly. Conversely, relying only on satellite networks for task processing results in high costs. Therefore, flexibly integrating LEO satellite links based on real-time traffic conditions, task demands, and the real-time state of ground network resources becomes an effective solution. However, achieving such a goal poses significant challenges in efficient allocation and balance between ground and LEO satellite network resources. Therefore, we propose a dynamic multi-task partial offloading algorithm based on LEO satellite-ground network collaboration to efficiently allocate resources between ground and satellite networks in real time. We first introduce the utility gain as a metric to evaluate task scheduling preference and design an improved iterative algorithm to jointly optimize the offloading ratio and channel allocation to maximize system utility. Finally, based on the real-world dataset of Shanghai (China), we demonstrate the significant advantages of the proposed strategy over representative methods in terms of delay, vehicle satisfaction, and system utility.

Integrated Sensing and Communication (ISAC) can assist Uncrewed Aerial Vehicle (UAV) secure communications by acquiring information about eavesdroppers. However, existing studies have not systematically investigated ISAC beamforming for simultaneously sensing the channel information of ground eavesdroppers, jamming eavesdropping links, and communicating with users, which poses significant challenges in ensuring both sensing accuracy and communication confidentiality. To address this issue, we propose a UAV-ISAC secure communication algorithm to maximize average secrecy rate by jointly optimizing communication and sensing beamforming, user scheduling, sensing time allocation, and UAV trajectory. We address the formulated NP-hard problem by decomposed it into three subproblems. We first relax binary user scheduling and sensing time allocation by a penalty-based successive convex approximation approach. The UAV trajectory is then iteratively optimized while beamforming is designed using semidefinite relaxation, with matrix lifting applied to handle the rank-one constraint. A triple-layer iterative algorithm is constructed by integrating these steps to achieve a suboptimal solution. Numerical experiment results and theoretical analysis validate the superiority of the proposed algorithm in terms of average secrecy rate, convergence and computational complexity.

The flexible deployment of Unmanned Aerial Vehicle (UAV) swarms holds significant potential for low-altitude economy, but their communication security is severely threatened by malicious jamming. Generally, existing anti-jamming methods often overlook multi-user interference in swarm scenarios and fail to exploit the full potential of Intelligent Reflecting Surface (IRS) architectures. To solve the above challenges, we propose for the first time an anti-jamming framework for UAV swarm communications assisted by a Hybrid-IRS-assisted UAV (H-UAV). We jointly optimize the H-UAV’s trajectory, the hybrid IRS’s beamforming and active/passive element allocation of IRSs, and Non-Orthogonal Multiple Access (NOMA) communication strategy under imperfect jammer Channel State Information (CSI), to maximize average system transmission rate while minimizing communication energy consumption. To handle the formulated highly-coupled non-convex problem, we decompose it into three sub-problems. Specifically, we employ Successive Convex Approximation (SCA) to optimize the H-UAV’s trajectories. The IRS beamforming and element allocation are then transformed into a semi-definite programming problem by a designed penalty-based approach. Finally, the NOMA decoding order and power allocation are optimized via a dynamic ordering scheme and an SCA-based algorithm. Compared to existing representative schemes, the proposed framework can achieve higher average transmission rates and lower energy consumption.

Over-the-Air Federated Edge Learning (OTA-FEEL) has emerged as a promising paradigm for collaborative AI model training across heterogeneous edge devices. Despite its advantages in communication efficiency and privacy preservation, OTA-FEEL faces critical challenges, including channel fading, energy constraints of edge devices, and non-i.i.d data distributions. This paper is the first to investigate a joint impact of local data distribution heterogeneity and transmission distortion on model convergence of OTA-FEEL. Accordingly, we analyze the gap between global expected and optimal losses, and formulate the gap minimization problem under long-term energy consumption constraints. To solve this problem, we propose an energy-aware alternating resource allocation algorithm based on Lyapunov optimization framework, jointly addressing transmit power control and device sampling rate selection. Specifically, we transform the non-convex problem based on inverse convex optimization. Then, we employ first-order Taylor expansion to linearize the non-convex constraint, and also develop an iterative framework based on block coordinate descent and successive convex approximation to enable rapid convergence. Extensive simulations under three types of non-i.i.d data distributions validate the effectiveness of the proposed EARA algorithm, which consistently outperforms representative algorithms by achieving test accuracy approaching the theoretical upper bound, while maintaining significantly low energy consumption.

This paper investigates the reliability enhancement of integrated access and backhaul (IAB) networks in urban environments by jointly leveraging reconfigurable intelligent surfaces (RIS) and unmanned aerial vehicles (UAVs). We propose a RIS and UAV collaboration-enhanced IAB (RUC-IAB) network, where UAVs serve as mobile IAB nodes and the RIS is employed to establish robust line-of-sight (LoS) backhaul links. Our collaborative approach effectively mitigates both blockage-induced and signal-to-noise ratio (SNR)-limited outages, which are the two primary factors compromising transmission reliability in urban IAB networks. To further reduce the outages caused by data accumulation at the IAB node, we develop a joint UAV deployment and RIS beamforming optimization (URO) scheme to balance the access and backhaul transmission rates. In this scheme, a closed-form lower bound on the non-outage probability is derived to facilitate low-complexity UAV placement, and a semidefinite relaxation (SDR)-based method is proposed to optimize the RIS phase shifts. Simulation results show that the proposed URO scheme achieves a 44.72% reduction in average outage probability compared to the phase-alignment-based scheme across various backhaul distances.

As AI services proliferate in the 6G era, computing power becomes a first-class network resource to meet the diverse demands of computation applications. EdgeCPN as the convergence of mobile edge computing and Computing Power Networks (CPN), promises higher allocation efficiency through resource pooling, yet existing work rarely treats computing power as a distinct commodity and typically overlooks uncertainty and partial information in AI demand reporting. This paper presents a two-stage resource transfer framework tailored for EdgeCPN-enhanced AI applications. In Stage I we formulate a cost-minimization problem that captures heterogeneous resource costs, and solve it via a generalized Benders decomposition to obtain decisions of local resource allocation and offloading. In Stage II we model on-demand resource transfer and pricing as a Stackelberg game, a learning-based algorithm is designed and shown to converge to a Stackelberg equilibrium that balances the utilities of provider and consumer. Comprehensive simulations demonstrate that the proposed method improves resource transfer performance under demand uncertainty, and yields stable, incentive-compatible pricing outcomes compared with baseline algorithm.

The existing wireless-optical switching units comprise spatial light modulators (SLM) and micro-electromechanical systems (MEMS), which steer the beam from a server port in a certain rack to another rack, establishing an inter-rack communication link. Nevertheless, this uni-cast communication approach results in a limited switching capacity. Fortunately, our passive reflective meta-surfaces (RMSs) demonstrate excellent beam-splitting capabilities, that is, after an incident light is reflected by our RMS, a pair of reflected beams can be simultaneously formed. In this paper, we further design a transmissive meta-surface (TMS), and after one incident light transmits through the TMS, N beams can be formed. Each of the N beams is subsequently reflected by the corresponding RMS, and ultimately, we can obtain 2×N beams. As a result, it significantly enhances the switching capacity of data centers (DCs). Simulation results validate the one-to-four (N=4) beam-splitting capacity of our TMS, with the insertion loss being as low as 0.5dB, efficiently being cascaded with RMS.

The rapid advancement of AI-generated content (AIGC) has enhanced the Artificial Intelligence of Things (AIoT) by offering a novel approach to content generation and creation. However, the heterogeneity of AIGC services and the large scale of AIGC models present significant challenges for providing these services. In this paper, we propose an edge-cloud collaborative framework to facilitate the provisioning of heterogeneous AIGC services. In this framework, we focus on three kinds of representative AIGC services, including lightweight AIGC services, computation-intensive AIGC services, and preprocessing-based AIGC services. We jointly optimize resource allocation and AIGC model deployment at an edge server to minimize the service delay for AIoT devices. The delay minimization problem involves mixed-integer nonlinear programming, which is inherently complex. To address this issue, we propose a dual-layer optimization algorithm that decouples the problem into an inner-layer resource allocation subproblem and an outer-layer model deployment subproblem. These subproblems are then addressed using the Karush-Kuhn-Tucker conditions and a cross-entropy-based technique. Finally, simulation results demonstrate the effectiveness of our proposed joint optimization scheme, which achieves an average performance improvement of approximately 23.2%.

Symbiotic radio (SR) presents an innovative wireless paradigm that simultaneously supports active primary and passive secondary transmissions. This technology significantly enhances spectrum and energy efficiency in network scenarios that support data transmission from a large number of Internet of Things (IoT) devices. Nonetheless, the received backscatter signal experiences attenuation due to the double path loss effect, thereby constraining the secondary network’s capacity to satisfy the data transmission requirements of IoT applications. To enhance the secondary network capacity with high energy efficiency in SR systems, we synergistically apply two promising technologies—wireless power transmission (WPT) and intelligent reflecting surfaces (IRS). Accordingly, this article explores the optimization of secondary network capacity in an SR system assisted by IRS and WPT, where high-density devices are organized into clusters. We adopt a hybrid access method that integrates time division multiple access (TDMA) for clusters accessing the Base Station (BS) and nonorthogonal multiple access (NOMA) for backscatter devices (BDs) communicating with each other in a cluster. By jointly optimizing active beamforming at the BS, passive beamforming at the IRS, and hybrid transmission time allocation, we maximize the sum data rate of the secondary links while ensuring that the communication requirements of primary links are met. To tackle this complex, high-dimensional, nonlinear problem, we propose a capacity optimization algorithm based on deep reinforcement learning (DRL). We conduct system performance evaluations, and the results validate the advantages of our proposed scheme in optimizing the secondary network capacity of SR systems compared to alternative approaches.

In the Internet of Everything (IoE) era, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) have emerged as crucial enabling technologies to support delay-sensitive and computation-intensive IoE services. Despite the potentials of RISs and MEC, achieving efficient service provisioning in IoE scenarios still faces significant challenges due to interdependencies among different types of resources. To address this issue, we propose a digital twin (DT)-empowered IoE framework that leverages real-time monitoring to virtually replicate network conditions, thereby assisting in decision-making in a physical IoE scenario. Specifically, the IoE scenario comprises a MEC server empowered by prestoring some service programs for task execution and a RIS that assists computation offloading. Taking into account deviations between DT and physical networks, we aim to minimize devices’ total task completion delay by jointly optimizing the service caching at the MEC server, the computation offloading of devices, the computing resource allocation at the MEC server, and the beamforming of the RIS. To handle the problem involving discrete and continuous factors, we develop a hybrid deep reinforcement learning (HDRL) algorithm that integrates the double deep Q-network (DDQN) and deep deterministic policy gradient (DDPG) approaches. In our HDRL algorithm, DDQN plays a crucial role in determining discrete variables representing service caching and computation offloading decisions, while DDPG focuses on optimizing resource allocation and RIS beamforming. We conduct simulations to evaluate the performance of the proposed scheme and compare it with several baselines. Simulation results demonstrate the superiority of our scheme in minimizing the task completion delay.

The development of Internet of Things (IoT) technology has led to the emergence of a large number of Intelligent Sensing Devices (ISDs). Since their limited physical sizes constrain the battery capacity, wireless powered IoT networks assisted by Unmanned Aerial Vehicles (UAVs) for energy transfer and data acquisition have attracted great interest. In this paper, we formulate an optimization problem to maximize system energy efficiency while satisfying the constraints of UAV mobility and safety, ISD quality of service and task completion time. The formulated problem is constructed as a Constrained Markov Decision Process (CMDP) model, and a Multi-agent Constrained Deep Reinforcement Learning (MCDRL) algorithm is proposed to learn the optimal UAV movement policy. In addition, an ISD-UAV connection assignment algorithm is designed to manage the connection in the UAV sensing range. Finally, performance evaluations and analysis based on real-world data demonstrate the superiority of our solution.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution to support wireless devices' computation-intensive services in the absence of terrestrial infrastructures. Nevertheless, the heterogeneous nature of MEC services and the security vulnerability of wireless channels present significant challenges to achieving efficient and secure computation offloading. In this paper, we investigate a multi-UAV-assisted MEC network in which wireless devices need to process diverse computation tasks. The devices can perform local computing or offload their computation tasks to UAV servers that have pre-cached relevant service programs in the presence of eavesdroppers. To facilitate secure service provisioning, we propose a cooperative jamming-based scheme in which a UAV jammer transmits jamming signals to interfere with eavesdroppers during devices' computation offloading processes. Taking into account UAV servers' constrained caching spaces and secure offloading requirements, we minimize the total task completion delay of devices by jointly optimizing multi-UAV deployment, task offloading decisions, service placement, UAV jammer's transmit power, and devices' transmit power. To tackle the formulated mixed-integer nonlinear programming problem, we design an optimization-embedding multi-agent twin delayed deep deterministic policy gradient (OE-MATD3) algorithm. Specifically, the MATD3 approach is leveraged to deal with optimization variables concerning UAVs, while a closed-form solution for devices' transmit power is derived and guides MATD3-based decision-making. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of devices' task completion delay.

In this paper, we propose a hierarchical optimization approach that guarantees the maximum age of information (AoI) for uncrewed aerial vehicle (UAV) assisted Internet-of-Things (IoT) data collection. Our model is based on an energy-efficient simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) beamforming model. We formulate the optimization to minimize the UAV flight energy consumption subject to a maximum average AoI threshold by optimizing the UAV trajectory, IoT device scheduling, and STAR-RIS beamforming. To solve this, we develop an optimization-based hierarchical deep reinforcement learning (OH-DRL) algorithm that decomposes the formulated problem into an inter-cluster UAV visiting policy and STAR-RIS-based intra-cluster IoT scheduling policy. In OH-DRL, we jointly optimize the two policies in a high-level loop and a low-level loop, respectively. In the high-level loop, we design an AoI-guided DRL algorithm to determine the AoI-guaranteed UAV hovering position with minimal flight distance. In the low-level loop, a semidefinite relaxation (SDR)-based optimization algorithm further reduces the UAV’s flying time by minimizing the average AoI. Simulation results validate that OH-DRL achieves better convergence performance and energy-saving efficiency across different network scales. Compared to the state-of-the-art DRL algorithm, OH-DRL reduces the UAV flight energy consumption by 14.4% and decreases the number of training episodes required for convergence by 66%

Federated learning (FL) is a promising solution to overcome data island and privacy issues in intrusion detection systems (IDSs) for the Industrial Internet of Things (IIoT). However, the heterogeneity of various IIoT devices poses formidable challenges to FL-based intrusion detection, especially the training cost relating to delay and energy consumption. In this article, we propose a delay and energy-efficient asynchronous FL (AFL) framework for intrusion detection (DEAFL-ID) in heterogeneous IIoT. Specifically, we address the shortcomings of low efficiency and high energy consumption in existing FL-based solutions involving all idle IIoT devices. To do so, we formulate an AFL-based optimal device selection problem which aims to select high-quality training devices in advance by exploring the device advantages in detection accuracy, delay reduction, and energy saving. Subsequently, a deep Q-network (DQN)-based learning algorithm is developed to quickly solve the above high-dimensional problem. In addition, to further improve the detection performance, we build a hybrid sampling-assisted convolutional neural network (CNN)-based IDS model, which can eliminate the imbalance of IIoT data and enable the selected devices to fully extract data features. Through simulations, we demonstrate that DEAFL-ID achieves a significant improvement in training cost and detection performance compared with existing IDS schemes.

Periodic data collection from numerous vehicular on-board sensors is necessary for aiding decision making in complex navigation and autonomous driving applications. The temporal freshness of data, represented by the Age of Information (AoI), thus holds critical significance. Integrating Unmanned Aerial Vehicle (UAV) relays with Reconfigurable Intelligent Surface (RIS) emerges as a promising strategy to establish reliable communication links between vehicles and data processing centers. Despite this potential, the current body of literature on the integration of UAV relays and RIS is insufficient, particularly in studying AoI. This paper addresses this gap by achieving a comprehensive optimization of the phase shifts at the RIS, spectrum allocation, and the UAV trajectory. The objective is to minimize the average AoI while adhering to the constraints associated with UAV energy consumption. This joint optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. It is tackled using an approach based on Multi-Step Dueling Double Deep Q Network (MSD3QN). Extensive simulations conducted across diverse scenarios prove the effectiveness of our proposed approach and demonstrate its ability in improving the timeliness of making decisions, reducing average AoI, and enhancing network coverage.

Mobile edge computing (MEC) pushes computing and caching resources close to the network edge, which allows devices to offload computation-intensive tasks to MEC servers. Considering that wireless signals may be easily blocked by obstacles, reconfigurable intelligent surface (RIS) has emerged as a promising technique to improve the efficiency of computation offloading. In this paper, we consider a RIS-assisted MEC network, where a MEC server caches service programs required for task execution and a RIS helps computation offloading in the presence of eavesdropping. Due to the diversity of services and the broadcast nature of wireless channels, it is challenging to achieve efficient and secure computation offloading in this network. Therefore, we first formulate a task completion delay minimization problem by jointly optimizing service caching, computation offloading decisions, RIS passive beamforming, and transmit power subject to the constraints of secure offloading rate and limited storage space. To address the highly non-convex nature of the problem, we then develop a dual-layer optimization algorithm via a vertical decomposition on its layered structure. The outer-layer problem, which deals with service caching and computation offloading decisions, is solved by a cross-entropy-based caching and offloading learning algorithm. For the inner-layer problem that optimizes RIS passive beamforming and transmit power, we utilize a horizontal decomposition by invoking the block coordinate descent method. Finally, simulation results demonstrate that the proposed scheme exhibits performance improvements compared to several baseline schemes.

To support the running of human-centric metaverse applications on mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Mobile Edge Computing (WPMEC) is promising to compensate for limited computational capabilities and energy supplies of mobile devices. The high-speed computational processing demands and significant energy consumption of metaverse applications require joint resource scheduling of multiple devices and UAVs, but existing WPMEC solutions address either device or UAV scheduling due to the complexity of combinatorial optimization. To solve the above challenge, we propose a two-stage alternating optimization algorithm based on multi-task Deep Reinforcement Learning (DRL) to jointly allocate charging time, schedule computation tasks, and optimize trajectory of UAVs and mobile devices in a wireless powered metaverse scenario. First, considering energy constraints of both UAVs and mobile devices, we formulate an optimization problem to maximize the computation efficiency of the system. Second, we propose a heuristic algorithm to efficiently perform time allocation and charging scheduling for mobile devices. Following this, we design a multi-task DRL scheme to make charging scheduling and trajectory design decisions for UAVs. Finally, theoretical analysis and performance results demonstrate that our algorithm exhibits significant advantages over representative methods in terms of convergence speed and average computation efficiency.

Multi-access Edge Cloud (MEC) networks are powerful for providing emerging computation-intensive and latency-sensitive applications with low latency leveraging ubiquitous edge devices. These networks enable complex applications to be split into multiple components/subtasks and deployed among multiple edge servers with limited computation and communication resources. However, multiple subtasks within an application are dependent on each other. They cannot be executed in parallel, resulting in non-trivial resource waste when allocating resources to every subtask throughout the lifetime of the application. This paper investigates the multi-component task offloading problem in MEC networks that addresses the dependencies among components and three-dimensional (3D) resource allocation, i.e., computation, communication, and time slots. The problem is NP-hard and challenging to solve due to the complex task dependencies, including triangular dependencies among multiple subtasks and the routing of edges between dependent subtasks. To address the challenge, we first propose a non-destructive task reconfiguration algorithm that transforms a task call graph into multiple sequential layers, breaking out the triangular dependency. Then, we develop a dePendency-awaRe task offloAding algorithm wIth taSk rEconfiguration (PRAISE) algorithm to maximize the total offloading benefit.PRAISEdecouples the original problem into task offloading and 3D convex resource optimization. Simulation results show thatPRAISEoutperforms baselines with higher system benefits and lower resource costs.

Multi-access edge computing (MEC) is a powerful technology that facilitates the provision of services to 6G users with ultra-low latency and high reliability, particularly in supporting artificial intelligence (AI) applications that rely on distributed machine learning (DL). However, the mobility of users poses challenges in offloading DL tasks to the MEC networks while ensuring satisfactory delay and blocking rates. Task replication emerges as a promising technique for achieving a cell-less design for mobile users. Nevertheless, existing research overlooks the replication of DL tasks involving multiple subtasks and users, as well as the high resource cost of task replication. Towards this challenge, this paper investigates the Mobility-awarE mulTi-replicA (META) DL task offloading problem in MEC networks. First, we propose a hybrid resource allocation mechanism that allocates resources to a replica with high access probability in a static manner and dynamically allocates resources to replicas with low access probabilities. Then, we develop an access base station (BS) clustering algorithm for each user to determine the optimal number of replicas. Additionally, we propose the META DL task offloading algorithms with proved approximation ratios to minimize the overall resource cost. Through simulations based on generated and real-world mobile users, we demonstrate the effectiveness of our proposed algorithms.

Due to the revolution of communication technology, the rapidly increasing number of mobile devices in edge networks generates various real-time service requests, requiring a considerable volume of heterogeneous resources all the time. However, edge devices with limited resources cannot afford substantial learning cost, while migrating services requires heterogeneous resources, especially for dynamic networks. To address these issues, we first establish a cooperative service migration framework and formulate a bi-objective optimization problem to optimize service performance and cost. By analyzing the optimal migration ratio of service cooperative migration, we propose an offline expert policy based on global states to provide optimal expert demonstrations. To realize real-time service migration based on observable states, we design a lightweight online agent policy to imitate expert demonstrations and leverage meta update to accelerate the model transfer. Experimental results show that our algorithm is exceptional in training cost and accuracy, and has significant superiors in multiple metrics such as the service latency and payment under different workloads, compared to other representative algorithms.

Driven by the increasing computational demand of real-time mobile applications, Unmanned Aerial Vehicle (UAV) assisted Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges and constructing high-throughput line-of-sight links for ground users. Most exsiting studies consider simplified scenarios, such as a single UAV, Service Provider (SP) or service type, and centralized UAV trajectory control. In order to be more in line with real-world cases, we intend to achieve distributed trajectory control of multiple UAVs in UAV-assisted MEC networks with multiple SPs providing differentiated services. Our objective is to minimize the short-term computational costs of ground users and the long-term computational cost of UAVs, simultaneously based on incomplete information. We first solve the formulated problem by reaching the Nash Equilibrium (NE) of the game among SPs based on complete information. We further formulate a Markov game model and propose a Deep Reinforcement Learning (DRL)-based UAV trajectory optimization algorithm, where only local observations of each UAV are required for each SP's flying action execution. Theoretical analysis and performance evaluation demonstrate the convergence, efficiency, scalability, and robustness of our algorithm compared with other representative algorithms.

For the first time, this paper proposes a new mathematical definition of communication: “Kong” element, and defines its operation and properties. It also proposes the XOR operation theorem, and proved the theorem. Based on the XOR operation of “Kong” elements, the existence theorem and absorption rate theorem of “Kong” elements in encrypted communication are proposed for the first time, and one typical case is proved in detail. Besides, this paper brought up the idea of “Kong” element encryption which is transmitted by defined operation, and enumerated the encrypted communication case of “Kong” element transmitting information. This example not only diffuses and confuses information, but also breaks the strong correlation of traditional ciphertext by adding random redundant bits to information. This communication mode based on “Kong” elements is expected to realize secure communication with a new idea.

The proliferation of Internet of Things (IoT) applications has led to the interconnection of multiaccess edge computing (MEC) systems through metro optical networks. To cater to these diverse applications, network slicing has become a popular tool for creating specialized virtual networks. However, the uneven utilization of multidimensional resources can result in resource fragmentation, thereby reducing the utilization of limited edge resources. This article focuses on mitigating multidimensional resource fragmentation in virtual network embedding (VNE) to maximize the profit of the infrastructure provider (InP). The problem is converted into a bilevel optimization problem, taking into account the interdependence between virtual node embedding and virtual link embedding. To solve this problem, we propose a nested bilevel VNE approach named BiVNE. BiVNE leverages an ant colony system (ACS) algorithm for the upper layer problem and utilizes the Dijkstra algorithm and an exact-fit spectrum slot assignment method for the lower layer problem. Evaluation results demonstrate that BiVNE can greatly improve the profit of the InP by increasing the acceptance ratio and avoiding resource fragmentation simultaneously.

This paper considers computation offloading for mobile applications with task-dependency requirements in mobile edge computing (MEC) systems. Based on the online arrival patterns and various delay constraints of practical applications, we focus on minimizing the system deadline violation ratio (DVR) to improve the overall reliability performance. Specifically, we propose a DVR minimization computation offloading scheme with task migration and merging, in which the task migration and merging model is designed to construct an overall directed acyclic graph (DAG) for all currently dependent tasks. We consider a multi-slot MEC system where applications arrive slot-by-slot without prior knowledge of future arrivals. Then given the number of application arrivals at each time slot, we equivalently transform the DVR minimization problem into a problem that maximizes the number of completed applications in a finite time horizon. The above problem is challenging to determine the optimal task execution order for different applications with various task dependencies and delay constraints. To address this, we develop a migration-enabled multi-priority task sequencing algorithm, which creatively introduces several task priority metrics and determines the optimal task execution order. Then, a deep deterministic policy gradient (DDPG)-based learning algorithm is developed to find the optimal offloading policy. Experimental results demonstrate that the proposed scheme can reduce the system DVR by 60.34%~70.3% compared with existing benchmark schemes under various network scenarios.

Virtual reality-embedded digital twin (VR-DT) service integrates digital twin with virtual reality to visualize the digital representation of real-world production, boosting the digital transformation of manufacturing industry in the Industrial Internet of Things (IIoT). Balanced against the advantages of the VR-DT service, its data-driven, computing-intensive, and security-sensitive features bring challenges to the current IIoT. Therefore, we propose a blockchain-based distributed resource allocation scheme to improve the average Quality of Service (QoS) of the VR-DT services with regard to service delay and transaction throughput. We formulate the joint optimization of channel assignment, subframe configuration, computing capacity allocation, and block size adjustment as a mixed-integer nonlinear programming problem. A fully decentralized multiagent compound-action actor–critic algorithm is developed to solve the QoS optimization problem. Simulation results demonstrate that our proposed scheme can efficiently improve the average QoS of the VR-DT services in a realizable way as compared to existing schemes.

Driven by the increasing demand of real-time mobile application processing, Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges. In this paper, we investigate an MEC network enabled by Unmanned Aerial Vehicles (UAV), and consider both the multi-user computation offloading and edge server deployment to minimize the system-wide computation cost under dynamic environment, where users generate tasks according to time-varying probabilities. We decompose the minimization problem by formulating two stochastic games for multi-user computation offloading and edge server deployment respectively, and prove that each formulated stochastic game has at least one Nash Equilibrium (NE). Two learning algorithms are proposed to reach the NEs with polynomial-time computational complexities. We further incorporate these two algorithms into a chess-like asynchronous updating algorithm to solve the system-wide computation cost minimization problem. Finally, performance evaluations based on real-world data are conducted and analyzed, corroborating that the proposed algorithms can achieve efficient computation offloading coupled with proper server deployment under dynamic environment for multiple users and MEC servers.

Blockchain has been leveraged to secure transactions for the m-commerce. However, the intensive computation in the mining process restricts the participation of mobile devices. Currently, some studies have deployed edge computing services to support the mining process, where edge servers managed by one Service Provider (SP) are considered. This paper investigates a more practical scenario with multiple SPs, where servers managed by different SPs have distinct capacities and prices, making miners’ offloading decisions rather complicated. To tackle the above challenges, we consider task offloading, block propagation and miner mobility comprehensively to maximize utilities of miners. Specifically, we first formulate a Markov game, and then design a learning-based offloading algorithm for off-chain computation, where a novel learning model is constructed by integrating Deep Reinforcement Learning (DRL) and Mean Field Theory (MFT) to guarantee a Nash equilibrium. Different from existing studies, each miner merely needs to respond to the average effect from others in our system, insteading of knowing policies of others. Finally, both theoritical and performance results show that our designed algorithm has superiority on average miner utilities and algorithm convergence time compared with other representative algorithms.

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed$\epsilon$-Nash equilibrium. Performance results show that our algorithm has significant superiority in terms of average profits, utilities and execution time compared with other representative algorithms.

Cloud Computing (CC) is powerful for the computation offloading of services, promoting the implementation of various modern applications. Mobile Edge Computing (MEC) can provide low-latency services utilizing edge servers locating in proximity to users. The combination of MEC and CC can give play to the dual advantages of both. However, it is a challenging problem to offload service requests to the collaborative edge-cloud networks aiming at minimizing costs due to the resource limitation of edge servers and the online feature of services. To address this issue, we mathematically model the service requests with multiple inter-connected functions. Then, the problem of computation offloading of multi-function service requests in collaborative edge-cloud networks is formulated to be an Integer Linear Programming (ILP) and is proved to be NP-hard. Furthermore, a Cost-minimized Computation Offloading with Reconfiguration (CCOR) algorithm is proposed to minimize the total cost of online services. Finally, simulation results show that the proposed CCOR algorithm can effectively reduce the cost of computation offloading with higher resource utilization of edge cloud compared with baseline algorithms.

Visible-light positioning (VLP) has been considered as a promising indoor positioning technology due to its high precision and low cost. However, current VLP techniques are greatly limited by the assumption that lights have to be turned on to emit shining light beams, which is not applicable to scenarios that do not need the illumination all the time. In this article, we design and implement a novel VLP system, DarkVLP, to achieve high-precision positioning when lights are “turned off.” The “turn off” state means lights emit the extremely low luminance that is imperceptible to human eyes. In order to realize such a system, we have to tackle nontrivial challenges in data encoding/decoding, modulation/demodulation, and positioning algorithm. Specifically, we propose the sliding rheostat-based modulation scheme to eliminate the need of a complex signal synchronization mechanism and landmark recognition algorithm at the receiver. We also propose the dual-photodiode-based positioning algorithm, which effectively mitigates effects of substantial signal strength fluctuation, to reliably achieve VLP. In the end, we design novel circuits and prototype our system DarkVLP on commercial off-the-shelf devices. The results of extensive experiments demonstrate that our DarkVLP system could achieve submeter precision under the extremely low luminance, which greatly broadens application scenarios of VLP.

Double auctions play a pivotal role in stimulating active participation of a large number of users comprising both task requesters and workers in mobile crowdsourcing. However, most existing studies have concentrated on designing offline two-sided auction mechanisms and supporting single-type tasks and fixed auction service models. Such works ignore the need of dynamic services and are unsuitable for large-scale crowdsourcing markets with extremely diverse demands (i.e., types and urgency degrees of tasks required by different requesters) and supplies (i.e., task skills and online durations of different workers). In this article, we consider a practical crowdsourcing application with an on-demand service strategy. Especially, we innovatively design three online service models, namely, online single-bid single-task (OSS), online single-bid multiple-task (OSM), and online multiple-bid multiple-task (OMM) models to accommodate diversified tasks and bidding demands for different users. Furthermore, to effectively allocate tasks and facilitate bidding, we propose a truthful online double auction mechanism for each service model based on the McAfee double auction. By doing so, each user can flexibly select auction service models and corresponding auction mechanisms according to their current interested tasks and online duration. To illustrate this, we present a three-demand example to explain the effectiveness of our on-demand service strategy in realistic crowdsourcing applications. Moreover, we theoretically prove that our mechanisms satisfy truthfulness, individual rationality, budget balance, and consumer sovereignty. Through extensive simulations, we show that our mechanisms can accommodate the various demands of different users and improve social utility, including platform utility and average user utility.

Integrating simultaneous wireless information and power transfer (SWIPT) and cognitive radio (CR) technologies into Internet-of-Things (IoT) networks, named SWIPT-enabled cognitive IoT networks, has become an effective approach to resolve the short lifetime of battery-constrained IoT Devices (IoDs) and spectrum scarcity. In this type of networks, IoDs are regarded as secondary users (SUs) being charged with wireless power. To improve the sum throughput of IoDs, we allow IoDs to switch among spectrum sensing, SWIPT and information transmission adaptively. Correspondingly, three-dimensional resources, i.e., time (for performing the three actions), power (including power transmitted from an IoT controller to each IoD and power for receiving information and charging at each IoD) and spectrum, are jointly and adaptively allocated to maximize the sum throughput of IoDs. Since the formulated problem is a mixed-integer nonlinear program (MINLP), we adopt an auxiliary variable to convert the original problem into a tractable problem, which is then solved by an efficient algorithm involving the Lagrangian dual method, the subgradient method and the multiple one-dimensional search algorithm. Simulation results show our adaptive design yields superior performance in terms of the sum throughput of IoDs.

With the development of the radar sensing technology, hand gesture sensing and recognition has attracted much attention. This letter adopts a frequency-modulated continuous wave (FMCW) radar to achieve short-range hand gesture sensing and recognition. Specifically, the range, Doppler, and angle parameters of hand gestures are measured by fast Fourier transformation (FFT) and multiple signal classification (MUSIC) algorithm, respectively. The mixup (MP) algorithm combined with augmentation (AU) algorithm using a weight factor is applied to expand the hand gesture data. Then, a complementary multidimensional feature fusion network-based hand gesture recognition (CMFF-HGR) is designed to extract the features and achieve HGR. Finally, a series of experiments are carried out to verify the effectiveness of the proposed approach, and the results show that the recognition accuracy is higher than the existing alternatives with low computational complexity.

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes.

Big data and cognitive computing have a wide range of applications in smart homes, smart cities, artificial intelligence, and computational social systems. Visible light positioning systems have attracted more and more attention as one of the application scenarios of computational social systems. In visible light positioning systems, the light-emitting diodes (LEDs) ceiling layout makes the smartphone usually obtain less than three LEDs in captured images. Due to the lack of necessary positioning information, most scholars combine the inertial measurement unit (IMU) with the modified filter algorithms to achieve positioning under the sparse light source. However, these systems have the following problems: 1) the azimuth angle obtained by the IMU is always not accurate, which decreases the positioning accuracy and 2) during the dynamic positioning process, the system’s initial position is difficult to automatically determine. In this article, we propose indoor high-precision visible light positioning under the sparse light source. First, we propose the geometric correction mechanism, which uses ellipse fitting to calibrate the azimuth angle, so as to increase the positioning accuracy for the static system. Then, we build a motion model for the entire positioning process through the unscented particle filter (UPF), which does not need to manually set initial state parameters, due to random generated particles. It can increase the positioning accuracy for the dynamic system. We evaluate our designed system, and the experimental results show that the average azimuth angle error is 2.04° and average positioning error is 8.8 cm, under the sparse light source.

The proliferation of the Internet of Things pushes the visible light positioning (VLP) system research. However, the existing positioning systems still have the following problems: 1) when the smartphone (receiver) is rotated or tilted during the positioning, existing collaborative LEDs’ positioning algorithms fail and 2) for different smartphone application scenarios, there is not an effective resource management solution between the server and the client. Therefore, in this article, we design and implement a robust and flexible indoor VLP system based on collaborative LEDs and edge computing. First, we propose the enhanced collaborative LEDs’ positioning algorithm, which uses the indoor hidden location information to obtain the rotation angle and tilt angle of the receiver, to achieve the robust system positioning. Then, we use the edge computing solution to balance between bandwidth resources and computing resources and propose a flexible functional segmentation scheme for different smartphone application scenarios. Finally, we conduct experimental tests to evaluate the positioning system performance by landmark decoding rate, positioning accuracy, and segmentation analysis. Test results show that the designed positioning system can achieve centimeter-level positioning. Meanwhile, the smartphone can exchange the least bandwidth resources for the most computing resources under Scheme-3.

The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices, it involves a large amount of users’ privacy. This also makes edge networks face more and more security issues, such as Denial-of-Service (DoS) attacks, unauthorized access, packet sniffing, and man-in-the-middle attacks. To combat these issues and enhance the security of edge networks, we propose a deep learning-based intrusion detection algorithm. Based on the generative adversarial network (GAN), we designed a powerful intrusion detection method. Our intrusion detection method includes three phases. First, we use the feature selection module to process the collaborative edge network traffic. Second, a deep learning architecture based on GAN is designed for intrusion detection aiming at a single attack. Finally, we propose a new intrusion detection model by combining several intrusion detection models that aim at a single attack. Intrusion detection aiming at multiple attacks is realized through the designed GAN-based deep learning architecture. Besides, we provide a comprehensive evaluation to verify the effectiveness of the proposed method.

Collaborative edge computing (CEC) can realize the cooperation and integration of heterogeneous resources distributed in adjacent areas, increasing the overall resource utilization efficiency. In a CEC-supported heterogeneous vehicular network composed of different access solutions, including cellular vehicle-to-everything (C-V2X) and dedicated short-range communications (DSRC), good network connections can guarantee timely access to edge resources. How to maintain stable and high-quality network connections for vehicles is a crucial issue. With traditional received signal strength (RSS)-based handover schemes, vehicles may encounter severe ping-pong effects and even direct handover failures leading to data packet loss. In this article, to overcome the frequent handover problem caused by vehicles’ high-speed motion and the ever-changing network environment, we propose a trajectory prediction-based handover scheme. In this scheme, the sojourn time of a vehicle staying in each candidate network’s coverage can be obtained through a social long short-term memory (social-LSTM)-based prediction model. Together with the signal strength, available bandwidth, and cost, the sojourn time is also taken as a handover decision attribute parameter. Simulation results show that our proposed scheme can reduce the number of handovers effectively.

It is critical to reduce the operating cost of shared buses for bus companies and improve the user experience of passengers. However, existing studies focus on either bus scheduling or route planning, which cannot accomplish the above mentioned goals concurrently. In this paper, we construct a joint bus scheduling and route planning framework to maximize the number of passengers, minimize the total length of routes and the number of required buses, as well as guarantee good user experience of passengers. First, we establish a system model based on a real-world scenario and formulate a multi-objective combinational optimization problem. Then, based on the extracted traffic topology of urban traffic networks and the generated candidate line set, we propose an offline algorithm to cope with the similar passenger flow distributions, e.g., morning or evening peak of every day. In order to cope with dynamic real-time passenger flows, an online algorithm is designed. Experiments are carried out based on real-word scenarios. The results show that the proposed algorithms can greatly reduce the operating cost of bus companies and guarantee good user experience based on real-world scheduling data in comparison with several existing methods.

A variety of novel mobile applications are developed to attract the interests of potential users in the emerging 5G-enabled vehicular networks. Although computation offloading and task scheduling have been widely investigated, it is rather challenging to decide the optimal offloading ratio and perform adaptive task scheduling in high-dynamic networks. Furthermore, the scheduling policy made by the network operator may be violated, since vehicular users are rational and selfish to maximize their own profits. By considering the incentive compatibility and individual rationality of vehicular users, we present POETS, an efficient partial computation offloading and adaptive task scheduling algorithm to maximize the overall system-wide profit. Specially, a two-sided matching algorithm is first proposed to derive the optimal transmission scheduling discipline. After that, the offloading ratio of vehicular users can be obtained through convex optimization, without any information of other users. Furthermore, a non-cooperative game is constructed to derive the payoff of vehicular users that can reach the equilibrium between users and the network operator. Theoretical analyses and performance evaluations based on real-world traces of taxies demonstrate the effectiveness of our proposed solution.

Intelligent Transportation System (ITS) is critical to cope with traffic events, e.g., traffic jams and accidents, and provide services for personal traveling. However, existing researches have not jointly considered the user data safety, utility and system latency comprehensively, to the best of our knowledge. Since both safe and efficient transmissions are significant for ITS, we construct a blockchain-enabled crowdsensing framework for distributed traffic management. First, we illustrate the system model and formulate a multi-objective optimization problem. Due to its complexity, we decompose it into two subproblems, and propose the corresponding schemes, i.e., a Deep Reinforcement Learning (DRL)-based algorithm and a DIstributed Alternating Direction mEthod of Multipliers (DIADEM) algorithm. Extensive experiments are carried out to evaluate the performance of our solutions, and experimental results demonstrate that the DRL-based algorithm can legitimately select active miners and transactions to make a satisfied trade-off between the blockchain safety and latency, and the DIADEM algorithm can effectively select task computation modes for vehicles in a distributed way to maximize their social welfare.

Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithm, network planning, and anomaly and intrusion detection. This article studies the problem of end-to-end network traffic prediction in IoV backbone networks, and proposes a deep learning-based method. The constructed system considers the spatio-temporal feature of network traffic, and can capture the long-range dependence of network traffic. Furthermore, a threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic dataset.

A novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC) emerges recently, which integrates Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies. It enables mobile clients to both extend their computing capacities by task offloading, and charge from edge servers via energy transmission. Existing studies generally focus on the centralized design of task scheduling and energy charging in WP-MEC networks. To meet the decentralization requirement of the near-coming 6G network, we propose an online learning algorithm for computation offloading in WP-MEC networks with a distributed execution manner. Specifically, we first define the delay minimization problem by considering task deadline and energy constraints. Then, we transform it into a primal-dual optimization problem based on the Bellman equation. After that, we design a novel neural model that learns both offloading and time division decisions in each time slot to solve the formulated optimization problem. To train and execute the designed algorithm distributivity, we form multiple learning models decentralized on edge servers and they work coordinately to achieve parameter synchronization. At last, both theoretical and performance analyses show that the designed algorithm has significant advantages in comparison with other representative schemes.

In this paper, we consider a mobile edge computing (MEC)-based secure computation offloading system, and design a practical multi-eavesdropper model including two specific scenarios of non-colluding and colluding eavesdropping. Furthermore, we design a requirement satisfaction model by exploring practical variations in user request patterns for security provisioning, delay reduction and energy saving. Based on these, we propose a satisfaction-maximized secure computation offloading (SMax-SCO) scheme, and then formulate an optimization problem aiming at maximizing users’ requirement satisfactions subject to secrecy offloading rate, tolerable delay, task workload and maximum power constraints. Since the optimization problem is nonconvex, we present an efficient successive convex approximation (SCA)-based algorithm to obtain suboptimal solutions. We demonstrate that the proposed SMax-SCO scheme achieves a significant improvement in security performance and requirement satisfaction compared with existing schemes. Moreover, we conclude that SMax-SCO can resist eavesdropping attacks of multiple eavesdroppers and even colluding eavesdroppers.

Virtual reality (VR) over wireless has emerged as an important application in future mobile networks. However, it is difficult for the existing mobile networks to meet the requirements of massive data transmissions and ultra-low latency for wireless VR. Multi-access edge computing (MEC) network, providing caching and computing capacities at network edge, emerges as a promising method to support wireless VR. However, mobile VR users' quality of experience (QoE) may be degraded by frequent handoffs. In this paper, we propose a proactive caching, computing and communication (3C) resource allocation method to provide smooth VR videos to handoff users. Specifically, the expected 3-dimensional (3D) video or 2D video for rendering is cached at a target base station (BS) ahead of time, and the size and quality of the video file are decided according to the 3C resources at the BS. Then, we model the the proactive 3C resource allocation as a Markov decision process and an effective allocation policy is obtained by a model-free algorithm based on deep reinforcement learning. Numerical results show that the proposed method can provide VR users with high QoE when they are moving between BSs.

This paper investigates the performance of cooperative device-to-device (C-D2D) communications in a cellular network, where the simultaneous wireless information and power transfer (SWIPT) technology is adopted by D2D transmitters (DTs). In this network, DTs can act as relays that consume a portion of energy harvested by a time switching (TS) strategy to satisfy the quality of service (QoS) requirements of cellular users (CUs) with poor channel conditions, in exchange for spectrum resources of CUs for D2D communications. To achieve the sum-throughput maximization of the network while guaranteeing the QoS requirements of both D2D and cellular links, we formulate a novel optimization problem that jointly determines user pairing between DTs and CUs, time allocation for energy harvesting and information transmission, and power allocation at DTs for relaying information and performing D2D communications. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) problem which is computationally prohibitive. To overcome this issue, a two-step policy-based algorithm is proposed to solve the problem in polynomial time. Simulation results validate the convergence of the proposed algorithm and the effectiveness of the joint user pairing and resource allocation scheme for improving network throughput.

With the emergence of computationally intensive and delay sensitive applications, mobile edge computing(MEC) has become more and more popular. Simultaneously, MEC paradigm is faced with security challenges, the most harmful of which is DDoS attack. In this paper, we focus on the resource orchestration algorithm in MEC scenario to mitigate DDoS attack. Most of existing works on resource orchestration algorithm barely take into account DDoS attack. Moreover, they assume that MEC nodes are unselfish, while in practice MEC nodes are selfish and try to maximize their individual utility only, as they usually belong to different network operators. To solve such problems, we propose a price-based resource orchestration algorithm(PROA) using game theory and convex optimization, which aims at mitigating DDoS attack while maximizing the utility of each participant. Pricing resources to simulate market mechanisms, which is national to make rational decisions for all participants. Finally, we conduct experiment using Matlab and show that the proposed PROA can effectively mitigate DDoS attack on the attacked MEC node.

Edge computing that utilizes ubiquitous edge devices locating in close proximity to users is powerful for providing Quality of Service guaranteed computation offloading services. Toward the limited resources of edge servers and wireless links, large services can be split into multiple interconnected components to be served by multiple edge servers cooperatively. The current works on service placement either assume unsplittable services or ignore the geographically isolated property of edge servers. They also ignore the interference among online services that share the same physical nodes/links in terms of executing delay. Namely, every service adds load to the placed nodes/links and every increment on load of nodes/links risks delay violation of existing services. To overcome above challenges, this article emphasizes on the interference-aware (IA) online multicomponent service placement in edge cloud networks. First, the delay of tree-like services is analyzed considering the dependency among components, based on which the IA residual capacities of physical nodes, links, and paths are defined and formulated theoretically. Furthermore, we reduce the problem of multicomponent service placement to be NP-hard and transform it into an ant colony optimization (ACO) problem to obtain the near-optimal solution. More importantly, a level traversal component ranking method and an IA dynamic pruning method are proposed for ACO to achieve faster convergence, interference awareness, and higher acceptance ratio of services. Simulation results are presented to validate the effectiveness of proposed methods. In addition, the classic artificial intelligence application of image classification is experimented to further strength the motivation of IA investigation in practical.

Wireless virtual reality (VR)-enabled medical treatment (WVMT) system, integrating the VR technology and the platform of the Internet of Medical Things (IoMT), is a promising application in future medical industries. Multiaccess edge computing (MEC) is an effective approach to support the ubiquitous applications of WVMT systems. Due to the high requirements of medical services, the computation efficiency and security are two issues in WVMT systems. In this article, we propose a blockchain-enabled task offloading scheme, where the viewport rendering tasks of VR devices (VDs) can be offloaded to edge access points (EAPs). The blockchain is integrated into the system to reach the consensus of the global information of task offloading and data processing to resist malicious attacks. To reduce VDs’ computation load under the promise of high VR QoE, we formulate the computation offloading and resource allocation to be a Markov decision problem, considering block consensus, content correlation, and fluctuating channel conditions. Then, a novel collective reinforcement learning (CRL) algorithm is proposed to adaptively allocate resources based on the requirements of viewport rendering, block consensus, and content transmission. In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. simulation results demonstrate the effectiveness of the proposed scheme.

The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

Wireless virtual reality (VR) is increasingly used in industrial Internet of Things (IIoTs). However, ultra-high viewport rendering demands and excessive terminal energy consumption restrict the application of wireless VR. Pervasive edge computing emerges as a promising method for wireless VR. In this article, we propose an energy-aware resource management scheme for wireless-VR-supported IIoTs. To reduce the energy consumption of VR equipments (VEs) while ensuring a smooth immersive VR experience, we formulate the viewport rendering offloading, computing, and spectrum resource allocation to be a joint optimization problem, considering content correlation between VEs, fluctuating channel conditions, and VR quality of experience. By applying dual approximation, the original problem is transformed to be a Markov decision process and an reinforcement learning (RL)-based online learning algorithm is designed to find the optimal policy. To improve the learning efficiency, the quantum parallelism is integrated into the RL to overcome “curse of dimensionality”. In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. Simulation results demonstrate the effectiveness of the proposed scheme.

Intelligent Internet of Things (IIoT) is comprised of various wireless and wired networks for industrial applications, which makes it complex and heterogeneous.The openness of IIoT has led to the intractable problems of network security and management. Many network security and management functions rely on network traffic prediction techniques, such as anomaly detection and predictive network planning. Predicting IIoT network traffic is significantly difficult because its frequently updated topology and diversified services lead to irregular network traffic fluctuations. Motivated by these observations, we proposed a reinforcement learning-based mechanism in this article. We modeled the network traffic prediction problem as a Markov decision process, and then, predicted network traffic by Monte Carlo Q-learning. Furthermore, we addressed the real-time requirement of the proposed mechanism and we proposed a residual-based dictionary learning algorithm to improve the complexity of Monte Carlo Q-learning. Finally, the effectiveness of our mechanism was evaluated using the real network traffic.

Recently, Internet of Vehicles (IoV) has become one of the most active research fields in both academic and industry, which exploits resources of vehicles and Road Side Units (RSUs) to execute various vehicular applications. Due to the increasing number of vehicles and the asymmetrical distribution of traffic flows, it is essential for the network operator to design intelligent offloading strategies to improve network performance and provide high-quality services for users. However, the lack of global information and the time-variety of IoVs make it challenging to perform effective offloading and caching decisions under long-term energy constraints of RSUs. Since Artificial Intelligence (AI) and machine learning can greatly enhance the intelligence and the performance of IoVs, we push AI inspired computing, caching and communication resources to the proximity of smart vehicles, which jointly enable RSU peer offloading, vehicle-to-RSU offloading and content caching in the IoV framework. A Mix Integer Non-Linear Programming (MINLP) problem is formulated to minimize total network delay, consisting of communication delay, computation delay, network congestion delay and content downloading delay of all users. Then, we develop an online multi-decision making scheme (named OMEN) by leveraging Lyapunov optimization method to solve the formulated problem, and prove that OMEN achieves near-optimal performance. Leveraging strong cognition of AI, we put forward an imitation learning enabled branch-and-bound solution in edge intelligent IoVs to speed up the problem solving process with few training samples. Experimental results based on real-world traffic data demonstrate that our proposed method outperforms other methods from various aspects.

Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.

With the rapid development of smart cities, white light‐emitting diodes (LEDs) are widely used in indoor lighting due to their characteristics of energy‐saving, long lifetimes, and low cost. At the same time, the high‐frequency modulated LEDs allow visible light to be used for indoor communications, positioning, and security. Visible light applications have many advantages, such as avoiding electromagnetic interference, high communication speed, great privacy, and high‐precise positioning. In this paper, we will survey the application prospects and research results of visible light from scenarios such as high‐speed communication, privacy security, and navigation localization and focus on analyzing and discussing the challenges and development trends encountered in visible light positioning.

Analyzing network vulnerability, especially connectivity vulnerability, is vital for network security planning. Traditionally, network vulnerability analysis methods separate the studies of global connectivity vulnerability and critical area vulnerability, and thus ignore joint failure of network connectivity and critical-area integrity that may cause grave damage to a network. To this end, this paper proposes a prioritizing critical area approach for connectivity analysis to identify the corresponding vulnerable elements. Specifically, we consider the worst-case scenario of a network and aim at finding the minimum disruption-cost set of elements whose removal not only severely damages network connectivity but also disrupts the critical-area integrity. Since the above optimization problem is NP-hard, a heuristic algorithm based on spectral partitioning is developed to solve it. Simulation results validate the effectiveness of our proposed scheme in accurately identifying the vulnerable elements in critical areas to prevent significant loss in the overall network connectivity and performance.

Non technical losses (NTL) detection plays a crucial role in protecting the security of smart grids. Employing massive energy consumption data and advanced artificial intelligence (AI) techniques for NTL detection are helpful. However, there are concerns regarding the effectiveness of existing AI-based detectors against covert attack methods. In particular, the tampered metering data with normal consumption patterns may result in low detection rate. Motivated by this, we propose a hybrid data-driven detection framework. In particular, we introduce a wide & deep convolutional neural networks (CNN) model to capture the global and periodic features of consumption data. We also leverage the maximal information coefficient algorithm to analysis and detect those covert abnormal measurements. Our extensive experiments under different attack scenarios demonstrate the effectiveness of the proposed method.

Crowdsourcing is a novel distributed problem-solving mechanism that can provide the collection and share of marine data in the Internet of Things for the smart ocean. Nevertheless, the privacy leakage issue caused by multirole information interaction in crowdsourcing brings a serious challenge to the smart ocean. In this article, we propose a crowdsourcing privacy protection scheme based on multiauthority ciphertext-policy attribute-based encryption to enhance privacy protection in the data sharing environment. In this scheme, we design an independent key component distribution approach through multiple authorities, which could effectively disperse the security responsibility from the crowdsourcing platform. Then, we present the idea of partial decryption on the platform to reduce the computing cost of mobile users and prevent the platform from snooping on users' data. Moreover, we put forward an efficient attribute revocation mechanism and a task search function in the scheme to achieve dynamic on-demand services while ensuring the forward and backward security of tasks. Theoretical analysis proves the correctness of both decryption and keywords matching, and the security of each involved entity. The simulation results show that our proposed scheme achieves a significant improvement in reducing time consumption compared with several related schemes.

Mobile crowdsourcing is a promising strategy for trusted data collection in Internet-of-Things (IoT) applications. In this article, we propose a new fog-blockchain distributed approach for crowdsourcing reputation management to prevent user's privacy leakage, malicious users' participation, and reputation tampering in wireless IoT systems. To protect the user's privacy, we design a cross-layer privacy protection model to separate the user's identity and tasks flexibly by means of a hierarchical structure based on fog computing. Moreover, considering the multiconstraint requirement of crowdsourcing tasks, we present a multifactor reputation evaluation method to accurately identify malicious users. Furthermore, to solve the multi-identity problem of users on multiple fog nodes, we propose an adaptive fog-blockchain reputation storage method, which efficiently reduces the system resource consumption by analyzing the adaptive classification of fog nodes. Exhaustive experimental simulation results validate the security and efficiency of our proposed reputation management system.

With the rapid increase in service requirements driven by Internet of Things (IoT) networks, mobile crowdsourcing has become a compelling paradigm that can efficiently solve complex tasks in the physical world. Nevertheless, we found that most IoT tasks have constraints on deadline, location, and resource consumption, which limit the application of crowdsourcing platforms in the IoT networks. In this article, we innovatively propose a reliable fog-based temporal-spatial crowdsourcing for serving the above tasks. In this scenario, the key point is to achieve the best match of the attributes among tasks, fog nodes, and workers. As the bridge of the other two parts, fog nodes determine the orientation of tasks. Therefore, we present a temporal-spatial task allocation (TS-TA) scheme in the fog layer, aiming to make task results more reliable. In this scheme, we build a temporal-spatial attribute learning model based on the user behaviors. Then, we use the users' interest attribute matching model to identify the candidate fog nodes that satisfy the requirements of temporal-spatial tasks. We choose the fog nodes with low spatial correlation that is benefit to defense the attack on the nodes in the intensive area. Meanwhile, we assign the redundancy nodes for intrusion response through replacing the attacked/negative node. Both theoretical and real-topology simulation results validate that the proposed scheme can get better performance in system resource consumption and system robustness compared with other benchmark schemes.

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.

The three-dimensional Network-on-Chips (3D NoCs) has become a mature multi-core interconnection architecture in recent years. However, the traditional electrical lines have very limited bandwidth and high energy consumption, making the photonic interconnection promising for future 3D Optical NoCs (ONoCs). Since existing solutions cannot well guarantee the fault-tolerant ability of 3D ONoCs, in this paper, we propose a reliable optical router (OR) structure which sacrifices less redundancy to obtain more restore paths. Moreover, by using our fault-tolerant routing algorithm, the restore path can be found inside the disabled OR under the deadlock-free condition, i.e., fault-node reuse. Experimental results show that the proposed approach outperforms the previous related works by maximum 81.1 percent and 33.0 percent on average for throughput performance under different synthetic and real traffic patterns. It can improve the system average optical signal to noise ratio (OSNR) performance by maximum 26.92 percent and 12.57 percent on average, and it can improve the average energy consumption performance by 0.3 percent to 15.2 percent under different topology types/sizes, failure rates, OR structures, and payload packet sizes.

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

With the incoming era of 5G communication, the number of mobile devices is anticipated to increase dramatically. Flexible network resource allocation is required urgently to meet the mobility needs of a large number of users, accelerating the rise of network virtualization. However, the existing researches on virtual network embedding (VNE) consider less the virtual node migration caused by user mobility. In this paper, we attempt to address the problem of VNE supporting user mobility. The concepts of interruption penalty and blocking penalty are proposed to quantify the impact of virtual node mobility on infrastructure providers (InPs) and refine the revenue model of InPs. Then we propose a location-constrained 5G VNE algorithm, where a virtual node and virtual link pair embedding method is designed to increase the probability of successful VNE. Based on the proposed VNE algorithm, we further propose a virtual network re-embedding algorithm that can dynamically migrate the embedding of virtual nodes following user mobility. The virtual node migration is triggered by predicting the locations of virtual nodes and selecting the target physical nodes with the minimum number of re-embedding. Simulation results show that the proposed algorithm outperforms the existing VNE algorithms with higher InP revenue.

Optical switching has been investigated for a long time, as a possible effective solution to overcome the limitations of power consumption, footprint and scalability in data center networks (DCNs). However, with the increasing DC traffic and narrow channel spacing between wavelengths for optical interconnects, traditional single-mode and multi-waveguide optical switching solutions have encountered bandwidth bottlenecks. To this end, we propose a 2×2 silicon-based on-chip optical switching architecture compatible with hybrid wavelength-andmode division multiplexing (WDM-MDM), and it is found experimentally that the proposed design increases the bandwidth 8× times with low crosstalk.

The blooming of Internet of Things enables our modern buildings to become more and more smart, e.g., the intelligent LED lighting system could automatically adjust LEDs' dimming levels based on the ambient sunlight. However, this brings blurring effects that could significantly affect the effectiveness of visible light localization. In this paper, we propose a high-precision visible light localization system DIMLOC under dimmable LEDs in smart buildings. We first propose to use a novel imaging processing framework that consists of efficient techniques of the second-order polynomial fitting, histogram equalization, and Sobel filter to cope with the blurring effects. We then propose a novel algorithm based on vision analysis and scaling factor that only needs two LEDs for positioning. We prototype our DIMLOC on commercial off-the-shelf devices, and extensive experiments demonstrate that DIMLOC could achieve centimeter precision, e.g., 4.5 cm. Overall, this paper could further broaden application scenarios of visible light localization in the era of smart buildings.

The automated diagnosis helps us better understand the complex landscape of diseases, leading to more effective, early and reliable medical diagnosis and therapy. The human-machine interactions in healthcare delivery relying on automated cyber-physical systems (ACPSs) play an important role in the automated diagnosis. Currently, the multicore accelerator used for ACPS has utilized the network-on-chip (NoC) for personalized healthcare. However, the discrete cores based on NoC are affected by limited computation speed, since the data have to pass through an electrical interconnect. In this paper, we propose a novel optical NoC (ONoC) solution of designing discrete cores to quickly understand biomarkers for early detecting abnormal pathophysiology, such as the deviation from the protein's native state. We analyze the performance of our ONoC-based ACPS accelerator for personalized healthcare by virtue of the tested proteins widely adopted in the lattice protein model. Our mathematical analysis and simulation results demonstrate that: 1) the chip area becomes smaller than a traditional design, which makes the personalized healthcare product more convenient; 2) the computation speed is promoted, resulting in the rapid understanding of biomarkers; and 3) we improve the data transmission reliability through accurately capturing the photonic effect so that desirable human-machine interactions can be guaranteed. Note to Practitioners-We design an on-chip hardware accelerator for automated diagnosis and personalized healthcare by predicting biological protein folding. The simulation results based on the lattice protein model can well guide the practitioners to design a more convenient and reliable product quickly detecting the biomarker, such as the deviation from the protein's native state.

The peer-to-peer energy trading has been achieved among nodes in industrial Internet of Things. To establish a secure private market, some meaningful works propose the concept of the energy chain, where one block is added in a linear and chronological order once the trading pair of nodes (buyer and seller) has a valid transaction verified by data audit (e.g., a hash value). Since the buyer applies virtual coins from the credit bank to buy others' surplus energy, a considerable credit utility is obtained if all nodes are encouraged to meet local power loads out of self-interest. However, such frequent transactions have huge operational overhead, including a long chain maintaining many blocks and an expensive energy transportation cost between trading pairs. To solve these challenging issues, our method enables nodes to satisfy their power loads through local stored energy (self-sufficiency), before participating as sellers if they still have considerable surplus electricity. Without transactions made by some self-sufficient nodes, the operational overhead can be mitigated in a more secure environment. Taking the classic Internet of energy as a case study, we demonstrate the effectiveness of our solutions, and it can achieve a good tradeoff between credit utility and operational overhead.

Currently, vehicles have the abilities to communicate with each other autonomously. For Internet of Vehicles (IoV), it is urgent to reduce the latency and improve the throughput for data transmission among vehicles. This article proposes a deep learning based transmission strategy by exploring trirelationships among vehicles. Specifically, we consider both the social and physical attributes of vehicles at the edge of IoV, i.e., edge of vehicles. The social features of vehicles are extracted to establish the network model by constructing triangle motif structures to obtain primary neighbors with close relationships. Additionally, the connection probabilities of nodes based on the characteristics of vehicles and devices can be estimated, by which a content sharing partner discovery algorithm is proposed based on convolutional neural network. Finally, the experiment results demonstrate the efficiency of our method with respect to various aspects, such as message delivery ratio, average latency, and percentage of connected devices.

Performing virtualization can further improve the infrastructure utilization of Inter-Data-Center Optical Networks (IDCONs). In virtualized IDCONs, a service requestor subscribes one lightpath for transferring the virtual machine to the specific server in the destination DC. However, the infrastructure overload leads to the denial of services. Thus, we design a novel service degradability (well-intentioned service degradation) framework, in order to minimize the number of service requirements declined by overloads, resulting in the maximal utility of the IDCON provider. We mathematically formulate the problem and determine the most appropriate degradation degree based on microeconomic theory. For heuristic algorithms, the availability-aware bandwidth degradability policy is invoked once there is the optical backbone overload; while for the server overload in DCs, the application-specific virtual machine degradability policy is considered. Finally, the simulation results demonstrate the effectiveness of our heuristic algorithms, and the optimal combination of degradation coefficients is achieved by microeconomic theory and LINGO.

Fog computing-based radio access networks (F-RANs) aim to extend the computing and storage facilities of the centralized cloud radio access networks (C-RANs) to the network edge. Compared with the centralized baseband unit pool in the C-RAN, F-RAN reduces the burden on fronthaul. Thus, the F-RAN is foreseen as a viable solution towards ultra-low latency service provisioning. However, due to limited computing and storage facilities in fog computing-enabled access points (F-APs), some tasks that cannot be executed on the primary F-APs are transferred to other F-APs. In worst-case, the tasks are sent to the resource-enriched centralized cloud for processing. The transmission latency between F-APs, F-AP-to-end- user, and fronthaul latency strongly depends on interference power from the undesired network element as well as end-users. At the same time, the computational latency increases with the queuing delay. In this paper, we propose a load balancing scheme to address the tradeoff between transmission and computing latencies in F- RANs. Finally, the extensive simulation results show that the proposed scheme outperforms the greedy approach to meet the critical requirements, such as low-latency and minimal task offloading to the cloud in the F-RAN for the low-latency communications.

Nowadays, owing to the advantages of high bandwidth and low power consumption, the wired optical network-on-chip (W-ONoC) has emerged as a high-performance on-chip communication solution. However, the W-ONoC suffers from increased latency and limited scalability since the multi-hop data transmission is frequently performed in wired structures. In addition, several problems such as photoelectric conversion and thermal sensitivity pose challenges to the design of WONoCs. In this paper, we develop a novel on-chip communication architecture based on free-space optics (FSO), and leverage a suite of emerging devices. The proposed architecture eliminates the power loss caused by the waveguide and microring resonator previously deployed in W-ONoCs, and the transmission latency is also reduced through simplifying the packet switching. Moreover, compared with the traditional single-layer FSO NoC, our 3D FSO NoC further decreases the number of consumed lasers and power. Extensive simulation results demonstrate the aforementioned advantages of our solution.

In sharing economy, people offer idle social resources to others in a sharing manner. Through community-based online platforms, the people offering services can earn commission while others can enjoy a better life via renting social resources. Consequently, the value-in-use of services is expectedly strengthened within the unit time, although the total amount of social resources remains constant. Influenced by sharing economy, some famous companies have developed intelligent systems to analyze the most appropriate coincidence between citizens' idle supply and renting demand from numerous data sets. However, the big data analysis of the optimal service-demand matching usually runs on the traditional multiprocessors equipped in intelligent systems, so-called “system-on-chip.” In this paper, we design a novel computer architecture - the accelerator based on optical network-on-chip (ONoC) - to further speed up the matching between citizens' offer and demand in sharing economy. Our ONoC-based accelerator is able to quickly calculate the optimal service-demand matching by processing computation tasks on parallel cores, i.e., task-core mapping. In addition, to improve the accelerator reliability, the assorted task-core mapping algorithm is also designed. The extensive simulation results based on real trace file demonstrate the effectiveness of our system and algorithm. Note to Practitioners - Sharing economy is of great importance for realizing green consumption and sustainable development in our human society. Sharing economy enterprise calls for intelligent system design for service-demand matching in the current big data era. In this paper, we design the accelerator based on ONoC to further speed up the matching between citizens' offer and demand in sharing economy. By processing computation tasks on parallel cores using our algorithm, the task-core mapping can be performed with high speed and reliability. The simulation results - based on the trace file of Amazon Mechanical Turk - can well guide the practitioners to design a more clever and reliable product by quickly calculating the optimal service-demand matching.

As an integrated environment deployed with wired and wireless infrastructures, the smart city heavily relies on the wireless-optical broadband access network. The information flows captured by indoor devices are sent to optical network units through front-end wireless mesh sensor networks (WMNs) and, finally, reach the optical line terminal for industrial/commercial decision making via the passive optical network backhaul. To reduce the backhaul bandwidth saturated by this conventional approach, edge devices are deployed at the front-end WMN to preprocess information flows. Based on collaborative edge computing, home users or factory workers customize their computing services as virtual networks embedded onto the common WMN. In this paper, we propose the green survivable virtual network embedding for the collaborative edge computing in smart cities. We mathematically formulate the problem and derive the corresponding bound. Extensive simulations with real traces demonstrate the algorithm effectiveness.

Passive Optical Network (PON) is a cost-effective way to become the front haul carrier for 5G ubiquitous wireless access connectivity. To hide the difference of frame formats and multiplexing methods among WiMAX, LTE and WiFi, a typical PON is proposed based on Orthogonal Frequency-Division-Multiplexing Access (OFDMA), i.e., OFDMA-PON. In OFDMA-PONs, by virtualization, low-bitrate Virtual Subcarriers (VSs) are assigned to segments, and VS loads can be migrated from one segment to another, thus sleeping unloaded segments while ensuring the service continuity. Although the subcarrier scheduling has been discussed for the OFDMA-PON merged with LTE, it is tailored to the single segment without allocating time and modulation level. In this paper, we investigate joint allocation of time, VS and modulation for 5G green OFDMA-PONs. We formulate the problem aimed at maximizing the energy-saving effect, and the upper bound is also derived. To solve the problem in a short span of time, we propose a novel heuristic algorithm. Finally, an extensive simulation is made to demonstrate the effectiveness of our design.

Internet of Things (IoT) devices have different operation principles, which weakens the data interoperability. Virtualization is an economic way of solving this problem. The data-collected by different vendors' sensors-share the same computing program encapsulated by the virtual machine (VM), so that the physical-layer difference can be hidden. To eliminate the extra cost and long delay of transferring VMs to the remote cloud, the edge device (ED) processes local VMs' requirements in prior. Hence, the sustainable strategy for recycling EDs is an important way to safeguard the network sustainability. To improve the recycling efficiency, most of the EDs should be upgraded simultaneously during one batch by migrating their local VMs to others for the service continuity. We investigate the least upgrade batch for recycling EDs in IoT networks. A two-step algorithm called minimized upgrade batch VM scheduling and bandwidth planning (MSBP) is designed to minimize the number of upgrade batches. As the frequent VM migration brings the bandwidth consumption and contention of trajectories, in our MSBP, two strategies: 1) shortest trajectory first and 2) least bandwidth utilization first (LBUF), are also considered. The simulation results show that: 1) MSBP has the optimal recycling efficiency (least number of upgrade batches) for EDs and 2) LBUF more effectively mitigates the negative impact of the path contention level on the recycling efficiency.

Broadband mobile networks utilize a radio resource control (RRC) state machine to allocate scarce radio resources. Current implementations introduce high latencies and cross-layer degradation. Recently, the RRC enhancements, continuous packet connectivity (CPC), and the enhanced forward access channel (Enhanced FACH), have emerged in UMTS. We measure the availability and performance of these enhancements on a network serving a market with a population in the millions. We demonstrate that these enhancements offer significant reductions in latency, mobile device energy consumption, and improved end user experience. We develop new over-the-air measurements that resolve existing limitations in measuring RRC parameters. We find CPC provides significant benefits with minimal resource costs, prompting us to rethink past optimization strategies. We examine the crosslayer performance of CPC and Enhanced FACH, concluding that CPC provides reductions in mobile device energy consumption for many applications. While the performance increase of HS-FACH is substantial, cross-layer performance is limited by the legacy uplink random access channel (RACH), and we conclude full support of Enhanced FACH is necessary to benefit most applications. Given that UMTS growth will exceed LTE for several more years and the greater worldwide deployment of UMTS, our quantitative results should be of great interest to network operators adding capacity to these networks. Finally, these results provide new insights for application developers wishing to optimize performance with these RRC enhancements.

Visible Light Communication (VLC) is a promising technique for high-speed, low-cost wireless services with the rapid development and wide deployment of Light-Emitting Diodes (LEDs). However, the broadcast nature of the VLC makes eavesdroppers easily intercept the light communication in various settings, e.g., offices, conference rooms and airport lobbies. Although previous work put forward physical layer mechanisms to improve VLC security, they ignored the impact of light reflection and channel correlation on VLC security, which is not practical for the indoor environment. In this paper, we propose a new eavesdropping-resilient framework to defend against eavesdropping attacks under both Single Input Single Output (SISO) and Multiple Input Single Output (MISO) models. We propose a random time reversal scheme, that makes transmitted signal automatically focus on legitimate receivers while interfering the eavesdropper's channel with VLC's multipath redundancy, time reversal, and random choice technique. We analyze the impact of channel correlation on VLC security under the MISO model and find that the system secrecy capacity decreases. Therefore, we propose to use Karhunen-LoEve (KL) transform to improve the system secrecy capacity. Finally, we conduct extensive simulations to show that, our framework can make the eavesdropper's Bit Error Rate (BER) be above 0.0038, which is the threshold of Forward Error Correction (FEC), even when he tries to attack with Constant Modulus Algorithm (CMA). Furthermore, the system secrecy capacity is improved up to 3 Bit/Sec/Hz.

To make full use of space/air resources, a Space-Air Integrated Network (SAIN) which is a hierarchical network with satellites, airships and hovering Unmanned Aerial Vehicles (UAVs) is constructed. Nowadays, Free Space Optical (FSO) links have been widely applied in SAINs since they provide high-rate and large-capacity data transmission. Unfortunately, the FSO links across the atmosphere would perform badly in adverse weather. Besides, the limited number of transceivers brings bottlenecks of link capacity and node energy. To solve these problems, in this paper, we first propose an adaptive RF/FSO switching mechanism based on predictions of atmosphere conditions, so that the high- rate and large-capacity data transmission can be achieved while overcoming the negative influences of bad weathers. Moreover, under the limited number of transceivers, we design a Dynamic Energy & Traffic Balance (DETB) topology control algorithm. Except for transmit power, both residual bandwidth and energy are taken into account for obtaining an optimal topology dynamically. Finally, a hierarchical routing policy combined with our DETB algorithm is proposed. It has been proved that our method extends the network lifetime, and the network throughput remains stable.

The use of secure HTTP calls is a first and critical step toward securing the Android application data when the app interacts with the Internet. However, one of the major causes for the unencrypted communication is app developer's errors or ignorance. Could the paradigm of literally repetitive and ineffective emphasis shift towards emphasis as a mechanism? This paper introduces emphaSSL, a simple, practical and readily-deployable way to harden networking security in Android applications. Our emphaSSL could guide app developer's security development decisions via real-time feedback, informative warnings and suggestions. At its core of emphaSSL, we use a set of rigorous security rules, which are obtained through an in-depth SSL/TLS security analysis based on security requirements engineering techniques. We implement emphaSSL via the PMD and evaluate it against 75 open- source Android applications. Our results show that emphaSSL is effective at detecting security violations in HTTPS calls with a very low false positive rate, around 2%. Furthermore, we identified 164 substantial SSL mistakes in these testing apps, 40% of which are potentially vulnerable to man-in-the-middle attacks. In each of these instances, the vulnerabilities could be quickly resolved with the assistance of our highlighting messages in emphaSSL. Upon notifying developers of our findings in their applications, we received positive responses and interest in this approach.

Fiber-Wireless (FiWi) access network, which could provide an anytime-anywhere access for end users with high bandwidth capacity and long distance, is facing the challenge of resource allocation and optimization due to the complexity and diversity of traffic demands. Though network virtualization becomes a promising solution, which allows heterogeneous virtual networks coexisting on the shared substrate network, previous works ignored both varied requirements of Quality of Service (QoS) satisfaction of virtual networks and the flexibility of reconfiguring the resource of substrate FiWi access network. In this paper, we propose a new Virtual Network Embedding (VNE) framework based on QoS satisfaction and network reconfiguration. By equipping each virtual network with a specific QoS satisfaction requirement, the characteristics of virtual network demands are formulated from a more practical point of view. Moreover, the adaptive bandwidth allocation of substrate network and virtual network reconfiguration are exploited to maximize the InP revenue. Simulation results demonstrate that our proposed VNE algorithm outperforms previous approaches with multifold increment of InP revenue.

Cooperative communications take full advantage of the broadcast nature of wireless channels and create spatial diversity, thereby achieving tremendous improvement in the network performance. This paper presents an energy-efficient cooperative MAC (EECO-MAC) protocol using cooperative communication and power control techniques in mobile ad hoc networks. The best partnership selection algorithm, which takes energy consumption into consideration is proposed to select the optimal cooperative helper for the cooperative transmission. Through exchanging control packets, the optimal transmission power is allocated for senders to transmit data packets to receivers. In order to reduce the influence induced by interference, space-time back off and time-space back off algorithms are proposed. Simulation results show that EECO-MAC consumes less energy and prolongs the network lifetime compared to IEEE 802.11 DCF and Coop MAC.

An integrated space/air information network is a convergence of satellite communication networks in space regions and aircraft communication networks in air regions. It supports direct internal information interactions. Along with the extensive application of the integrated space/air information network especially in military fields, its routing problem becomes a key point of research. However, the existing routing algorithms are mainly designed for the space or air region separately and there is little study on the generalized routing for the integrated space/air information network. In this paper, we propose a Hybrid time-space Graph based Hierarchical Routing (HGHR) scheme for this integrated network. The hybrid time-space graph includes two subgraphs: a deterministic one and a semi-deterministic one. As satellite orbits are pre- known in the space region, we introduce a deterministic time-space subgraph. While each aircraft has a cyclic movement with the predictable contact probability and contact time in the air region, we construct a semi-deterministic time-space subgraph according to the prediction results of a discrete time homogeneous semi-Markov model. Simulation results show that HGHR has good performance in terms of data delivery ratio and end- to-end delay.

There is growing interest in new physical-layer transmission methods based on known-interference cancellation (KIC). These KIC-based methods share the common idea that the interference can be cancelled when the bit-sequence of it is known, which can improve the efficiency of wireless data communications. Existing work on KIC mainly focuses on single-hop or two-hop networks, with physical-layer network coding (PNC) and full-duplex (FD) communications as typical examples. This paper extends the idea of KIC to multi-hop networks, and proposes a bidirectional end-to-end KIC (BE2E-KIC) transmission method for the scenario where two nodes intend to exchange packets through multiple intermediate nodes. With BE2E-KIC, the involved nodes can simultaneously transmit and receive on the same channel. We first discuss the procedure of BE2E-KIC and provide a theoretical analysis on its feasibility and effectiveness. Then, we propose a medium access control (MAC) scheme that supports BE2E-KIC, which schedules packet transmissions in more realistic cases with the presence of packet-loss. Simulation results illustrate that BE2E-KIC can improve the network throughput and reduce the end-to-end delay compared with other existing transmission methods.

Broadband mobile networks utilize a radio resource control (RRC) state machine to allocate scarce radio resources. Current implementations introduce high latencies and cross-layer degradation. Recently, the RRC enhancements, continuous packet connectivity (CPC) and the enhanced forward access channel (Enhanced FACH), have emerged in UMTS. We study the availability and performance of these enhancements on a network serving a market with a population in the millions. Our experience in the wild shows these enhancements offer significant reductions in latency, mobile device energy consumption, and improved end user experience. We develop new over-the-air measurements that resolve existing limitations in measuring RRC parameters. We find CPC provides significant benefits with minimal resource costs, prompting us to rethink past optimization strategies. We examine the cross-layer performance of CPC and Enhanced FACH, concluding that CPC provides reductions in mobile device energy consumption for many applications. While the performance increase of HS-FACH is substantial, cross-layer performance is limited by the legacy uplink random access channel (RACH), and we conclude full support of Enhanced FACH is necessary to benefit most applications. Given that UMTS growth will exceed LTE for several more years and the greater worldwide deployment of UMTS, our quantitative results should be of great interest to network operators adding capacity to these networks. Finally, these results provide new insights for application developers wishing to optimize performance with these RRC enhancements.

Resource allocation is an important topic with a wide range of applications. In many practical cases, users and resource suppliers are players in the market. As a result, much effort has been made in applying market mechanisms (such as auction and game-theoretic results) to resource allocation. The conventional approach in such studies is to consider cases where users' resource demands are fixed. However, in practice, resource demands are often elastic, which can be related to the quality of experience (QoE) that the user receives. We consider elastic resource demands in this paper, and propose a double auction and negotiation (DAN) scheme, which includes a conventional auction stage as well as a negotiation stage, where the latter allows users to dynamically adjust their demands. The proposed DAN scheme not only allows more users to get access to some amount of resource (thereby avoiding users becoming completely disconnected), but also increases the payoff of resource suppliers, as is confirmed by simulations. We also discuss the conditions of having Nash equilibrium in the users' resource demands and suppliers' pricing in the negotiation stage.

Cooperation among nodes plays an important role in the commercial development of wireless networks. Efficient cooperation should include not only encouraging selfish nodes to forward packets for one another but also selecting optimal transmission methods. Therefore, in this paper, in order to improve network performance, we propose a social-oriented adaptive transmission scheme for wireless ad hoc networks. Firstly, next-hop node for each transmission is decided by a double auction-based social awareness mechanism. Then in the case of relay-aided transmissions, optimal relaying method is selected by jointly considering network coding and spectrum spatial reuse. Simulation results demonstrate that the proposed scheme has significant advantages in social welfare and throughput improvement.

Physical-layer network coding (PNC) is an effective strategy for increasing the throughput of wireless networks. In the current literatures, PNC without rate and power adaptation is mainly focused. Realizing that the transmission efficiency can be improved through rate and power adaptation in wireless networks, this paper focuses on developing a rate and power adaptation scheme for PNC. Through formulating how the data rate and transmission power affect the bit error rate (BER) of involved links in PNC, we observe that with a given data rate, the transmission power has to satisfy some constraints. Using these power constraints, we obtain a candidate set of optimal transmission power. By traversing the candidate set and the data rates supported by nodes, a rate and power adaptation scheme is developed. To test its performance, we apply the proposed scheme into an existing PNC-supported MAC protocol. Simulation results demonstrate that the proposed scheme can improve the throughput and delay performance in various scenarios.

We study scheduling and transmission of packets with deadline constraints in cooperative wireless networks. The packets which miss their deadlines become useless and have to be dropped. To minimize packet dropping probability, we consider multiple transmission methods and integrate packet scheduling with adaptive transmission method selection. We first introduce an exhaustive search method to obtain the optimal scheduling sequences and the corresponding transmission methods, under different channel conditions. Through observing the optimal results, we propose a heuristic method based on a dynamic graph. Simulation results show that the proposed heuristic method can obtain results which are similar to those achieved with the exhaustive search method, but with low computational complexity.

In a many-to-many session, a participant distributes and receives traffic flows to/from all others in the same session. It is desirable to achieve the high resource utilizations and low energy consumptions when we provision such sessions. In the Wavelength-Division-Multiplexing (WDM) optical network, traffic grooming has been widely applied as the key technology for the routing and aggregation of sessions due to the high resource utilizations. However, the current many-to-many grooming approaches, whether the lightpath circle or the hubbed light-tree, do not make effort on reducing energy consumptions of many-to-many sessions. In this paper, we formulate the “Green Routing and Aggregation of Many-to-Many Sessions (GRAMMS)” problem. For the problem solving, we propose a novel heuristic that applies a rational combination of lightpaths in green WDM optical networks. We further give an illustrative comparison between our heuristic and existing approaches in terms of energy savings. Extensive simulation results demonstrate that, compared to benchmark approaches, our heuristic can achieve much higher energy efficiency.

Recently, physical-layer network coding (PNC) attracts much attention due to its ability to improve throughput in relay-aided communications. However, the implementation of PNC is still a work in progress, and synchronization is a significant and difficult issue. This paper investigates the feasibility of synchronous PNC with M-ary quadrature amplitude modulation (M-QAM). We first propose a synchronization scheme for PNC. Then, we analyze the synchronization errors and overhead of potential synchronization techniques, which includes phase-locked loop (PLL) and maximum likelihood estimation (MLE) based synchronization schemes. Their effects on the average symbol error rate and the goodput are subsequently discussed. Based on the analysis, we perform numerical evaluations and reveal that synchronous PNC can outperform conventional network coding (CNC) even when taking synchronization errors and overhead into account. The theoretical throughput gain of PNC over CNC can be approached when using the MLE based synchronization method with optimized training sequence length. The results in this paper provide some insights and benchmarks for the implementation of synchronous PNC.

The denoise-and-forward (DNF) method of physical-layer network coding (PNC) is a promising approach for wireless relaying networks. In this paper, we consider DNF-based PNC with M-ary quadrature amplitude modulation (M-QAM) and propose a mapping scheme that maps the superposed M-QAM signal to coded symbols. The mapping scheme supports both square and non-square M-QAM modulations, with various original constellation mappings (e.g. binary-coded or Gray-coded). Subsequently, we evaluate the symbol error rate and bit error rate (BER) of M-QAM modulated PNC that uses the proposed mapping scheme. Afterwards, as an application, a rate adaptation scheme for the DNF method of PNC is proposed. Simulation results show that the rate-adaptive PNC is advantageous in various scenarios.

Survivability is one of the key issues in Fiber-Wireless (FiWi) broadband access network since huge data loss could be caused by single segment failure. Previous schemes focus on protecting FiWi against single segment failure by deploying backup fibers. However, these schemes suffer from two key problems. First, they ignore optimizing the selection of backup ONUs, which determines the recovery delay of the traffic interrupted by failure. Second, they underutilize the residual capacity of segments, thus require high cost of backup fibers. In this paper, aiming to tackle the inefficiency of previous schemes, we propose an efficient protection scheme, called Optimizing Backup ONUs selection and backup Fibers deployment (OBOF), to enhance the survivability of FiWi against single segment failure. Extensive experimental results demonstrate that our OBOF scheme outperforms the previous schemes significantly, especially in the scenario of higher traffic demand.

With the increasing of number of multimedia network applications, multi-cast services are becoming more and more popular. At the same time, the waveband switching technique is proposed to save ports and to reduce the cost of optical cross-connect. However, the existing multi-cast waveband grooming algorithms are mostly limited to single-domain optical networks. The size of optical backbones keeps enlarging, and the backbones are actually divided into multiple independent domains to provide the needed scalability and confidentiality. To solve these problems, the authors propose three heuristic algorithms: per-domain multi-cast grooming (PDMG), virtual topology multi-cast grooming (VTMG) and hierarchical multi-domain multi-cast grooming (HMMG). The main difference between these algorithms is that the inter-domain routing is performed in a different manner; in other words, the routings in PDMG, VTMG and HMMG are computed based on the domain-by-domain fixed routing table, the high layer in the aggregative virtual topology of multi-domains and the hierarchical integrated multi-cast auxiliary graph, respectively. For the intra-domain routing, the three algorithms employ the same method based on an intra-domain multi-cast integrated auxiliary graph. Simulation results show that compared with PDMG and VTMG, HMMG has the best performance in terms of the number of ports saved and the blocking probability.

Since current optical network is actually divided into multiple domains each of which has its own network provider for independent management, the development of multi-domain networks has become the trend of next-generation intelligent optical networks, and then the survivability has also become an important and challenging issue in fault-tolerant multi-domain optical networks. In this paper, we study protection algorithms in multi-domain optical networks and propose a new heuristic algorithm called multi-domain Hamiltonian cycle protection (MHCP) to tolerate the single-fiber link failure. In MHCP, we present the local Hamiltonian cycle (LHC) method based on the physical topology of each single-domain and the global Hamiltonian cycle (GHC) method based on the abstracted virtual topology of multi-domains to protect the intra-fiber link and inter-fiber link failures, respectively. We also present the link-cost formulas to encourage the load balancing and proper links selection for computing the working path of each connection request. Simulation results show that, compared with previous multi-domain protection algorithm, MHCP can obtain better performances in resource utilization ratio, blocking probability, and computation complexity.

In this paper, a QoS unicast routing scheme with ABC supported is proposed. With gaming analysis and particle swarm optimization algorithm, it tries to find a QoS unicast path with Pareto optimum under Nash equilibrium on both the network provider utility and the user utility achieved or approached. Simulation results have shown that it is both feasible and effective.

This paper proposes a new survivable algorithm, enhanced shared-path protection (ESPP), to tolerate multi-link failures in WDM optical networks. In ESPP, we consider the load balancing to reduce the blocking probability, use resources sharing to save backup resources, and perform recovery escalation to carry the affected traffic. Compared with the conventional algorithm, ESPP has better resource utilization ratio, lower blocking probability and higher protection ability. Simulation results are shown to be promising.