Recommender systems (RSs) are widely adopted due to their effectiveness in modeling user preferences and generating personalized recommendations. However, data poisoning attacks (PAs) manipulate recommendation results by injecting fake user profiles, thereby affecting the quality and accuracy of RSs. Moreover, emerging high-concealed PAs (HCPAs) achieve greater evasion of detection by controlling the cost of the attack, simulating the behavior patterns of benign users, and carrying out the attack with less prior knowledge. The HCPAs bring challenges: (1) the very low cost of attacks not only leads to an imbalance in data distribution but also introduces a large amount of accidental co-occurrence noise; (2) the behavioral patterns similar to benign users make it difficult to describe the characteristics of HCPAs; and (3) the prior knowledge for detecting HCPAs in real scenarios is very limited. To address these challenges, we propose STOP, an orthogonal projection bi-hypersphere detection method built on multi-view relational disentanglement and information-consistent fusion. First, we model the distributional preferences of user ratings to eliminate rating and popularity bias, and construct a co-occurrence association graph to suppress accidental overlaps. To address data imbalance caused by HCPAs, second, we introduce a distributional-consensus importance screening method that filters out benign users weakly associated with potential attackers. To address the issues of noise and the difficulty in feature characterization, third, we propose a multi-view relational disentanglement and information-consistent fusion method, which can eliminate redundant relationships, separate key relations into sequence-varying and sequence-stable components over rating sequences, and retain task-related relationships. Finally, inspired by the “convergence theorem”, we design an orthogonal projection bi-hypersphere boundary learning detection method to reduce the high false alarm rate (FAR). We extensively evaluate STOP under various HCPA scenarios, demonstrating its superiority over existing methods with an average 12.34% improvement in detection rate and an average 2.75% reduction in FAR. Furthermore, forensic analysis on real-world unlabeled data reveals distinct attacker “fingerprints”, such as extreme ratings, contradictory review styles, and analysis of target items, validating STOP's reliability in practical applications.

This article introduces a radar perception learning framework guided by data collected from commonly equipped visual-inertial (VI) sensor suites on smart vehicles. Unlike existing approaches that rely on dense point clouds from 3D LiDARs, which are costly and not widely deployed, this method leverages the broader availability of VI data. However, visual images alone lack the ability to capture the three-dimensional motion of moving targets, which limits their effectiveness in supervising motion-related tasks. To overcome this limitation, the framework integrates multiple perception tasks such as odometry estimation, motion segmentation, and scene flow prediction into a unified learning process. The first component is an odometry estimation module that combines deterministic ego-motion models with data-driven learning results. This fusion helps accurately infer the scene flow of static background points while minimizing drift. The second component is a supervision signal extraction module that aligns optical and millimeter-wave radar measurements to guide the learning of radar scene flow and rigid transformations. This module improves the reliability of dynamic point supervision through joint constraints across sensing modalities. The third component introduces a feature-selection module designed for cross-modal learning. It enhances the accuracy of motion segmentation and enforces consistency between odometry and scene flow, resulting in more coherent radar perception outputs. Experimental evaluations show that this framework achieves superior performance in challenging conditions such as smoke-obscured environments. It surpasses state-of-the-art (SOTA) methods that depend on high-cost LiDAR systems. The implementation of VISC+ will be open-source athttps://github.com/weini-Eve/VISC

Vehicle visual perception is essential for safe autonomous driving, especially in challenging low-visibility conditions. Polarimetric imaging has shown enhanced perception by improving target-background contrast and reducing glare. However, current research in polarimetric imaging for autonomous driving largely focuses on singular use of polarimetric features. Exploiting polarimetric information to enhance vehicle visual perception in low-visibility scenarios remains a critical challenge. This paper addresses this challenge by integrating multiple polarimetric parameter characterization with a deep learning model for semantic segmentation visual perception task, which is called TransWNet. The model combines Convolutional Neural Networks (CNN) and Transformer architectures, extracting features and contextual information of both Degree of Polarization (DoP) and Angle of Polarization (AoP) comprehensively during the encoding phase. In the decoding phase, it incorporates skip connections to effectively retain multimodal polarimetric information across deep and shallow features, generating output through feature fusion. To the best of our knowledge, this is the first work that jointly exploits DoP and AoP for vehicle scene understanding in degraded visibility. Experimental results demonstrate that TransWNet, by effectively leveraging multimodal polarimetric information, achieves significantly better performance in semantic segmentation of low-visibility traffic scenes, with marked improvements in mIoU, mPA, and Accuracy over all single-feature baselines. Compared with the baseline method, TransWNet improves the Accuracy by 3.44%.

Existing AI-based meeting summarization tools have enabled rapid generation of meeting notes, yet their reliability and user controllability remain limited. This paper explores human-AI collaboration for mobile meeting summarization and presents MeetSumAid, a multifunctional system that integrates summarization algorithms with an interactive user interface. The system is designed to support users in understanding, validating, and refining AI-generated summaries through natural interactions and flexible control mechanisms. By enabling real-time inspection, editing, and feedback, MeetSumAid facilitates reliable collaboration between humans and AI in dynamic meeting scenarios. A user study with 20 participants shows that MeetSumAid significantly improves summary quality, generation efficiency, and user-perceived reliability compared with baseline AI summarizers, while reducing cognitive load. Further analysis reveals how different interface components enhance users' engagement and confidence during collaboration. This work provides a practical step toward reliable and user-centered human-AI collaboration in mobile meeting summarization and offers actionable design implications for future intelligent collaborative systems.

This work proposes a mmWave radar’s scene flow estimation framework supervised by data from a widespread visual-inertial (VI) sensor suite, allowing crowdsourced training data from smart vehicles. Current scene flow estimation methods for mmWave radar are typically supervised by dense point clouds from 3D LiDARs, which are expensive and not widely available in smart vehicles. While VI data are more accessible, visual images alone cannot capture the 3D motions of moving objects, making it difficult to supervise their scene flow. Moreover, the temporal drift of VI rigid transformation also degenerates the scene flow estimation of static points. To address these challenges, we propose a drift-free rigid transformation estimator that fuses kinematic model-based ego-motions with neural network-learned results. It provides strong supervision signals to radar-based rigid transformation and infers the scene flow of static points. Then, we develop an optical-mmWave supervision extraction module that extracts the supervision signals of radar rigid transformation and scene flow. It strengthens the supervision by learning the scene flow of dynamic points with the joint constraints of optical and mmWave radar measurements. Extensive experiments demonstrate that, in smoke-filled environments, our method even outperforms state-of-the-art (SOTA) approaches using costly LiDARs.

Utilizing an autonomous underwater vehicle (AUV) for data collection in underwater wireless sensor networks (UWSNs) is a promising approach. However, since underwater sensor nodes are typically battery-powered and difficult to replace, effective energy management is crucial for extending the network lifetime of UWSNs. Additionally, the limited energy of the AUV presents further challenges. To address these issues, this paper proposes an energy-aware AUV-assisted data collection scheme based on dynamic clustering and cluster head selection (DCCHS) to maximize network lifetime. Specifically, we utilize the energy center to define the cluster center and employ a bottom-up hierarchical clustering approach to address the node dynamic clustering problem under the AUV movement distance constraint. Subsequently, we introduce a cluster head (CH) selection algorithm based on iterative optimization, and adds auxiliary CHs near the AUV path to reduce the energy consumption of CHs. Simulation results demonstrate that the proposed DCCHS scheme significantly extends the network lifetime compared to existing schemes, particularly in scenarios with dense node deployment.

Cooperative platooning holds great potential for driving safety and road efficiency. However, limited communication resources and dynamic network topologies pose challenges to reliable and timely vehicular negotiation on joint platoon control (e.g., changing lanes and giving ways) in cooperative platooning. In this article, we propose a new hierarchical consensus (HC) framework to support reliable and fast coordination among multiple platoons for safe and efficient driving control. The HC framework consists of intraplatoon and interplatoon schemes. For the intraplatoon scheme, we propose a new practical Byzantine fault tolerance (PBFT) enabled intraplatoon consensus mechanism. An adaptive local consensus scheme is designed to reduce the local consensus delay and improve the successful local consensus ratio. For the interplatoon consensus, we develop a new Raft and 5G time sensitive networking (5G-TSN)-based scheme to enhance the responsiveness and scalability of multiplatoon negotiations. Furthermore, we design a dynamic prioritization scheme for 5G-TSN flows and develop an intelligent flow scheduling algorithm to improve interactions among platoons and shorten the total negotiation delay, while ensuring successful multiplatoon negotiations. Simulation results indicate that the proposed scheme can significantly enhance the multiplatoon negotiation performance for cooperative control, with more than 16.9% higher successful consensus ratio and 14% lower negotiation delay than existing approaches.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) network, where multiple UAVs with caching and computation functionalities are deployed to satisfy the heterogeneous content and service requests from the user equipments (UEs). In order to comprehensively characterize the capability of our considered network in satisfying the UEs’ requests, we define the weighted sum of the content cache hit ratio and the service delay shrinkage ratio as the average quality-of-experience (QoE) of our network and adopt it as the performance metric. Through analysis, we show how the average QoE of our network is dependent on the content cache and service placement decisions at the UAVs, as well as the computation task offloading decisions at the UEs, thus enabling us to formulate an average QoE maximization problem, subject to practical constraints on the UAVs’ caching and computation capabilities. To solve this NP-hard problem, we decompose it into two sub-problems, namely, the content cache and service placement optimization sub-problem and the task offloading optimization sub-problem. Gibbs sampling-based and matching game-based algorithms are proposed to efficiently solve these sub-problems iteratively. Via numerical results, we validate the effectiveness of our proposed algorithms. Compared to various benchmarks, we demonstrate that our proposed algorithms can significantly improve the average QoE of our considered network, especially when the caching and computation resources of the UAVs are limited.

Large Language Models (LLMs), with their strong generalization and inference capabilities, have been increasingly leveraged to address the challenges of handling corner cases in autonomous driving (AD). However, a critical unresolved issue remains: the lack of a comprehensive understanding and formal assessment of LLMs’ driving theory knowledge and practical skills. To address this issue, we propose the first dedicated driving theory test framework and benchmark for LLMs. That is a crucial yet unexplored area in the literature, particularly for safety-critical applications in autonomous driving and driver assistance. Our framework systematically evaluates LLMs’ competence in driving theory and hazard perception, akin to the official UK driving theory test, ensuring their qualification for critical driving-related tasks. To facilitate rigorous benchmarking, we construct a comprehensive dataset comprising over 700 multiple-choice questions (MCQs) and 54 hazard perception video tests sourced from the official UK driving theory examination. Additionally, we incorporate two standardized MCQ sets from the UK’s Driver and Vehicle Standards Agency (DVSA). For these two types of theoretical test items, we design tailored assessment methodologies and evaluation metrics, including accuracy, recall, precision, F1-score, real-time performance, and computational efficiency. The experimental results reveal that among all LLMs tested, only GPT-4o achieved an accuracy of 88. 21% in the MCQs test, successfully passing this component. However, in hazard perception testing, none of the evaluated models met the passing criteria under the given settings, highlighting the substantial improvements required before these models can be practically deployed for real-world driving applications. Our key insight is that the specific test questions LLMs fail to answer correctly directly reflect their deficiencies in understanding and flexibly applying traffic regulations, as well as in analyzing and responding to complex driving scenarios. This provides clear directions for future improvements.

Vehicular edge computing (VEC) is critical for the safe and efficient driving of intelligent vehicles, by which they can offload computation-intensive tasks (such as driving environment perception) to edge servers to overcome the limitations of onboard computational resources and cooperate with others. One of the major challenges faced by VEC is that the offloaded intelligent driving tasks generally generate large amounts of data, which can easily stretch and congest the vehicle communication channels. To address the above challenges, we first propose a novel semantic VEC (SVEC) architecture, which can extract the semantic information of tasks and offload them to edge servers, thereby achieving reliable and efficient offloaded task communication and computation adaptively. Considering the scarce channel resources of vehicles and the intelligent tasks with different priorities and modalities, we define a novel user utility model for SVEC and transform the problem of maximizing user utility into a joint optimization problem of semantic feature extraction, task offloading and resource allocation. Furthermore, to cope with the complexity of the solution space of the optimization problem, we propose a diffusion-based multi-agent reinforcement learning algorithm, which improves the ability of agents to explore the solution space through the diffusion process, thereby achieving optimal decisions for semantic feature extraction, task offloading and resource allocation. Simulation results show that the proposed scheme improves the overall performance of SVEC while reducing offload latency and average system cost.

Reliable and timely data collection is an important and challenging problem for underwater wireless sensor networks (UWSNs), partly due to the very slow underwater communication and the difficulty in recharging the sensors. In this paper, we exploit the use of autonomous underwater vehicles (AUVs) for UWSN data collection task. Specifically, we investigate how to maximize the value of information (VoI) for data collection through joint optimization of cluster head (CH) selection and multi-AUV path planning. We formulate the joint optimization problem for the task, taking into account the energy constraints of sensor nodes. To solve the problem, we propose a deep reinforcement learning algorithm based on an encoder-decoder architecture. The entire UWSN system is fed into the encoder network, followed by a composite decoder consisting of an AUV selection decoder and a cluster access sequence decoder to obtain the cluster access sequence for each AUV. Based on the determined sequences, we further utilize the dynamic programming algorithm to achieve optimal CH selection. Finally, we obtain the sequence of AUVs accessing the selected CHs. Simulation results demonstrate that the proposed learning-based approach converges and achieves a higher VoI than the existing benchmark algorithms.

With the undetectable characteristic, adaptive model poisoning attacks can combine with any other attacks, bypassing the detection and violating the availability of federated learning (FL) systems. Existing defences are vulnerable to adaptive model poisoning attacks, as model poisoning-related features are tailored to these methods and compromise the accuracy of the FL model. We first present a unified reformulation of existing adaptive model poisoning attacks. Analyzing the reformulated attacks, we find that the detectors should reduce the attacker’s optimization cost functions to defeat adaptive attacks. However, existing defences do not consider the causes of model parameters’ high dimensionality and data heterogeneity. We propose a novel robust FL algorithm, FedDet, to tackle the problems. By splitting the local models into layers for robust aggregation, FedDet can overcome the issue with high dimensionality while keeping the functionality of layers. During the robust aggregation, FedDet normalizes every slice of local models by the median norm value instead of excluding some clients, which can avoid deviation from the optimal model. Furthermore, we conduct a comprehensive security analysis of FedDet and an existing robust aggregation method. We propose the upper bounds on the perturbations disturbed by these adaptive attacks. It is found that FedDet can be more robust than Krum with a smaller perturbation upper bound under attacks. We evaluate the performance of FedDet and four baseline methods against these attacks under two classic data sets. It demonstrates that FedDet significantly outperforms the existing compared methods against adaptive attacks. FedDet can achieve 60.72% accuracy against min–max attacks.

Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety-critical nature. While multi-agent reinforcement learning (MARL) algorithms have been proposed for MVP in structured grid-pattern roads, the existing algorithms use random training samples in centralized learning, which leads to homogeneous agents showing low collaboration performance. For the more challenging problem of pursuing multiple evaders, these algorithms typically select a fixed target evader for pursuers without considering dynamic traffic situation, which significantly reduces pursuing success rate. To address the above problems, this paper proposes a Progression Cognition Reinforcement Learning with Prioritized Experience for MVP (PEPCRL-MVP) in urban multi-intersection dynamic traffic scenes. PEPCRL-MVP uses a prioritization network to assess the transitions in the global experience replay buffer according to each MARL agent’s parameters. With the personalized and prioritized experience set selected via the prioritization network, diversity is introduced to the MARL learning process, which can improve collaboration and task-related performance. Furthermore, PEPCRL-MVP employs an attention module to extract critical features from dynamic urban traffic environments. These features are used to develop a progression cognition method to adaptively group pursuing vehicles. Each group efficiently targets one evading vehicle. Extensive experiments conducted with a simulator over unstructured roads of an urban area show that PEPCRL-MVP is superior to other state-of-the-art methods. Specifically, PEPCRL-MVP improves pursuing efficiency by 3.95$\%$over Twin Delayed Deep Deterministic policy gradient-Decentralized Multi-Agent Pursuit and its success rate is 34.78$\%$higher than that of Multi-Agent Deep Deterministic Policy Gradient. Codes are open-sourced.

Real-time semantic segmentation (SS) is a major task for various vision-based applications such as self-driving. Due to the limited computing resources and stringent performance requirements, streaming videos from camera-embedded mobile devices to edge servers for SS is a promising approach. While there are increasing efforts on task-oriented video compression, most SS-applicable algorithms apply more uniform compression, as the sensitive regions are less obvious and concentrated. Such processing results in low compression performance and significantly limits the capacity of edge servers supporting real-time SS. In this paper, we propose STAC, a novel task-oriented DNN-driven video compressive streaming algorithm tailed for SS, to strike accuracy-bitrate balance and adapt to time-varying bandwidth. It exploits DNN's gradients as sensitivity metrics for fine-grained spatial adaptive compression and includes a temporal adaptive scheme that integrates spatial adaptation with predictive coding. Furthermore, we design a new bandwidth-aware neural network, serving as a compatible configuration tuner to fit time-varying bandwidth and content. STAC is evaluated in a system with a commodity mobile device and an edge server with real-world network traces. Experiments show that STAC can save up to 63.7–75.2% of bandwidth or improve accuracy by 3.1–9.5% compared to state-of-the-art algorithms, while capable of adapting to time-varying bandwidth.

Federated learning (FL) is an efficient, scalable, and privacy-preserving technology in which clients collaborate on machine learning or deep learning model training. However, malicious clients can send poisoned model updates to the central server without being identified, which makes FL vulnerable to backdoor attacks. In this work, we propose a novel defence approach, FLSec, to mitigate backdoor attacks caused by adversarial local model updates. FLSec utilizes an original measurement, GradScore, computed from the loss gradient norm of the final layer of the local models for backdoor defence. We show that GradScore is efficient and robust in identifying malicious model updates through analysis and experiments. Our extensive evaluation also demonstrates FLSec is highly effective in mitigating three state-of-the-art backdoor attacks on well-known datasets, MNIST, LOAN, and CIFAR-10. The accuracy on a benign dataset with the proposed defence approach is nearly unchanged, with the accuracy on the backdoor dataset being reduced to 0%. In addition, our experiments show that FLSec significantly outperforms existing backdoor defences in multi-round backdoor attacks.

Offloading computing to edge servers is a promising solution to support growing video understanding applications at resource-constrained IoT devices. Recent efforts have been made to enhance the scalability of such systems by reducing inference costs on edge servers. However, existing research is not directly applicable to pixel-level vision tasks such as video semantic segmentation (VSS), partly due to the fluctuating VSS accuracy and segment bitrate caused by the dynamic video content. In response, we present Penance, a new edge inference cost reduction framework. By exploiting softmax outputs of VSS models and the prediction mechanism of H.264/AVC codecs, Penance optimizes model selection and compression settings to minimize the inference cost while meeting the required accuracy within the available bandwidth constraints. We implement Penance in a commercial IoT device with only CPUs. Experimental results show that Penance consumes a negligible 6.8% more computation resources than the optimal strategy while satisfying accuracy and bandwidth constraints with a low failure rate.

Despite the prevalence of GPS services, they still suffer from intermittent positioning with poor accuracy in partially shadowed regions like urban canyons, flyover shadows, and factories' indoor areas. Existing wisdom relies on hardware modifications of GPS receivers or power-hungry infrastructures requiring continuous plug-in power supply which is hard to provide in outdoor regions and some factories. This paper fills the gap with GPSMirror, the first GPS-strengthening system that works for unmodified smartphones with the assistance of newly-designed GPS backscatter tags. The key enabling techniques in GPSMirror include: (i) a meticulous hardware design with microwatt-level power consumption that pushes the limit of backscatter sensitivity to re-radiate extremely weak GPS signals with enough coverage approaching the regulation limit; and (ii) a novel GPS positioning algorithm achieving meter-level accuracy in shadowed regions as well as expanding locatable regions under inadequate satellites where conventional algorithms fail. We build a prototype of the GPSMirror tags and conduct comprehensive experiments to evaluate them. Our results show that a GPSMirror tag can provide coverage up to 27.7 m. GPSMirror achieves median positioning accuracy of 3.7 m indoors and 4.6 m in urban canyon environments, respectively.

Multirobot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with significant operational cost savings and productivity improvement, where unmanned aerial vehicles (UAVs) are employed to control and implement collaborative productions without human intervention. This mission-critical system relies on 3-dimension (3-D) scene recognition to improve operation accuracy in the production line and autonomous piloting. However, implementing 3-D point cloud learning, such as Pointnet, is challenging due to limited sensing and computing resources equipped with UAVs. Therefore, we propose a digital twin (DT) empowered knowledge distillation (KD) method to generate several lightweight learning models and select the optimal model to deploy on UAVs. With a digital replica of the UAVs preserved at the edge server, the DT system controls the model-sharing network topology and learning model structure to improve recognition accuracy further. Moreover, we employ network calculus to formulate and solve the model sharing configuration problem toward minimal resource consumption, as well as convergence. Simulation experiments are conducted over a popular point cloud data set to evaluate the proposed scheme. Experiment results show that the proposed model-sharing scheme outperforms the individual model in terms of computing resource consumption and recognition accuracy.

Cellular vehicle-to-vehicle (V2V) communications can support advanced cooperative driving applications such as vehicle platooning and extended sensing. As the safety critical applications require ultra-low communication latency and deterministic service guarantee, it is vital to characterize the latency upper bound of cellular V2V communications. However, the contention-based Medium Access Control (MAC) and dynamic vehicular network topology brings many challenges to model the upper bound of cellular V2V communication latency and assess the link capability for quality of service (QoS) guarantee. In this paper, we are motivated to reduce the research gap by modelling the latency upper bound of cellular V2V with network calculus. Based on the theoretical model, the probability distribution of the delay upper bound can be obtained under the given task features and environment conditions. Moreover, we propose an intelligent scheme to reduce upper bound of end-to-end latency in vehicular platoon scenario by adaptively adjusting the V2V communication parameters. In the proposed scheme, a deep reinforcement learning model is trained and implemented to control the time slot selection probability and the number of time slots in each frame. The proposed approaches and the V2V latency upper bound are evaluated by simulation experiments. Simulation results indicate that our network calculus based analytical approach is effective in terms of the latency upper bound estimations. In addition, with fast iterative convergence, the proposed intelligent scheme can significantly reduce the latency by about 80% compared with the conventional V2V communication protocols.

Deep learning has shown impressive performance in semantic segmentation, but it is still unaffordable for resource-constrained mobile devices. While offloading computation tasks is promising, the high traffic demands overwhelm the limited bandwidth. Existing compression algorithms are not fit for semantic segmentation, as the lack of obvious and concentrated regions of interest (RoIs) forces the adoption of uniform compression strategies, leading to low compression ratios or accuracy. This paper introduces STAC, a DNN-driven compression scheme tailored for edge-assisted semantic video segmentation. STAC is the first to exploit DNN’s gradients as spatial sensitivity metrics for spatial adaptive compression and achieves superior compression ratio and accuracy. Yet, it is challenging to adapt this content-customized compression to videos. Practical issues include varying spatial sensitivity and huge bandwidth consumption for compression strategy feedback and offloading. We tackle these issues through a spatiotemporal adaptive scheme, which (1) takes partial strategy generation operations offline to reduce communication load, and (2) propagates compression strategies and segmentation results across frames through dense optical flow, and adaptively offloads keyframes to accommodate video content. We implement STAC on a commodity mobile device. Experiments show that STAC can save up to 20.95% of bandwidth without losing accuracy, compared to the state-of-the-art algorithm.

The recent decade has witnessed an upsurge in the demands of intelligent and simplified Internet of Things (IoT) networks that provide ultra-low-power communication for numerous miniaturized devices. Although the research community has paid great attention to wireless protocol designs for these networks, researchers are handicapped by the lack of an energy-efficient software-defined radio (SDR) platform for fast implementation and experimental evaluation. Current SDRs perform well in battery-equipped systems, but fail to support miniaturized IoT devices with stringent hardware and power constraints. This paper takes the first step toward designing an ultra-low-power SDR that satisfies the ultra-low-power or even battery-free requirements of intelligent and simplified IoT networks. To achieve this goal, the core technique is the effective integration of µW-level backscatter in our SDR to sidestep power-hungry active radio frequency chains. We carefully develop a novel circuit design for efficient energy harvesting and power control, and devise a competent solution for eliminating the harmonic and mirror frequencies caused by backscatter hardware. We evaluate the proposed SDR using different modulation schemes, and it achieves a high data rate of 100 kb/s with power consumption less than 200 µW in the active mode and as low as 10 µW in the sleep mode. We also conduct a case study of railway inspection using our platform, achieving 1 kb/s battery-free data delivery to the monitoring unmanned aerial vehicle at a distance of 50 m in a real-world environment, and provide two case studies on smart factories and logistic distribution to explore the application of our platform.

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

The emergence of Intelligent Connected Vehicles (ICVs) shows great potential for future intelligent traffic systems, enhancing both traffic safety and road efficiency. However, the ICVs relying on data driven perception and driving models face many challenges, such as the lack of comprehensive knowledge to deal with complicated driving context. In this paper, we investigate cooperative knowledge sharing for ICVs. We propose a secure and efficient blockchain based knowledge sharing framework, wherein a distributed learning based scheme is utilized to enhance the efficiency of knowledge sharing and a directed acyclic graph (DAG) system is designed to guarantee the security of shared learning models. To cater for the time-intense demand of highly dynamic vehicular networks, a lightweight DAG is designed to reduce the operation latency in terms of fast consensus and authentication. Moreover, to further enhance model accuracy as well as minimizing bandwidth consumption, an adaptive asynchronous distributed learning (ADL) based scheme is proposed for model uploading and downloading. Experiment results show that the DAG based framework is lightweight and secure, which reduces both chosen and confirmation delay as well as resisting malicious attacks. In addition, the proposed adaptive ADL scheme enhances driving safety related performance compared to several existing algorithms.

Vehicle platooning is one of the advanced driving applications expected to be supported by the 5G vehicle to everything (V2X) communications. It holds great potentials on improving road efficiency, driving safety and fuel efficiency. Apart from the organization and internal communication of the platoons, real-time prediction of surrounding road users (such as vehicles and cyclists) is another critical issue. While artificial intelligence (AI) is receiving increasing interests on its application to trajectory prediction, there is a potential problem that the pre-trained neural network models may not well fit the current driving environment and needs online fine-tuning to maintain an acceptable high prediction accuracy. In this paper, we propose a digital twin based real-time trajectory prediction scheme for platoons of connected intelligent vehicles. In this scheme the head vehicle of a platoon senses the surrounding vehicles. A LSTM neural network is applied for real-time trajectory prediction with the sensing outcomes. The head vehicle controls the offloading of the trajectory data and maintains a digital twin to optimize the update of LSTM model. In the digital twin a Deep-Q Learning (DQN) algorithm is utilized for adaptive fine tuning of the LSTM model, to ensure the prediction accuracy and minimize the consumption of communication and computing resources. A real-world dataset is developed from the KITTI datasets for simulations. The simulation results show that the proposed trajectory prediction scheme can maintain a prediction accuracy for safe platooning and reduce the delay of updating the neural networks by up to 40%.

Connected vehicles (CV) and automated vehicles (AV) are promising technologies for reducing road accidents and improving road efficiency. Significant advances have been achieved for AV and CV technologies, but they both have inherent shortcomings such line of sight sensing for AV. Connected autonomous vehicles (CAV) has been proposed to address the problems through sharing sensing and cooperative driving. While the focus of the research on CAV has been on the vehicles so far, cooperative and connected smart road infrastructure can play a critical role to enhance CAV and safe driving. In this paper we present an investigation of connected smart road infrastructure and AVs (CRAV). We discuss the potentials and challenges of CRAV, then propose a scalable simulation framework for the CRAV to facilitate fast, economic and quantitative study of CRAV. A case study of CRAV on smart road side unit (RSU) assisted vulnerable road users (VRU) collision warning is conducted, where the identification of VRU such as pedestrians on the road by the AVs is compared with and without RSU assistance. The impact of the location of RSUs on avoiding potential collisions is evaluated for vehicles with different sensor configurations. Preliminary simulation results show that with the support of smart RSUs, the CAVs could be notified of the existence of the VRUs on the road by the RSUs much earlier than they can detect with their own onboard sensors, and collisions with VRUs can be reduced. This study demonstrates the effectiveness of the proposed CRAV simulation framework and the great potentials of CRAV.

Research on the sixth-generation cellular networks (6G) is gaining huge momentum to achieve ubiquitous wireless connectivity. Connected autonomous vehicles (CAVs) is a critical vertical application for 6G, holding great potentials of improving road safety, road and energy efficiency. However, the stringent service requirements of CAV applications on reliability, latency, and high speed communications will present big challenges to 6G networks. New channel access algorithms and intelligent control schemes for connected vehicles are needed for 6G-supported CAV. In this article, we investigated 6G-supported cooperative driving, which is an advanced driving mode through information sharing and driving coordination. First, we quantify the delay upper bounds of 6G vehicle-to-vehicle (V2V) communications with hybrid communication and channel access technologies. A deep learning neural network is developed and trained for the fast computation of the delay bounds in real-time operations. Then, an intelligent strategy is designed to control the intervehicle distance for cooperative autonomous driving. Furthermore, we propose a Markov chain-based algorithm to predict the parameters of the system states, and also a safe distance mapping method to enable smooth vehicular speed changes. The proposed algorithms are implemented in the AirSim autonomous driving platform. Simulation results show that the proposed algorithms are effective and robust with safe and stable cooperative autonomous driving, which greatly improve the road safety, capacity, and efficiency.

In this paper we investigate the performance of Dedicated Short-Range Communications (DSRC) or IEEE 802. 11p/OCB transceiver gathering performance statistics and relating its usage for ensuring safety and mitigating collision avoidance in Vehicle-to-Vehicle (V2V) communications. To achieve this, we created a low-cost Software Defined Radio (SDR) based DSRC testbed. We focus on quality of service (QoS) and robustness of such a system to assess how it could be used to support road safety applications and assist cooperative awareness in smart cities. To deal with real-world scenarios, we established a testbed using SDR devices and tested in varied scenarios. Our experiments are tailored to give insights into the fundamental challenges regarding how the practical driving safety issues can be effectively addressed by different technologies such as SDR, V2V communications, sensing and their combination. The results we collected and analysed can be used to support future V2V experimentation.

Object detection is a critical problem for advanced driving assistance systems (ADAS). Recently, convolutional neural networks (CNN) achieved large successes on object detection, with performance improvement over traditional approaches, which use hand-engineered features. However, due to the challenging driving environment (e.g., large object scale variation, object occlusion, and bad light conditions), popular CNN detectors do not achieve very good object detection accuracy over the KITTI autonomous driving benchmark dataset. In this paper, we propose three enhancements for CNN-based visual object detection for ADAS. To address the large object scale variation challenge, deconvolution and fusion of CNN feature maps are proposed to add context and deeper features for better object detection at low feature map scales. In addition, soft non-maximal suppression (NMS) is applied across object proposals at different feature scales to address the object occlusion challenge. As the cars and pedestrians have distinct aspect ratio features, we measure their aspect ratio statistics and exploit them to set anchor boxes properly for better object matching and localization. The proposed CNN enhancements are evaluated with various image input sizes by experiments over KITTI dataset. The experimental results demonstrate the effectiveness of the proposed enhancements with good detection performance over KITTI test set.

Internet-of-Vehicles (IoV) has the potentials of enhancing automatic driving in various transportation environment. However, there is very little investigation on quantifying the potential influence of automatic driving applications with the road efficiency in IoV. This paper studies the connection of safety distance to the road congestion under different IoV resource conditions. We propose an elastic wave equation model to reveal the relation between safety distance and road congestion. It can be found that the propagation speed of road congestion is largely affected by the safety distance. To recoup the efficient road safety and alleviate road congestion, an optimization problem is formulated with cooperative communication and computing via platoons that aims to minimize the total safety distance. Since the optimization is a complicated 0-1 programming problem, we propose a practical resource allocation algorithm and solve the problem through Lagrangian relaxation. Simulation experiments show that the proposed algorithm leads to near-optimal results with low complexity but no overhead of vehicular information exchange.

The ICDAR 2019 Challenge on "Scanned receipts OCR and key information extraction" (SROIE) covers important aspects related to the automated analysis of scanned receipts. The SROIE tasks play a key role in many document analysis systems and hold significant commercial potential. Although a lot of work has been published over the years on administrative document analysis, the community has advanced relatively slowly, as most datasets have been kept private. One of the key contributions of SROIE to the document analysis community is to offer a first, standardized dataset of 1000 whole scanned receipt images and annotations, as well as an evaluation procedure for such tasks. The Challenge is structured around three tasks, namely Scanned Receipt Text Localization (Task 1), Scanned Receipt OCR (Task 2) and Key Information Extraction from Scanned Receipts (Task 3). The competition opened on 10th February, 2019 and closed on 5th May, 2019. We received 29, 24 and 18 valid submissions received for the three competition tasks, respectively. This report presents the competition datasets, define the tasks and the evaluation protocols, offer detailed submission statistics, as well as an analysis of the submitted performance. While the tasks of text localization and recognition seem to be relatively easy to tackle, it is interesting to observe the variety of ideas and approaches proposed for the information extraction task. According to the submissions' performance we believe there is still margin for improving information extraction performance, although the current dataset would have to grow substantially in following editions. Given the success of the SROIE competition evidenced by the wide interest generated and the healthy number of submissions from academic, research institutes and industry over different countries, we consider that the SROIE competition can evolve into a useful resource for the community, drawing further attention and promoting research and development efforts in this field.

Automatic recognition and error correction of texts from images are critical for many commercial applications such as receipt recognition, which have very high accuracy requirements. In this paper we propose an integrated image based text recognition and correction approach to improve accuracy. There are two levels of text recognition and correction integration in the proposed approach. Firstly, a beam search strategy is designed to generate a set of text candidates, based on the probability distribution of text prediction outcomes from a deep learning recognition model. Then a word-level lexicon check is applied to select only one from the candidate text sentences, which has the highest prediction probability among those with all words present in the lexicon. Jointly the beam search and lexicon check can effectively correct some recognition errors. Secondly, an encoder-decoder language model based corrector is developed to correct potential recognition errors in the selected output texts that fail the lexicon check. Training samples for the corrector are created from the recognition outcomes and can be expanded by associating multiple text candidates with one image label. We conduct experiments on ICDAR'13 and CH10K datasets to evaluate the proposed approach and the impact of these two levels of integration on accuracy. Experiment results show that the proposed approach outperforms the existing one with higher recall and much higher recognition accuracy through effective exploitation of joint recognition and correction design.

Road accidents and traffic congestion are two critical problems for global transport systems. Connected vehicles (CV) and automated vehicles (AV) are among the most heavily researched and promising automotive technologies to reduce road accidents and improve road efficiency. However, both AV and CV technologies have inherent shortcomings, for example, line of sight sensing limitation of AV sensors and the dependency of high penetration rate for CVs. In this paper we present a cooperative connected intelligent vehicles (CAV) framework. It is motivated by the observation that vehicles are increasingly intelligent with various levels of autonomous functionalities. The vehicles intelligence is boosted by more sensing and computing resources. These sensor and computing resources of CAV vehicles and the transport infrastructure could be shared and exploited. With resource sharing and cooperation CAVs can have comprehensive perception of driving environments, and novel cooperative applications can be developed to improve road safety and efficiency (RSE). The key feature of the cooperative CAV system is the cooperation within and across the key players in the road transport systems and across system layers. For example, the various levels of cooperation include cooperative sensing, cooperative RSE applications and cooperation among the vehicles and among the vehicles and infrastructure. We will present the potentials that could be brought by cooperative CAV, the roadmap for research and development, the preliminary research results and open issues.

With increasingly smaller size, more powerful sensing capabilities and higher level of autonomy, multiple unmanned aerial vehicles (UAVs) can form UAV networks to collaboratively complete missions more reliably, efficiently, and economically. While UAV networks are promising for many applications, there are many outstanding issues to be resolved before large scale UAV networks are practically used. In this paper we study the application of cognitive radio (CR) technology for UAV communication networks, to provide high capacity and reliable communication with opportunistic and timely spectrum access. Compressive sensing is applied in the CR to boost the performance of spectrum sensing. However, the performance of existing compressive spectrum sensing schemes is constrained with nonstrictly sparse spectrum. In addition, the reconstruction process applied in existing schemes has unnecessarily high computational complexity and low energy efficiency. We proposed a new compressive signal processing algorithm, called iterative compressive filtering, to improve the UAV network communication performance. The key idea is using orthogonal projection as a bandstop filter in compressive domain. The components of primary users in the recognized subchannels are adaptively eliminated in compressive domain, which can directly update the measurement for further detection of other active users. Experiment results showed increased efficiency of the proposed algorithm over existing compressive spectrum sensing algorithms. The proposed algorithm achieved higher detection probability in identifying the occupied subchannels under the condition of nonstrictly sparse spectrum with large computational complexity reduction, which can provide strong support of reliable and timely communication for UAV networks.

Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multitier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multitier fog is consisted of ad-hoc fogs and dedicated fogs with opportunistic and dedicated computing resources, respectively. The proposed new fog computing model with clear functional modules is able to mitigate the potential problems of dedicated computing infrastructure and slow response in cloud computing. We run analytics benchmark experiments over fogs formed by Rapsberry Pi computers with a distributed computing engine to measure computing performance of various analytics tasks, and create easy-to-use workload models. Quality of services (QoS) aware admission control, offloading, and resource allocation schemes are designed to support data analytics services, and maximize analytics service utilities. Availability and cost models of networking and computing resources are taken into account in QoS scheme design. A scalable system level simulator is developed to evaluate the fog-based analytics service and the QoS management schemes. Experiment results demonstrate the efficiency of analytics services over multitier fogs and the effectiveness of the proposed QoS schemes. Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility.

Text detection in a natural environment plays an important role in many computer vision applications. While existing text detection methods are focused on English characters, there are strong application demands on text detection in other languages, such as Chinese. In this paper, we present a novel text detection algorithm for Chinese characters based on a specific designed convolutional neural network (CNN). The CNN contains a text structure component detector layer, a spatial pyramid layer, and a multi-input-layer deep belief network (DBN). The CNN is pre-trained via a convolutional sparse auto-encoder, specifically designed for extracting complex features from Chinese characters. In particular, the text structure component detectors enhance the accuracy and uniqueness of feature descriptors by extracting multiple text structure components in various ways. The spatial pyramid layer enhances the scale invariability of the CNN for detecting texts in multiple scales. Finally, the multi-input-layer DBN replaces the fully connected layers in the CNN to ensure features from multiple scales are comparable. A multilingual text detection dataset, in which texts in Chinese, English, and digits are labeled separately, is set up to evaluate the proposed text detection algorithm. The proposed algorithm shows a significant performance improvement over the baseline CNN algorithms. In addition the proposed algorithm is evaluated over a public multilingual benchmark and achieves state-of-the-art result under multiple languages. Furthermore, a simplified version of the proposed algorithm with only general components is evaluated on the ICDAR 2011 and 2013 datasets, showing comparable detection performance to the existing general text detection algorithms.

Identifying topic trends on microblogging services such as Twitter and estimating those topics’ future popularity have great academic and business value, especially when the operations can be done in real time. For any third party, however, capturing and processing such huge volumes of real-time data in microblogs are almost infeasible tasks, as there always exist API (Application Program Interface) request limits, monitoring and computing budgets, as well as timeliness requirements. To deal with these challenges, we propose a cost-effective system framework with algorithms that can automatically select a subset of representative users in microblogging networks in offline, under given cost constraints. Then the proposed system can online monitor and utilize only these selected users’ real-time microposts to detect the overall trending topics and predict their future popularity among the whole microblogging network. Therefore, our proposed system framework is practical for real-time usage as it avoids the high cost in capturing and processing full real-time data, while not compromising detection and prediction performance under given cost constraints. Experiments with real microblogs dataset show that by tracking only 500 users out of 0.6 million users and processing no more than 30,000 microposts daily, about 92% trending topics could be detected and predicted by the proposed system and, on average, more than 10 hours earlier than they appear in official trends lists.

Scene text information extraction plays an important role in many computer vision applications. Unlike most existing text extraction algorithms for English texts, in this paper, we focus on Chinese texts, which are more complex in stroke and structure. To tackle this challenging problem, we propose a novel convolutional neural network (CNN) based text structure feature extractor for Chinese texts. Each Chinese character contains its specific types and combination of text structure components, which is rarely seen in backgrounds. Thus, different from the features only applicable to one text extraction stage (text detection or text recognition), the text structure component feature is suitable for both Chinese text detection and recognition. A text structure component detector (TSCD) layer is designed to detect the large amount of component types, which is the most challenging part of extracting text structure component features. Through statistical classification various types of text structure component are detected by their specially designed convolutional units in the TSCD layer. With the TSCD layer, the CNN has improvements in the accuracy and uniqueness of text feature description. In the evaluation, both text detection and recognition algorithms based on the proposed text structure feature extractor achieve state-of-the-art results in two datasets.

Candidate text region extraction plays a critical role in convolutional neural network (CNN) based text detection from natural images. In this paper, we propose a CNN based scene text detection algorithm with a new text region extractor. The so called candidate text region extractor I-MSER is based on Maximally Stable Extremal Region (MSER), which can improve the independency and completeness of the extracted candidate text regions. Design of I-MSER is motivated by the observation that text MSERs have high similarity and are close to each other. The independency of candidate text regions obtained by I-MSER is guaranteed by selecting the most representative regions from a MSER tree which is generated according to the spatial overlapping relationship among the MSERs. A multi-layer CNN model is trained to score the confidence value of the extracted regions extracted by the I-MSER for text detection. The new text detection algorithm based on I-MSER is evaluated with wide-used ICDAR 2011 and 2013 datasets and shows improved detection performance compared to the existing algorithms.

Higher order sectorization (HOS), which splits macrocells into a larger number of smaller sectors, are receiving significant interest as a cost-effective means of improving network capacity. Potentially, the capacity gain with HOS is proportionally linear to the number of sectors per cell due to spatial reuse, but factors such as non-ideal antenna radiation patterns together with inter-cell interference can significantly reduce this capacity gain. We develop a statistical model to theoretically characterize the performance of HOS deployments in wireless networks using orthogonal frequency division multiple access. Moreover, a fractional frequency reuse scheme is considered, which aids to mitigate inter-cell interference. The model provides a fast and effective tool for studying network performance in terms of user signal quality, site throughput, and outage probability, and it can be used to speed up network planning and optimization. In addition, we consider the impact of user classification methods in the analysis, and propose a new spectrum efficiency-based user classification method that improves resource utilization and allocation fairness. Performance results indicate that the proposed model is accurate, and shows a diminishing performance gain of HOS deployments with the number of sectors. The proposed user classification method improves network performances with respect to the state-of-the-art approaches.

Machine to machine (M2M) communication has raised significant interests. However, due to the massive number of machine type communication (MTC) devices that are anticipated to communicate using cellular networks, there is a major problem on efficient accommodation of the heavy Random Access (RA) loads from the MTC devices. Use of small cells has been specified to provide network densification by 3GPP. In this paper we investigate the use of small cells to support RA and the allocation of Zadoff-Chu sequences to the small cells, which are used to generate preambles for the RA procedure. Small cells can be deployed on demand to handle mainly RA loads from MTC devices, which may generate much less data traffic compared to human devices. It is demonstrated that, with small cell support, more random channel access opportunities are provided and this can effectively support a massive number of machine devices. Using both simulations and analytical model the proposed implementation is evaluated and compared to two existing random access schemes without small cell support (the basic random access scheme and the access class barring (ACB) scheme). It is observed that the capacity of the networks in terms of the number of supported machine devices with small cell support can be increased significantly. The proposed implementation shows large potential to handle random channel access for massive machine devices.

As microblog services such as Twitter become a fast and convenient communication approach, identification of trendy topics in microblog services has great academic and business value. However detecting trendy topics is very challenging due to huge number of users and short-text posts in microblog diffusion networks. In this paper we introduce a trendy topics detection system under computation and communication resource constraints. In stark contrast to retrieving and processing the whole microblog contents, we develop an idea of selecting a small set of microblog users and processing their posts to achieve an overall acceptable trendy topic coverage, without exceeding resource budget for detection. We formulate the selection operation of these subset users as mixed-integer optimization problems, and develop heuristic algorithms to compute their approximate solutions. The proposed system is evaluated with real-time test data retrieved from Sina Weibo, the dominant microblog service provider in China. It's shown that by monitoring 500 out of 1.6 million microblog users and tracking their microposts (about 15,000 daily) with our system, nearly 65% trendy topics can be detected, while on average 5 hours earlier before they appear in Sina Weibo official trends.

Vehicle-to-grid (V2G) system with efficient demand response management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at electric vehicles (EVs). An incentivized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling-based auction. In the proposed approach, a number of aggregators sit on the first-level auction responsible to communicate with a group of EVs. EVs as bidders consider quality of energy (QoE) requirements, and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate that the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.

GraphChi is the first reported disk-based graph engine that can handle billion-scale graphs on a single PC efficiently. GraphChi is able to execute several advanced data mining, graph mining and machine learning algorithms on very large graphs. With the novel technique of parallel sliding windows (PSW) to load subgraph from disk to memory for vertices and edges updating, it can achieve data processing performance close to and even better than those of mainstream distributed graph engines. GraphChi mentioned that its memory is not effectively utilized with large dataset, which leads to suboptimal computation performances. In this paper we are motivated by the concepts of “pin ” from TurboGraph and “ghost” from GraphLab to propose a new memory utilization mode for GraphChi, which is called Part-in-memory mode, to improve the GraphChi algorithm performance. The main idea is to pin a fixed part of data inside the memory during the whole computing process. Part-in-memory mode is successfully implemented with only about 40 additional lines of code to the original GraphChi engine. Extensive experiments are performed with large real datasets (including Twitter graph with 1.4 billion edges). The preliminary results show that Part-in-memory mode memory management approach effectively reduces the GraphChi running time by up to 60% in PageRank algorithm. Interestingly it is found that a larger portion of data pinned in memory does not always lead to better performance in the case that the whole dataset cannot be fitted in memory. There exists an optimal portion of data which should be kept in the memory to achieve the best computational performance.

With careful calculation of signal forwarding weights, relay nodes can be used to work collaboratively to enhance downlink transmission performance by forming a virtual multiple-input multiple-output beamforming system. Although collaborative relay beamforming schemes for single user have been widely investigated for cellular systems in previous literatures, there are few studies on the relay beamforming for multiusers. In this paper, we study the collaborative downlink signal transmission with multiple amplify-and-forward relay nodes for multiusers in cellular systems. We propose two new algorithms to determine the beamforming weights with the same objective of minimizing power consumption of the relay nodes. In the first algorithm, we aim to guarantee the received signal-to-noise ratio at multiusers for the relay beamforming with orthogonal channels. We prove that the solution obtained by a semidefinite relaxation technology is optimal. In the second algorithm, we propose an iterative algorithm that jointly selects the base station antennas and optimizes the relay beamforming weights to reach the target signal-to-interference-and-noise ratio at multiusers with nonorthogonal channels. Numerical results validate our theoretical analysis and demonstrate that the proposed optimal schemes can effectively reduce the relay power consumption compared with several other beamforming approaches.

This paper researches on Matthew Effect in Sina Weibo microblogger. We choose the microblogs in the ranking list of Hot Microblog App in Sina Weibo microblogger as target of our study. The differences of repost number of microblogs in the ranking list between before and after the time when it enter the ranking list of Hot Microblog app are analyzed. And we compare the spread features of the microblogs in the ranking list with those hot microblogs not in the list and those ordinary microblogs of users who have some microblog in the ranking list before. Our study proves the existence of Matthew Effect in social network.

IEEE 802.15.4 standard is a relatively new standard designed for low power low data rate wireless sensor networks (WSN), which has a wide range of applications, e.g., environment monitoring, e-health, home and industry automation. In this paper, we investigate the problems of hidden devices in coverage overlapped IEEE 802.15.4 WSNs, which is likely to arise when multiple 802.15.4 WSNs are deployed closely and independently. We consider a typical scenario of two 802.15.4 WSNs with partial coverage overlapping and propose a Markov-chain based analytical model to reveal the performance degradation due to the hidden devices from the coverage overlapping. Impacts of the hidden devices and network sleeping modes on saturated throughput and energy consumption are modeled. The analytic model is verified by simulations, which can provide the insights to network design and planning when multiple 802.15.4 WSNs are deployed closely.

ABSTRACT With the reformation of spectrum policy and the development of cognitive radio, secondary users will be allowed to access spectrums licensed to primary users. Spectrum auctions can facilitate this secondary spectrum access in a market‐driven way. To design an efficient auction framework, we first study the supply and demand pressures and the competitive equilibrium of the secondary spectrum market, considering the spectrum reusability. In well‐designed auctions, competition among participants should lead to the competitive equilibrium according to the traditional economic point of view. Then, a discriminatory price spectrum double auction framework is proposed for this market. In this framework, rational participants compete with each other by using bidding prices, and their profits are guaranteed to be non‐negative. A near‐optimal heuristic algorithm is also proposed to solve the auction clearing problem of the proposed framework efficiently. Experimental results verify the efficiency of the proposed auction clearing algorithm and demonstrate that competition among secondary users and primary users can lead to the competitive equilibrium during auction iterations using the proposed auction framework. Copyright © 2011 John Wiley & Sons, Ltd.

In this poster we report our study on the microblog spammers with samples attracted by 50 honeyspots from two popular Chinese microblogging networks: Sina Weibo (weibo.com), and Ten cent Weibo (t.QQ.com) in seven months. We studied their features such as social information, activity, account age and spamming strategy. Several distinguishing characteristics of spammers on these two social network communities are observed, which can be helpful to the further study on automatic detection of microblog spammers. To our best knowledge our work is the first of its kind on the analysis of features of Chinese micloblog spammers.

Congestion control is critical for the provisioning of quality of services (QoS) over dedicated short range communications (DSRC) vehicle networks for road safety applications. In this paper we propose a congestion control method for DSRC vehicle networks at road intersection, with the aims of providing high availability and low latency channels for high priority emergency safety applications while maximizing channel utilization for low priority routine safety applications. In this method a offline simulation based approach is used to find out the best possible configurations of message rate and MAC layer backoff exponent (BE) for a given number of vehicles equipped with DSRC radios. The identified best configurations are then used online by an roadside access point (AP) for system operation. Simulation results demonstrated that this adaptive method significantly outperforms the fixed control method under varying number of vehicles. The impact of estimation error on the number of vehicles in the network on system level performance is also investigated.

Dedicated Short Range Communication (DSRC) is a promising technique for vehicle ad-hoc network (VANET) and collaborative road safety applications. As road safety applications require strict quality of services (QoS) from the VANET, it is crucial for DSRC to provide timely and reliable communications to make safety applications successful. In this paper we propose two adaptive message rate control algorithms for low priority safety messages, in order to provide highly available channel for high priority emergency messages while improve channel utilization. In the algorithms each vehicle monitors channel loads and independently controls message rate by a modified additive increase and multiplicative decrease (AIMD) method. Simulation results demonstrated the effectiveness of the proposed rate control algorithms in adapting to dynamic traffic load.

In this paper, the implementation aspects and constraints of the simplest network coding (NC) schemes for a two-way relay channel (TWRC) composed of a user equipment (mobile terminal), an LTE relay station (RS) and an LTE base station (eNB) are considered in order to assess the usefulness of the NC in more realistic scenarios. The information exchange rate gain (IERG), the energy reduction gain (ERG) and the resource utilization gain (RUG) of the NC schemes with and without subcarrier division duplexing (SDD) are obtained by computer simulations. The usefulness of the NC schemes are evaluated for varying traffic load levels, the geographical distances between the nodes, the RS transmit powers, and the maximum numbers of retransmissions. Simulation results show that the NC schemes with and without SDD, have the throughput gains $0.5\%$ and $25\%$, the ERGs $7-12\%$ and $16-25\%$, and the RUGs $0.5-3.2\%$, respectively. It is found that the NC can provide performance gains also for the users at the cell edge. Furthermore, the ERGs of the NC increase with the transmit power of the relay while the ERGs of the NC remain the same even when the maximum number of retransmissions is reduced.

It is desirable that energy performance improvement is not realized at the expense of other network performance parameters. This paper investigates the trade off between energy efficiency, spectral efficiency and user QoS performance for a multi-cell multi-user radio access network. Specifically, the energy consumption ratio (ECR) and the spectral efficiency of several common frequency domain packet schedulers in a cellular E- UTRAN downlink are compared for both the SISO transmission mode and the 2×2 Alamouti Space Frequency Block Code (SFBC) MIMO transmission mode. It is well known that the 2×2 SFBC MIMO transmission mode is more spectrally efficient compared to the SISO transmission mode, however, the relationship between energy efficiency and spectral efficiency is undecided. It is shown that, for the E-UTRAN downlink with fixed transmission power, spectral efficiency improvement results into energy efficiency improvement. The effect of SFBC MIMO versus SISO on the user QoS performance is also studied.

Recently underwater sensor networks (UWSN) attracted large research interests. Medium access control (MAC) is one of the major challenges faced by UWSN due to the large propagation delay and narrow channel bandwidth of acoustic communications used for UWSN. Widely used slotted aloha (S-Aloha) protocol suffers large performance loss in UWSNs, which can only achieve performance close to pure aloha (PAloha). In this paper we theoretically model the performances of S-Aloha and P-Aloha protocols and analyze the adverse impact of propagation delay. According to the observation on the performances of S-Aloha protocol we propose two enhanced S-Aloha protocols in order to minimize the adverse impact of propagation delay on S-Aloha protocol. The first enhancement is a synchronized arrival S-Aloha (SA-Aloha) protocol, in which frames are transmitted at carefully calculated time to align the frame arrival time with the start of time slots. Propagation delay is taken into consideration in the calculation of transmit time. As estimation error on propagation delay may exist and can affect network performance, an improved SA-Aloha (denoted by ISAAloha) is proposed, which adjusts the slot size according to the range of delay estimation errors. Simulation results show that both SA-Aloha and ISA-Aloha perform remarkably better than S-Aloha and P-Aloha for UWSN, and ISA-Aloha is more robust even when the propagation delay estimation error is large.

Abstract Dedicated short range communications (DSRC) has been regarded as one of the most promising technologies to provide robust communications for large scale vehicle networks. It is designed to support both road safety and commercial applications. Road safety applications will require reliable and timely wireless communications. However, as the medium access control (MAC) layer of DSRC is based on the IEEE 802.11 distributed coordination function (DCF), it is well known that the random channel access based MAC cannot provide guaranteed quality of services (QoS). It is very important to understand the quantitative performance of DSRC, in order to make better decisions on its adoption, control, adaptation, and improvement. In this paper, we propose an analytic model to evaluate the DSRC‐based inter‐vehicle communication. We investigate the impacts of the channel access parameters associated with the different services including arbitration inter‐frame space (AIFS) and contention window (CW). Based on the proposed model, we analyze the successful message delivery ratio and channel service delay for broadcast messages. The proposed analytical model can provide a convenient tool to evaluate the inter‐vehicle safety applications and analyze the suitability of DSRC for road safety applications. Copyright © 2009 John Wiley & Sons, Ltd.

This paper attempts to address the effectiveness of physical-layer network coding (PNC) on the capacity improvement for multi-hop multicast in random wireless ad hoc networks (WAHNs). While it can be shown that there is a capacity gain by PNC, we can prove that the per session throughput capacity with PNC is θ (nR(n))-1), where n is the total number of nodes, R(n) is the communication range, and each multicast session consists of a constant number of sinks. The result implies that PNC cannot improve the capacity order of multicast in random WAHNs, which is different from the intuition that PNC may improve the capacity order as it allows simultaneous signal reception and combination.

Link adaptation is a critical component of IEEE 802.11 systems, which adapts transmission rates to dynamic wireless channel conditions. In this paper we investigate a general cross-layer link adaptation algorithm which jointly considers the physical layer link quality and random channel access at the MAC layer. An analytic model is proposed for the link adaptation algorithm. The underlying wireless channel is modeled with a multiple state discrete time Markov chain. Compared with the pure link quality based link adaptation algorithm, the proposed cross-layer algorithm can achieve considerable performance gains of up to 20%.

The objective of this paper is to combine the antenna downtilt selection with the cell size selection in order to reduce the overall radio frequency (RF) transmission power in the homogeneous High-Speed Packet Downlink (HSDPA) cellular radio access network (RAN). The analysis is based on the concept of small cells deployment. The energy consumption ratio (ECR) and the energy reduction gain (ERG) of the cellular RAN are calculated for different antenna tilts when the cell size is being reduced for a given user density and service area. The results have shown that a suitable antenna tilt and the RF power setting can achieve an overall energy reduction of up to 82.56%. Equally, our results demonstrate that a small cell deployment can considerably reduce the overall energy consumption of a cellular network.

Abstract The performance of wireless networks is limited by multiple access interference (MAI) in the traditional communication approach where the interfered signals of the concurrent transmissions are treated as noise. In this paper, we treat the interfered signals from a new perspective on the basis of additive electromagnetic (EM) waves and propose a network coding based interference cancelation (NCIC) scheme. In the proposed scheme, adjacent nodes can transmit simultaneously with careful scheduling; therefore, network performance will not be limited by the MAI. Additionally we design a space segmentation method for general wireless ad hoc networks, which organizes network into clusters with regular shapes (e.g., square and hexagon) to reduce the number of relay nodes. The segmentation method works with the scheduling scheme and can help achieve better scalability and reduced complexity. We derive accurate analytic models for the probability of connectivity between two adjacent cluster heads which is important for successful information relay. We proved that with the proposed NCIC scheme, the transmission efficiency can be improved by at least 50% for general wireless networks as compared to the traditional interference avoidance schemes. Numeric results also show the space segmentation is feasible and effective. Finally we propose and discuss a method to implement the NCIC scheme in a practical orthogonal frequency division multiplexing (OFDM) communications networks. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we study the localization problem in large-scale Underwater Wireless Sensor Networks (UWSNs). Unlike in the terrestrial positioning, the global positioning system (GPS) can not work efficiently underwater. The limited bandwidth, the severely impaired channel and the cost of underwater equipment all makes the localization problem very challenging. Most current localization schemes are not well suitable for deep underwater environment. We propose a hierarchical localization scheme to address the challenging problems. The new scheme mainly consists of four types of nodes, which are surface buoys, Detachable Elevator Transceivers (DETs), anchor nodes and ordinary nodes. Surface buoy is assumed to be equipped with GPS on the water surface. A DET is attached to a surface buoy and can rise and down to broadcast its position. The anchor nodes can compute their positions based on the position information from the DETs and the measurements of distance to the DETs. The hierarchical localization scheme is scalable, and can be used to make balances on the cost and localization accuracy. Initial simulation results show the advantages of our proposed scheme.

To ensure state synchronization of signalling operations, many signaling protocol designs choose to establish ldquosoftrdquo state that expires if it is not refreshed. The approaches of refreshing state in multi-hop signaling system can be classified as either end-to-end (E2E) or hop-by-hop (HbH). Although both state refresh approaches have been widely used in practical signaling protocols, the design tradeoffs between state synchronization and signaling cost have not yet been fully investigated. In this paper, we investigate this issue from the perspectives of state refresh and state removal. We propose simple but effective Markov chain models for both approaches and obtain closed-form solutions which depict the state refresh performance in terms of state consistency and refresh message rate, as well as the state removal performance in terms of state removal delay. Simulations verify the analytical models. It is observed that the HbH approach yields much better state synchronization at the cost of higher signaling cost than the E2E approach. While the state refresh performance can be improved by increasing the values of state refresh and timeout timers, the state removal delay increases largely for both E2E and HbH approaches. The analysis here shed lights on the design of signaling protocols and the configuration of the timers to adapt to changing network conditions.

In this paper new architectural approaches that improve the energy efficiency of a cellular radio access network (RAN) are investigated. The aim of the paper is to characterize both the energy consumption ratio (ECR) and the energy consumption gain (ECG) of a cellular RAN when the cell size is reduced for a given user density and service area. The paper affirms that reducing the cell size reduces the cell ECR as desired while increasing the capacity density but the overall RAN energy consumption remains unchanged. In order to trade the increase in capacity density with RAN energy consumption, without degrading the cell capacity provision, a sleep mode is introduced. In sleep mode, cells without active users are powered-off, thereby saving energy. By combining a sleep mode with a small-cell deployment architecture, the paper shows that the ECG can be increased by the factor n = (Rlarge/Rsmall)2while the cell ECR continues to decrease with decreasing cell size.

In this paper a Markov chain based analytical model is proposed to evaluate the slotted CSMA/CA algorithm specified in the MAC layer of IEEE 802.15.4 standard. The analytical model consists of two two-dimensional Markov chains, used to model the state transition of an 802.15.4 device, during the periods of a transmission and between two consecutive frame transmissions, respectively. By introducing the two Markov chains a small number of Markov states are required and the scalability of the analytical model is improved. The analytical model is used to investigate the impact of the CSMA/CA parameters, the number of contending devices, and the data frame size on the network performance in terms of throughput and energy efficiency. It is shown by simulations that the proposed analytical model can accurately predict the performance of slotted CSMA/CA algorithm for uplink, downlink and bi-direction traffic, with both acknowledgement and non-acknowledgement modes.

Abstract In cognitive radio cellular networks (CogCell), the secondary users (SUs) are allowed to access the channels licensed to the primary users (PUs) including Primary Transmitters (PTs) and Primary Receivers (PRs), only if the interference to the PRs is less than the predefined threshold, and the quality of service (QoS) requirements of PTs are guaranteed. In addition, different SUs may require different levels of QoS, and pay differently depending on the provided QoS. The network operator achieves different secondary revenues by admitting SUs in different QoS levels. The problem we address in this paper is to maximize the total secondary revenue relative to the interference constraints on PRs, and QoS requirements for both PTs and SUs. We formulate this optimization problem, and propose a power control scheme for both PTs and SUs. Then, we introduce three solutions including an exact solution using dynamic programming, a greedy heuristic algorithm, and a minimal signal‐interference‐plus‐noise‐ratio (SINR) removal algorithm. Based on these algorithms, we propose three QoS aware admission and power control (QAPC) schemes, one optimal solution called QAPC‐dynamic, and two approximate solutions called QAPC‐greedy and QAPC‐minimal SINR removal algorithm (MSRA), respectively. Numerical results show that QAPC‐dynamic always achieves the highest secondary revenue while QAPC‐MSRA gives the lowest secondary revenue. Since the time complexity of QAPC‐dynamic is much higher than the other two schemes, QAPC‐greedy is recommended considering the trade‐off between the computation complexity and performance gain. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we develop an analytical model for IEEE 802.11 MAC protocol with arbitrary buffer size in unsaturated conditions. The model comprises of a generalized Markov chain model and an M/G/1/K queuing model. We give the thorough and accurate queuing analysis of IEEE 802.11 MAC layer. It's shown that for practical 802.11 WLAN (i.e., buffer size larger than one and number of stations comparatively large) under the optimal offered load, the total throughput is maximized, the packet blocking probability (due to limited buffer size) and the average queuing delay tends to zero, the average MAC service delay as well as its standard deviation is much lower than that in saturated conditions. The simulations show the analytical model is highly accurate.

A dynamic bandwidth reservation (DBR) scheme for hybrid IEEE 802.16 wireless networks is investigated, in which 802.16 networks serve as the backhaul for client networks, such as WiFi hotspots and cellular networks. The DBR scheme implemented in the subscription stations (SSs) (co-locating with access pointers) consists of two components: connection admission controller (CAC), and bandwidth controller (BC). The CAC processes the received connection set-up requests from the client networks connected to the SSs. The BC manages the request and release of bandwidth from the base station (BS). It dynamically changes the reserved bandwidth between a small number of values. Hysteresis is incorporated in bandwidth release to reduce bandwidth request signalling load and connection blocking probability. An analytical model is proposed to evaluate the performances of reserved bandwidth, connection blocking probability and signalling load. The impacts of hysteresis mechanism and probability of reservation request blocking are taken into account. Simulation verifies the analytical model.

This paper investigates the random channel access mechanism specified in the IEEE 802.16 standard for the uplink traffic in a point-to-multipoint (PMP) network architecture. An analytical model is proposed to study the impacts of the channel access parameters, bandwidth configuration and piggyback policy on the performance. The impacts of physical burst profile and non-saturated network traffic are also taken into account in the model. Simulations validate the proposed analytical model. It is observed that the bandwidth utilization can be improved if the bandwidth for random channel access can be properly configured according to the channel access parameters, piggyback policy and network traffic.

This paper investigates a cross-layer design approach for minimizing energy consumption and maximizing network lifetime (NL) of a multiple-source and single-sink (MSSS) WSN with energy constraints. The optimization problem for MSSS WSN can be formulated as a mixed integer convex optimization problem with the adoption of time division multiple access (TDMA) in medium access control (MAC) layer, and it becomes a convex problem by relaxing the integer constraint on time slots. Impacts of data rate, link access and routing are jointly taken into account in the optimization problem formulation. Both linear and planar network topologies are considered for NL maximization (NLM). With linear MSSS and planar single-source and single-sink (SSSS) topologies, we successfully use Karush-Kuhn-Tucker (KKT) optimality conditions to derive analytical expressions of the optimal NL when all nodes are exhausted simultaneously. The problem for planar MSSS topology is more complicated, and a decomposition and combination (D&C) approach is proposed to compute suboptimal solutions. An analytical expression of the suboptimal NL is derived for a small scale planar network. To deal with larger scale planar network, an iterative algorithm is proposed for the D&C approach. Numerical results show that the upper-bounds of the network lifetime obtained by our proposed optimization models are tight. Important insights into the NL and benefits of cross-layer design for WSN NLM are obtained.

Distributed source coding (DSC) has recently been considered as an efficient approach to data compression in wireless sensor networks (WSN). Using this coding method multiple sensor nodes compress their correlated observations without inter-node communications. Therefore energy and bandwidth can be efficiently saved. In this paper, we investigate a random-binning based DSC scheme for remote source estimation in WSN and its performance of estimated signal to distortion ratio (SDR). With the introduction of a detailed power consumption model for wireless sensor communications, we quantitatively analyze the overall network energy consumption of the DSC scheme. We further propose a novel energy-aware transmission protocol for the DSC scheme, which flexibly optimizes the DSC performance in terms of either SDR or energy consumption, by adapting the source coding and transmission parameters to the network conditions. Simulations validate the energy efficiency of the proposed adaptive transmission protocol.

The environment of a mobile ad hoc network may vary greatly depending on nodes' mobility, traffic load and resource conditions. In this paper we categorize the environment of an ad hoc network into three main states: an ideal state, wherein the network is relatively stable with sufficient resources; a congested state, wherein some nodes, regions or the network is experiencing congestion; and an energy critical state, wherein the energy capacity of nodes in the network is critically low. Each of these states requires unique routing schemes, but existing ad hoc routing protocols are only effective in one of these states. This implies that when the network enters into any other states, these protocols run into a sub optimal mode, degrading the performance of the network. We propose an ad hoc network state aware routing protocol (ANSAR) which conditionally switches between earliest arrival scheme and a joint load-energy aware scheme depending on the current state of the network. Comparing to existing schemes, it yields higher efficiency and reliability as shown in our simulation results.

Medium access control (MAC) protocols have a large impact on the achievable system performance for wireless ad hoc networks. Because of the limitations of existing analytical models for ad hoc networks, many researchers have opted to study the impact of MAC protocols via discreteevent simulations. However, as the network scenarios, traffic patterns and physical layer techniques may change significantly, simulation alone is not efficient to get insights into the impacts of MAC protocols on system performance. In this paper, we analyze the performance of IEEE 802.11 distributed coordination function (DCF) in multihop network scenario. We are particularly interested in understanding how physical layer techniques may affect the MAC protocol performance. For this purpose, the features of interference range is studied and taken into account of the analytical model. Simulations with OPNET show the effectiveness of the proposed analytical approach.

The ping-pong type of movement is a typical motion manner in mobile IPv6 networks, which will bring frequent handovers and thus increase signaling burden. On the other hand, reducing handover delay in this case seems to be more significant. In this paper, we propose a fast seamless handover scheme for the ping-pong type of movement as an extension to the hierarchical mobile IPv6. Based on the simulation results, it can be observed that, by setting the reservation active flag (RAF) and the offline count down timer (CDT), the scheme significantly reduces QoS signaling cost and handover delay. Furthermore, the simulations work out an optimized CDT for acquiring better cost performance of resource reservation

A new generalized sphere decoder (GSD), called a double-layer sphere decoder (DLSD), is proposed for under-determined MIMO systems with fewer receive antennas N than transmit antennas M. The proposed algorithm is significantly faster than those introduced in and . The basic idea is to partition the transmitted signal vector into two subvectors with M-N+1 and N-1 elements. After some simple transformations, we can use an outer layer sphere decoder (SD) to choose proper subvectors of length M-N+1 and then use an inner layer SD to decide the other subvector of length N-1, thus the whole transmitted signal vector is obtained. Simulation results show that DLSD has far less complexity than the existing GSDs.

The DiffServ Assured Forwarding (AF) service provides a scalable solution to QoS guarantee. Currently, AF only provides qualitative differentiation of delay or loss between classes of service, but not quantitative guarantee. By studying the quantitative behaviour of the steady state operating point for RIO, we propose two adaptive RIO algorithms for per-hop QoS provisioning in AF service. These two algorithms, ARIO-D and ARIO-L, work by dynamically adjusting the RIO parameters so that the packet delay and the packet loss are, respectively, kept at their target level. Simulation results show that they can provide both high link utilization as well as stable and differentiated delay or loss for different AF classes on a single router.

RIO is the primary queue management mechanism proposed for assured forwarding in the DiffServ framework. Although RIO can generally provide bandwidth guarantee, its performance in terms of both delay and loss is sensitive to traffic level. In this paper, we demonstrate this sensitivity problem by simulation and present a qualitative explanation for its origin. We propose two adaptive algorithms to overcome this problem. Simulation results show that they can effectively improve the robustness of RIO under different and dynamic traffic, and provide stable and quantitative performance of delay or loss.

This paper presents an analytical model for saturation throughput of IEEE 802.11 distributed coordination function (DCF) with multiple classes of service based on varying the parameters used in medium access control. In particular, we show that relative service differentiation can easily be achieved by varying the initial contention window alone. The simulation results show the validity of this model.

Based on an assumption that a steady state exists in the full-memory multidestination automatic repeat request (ARQ) scheme, we propose a novel analytical method called steady-state function method (SSFM), to evaluate the performance of the scheme with any size of receiver buffer. For a wide range of system parameters, SSFM has higher accuracy on throughput estimation as compared to the conventional analytical methods.