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With the rapid development of Internet of Things (IoT), digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of digital twin in IoT. In this paper, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through access point (AP) to help resource limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging Directed Acyclic Graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.

The fundamental communication paradigms in the next-generation mobile networks are shifting from connected things to connected intelligence. The potential result is that current communication-centric wireless systems are greatly stressed when supporting computation-centric intelligent services with distributed big data. This is one reason that makes federated learning come into being, it allows collaborative training over many edge devices while avoiding the transmission of raw data. To tackle the problem of model aggregation in federated learning systems, this paper resorts to multiple reconfigurable intelligent surfaces (RISs) to achieve efficient and reliable learning-oriented wireless connectivity. The seamless integration of communication and computation is actualized by over-the-air computation (AirComp), which can be deemed as one of uplink non-orthogonal multiple access (NOMA) techniques without individual information decoding. Since all local parameters are uploaded via noisy concurrent transmissions, the unfavorable propagation error inevitably deteriorates the accuracy of the aggregated global model. The goals of this work are to 1) alleviate the signal distortion of AirComp over shared wireless channels, and 2) speed up the convergence rate of federated learning. More specifically, both the mean-square-error (MSE) and the device set in the model uploading process are optimized by jointly designing transceivers, tuning reflection coefficients, and selecting clients. Compared to baselines, extensive simulation results show that 1) the proposed algorithms can aggregate model more accurately and accelerate convergence; 2) the training loss and inference accuracy of federated learning can be improved significantly with the aid of multiple RISs.

Inspired by excellent precision of carrier phase positioning, this paper presents a new carrier phase positioning technique for 5G new radio cellular networks with a focus on clock synchronization and integer ambiguity resolution. A carrier-phase based clock offset estimation method is first proposed to achieve precise clock synchronization among base stations, and proved to achieve the Cramér-Rao Lower Bound (CRLB) asymptotically. A fusion method is developed to fuse the estimated positions of a mobile station (MS) based on time-difference-of-arrival, with the estimated position changes based on the temporal changes of carrier phase measurements. While circumventing the integer ambiguities of the carrier phase measurements, the fusion method provides quality interim estimates of the MS positions, at which the measurements can be linearized to resolve the integer ambiguities. As a result, precise MS positions can be obtained based on the disambiguated carrier phase measurements. Numerical simulations show that the proposed carrier phase positioning can achieve a centimeter-level accuracy in wireless cellular networks.

More and more cloud data centers are turning to leverage on-site renewable energy to reduce power cost for sustainable development. But how to effectively coordinate the intermittent renewable energy with workload remains to be a great challenge. This paper investigates the problem of workload scheduling for power cost minimization under the constraints of different Service Level Agreements (SLAs) of delay tolerant workload and delay sensitive workload for green data centers in a smart grid. Different from the existing studies, we take into consideration of the impact of zero price in the smart grid and the cost of on-site renewable energy. To handle the randomness of workload, electricity price and renewable energy availability, we first formulate the problem as a constrained stochastic problem. Then we propose an efficient online control algorithm named ODGWS (Online Delay-Guaranteed Workload Scheduling) which makes online scheduling decisions achieve a bounded guarantee from the worst scheduling delay for delay tolerant workload. Compared with the existing solutions, our ODGWS decomposes the problem into that of solving a simple optimization problem within each time slot in O ( 1 ) time without needing any future information. The rigorous theoretical analysis demonstrates that our algorithm achieves a [ O ( 1 V ) , O ( V ) ] cost-delay tradeoff, where V is a balance parameter between the cost optimality and service quality. Extensive simulations based on real-world traces are done to evaluate the performance of our algorithm. The results show that ODGWS saves about 5% average power cost compared with the baseline algorithms.

In this letter, we study the secrecy performance of an intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) relay communication system in the presence of multiple ground eavesdroppers (Eves). Specifically, a UAV equipped with an IRS is deployed as a passive relay to forward the signals transmitted from the base station to users. By modeling the distribution of Eves with stochastic geometry theory and taking the estimation errors into account, we extract the novel expressions for the statistical characterizations of the signal-to-noise ratio, for both legitimate receiver and Eves. For completeness, different eavesdropping channels are investigated, namely, the cooperative and independent Eve cases. The secrecy outage probability (SOP) is analytically evaluated to unveil the impact of the number of reflecting elements and the location of UAV on SOP performance. Finally, numerical results corroborate the theoretical analysis.

Salient object detection (SOD) focuses on mimicking the attention mechanism in human vision system. Due to the limited information provided from the traditional image data, the image-based SOD advances are very challenging in some complex scenes. Inspired by the emerging event cameras that provide asynchronous measurements of local temporal contrast over a large dynamic range, we propose a new idea to extract more effective information from the combination of event flow and RGB images. In this paper, we construct an end-to-end joint network for salient object detection (ERSOD-Net), which simultaneously supervise the RGB image and the event data within the corresponding image exposure time. To fully exploit temporal information of event data, Long Short-Term Memory module is utilized to effectively process event and learn salient object event surfaces. Moreover, multi-level feature interaction is designed to fuse two complementary branches of image and event and to predict significance mapping. Finally, to demonstrate the effectiveness of our model, a real Event-RGB SOD dataset (ERSOD) is built by DAVIS camera. Experiments on both benchmark and ERSOD datasets show that the proposed event guided network greatly improves the SOD performance in different evaluation metrics. The code and datasets will be released later at https://github.com/jxr326/ERSOD-Net.

The unmanned aerial vehicle (UAV) swarm has drawn much attention for its wide range of potential applications. However, most UAV swarms rely on manual remote control or preprogramming, leaving autonomous cooperation as a challenging issue. Control and communication are two important pillars intra-autonomous-swarm, and the competition for limited resources between control and communication needs to be coordinated effectively in a swarm. In this article, the challenges and potentials of autonomous UAV swarm control and communication are discussed first. Then a new perspective on joint optimization of the two issues is presented, aiming at the swarm's task handling capability. Furthermore, a task-oriented integration of communication and control (ToICC) framework is proposed, including a route of mathematization for the task handling capability and a game-based distributed multi-agent cooperative learning approach. Finally, some potential applications and an illustrative case are provided to demonstrate the usefulness and effectiveness of the proposed ToICC framework.

This work considers a secure multiple-input multiple-output (MIMO) channel where the energy receiver (ER) is a potential eavesdropper (Eve). Specifically, we aim to maximize the secrecy energy efficiency (SEE) via jointly designing the transmit precoding matrix and the artificial noise (AN) covariance at the base station (BS), as well as the power splitting (PS) ratio at the desired receiver (DR), subject to the constraints of the transmit power budget and harvested energy threshold. To handle the formulated highly nonconvex fractional problem, we apply the successive convex approximation (SCA) method to transform the objective and constraint into a tractable form. Then, a penalty-based iterative algorithm is proposed. Finally, simulation results validate the performance of the proposed design.

Abstract In this article, a multilabel support vector machine (SVM)-based approach is investigated to address the simultaneous decay detection of the marine propulsion system. To verify the performance of the algorithm, we perform some experiments using a simulation dataset from a real-data validated numerical simulator of a Frigate. In particular, we try to train the model without simultaneous decay data, considering the great difficulty of obtaining simultaneous decay data in practice. The experimental results show that the proposed approach can identify the complex decay modes of the marine propulsion system effectively using only simple decay data in the training process. Introduction The propulsion system is considered to be the “heart” of a marine ship (Li et al. 2019a). Its safety and reliability are critical to the regular operation of the ship (Bayer et al. 2018; Cheliotis & Lazakis, 2018; Lazakis et al. 2016). However, performance decay may occur to the propulsion system due to the high humidity and high salt characteristics of the marine environment (Fang et al. 2018; Kang et al. 2019; Wang et al. 2019). The decay modes can be divided into single decay and simultaneous decay. Single decay indicates a simple decay mode that only one kind of decay occurs at a time, and simultaneous decay indicates a complex decay mode that multiple decays occur at the same time. To improve the safety and reliability of the marine propulsion system, researchers have proposed many related approaches from the perspective of fault diagnosis.

Pedestrian dead reckoning based on particle filter is commonly used for enabling seamless smartphone-based indoor positioning. However, compass directions indoor are heavily distorted due to the presence of ferromagnetic materials. Conventional particle filters convert the raw compass direction to a distribution adding a constant variance noise and leveraging a particle swarm to simulate the distribution. Finally, the selection of eligible directions is performed applying external constraints mainly imposed from the indoor map. However, the choice of a constant parameter decreases the positioning performances because the variance of nearby context, including topography, ferromagnetic materials, and particle distribution, is not represented. Therefore, we propose the particle filter reinforcement able to adaptively learn and adjust the variance of the direction observing the context in real-time. Experiments in real-world scenarios show that the proposed method improves the positioning accuracy by more than 20% at the 80% probability compared with state-of-the-art methods.

The Intelligent Transportation System (ITS) provides more possibilities for the realization of smart cities by integrating the Internet of Things (IoT) and cloud computing. However, how to ensure security of IoT data stored in the cloud has become one of the biggest challenges at present. As a promising solution for realizing fine-grained access control, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) can be used to ensure data security. However, the traditional CP-ABE schemes may leak privacy of ITS users. Moreover, due to their high computational overheads, the current privacy-preserving techniques are not suitable for IoT lightweight devices. To fill this gap, this article presents ABE-FPP, a lightweight attribute-based access control scheme with full privacy protection (FPP), which can achieve full privacy protection in the three key stages (i.e., key generation, access control, and partial decryption), while reducing consumption overhead on the user side. Specifically, to protect privacy during key generation, a lightweight two-party secure computing protocol between the user and the authority is designed to generate secret keys; to protect privacy during the access control policy setting, we present an efficient policy hidden strategy, which only reveals attribute names and efficiently hides attribute values; to protect privacy during partial decryption, we propose a hybrid authentication method that does not need to submit attribute values to the cloud. Moreover, to achieve lightweight computation for IoT devices, online/offline encryption and outsourced decryption are employed in ABE-FPP. Finally, formal security proofs show that our scheme is secure in the standard model. The asymptotic complexity analyses and experimental results demonstrate that the presented scheme achieves higher computation efficiency than the state-of-the-art ones.

Soft fault diagnosis has been validated as a very challenging problem in analog circuits. In order to improve the generalization ability and close to the practical application of fault diagnosis models, a novel method for obtaining a large number of training samples in soft fault interval is proposed in the present work. Training samples are randomly generated in the interval of soft fault to adapt the continuously change of component parameters. Limits of experimental conditions, lead to the limited number of samples labeled by experts, so that training samples are classified using the semi-supervised support vector machine (S3VM) algorithm. Manifold learning algorithm has been used for the feature extraction and the dimension reduction of soft fault time domain response data in analog circuits. Then the semi-supervised Gaussian mixture model (SGMM) is applied to cluster decision trees. Finally, the S3VM classification is used for the soft fault diagnosis in analog circuits. Experimental results show that the proposed method can be utilized in the single and double soft fault diagnosis of analog circuits. It is observed that the S3VM method is extremely significant as a guide in actual engineering applications, although the diagnosis rate is slightly less than the fixed-parameter offset soft faults.

In view of the problems of low stiffness, small driving force and large balloon effect existing in the current soft actuator, this paper proposes an optimization method to enhance the overall stiffness of the soft gripper by using rigid components based on the multi-cavity soft pneumatic actuator. This paper introduces the main components of the actuator: the soft part poured by liquid silica gel, and the open rectangular rigid structures by 3D printed. The kinematics model of the finger is established based on the Piecewise Constant Curvature model(PCC). The bending performance of the enhanced stiffness gripper is verified by finite element analysis(FEA): the tip force of actuator increased with the increase of the number of rigid structures when the bending angle is constant. According to the and experimental data, the overall stiffness of soft gripper is increased by the rigid structure without affecting the flexibility of operation. And the maximum weight which can grasp is 3.4 times that of the traditional soft gripper, improved the grasping range of the soft gripper effectively.

This paper proposes a new framework of lossless data hiding (LDH) in JPEG images. The proposed framework contains two algorithms, i.e., the optimized basic LDH and the relay transfer based extension. In the basic algorithm, we aim to preserve the filesize after data embedding. The data hiding process is optimized by variable-length-code (VLC) mapping, combination and permutation. In the extended algorithm, we focus on embedding more bits into the bitstream with a condition that the filesize increment is allowed. To decrease the filesize increment, we propose a relay transfer based algorithm to preprocess the JPEG bitstream. Subsequently, we embed data into the processed bitstream using the basic LDH. Both algorithms provide better performances than previous arts. After lossless data hiding, the marked JPEG bitstream is compliant to common JPEG decoders. Since all operations are implemented on VLCs and the Huffman codes, no distortion is generated on the image pixels. Experimental results demonstrate that the proposed approach outperforms previous methods.

This paper aims to produce an effective online scheduling technique, where a base station (BS) schedules the transmissions of energy harvesting-powered Internet-of-Things (IoT) devices only based on the (differently outdated) in-band reports of the devices on their states. We establish a new primal-dual learning framework, which learns online the optimal proactive schedules to maximize the time-average throughput of all the devices. Batch gradient descent is designed to enable stochastic gradient descent (SGD)-based dual learning to learn the network dynamics from the outdated reports. Replay memory is deployed to allow online convex optimization (OCO)-based primal learning to predict channel conditions and prevent over-fitting. We also decentralize the online learning between the BS and devices, and speed up learning by leveraging the instantaneous knowledge of the devices on their states. We prove that the proposed framework asymptotically converges to the global optimum, and the impact of the outdated knowledge of the BS diminishes. Simulation results confirm that the proposed approach can increasingly outperform state of the art, as the number of devices grows.

Covert timing channels (CTCs) are defined as a mechanism that embeds covert information into network traffic. In a manner, information leakage caused by CTCs brings serious threat to network security. In recent years, detection of CTCs is a focus and a challenging task in the field of covert channel research. However, existing detection schemes based on statistical methods have poor performance in detecting multiple CTCs, and require so many inter-arrival times of packets that these schemes cannot detect CTCs in real time. In this paper, we propose a novel deep learning approach for CTCs detection, namely, covert timing channels detection based on auxiliary classifier generative adversarial network (CD-ACGAN). The network structure and loss function of CD-ACGAN are designed to be suitable for CTCs detection task. We first encode traffic flows into single-channel Gramian Angular Field (GAF) images. Then we use CD-ACGAN to learn features from GAF images and predict the classes of CTCs. Our experimental results show that our approach has high accuracy and strong robustness in detecting various CTCs.

Covert Timing Channels(CTCs) is a technique to leak information. CTCs only modify inter-arrival time sequence(IATs) between packets, consequently, traditional network security mechanisms, such as firewalls and proxies, can not effectively detect CTCs. If CTCs are maliciously utilized by criminals, will pose a great threat to network security. Classic CTCs detection methods, such as KS-test, Entropy-test, etc, not only have less universality and robustness, but also require more sampled IATs to detect CTCs, therefore, how to improve performance of detection methods against CTCs, has became a popular research in recent years. In this paper, a new CTCs detection method based on time series symbolization is proposed. It firstly converts the sampled IATs to symbolic time series, and regards each discrete value as a status. Then counts the times of transition for each status to status, and calculates the status transition probability matrix(STPM). Finally, it differentiates the label(overt or covert) of sampled IATs, by calculating similarity score. Experimental results about detection accuracy show that, in an ideal network environment, compared with classic methods, our method has better performance, with average accuracy of about 96%. Besides, our method has better performance as well, with the existence of network interference.

With the expansion of the urban areas and the rapid growth of the urban population, the disposal of large amounts of domestic waste has become a problem faced by large cities. To solve this problem, many MSW incineration power plants have been continuously built. As a typical large-span spatial structure of waste incineration power plant, how to achieve economic, beautiful, and environmentally friendly design goals under the premise of meeting the requirements of the production process is an important problem currently facing. In this paper, a structural design optimization method based on damage index is proposed. Taking a large-span mixed structure of a MSW incineration power plant as an example, the paper firstly uses the dynamic elastic-plastic time history analysis method to evaluate its seismic performance, and then optimizes the structure according to the damage degree of the structure under small and large earthquakes design. The results show that under the premise of meeting the requirements of the code, this method can ensure that the degree of structural damage under the earthquake remains almost unchanged, while significantly reducing the amount of building materials and reducing the cost. At the same time, this method is more direct, simple, and effective than optimization design methods based on experience and internal forces of structural members.

Ductility is an important aspect of the seismic performance of structures. Strong column and weak beam are important means to ensure the hinge plastic failure mechanism of frame structures. The corresponding provisions on failure mechanism of strong column-weak beam has been specified in design codes of various countries, but their differences are relatively large. This paper takes a plane frame as example which is designed according to the seismic code regulations of various countries, and then the difference in the failure mechanism of the designed frame under seismic actions is compared and analyzed. Finally, some suggestions are given to ensure the realization of strong column-weak beam mechanism.

The increasing growth in the use of mobile news apps has raised questions on how their interface design features affect users’ gaze behaviours and behavioural performance. To tackle these issues, t...

Owing to the developments of wireless communication technologies, mobile health (M-Health) has been postulated as a promising means to improve healthcare quality and save lives in emergencies. However, using wireless communications in M-Health faces the following challenges. First, the wireless transmission may generate electromagnetic interference (EMI) and trigger critical malfunctions to the medical devices. Second, different types of M-Health applications and time-varying patient conditions require dynamic quality of service (QoS). Third, energy efficiency (EE) should be optimized to guarantee the reliability of M-Health services, considering the limitations of the existing battery technologies. To address these challenges, this paper investigates the joint channel and power resource allocation problem for vehicular M-Health communications. The problem is formulated as a mixed-integer nonlinear program (MINLP) to maximize the system EE with the consideration of EMI constraints on medical equipment and dynamic QoS requirements of medical users. To find possible solutions, we reformulate the MINLP problem by relaxing the integer variables and transforming the objective to convex forms. Based on the dual-decomposition method, we first obtain the optimal power allocation and then recover the channel allocation variables to integers. To satisfy the QoS requirements, we develop two channel allocation strategies, i.e., the QoS fulfillment strategy and the QoS compensation strategy. The swap-blocking allocation concept is introduced to guarantee the optimality of the obtained solutions. Simulation results show that the proposed resource allocation scheme improves the system EE and service satisfaction degree.

By reviewing the evolution law and ubiquity of mobile communication, this review investigates Sixth Generation (6G) Mobile Communication System. To support the new object, “genie" which represents knowledge and consciousness, a Ubiquitous-X network is proposed. The proposed network is a 6G information exchange hub network that enables cooperation and integration between the human-machine-thing-genie communication objects and the existing networks in terms of access, resources, and structure. To construct the future 6G network, Ubiquitous-X network architecture faces three challenges: nonlinear propagation caused by non-uniform propagation of information, high information space dimensionality caused by increased information dimensions, and complex information processing caused by service differentiation. To address these challenges and offer insights for 6G research, this review relies on dissipative theory as applied to complex systems and the idea that information negative entropy promotes order, and provides the evolution paradigm and core technologies for designing and optimizing the 6G Ubiquitous-X network.

© 2014 IEEE. Lyapunov optimization has shown to be effective for online optimization of fog computing, asymptotically approaching the optimality only achievable offline. However, it is not directly applicable to the Internet of Things, as inexpensive sensors have small buffers and cannot generate sufficient backlogs to activate the optimization. This article proposes an enabling technique for the Lyapunov optimization to operate under finite buffers without loss of asymptotic optimality. This is achieved by optimizing the biases (namely, 'virtual placeholders') of the buffers to create sufficient backlogs. The optimization of the placeholders is proved to be a new three-layer shortest path problem and solved in a distributed manner by extending the Bellman-Ford algorithm. The sizes of the virtual placeholders decline fastest along the shortest paths from the sensors to the data center, thereby preventing unnecessary detours and reducing end-to-end delays. Corroborated by simulations, the proposed approach is able to operate under the conditions the direct application of the Lyapunov optimization fails, and significantly increase the throughput and reduce the delays in other cases.

Due to the inherent flexibility of the material, the flexible manipulator is highly adaptable to the environment. Compared with rigid robots, it is characterized by better adaptability, better flexibility, and better human–computer interaction performance. Therefore, it has gradually become a field popularly studied in the world. In this paper, a multi-cavity flexible actuator is fabricated, which consists of two parts, a main chamber and a base. The main body chamber is made of super-elastic material, and the base is mainly made of super-elastic material. There is a strain-limiting layer in the middle of the base, and the differential effect between these two parts is used to realize the bending action of the actuator. The pneumatic driving method is adopted, and the grasping action of the flexible gripper is realized by changing the air pressure inside the chamber. The mathematical model of flexible actuator was established and simulated by Abaqus finite element software to explore the effect of the length, the width and the height of the single chamber of the actuator on its bending performance. The experimental results show that: when the air pressure is constant, (1) the height and the width of the single chamber have a great influence on the bending performance, and all show a positive correlation; (2) compared with the height and the width, the length of the single chamber has a relatively smaller effect on the bending performance, and there is a negative correlation.

In this paper, an Uncertainty-based Multi-disciplinary Design Optimization (UMDO) method combining with fuzzy theory and Multi-Discipline Feasible (MDF) method is developed for the conceptual design of a Hybrid Rocket Motor (HRM) powered Launch Vehicle (LV). In the method proposed, membership functions are used to represent the uncertain factors, the fuzzy statistical experiment is introduced to analyze the propagation of uncertainties, and means, standard deviations and credibility measures are used to delineate uncertain responses. A geometric programming problem is solved to verify the feasibility of the Fuzzy-based Multi-Discipline Feasible (F-MDF) method. A multi-disciplinary analysis of a three-stage HRM powered LV involving the disciplines of propulsion, structure, aerodynamics and trajectory is implemented, and the mathematical models corresponding to the F-MDF method and the MDF method are established. A two-phase optimization method is proposed for multi-disciplinary design optimization of the LV, including the orbital capacity optimization phase based on the Ziolkowski formula, and the scheme trajectory verification phase based on the 3-degree-of-freedom point trajectory simulation. The correlation coefficients and the quadratic Response Surface Method (RSM) based on Latin Hypercube Sampling (LHS) are adopted for sensitive analysis of uncertain factors, and the Multi-Island Genetic Algorithm (MIGA) is adopted as the optimization algorithm. The results show that the F-MDF method is applicable in LV conceptual design, and the design with the F-MDF method is more reliable and robust than that with the MDF method.

Machine learning is an effective way to realise the condition monitoring of marine machinery. However, it is challenging to realise this purpose based on supervised learning in practice due to the ...

The powerful computing and storage capability of cloud computing can inject new vitality into wireless sensor networks (WSNs) and have motivated a series of new applications. However, data collection from WSNs to the Cloud is a bottleneck because the poor communication ability of WSNs, especially in delay-sensitive applications, limits their further development and applications. We propose a fog structure composed of multiple mobile sinks. Mobile sinks act as fog nodes to bridge the gap between WSNs and the Cloud. They cooperate with each other to set up a multi-input multi-output (MIMO) network, aiming to maximize the throughput and minimize the transmission latency. We district collecting zones for all sinks and then assign sensors to the corresponding sinks. For those assigned sensors, hops and energy consumption are considered to solve the hopspot problem. Sensor data are uploaded to the Cloud synchronously through sinks. The problem is proved to be NP-hard, and we design an approximation algorithm to solve this problem with several provable properties. We also designed a detailed routing algorithm for sensors considering hops and energy consumption. We compare our method to several traditional solutions. Extensive experimental results suggest that the proposed method significantly outperforms traditional solutions.

Many facial landmark detection and tracking methods suffer from instability problems that have a negative influence on real-world applications, such as facial animation, head pose estimation and real-time facial 3D reconstruction. The instability results of landmark tracking cause face pose shaking and face movement that is not fluent enough. However, most of the existing landmark detection and tracking methods only consider the stability of face location but neglect the stability of local landmark movement. To solve the problem of landmark local shaking, we present a novel hierarchical filtering method for stabilized facial landmark detection and tracking in video frames. The proposed method addresses the challenging landmark local shaking problem and provides effective remedies to solve them. The main contribution within our solution is a novel hierarchical filtering strategy, which guarantees the robustness of global whole facial shape tracking and the adaptivity of local facial parts tracking. The proposed solution does not depend on specific face detection and alignment algorithms, and thus, can be easily deployed into existing systems. Extensive experimental evaluations and analyses on benchmark datasets and 3D head pose datasets verify the effectiveness of our proposed stabilizing method.

Mobile edge computing (MEC) supporting localized context awareness creates a new technological frontier for 5G and beyond. Due to very asymmetric traffic related to MEC and the time division duplexing (TDD) system, we efficiently exploit the networking and computing functionalities for TDD orthogonal frequency division multiple access (TDD-OFDMA) technology supporting multiple services. The primary technical challenge of TDD-OFDMA systems lies in dynamic configuring based on the unknown characteristics of future traffic, i.e., the information lag. Therefore, a model-free online TDD configuration scheme is proposed based on context analysis and multi-armed bandit (MAB) optimization. The characteristics of future traffic are predicted by the context-aware MEC computing, so that TDD configuration is novelly modeled as a contextual MAB problem. Solving MAB by the contextual upper-confidence-bound, TDD configuration can be dynamically adjusted according to network traffic. To simultaneously reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is further developed to select MDs and allocate resources. GRA algorithm decomposes the complex multi-factor coupling non-convex problem into a series of convex sub-problems, thereby asymptotically obtaining the selection and allocation with polynomial time complexity. Simulations justify significant performance gain in mobile networking and MEC.

© 2002-2012 IEEE. IoT provides ubiquitous connectivity and pervasive intelligence. Challenges arise from the data-intensive applications pertaining to IoT. The major contribution of this article is a new asymptotically optimal, fully decentralized, real-time framework which seamlessly integrates wireless computation offloading and fog computing in IoT networks with random traffic variations over space and time. Other enabling techniques are discussed to address practical challenges, such as outdated incentive for the cooperation of multiple service providers, massive access requests within limited system bandwidth, and computing acceleration. The new framework is able to provide ubiquitous computing for continuously increasing IoT services. Numerical experiments indicate that ubiquitous fog computing can substantially improve throughput, reduce computing latency, and cut off signaling overhead, without compromising the optimality of network operations.

Satellites have shown free-space quantum-communication ability; however, they are orbit-limited from full-time all-location coverage. Meanwhile, practical quantum networks require satellite constellations, which are complicated and expensive, whereas the airborne mobile quantum communication may be a practical alternative to offering full-time all-location multi-weather coverage in a cost-effective way. Here, we demonstrate the first mobile entanglement distribution based on drones, realizing multi-weather operation including daytime and rainy nights, with a Clauser-Horne-Shimony-Holt S-parameter measured to be 2.41 ± 0.14 and 2.49 ± 0.06, respectively. Such a system shows unparalleled mobility, flexibility and reconfigurability compared to the existing satellite and fiber-based quantum communication, and reveals its potential to establish a multinode quantum network, with a scalable design using symmetrical lens diameter and single-mode-fiber coupling. All key technologies have been developed to pack quantum nodes into lightweight mobile platforms for local-area coverage, and arouse further technical improvements to establish wide-area quantum networks with high-altitude mobile communication., We demonstrate the first drone-based entanglement distribution under multi-weather conditions, which exhibits the third possible experimental strategy for quantum communication, in addition to fiber and satellite.

The convergence of wireless power transfer (WPT) and mobile edge computing (MEC) has fostered the rise of wireless powered MEC, which is taken as a crucial technology for the sustainability of the Industrial Internet of Things (IIoT) systems. This article provides an overview of wireless powered MEC enabled IIoT systems, including use cases, network requirements, system architecture, and resource management. First, we highlight four key requirements to drive operational efficiency from the perspective of being transformed IIoT projects. Then an integration architecture of wireless powered MEC for IIoT is proposed. Designed with joint consideration of energy, communication, and computing resources, the architecture is enabled by an efficient system schedule mechanism to achieve age-aware data update, green and sustainable energy supply, as well as hierarchical and resilient computation. Finally, a case study is provided to verify the feasibility of the proposed architecture and demonstrate the efficiency of the proposed resource management approach.

The emergence of mobile Internet of Things (IoT) has brought much more convenience to our daily life than ever before, modern intelligent vehicles are usually equipped with various mobile IoT components or devices. However, when we are enjoying the convenience from the rapid development of mobile IoT services, our privacy can be misused by attackers in an easier way. In this paper, we devise a query scheme with intelligent vehicle privacy guarantees, which enables a vehicle to acquire accurate query service from the service provider (SP) without providing its explicit private information, such as location or query interest privacy. And more than that, we introduce network coding for the first time to make our scheme applicable to a more sophisticated top-K query by multiple vehicles cooperation in mobile IoT. Furthermore, we also consider the unnecessary reveal issue of service data at SP side since the data is its asset. Performance analysis and experiments verify the validity of our scheme and demonstrate a better accuracy and efficiency compared with existing solutions in mobile IoT scenario.

Steganography in inactive Voice-over-IP frames is a new technique of information hiding, which can achieve large steganographic capacity while maintaining excellent imperceptibility. To prevent the illegitimate use of this technique, the entropy-based and poker test-based steganalysis methods have been presented. However, the detection performance of these two methods is not so good for the cases of having small quantity of inactive frames or low embedding rates. Thus, we present a new steganalysis method based on statistic characteristics of fundamental frequency. Specifically, we employ the statistics for zero-crossing count (ZCC), including the average ZCC of inactive frames, the ratio between the average ZCC of inactive frames and that of all frames, and the difference between the average ZCC of inactive frames and their calibrated versions, to characterize the frame-level dynamic characteristic of speech signals; we utilize the average values of Mel-frequency cepstral coefficients (MFCCs) to represent the invariant characteristic of inactive frames; further, using the feature set consisting of the zero-crossing statistics and average MFCCs, we propose a support-vector-machine based steganalysis for inactive speech frames. The proposed steganalysis method is evaluated with a large number of ITU-T G.723.1 encoded speech samples, and compared with the existing methods. The experimental results demonstrate that the proposed method significantly outperforms the previous ones on detection accuracy, false positive rate and false negative rate for any given embedding rates or using the same number of inactive frames. Particularly, the proposed method can provide accurate detecting results for the existing steganographic methods only using very small quantity of inactive frames, and thereby be employed to detecting potential inactive-frame steganography behaviors in real-time speech streams.

This letter aims at analyzing variant transmission range and the hop progress of two-layer VANETs under Rayleigh fading channel. Different with the existing studies that are based on the constant transmission range and two-dimension assumptions, this letter adopts a more practical two-layer viaducts scenario. Both the wireless channel propagation and the effect of interference generated by other vehicles are considered. The closed-form expressions of the hop progress and time-varying effective transmission range are obtained. The achieved expression indicates that the actual one-hop progress is bounded by the non-negligible effective transmission range. Simulation results show that the packet delivery ratio can be improved if the variant transmission range is used in the protocols.

Surface acoustic wave (SAW) devices are one of the indispensable components in the radio frequency (RF) front-end of mobile phones. With the development of mobile communication technology, the requirements for linear specification of devices are more and more strict. Nonlinear distortions of SAW devices have a serious influence on the application of mobile RF modules. To satisfy the strict requirement of linearity of communication system, it is necessary to understand the generation mechanism of nonlinearity and study the accurate modeling, appropriate measurement methods, and nonlinear response elimination technology. In this paper, we summarize the research progress on the nonlinearity of SAW devices in recent years from four aspects: the generation mechanism, simulation methods, measurement system, and suppression technology. The nonlinear harmonics with the nonlinear Mason equivalent circuit model are simulated. Furthermore, harmonics and intermodulation signals of SAW filters are tested by the authors. Thanks to these research studies, it is of great significance to the development of future RF front-end modules with high linear SAW devices.

Parallel and distributed computing has been the basis to many emerging areas, such as smart networks, cloud computing, big data analysis, and blockchain technology. Without the development of parallel and distributed computing technologies and various types of systems, it is not possible to meet the requirement on efficiency, accuracy, scalability, and reliability for various critical applications that support our modern economy and society. While parallel and distributed computing played a vital role in modern science, engineering, biology, medicine, pharmacy, astronomy, geology, and archaeology, its application has also been extended to business, finance, economics, management, government, and defense, covering all aspects of our modern society and life. Furthermore, parallel and distributed computing has emerged in recent advances of many hotspot research directions including artificial intelligence, machine learning, Internet of Things, bioinformatics, digital medicine, cybersecurity, and social computing, resulting in numerous ground-breading discoveries that are changing our society and life.

Structural Hole (SH) spanners are the set of users who bridge different groups of users and are vital in numerous applications. Despite their importance, existing work for identifying SH spanners focuses only on static networks. However, real-world networks are highly dynamic where the underlying structure of the network evolves continuously. Consequently, we study SH spanner problem for dynamic networks. We propose an efficient solution for updating SH spanners in dynamic networks. Our solution reuses the information obtained during the initial runs of the static algorithm and avoids the recomputations for the nodes unaffected by the updates. Experimental results show that the proposed solution achieves a minimum speedup of 3.24 over recomputation. To the best of our knowledge, this is the first attempt to address the problem of maintaining SH spanners in dynamic networks.

This paper investigates the problem of secure communication in the unmanned aerial vehicle (UAV) enabled net-works aided by a reconfigurable intelligent surface (RIS) from the physical layer security perspective. Specifically, the RIS is deployed to assist the wireless transmission from the UAV to the ground user in the presence of an eavesdropper. The objective of this work is to maximize the secrecy rate by jointly optimizing the phase shifts at the RIS as well as the transmit beamforming vector and location of the UAV. However, the formulated problem is difficult to solve directly due to the non-linear and non-convex objective function and constraints. By invoking the successive convex approximation and fractional programming techniques, the intractable original problem is transformed into convex ones, then an alternating algorithm is proposed to solve the challenging problem effectively. Simulations results demonstrate that the designed algorithm for RIS-aided UAV communications can achieve higher secrecy rate than benchmarks.

This paper investigates the model aggregation process in an over-the-air federated learning (AirFL) system, where an intelligent reflecting surface (IRS) is deployed to assist the transmission from users to the base station (BS). Since the successive interference cancellation (SIC) is adopted as a basis to decode local model parameters and analyze their statistic characteristics for detecting malicious devices, the quality-of-service (QoS) requirement is ensured. The objective of this paper is to minimize the mean-square-error by jointly optimizing the receive beamforming vector at the BS, transmit power allocation at users, and phase shift matrix of the IRS, subject to the transmit power constraint for devices, unit-modulus constraint for reflecting elements, SIC decoding order constraint and QoS constraint. To address this complicated problem, alternating optimization is employed to decompose it into three subproblems, where the optimal receive beamforming vector is obtained by solving the first subproblem with the Lagrange dual method. Then, the convex relaxation method is applied to the transmit power allocation subproblem to find a suboptimal solution. Eventually, the phase shift matrix subproblem is addressed by invoking the semidefinite relaxation. Simulation results validate the availability of IRS and the effectiveness of the proposed scheme in improving federated learning performance.

Robots have been widely used in the era of Inter-net of Things (IoT) and become an important driver behind 6G systems. However, due to the mobility of robots and the complicated wireless environment, it is challenging to control robots without prior knowledge, especially for the dynamic multi-robot systems. This paper investigates data transmission and path planning problems in an indoor multi-robot navigation system, where non-orthogonal multiple access (NOMA) is adopted to provide massive connectivity and improve spectrum efficiency. The considered system is designed for the long-term throughput maximization by jointly designing the downlink transmit power at the access point (AP) and the motion paths of the robots, while satisfying the power budget and mobility constraints. In this paper, a novel federated deep reinforcement learning approach called federated deep Q-network learning (F-DQN) algorithm is proposed to tackle the formulated problem. Simulation results demonstrate that the proposed algorithm not only speeds up the convergence rate and promotes the system throughput, but also is scalable for the number of robots.

Cooperative caching can unify storage across edge clouds and provide efficient delivery of popular contents under effective content placement. However, the placement and delivery are non-trivial in cooperative caching due to the decentralized property of edge clouds, as well as the temporal and spatial correlation of the placement. We propose a new distributed online learning approach to jointly optimize content placement and delivery without the a-priori knowledge on file popularity and link availability. Content placement and delivery can be asymptotically optimized in real-time by running distributed online learning at individual edge servers by exploiting stochastic gradient descent (SGD). The proposed approach can allow operations at different timescales by integrating mini-batch learning for farsighted content placement. The optimality loss, stemming from the different timescales, can asymptotically reduce, as the SGD stepsize declines. Simulations confirm that the proposed approach outperforms existing techniques in terms of cache hit ratio and cost effectiveness. Insights are shed on the optimal placement of popular contents.

The ever-increasing communication requirement of the next generation network imposes great challenges in the existing cellular architecture and requires energy-efficient resource allocation. In this paper, we consider a cell-free (CF) massive multiple-input multiple-output (MIMO) network, where a large number of distributed access points with multiple antennas simultaneously serve a smaller number of users in a geographic coverage without cell boundary. To alleviate the limitation of orthogonal resource and further enhance network capacity, non-orthogonal multiple access is applied to the CF massive MIMO network in both channel training and uplink transmission phases. First, we derive the closed-form expression of uplink transmission rate by taking into account the imperfect channel state information (CSI) caused by pilot contamination and channel statistics. Second, based on the obtained uplink rate expression, an energy efficiency (EE) maximization problem is formulated by optimizing power control scheme, and we propose a low-complexity algorithm to address the non-convex optimization. Finally, simulation results validate that the designed algorithm can improve EE significantly compared to benchmarks in different CSI cases and network setups.

This paper investigates the problem of resource allocation for joint communication and radar sensing system on rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) system. UAV simultaneously communicates with multiple users and probes signals to targets of interest to exploit cooperative sensing ability and achieve substantial gains in size, cost and power consumption. By virtue of using linearly precoded rate splitting at the transmitter and successive interference cancellation at the receivers, RSMA is introduced as a promising paradigm to manage interference as well as enhance spectrum and energy efficiency. To maximize the energy efficiency of UAV networks, the deployment location and the beamforming matrix are jointly optimized under the constraints of power budget, transmission rate and approximation error. To solve the formulated non-convex problem efficiently, we decompose it into the UAV deployment subproblem and the beamforming optimization subproblem. Then, we invoke the successive convex approximation and difference-of-convex programming as well as Dinkelbach methods to transform the intractable subproblems into convex ones at each iteration. Next, an alternating algorithm is designed to solve the non-linear and non-convex problem in an efficient manner, while the corresponding complexity is analyzed as well. Finally, simulation results reveal that proposed algorithm with RSMA is superior to orthogonal multiple access and power-domain non-orthogonal multiple access in terms of power consumption and energy efficiency.

Compared to conventional cloud computing, non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) can provide scalable and low-latency computing services for the next generation network. However, considering the prevalent stochasticity of wireless networks and the sophisticated signal processing of NOMA, it is critical but challenging to design an efficient and low-complexity task offloading strategy for NOMA-based MEC, especially in large-scale networks. This paper investigates the offloading scheduling and resource allocation problem to maximize the long-term system utility (a measure of throughput and fairness) under complete and partial device knowledge, respectively. Due to the combinatorial property of the temporary coupling and non-convexity, we first apply the Lyapunov optimization to optimally decouple the stochastic optimization over a long time horizon into a series of per-slot deterministic subproblems without a prior knowledge of network dynamics. Then, we propose to equivalently transform the nonconvex per-slot subproblems into convex ones by introducing a set of auxiliary variables. The designed online algorithm is proved to be asymptotically optimal, even under the partial knowledge of device states. Simulation results validate the superiority of the online scheduling algorithm in terms of system utility and stability improvement compared with benchmarks.

Federated learning (FL) and edge computing are both important technologies to support the future Internet of Things (IoT). Despite that the network supported by edge computing has great potential to promote FL, it is more challenging to achieve efficient FL due to more complex resource coupling in it. Focus on this problem, we formulate a problem which minimizes the weighted sum of system cost and learning cost by jointly optimizing bandwidth, computation frequency, transmission power allocation and subcarrier assignment. In order to solve this mixed-integer non-linear problem, we first decouple the bandwidth allocation subproblem from the original problem and obtain a closed-form solution. Further considering the remaining joint optimization problem of computation frequency, transmission power and subcarrier, an iterative algorithm with polynomial time complexity is designed. In an iteration, the latency and computation frequency optimization subproblem and transmission power and subcarrier optimization subproblem are solved using the proposed algorithms in turn. The iterative algorithm is repeated until convergence. Finally, to verify the performance of the algorithm, we compare the proposed algorithm with five baselines. Numerical results show the significant performance gain and the robustness of the proposed algorithm.

Proportional Orthogonal Projection Iteration (POSIT) is the mainstream iterative algorithm in camera pose estimation. The traditional POSIT algorithm has many iterations, too long operating time, and low algorithm robustness. In response to these problems, it proposes a POSIT optimization algorithm based on mean convergence in this paper. The algorithm is based on the traditional POSIT, which the input of each iteration is not the last output, but is determined by the weighted sum of all the previous outputs. The weights are determined by the differences between the outputs and the mean. In order to reduce the number of iterations of the algorithm and the time complexity, the algorithm pre-stores some of the key-value pairs of input and output in the actual scene into the hash table, and uses Bloom filters to save memory space. In theory, the POSIT algorithm can be used down to O(1). Finally, the algorithm is verified by experiments with other existing algorithms in this paper. The results show that the optimized POSIT has significantly improved accuracy and robustness, and its time complexity is lower than that of the traditional POSIT.

With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a novel reconfigurable intelligent surface (RIS)-unified network by leveraging the RIS to flexibly adjust the signal processing order of hybrid users. The objective of this work is to maximize the achievable hybrid rate of all users by jointly optimizing the transmit power at users, controlling the receive scalar at the base station, and designing the phase shifts at the RIS. Due to the fact that all signals of computation and communication are combined into one concurrent transmission, the formulated resource allocation problem is very challenging. To solve this problem, the alternating optimization is invoked to address non-convex subproblems iteratively for finding a suboptimal solution with low complexity. Simulation results demonstrate that i) the proposed RIS-unified network can support the on-demand communication and computation efficiently, and ii) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users.

Person re-identification (re-ID) is the task of associating the image patches of the identical person across disjoint cameras, which is a very challenging topic in computer vision. Recently, with the advance of deep learning techniques and the public of huge datasets, person re-ID has achieved a great success. However, an obvious but ignored issue is that the sample pairs are biased, where the negative sample pairs are far more than the positive ones. In this paper, we develop an adaptive verification loss, termed as ADV-Loss to handle the imbalance of sample pairs. Our ADV-Loss empowers the popular verification loss with the flexibility of adaptively addressed the hard triple units. Specifically, we learn a re-weighted strategy from the triplet loss to balance the sample pairs. According to the objective values of triple loss, the hard triple units will be endowed with larger weights, while the less important triple units are de-emphasized or simply dropped. Thus, ADV-Loss, on the basis of the cross-entropy loss, handles the biased issue by only holding the equal number but informative sample pairs. Moreover, theoretical analysis of our designed ADV-Loss is provided to guarantee the important sample pairs not to be despised during the backpropagation. Ablation analysis verifies the potential efficacy of the proposed loss. Moreover, experiments of person re-ID on three large-scale benchmark datasets show that two popular deep networks with the proposed loss achieve satisfactory performance against several well-established person re-ID methods.

Public transit systems (PTSs) in a city usually consist of multiple systems with different transit modes, such as bus and subway systems, and the connection relationships among those systems can be described in terms of the geographical proximity of different stations. This paper proposes a methodology to analyze the vulnerability of the PTSs in a city from the perspective of their common functionality of providing residential communities access to critical services, including commuting, medical, commercial and education services. The proposed methodology is built upon a multi-layer network, with each layer describing a particular type of public transit system. This paper applies the proposed methodology to analyze the vulnerability of the bus and subway systems in Wuhan, China, under several types of disruptions, including random vehicle accidents, recurrent traffic jams and a real rainstorm disaster.