Edge computing has recently emerged as a promising paradigm to support mobile access in Internet of Things (IoT) multinetworks, where heterogeneous wireless communication solutions coexist. Meanwhile, software-defined networking (SDN) presents a potential infrastructure to monitor and manage mobile edge computing. However, resilient access in the integrated IoT-Edge-SDN environment is a key challenge. In this article, we present location-aware spatio-temporal solution (LASTS) as an edge computing-empowered software-defined system to scalably control mobile IoT access and detect sequential anomaly. LASTS utilizes a Personal access point protocol to enable switching between multiple networks. In addition, it supports efficient control and transfer of the mobile device’s spatio-temporal context. This context information plays an important role in a deep learning model employed for sequential anomaly detection in the LASTS system. Realistic testbed experiments confirm that LASTS can successfully achieve scalable access control and sequential anomaly detection in mobile IoT.

In this paper, we consider the k-center problem with outliers (the (k, z)-center problem) in the context of Massively Parallel Computation (MPC). Existing MPC algorithms for the (k, z)-center problem typically require Ω(k) local space per machine. While this may be feasible when k is small, these algorithms become impractical for large k, where each machine may lack sufficient space for computation. This motivates the study of fully-scalable algorithms with sublinear local space. We propose the first fully-scalable MPC algorithm for the (k, z)-center problem. The main challenge is to design an MPC algorithm that operates with sublinear local space for finding the inliers close to the optimal clustering centers, and ensuring the approximation loss remains bounded. To address this issue, we propose an iterative sampling-based algorithm with sublinear local space in the data size. A key component of our approach is an outliers-removal algorithm that adjusts the sample size in each iteration to select inliers as clustering centers. However, the number of discarded inliers increases with the iteration of the outliers-removal algorithm, making it difficult to bound. To address this, we propose a self-adaptive method that can automatically adjust sample size to account for different data distributions on each machine, ensuring a lower bound on the sampling success probability. With these techniques, we present an O(log^*n)-approximation MPC algorithm for the (k, z)-center problem in constant-dimensional Euclidean space. The algorithm discards at most (1 + ε)z outliers, completing in O(log log n) computation rounds while using Θ(n^δ) local space per machine.

Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) aims to minimize the distance between sketches and corresponding images in the embedding space. However, scalability is hindered by the growing complexity of solutions, mainly due to the abstract nature of fine-grained sketches. In this paper, we propose an effective approach to narrow the gap between the two domains. It mainly facilitates unified mutual information sharing both intra- and inter-samples, rather than treating them as a single feature alignment problem between modalities. Specifically, our approach includes: (i) Employing dual weight-sharing networks to optimize alignment within the sketch and image domain, which also effectively mitigates model learning saturation issues. (ii) Introducing an objective optimization function based on contrastive loss to enhance the model's ability to align features in both intra- and inter-samples. (iii) Presenting a self-supervised Multi-Scale Token Recycling (MSTR) Module featured by recycling discarded patch tokens in multi-scale features, further enhancing representation capability and retrieval performance. Our framework achieves excellent results on CNN- and ViT-based backbones. Extensive experiments demonstrate its superiority over existing methods. We also introduce Cloths-V1, the first professional fashion sketch-image dataset, utilized to validate our method and will be beneficial for other applications.

Micro Advice is a mobile platform for democratic governance in China, allowing access to voice social issues and advice to the government with the aim of improving people's livelihood. However, due to the lack of first-hand experience, the current understanding of how end-users utilize Micro Advice to participate in democratic governance is incomplete. We interviewed 12 users to understand their practices and challenges in using the platform. Specifically, we illustrate the user's experience, introduce what difficulties they encountered, and how they strategically use the platform to improve people's livelihood. We also investigate the socio-technical aspects of Micro Advice within the Chinese political context, discussing how to accept and utilize Micro Advice in China's social environment, and develop technological solutions adapted to these backgrounds. Finally, we propose some design implications for civic technology participation platforms. Micro Advice provides a novel, open, and real-time channel for civic engagement, showcasing the practical effects and impact of digitized civic engagement in China. It offers researchers a new perspective for expressing and addressing societal issues. We believe that the innovation and insights of Micro Advice can extend to other types of digitized civic engagement initiatives. We will continue to explore the interactive processes between the government and the public, along with innovative technological approaches.

Controller area network (CAN) protocol is an efficient standard enabling communication among electronic control units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. In this article, a novel vehicle intrusion detection system (IDS) is developed. The challenge is that existing techniques of IDSs rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges multidimensional long short-term memory (MD-LSTM) and self-attention mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including stacked long short-term memory (LSTM), MD-LSTM, etc. Experiments show that our approach has the best-detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies, such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better-energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high-detection accuracy of 98.81% and a low latency of 1.88 ms.

Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a pivotal role as a means of expressing design creativity. However, designers often tend to not openly share these meticulously crafted sketches. This phenomenon of data island in the design area hinders its digital transformation under the third wave of AI. In this paper, we introduce a Federated Generative Artificial Intelligence Clothing system, namely StyleWe, employing federated learning to aid in sketch design. StyleWe is committed to establishing an ecosystem wherein designers can exchange sketch styles among themselves. Through StyleWe, designers can generate sketches that incorporate various designers' styles from their peers, drawing inspiration from collaboration without the need for data disclosure or upload. Extensive performance evaluations and user studies indicate that our StyleWe system can produce multi-styled sketches of comparable quality to human-designed ones while significantly enhancing efficiency compared to hand-drawn sketches.

Quantized neural networks (QNNs) have become a standard operation for efficiently deploying deep learning models on hardware platforms in real application scenarios. An empirical study on German traffic sign recognition benchmark (GTSRB) dataset shows that under the three white-box adversarial attacks of fast gradient sign method, random + fast gradient sign method and basic iterative method, the accuracy of the full quantization model was only 55%, much lower than that of the full precision model (73%). This indicates the adversarial robustness of the full quantization model is much worse than that of the full precision model. To improve the adversarial robustness of the full quantization model, we have designed an adversarial attack defense platform based on field-programmable gate array (FPGA) to jointly optimize the efficiency and robustness of QNNs. Various hardware-friendly techniques such as adversarial training and feature squeezing were studied and transferred to the FPGA platform based on the designed accelerator of QNN. Experiments on the GTSRB dataset show that the adversarial training embedded on FPGA can increase the model's average accuracy by 2.5% on clean data, 15% under white-box attacks, and 4% under black-box attacks, respectively, demonstrating our methodology can improve the robustness of the full quantization model under different adversarial attacks.

In the foreseeable Intelligent Transportation System, Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge Computing helps overcome this limitation effectively. This paper addresses the vehicle-to-edge server computation offloading conundrum by optimizing the trade-offs in partial offloading and resource allocation. Proposing a distributed approach, this study confronts the multi-variable non-convex challenge directly by decoupling variables and deriving constraint-based bounds that guide the decisions for offloading and allocation. A novel low-complexity distributed algorithm is introduced that not only tends toward optimal but also demonstrates superior real-time applicability and efficiency, illustrated through enhanced performances both in simulated trials and genuine vehicular edge computing settings. The algorithm’s practical effectiveness addresses a notable gap between the theoretical models for computation offloading and actual real-life execution, reinforcing the soundness and relevance of the proposed method. Furthermore, its advanced integration with federated learning frameworks marks a leading-edge application, substantiating significant enhancements in computational efficiency and robustness.

The accurate prediction of traffic conditions, including speed, flow, and travel time, poses a critical challenge in urbanization that significantly impacts car owners and road administrators. However, in certain scenarios with restricted road data availability (e.g. lack of traffic light status and signal control strategies, cooperation between road administrators and third parties, etc.), it is imperative to make effective use of basic road information (e.g. historical traffic data and road connectivity) to improve both prediction accuracy and scalability on various arterial road networks against state-of-art deep learning models. In this paper, we propose a spatial-temporal learning framework TPGraph for an accurate prediction of arterial roads’ traffic data by effectively utilizing upstream and downstream road information. TPGraph is composed of three major parts: 1) A multi-scale temporal feature fusion module that utilizes a multi-head attention mechanism to integrate recently-periodic features, daily-periodic features, and weekly-periodic features; 2) A multi-graph convolution module that employs graph fusion and graph convolution networks to capture richer spatial semantics, and 3) A dynamic spatial-temporal prediction module that leverages a spatial-temporal transformer for single or multiple traffic-state predictions. Our proposed framework, TPGraph, leverages just multi-scale historical traffic conditions and readily accessible spatial factors as input to generate accurate predictions of future traffic conditions. We mainly evaluate the performance of our approach through multi-step prediction experiments conducted at hourly intervals, forecasting travel time or travel speed for each road at 15 mins, 30 mins, and 1 hour. Furthermore, we conduct extensive experiments on real-world arterial road datasets to demonstrate the superior predictive performance of TPGraph compared to existing methods.

The “mind-controlling” capability has always been in mankind's fantasy. With the recent advancements in electroencephalograph (EEG) techniques, brain-computer interface (BCI) researchers have explored some solutions to allow individuals to perform various tasks using their minds. However, the commercial off-the-shelf devices to run accurate EEG signal collection are usually expensive and the comparably cheaper devices can only present coarse results, which prevents the practical application of these devices in domestic services. To tackle this challenge, we propose and develop an end-to-end solution that enables fine brain-robot interaction (BRI) through embedded learning of coarse EEG signals from low-cost devices, namely PerBCI, so that people having difficulty moving, such as the elderly, can mind command and control a robot to perform some basic household tasks. Our contributions are three folds: 1) We present a stacked long short-term memory (BiLSTM) structure, along with specific pre-processing techniques to handle the time-dependency of EEG signals and their classification. 2) We propose a personalized design to adaptively capture multiple features and achieve accurate recognition of individual EEG signals by enhancing the signal interpretation of BiLSTM with an attention mechanism. 3) We develop a low-cost, real-time and end-to-end BRI system that can run our PerBCI models and algorithms in the embedded robot platform to perform more than one type of domestic task based on the users' EEG signal inputs. Our real-world experiments with elderly participants of diverse backgrounds in a home setting and system comparison with other approaches show that the proposed end-to-end solution with low cost can achieve satisfactory run-time speed, accuracy and energy-efficiency.

Recent advancements in EEG-based BCI technologies have been explored to assist individuals in executing brain-to-robot tasks. In the future, using BCI systems in both personal and professional domains is anticipated in widespread adoption. One promising application is facing home life, to enable people to use commercial EEG equipment to implement daily tasks. However, current BCI studies mainly focus on single-brain-to-single-robot interaction, which has limitations in representing diverse human intentions. A generalized BCI system with multiple EEG devices allows users to more precisely or collaboratively control robots. Therefore, it is imperative to extend the BCI techniques to future collaboration scenarios. In this paper, we present a new system, NeuroBCI, for multi-brain-to-multi-robot interaction through the integration of sensing, computing, communication, and control. To improve sensing efficiency, NeuroBCI employs a sparse attention mechanism to extract joint features from heterogeneous EEG data. Parallel computation and transmission for multi-user multi-task scenarios are handled by semantic autoencoder and autodecoder communications. A code map is designed to ensure concurrent control and model compression methods are used on both transmitter and receiver sides. Our experiments in comparison with state-of-the-art works show the superior performance of NeuroBCI on sensing, computing, communication, and control as a holistic system.

Hand-drawn sketches and sketch colourization are the most laborious but necessary steps for fashion designers to design exquisite clothes, especially when the fashion design requires distinctive and personal characteristics from designer style. This paper presents an artificial intelligent aided fashion design system, namely StyleMe, to support the automatic generation of clothing sketches with designer style. Given the clothing pictures specified by the designer, StyleMe can use deep learning based generative model to generate clothing sketches that are consistent with the designer style. The system also supports intelligent colourization on clothing sketch by style transfer, according to specified styles from the real fashion images. Through a series of performance evaluations and user studies, we found that our system can generate effective clothing sketches as good as fashion designers’ human work, and significantly improve the efficiency of fashion design with its sketch colourization method.

Recent years have witnessed significant developments in point cloud processing, including classification and segmentation. However, supervised learning approaches need a lot of well-labeled data for training, and annotation is labor-and time-intensive. Self-supervised learning, on the other hand, uses unlabeled data, and pretrains a back-bone with a pretext task to extract latent representations to be used with the downstream tasks. Compared to 2D images, self-supervised learning of 3D point clouds is under-explored. Existing models, for self-supervised learning of 3D point clouds, rely on a large number of data samples, and require significant amount of computational re-sources and training time. To address this issue, we propose a novel contrastive learning approach, referred to as To ThePoint. Different from traditional contrastive learning methods, which maximize agreement between features obtained from a pair of point clouds formed only with different types of augmentation, ToThePoint also maximizes the agreement between the permutation invariant features and features discarded after max pooling. We first perform self-supervised learning on the ShapeNet dataset, and then evaluate the performance of the network on different downstream tasks. In the downstream task experiments, performed on the ModelNet40, ModelNet40C, ScanobjectNN and ShapeNet-Part datasets, our proposed ToThe-Point achieves competitive, if not better results compared to the state-of-the-art baselines, and does so with significantly less training time (200 times faster than baselines).

With the development of artificial intelligence and the improvement of hardware computing power, deep learning models have become widely used in the Internet of Things (IoT) field, especially for analyzing spatiotemporal data collected by wireless sensors. Recurrent neural networks (RNNs) such as long short-term memory (LSTM) network are generally used for these time-series data. Hyperparameter settings of model training are regarded as essential factors for the performance of deep learning models. Manually optimizing hyperparameters not only cost more resources but also be more likely to set hyperparameters that follow stereotypes, resulting in unreasonable hyperparameter settings and poor model performance. As one of the most important fields in automated machine learning research, automated hyperparameter optimization (HPO) mainly includes grid search, hyperparameter search based on genetic algorithm, etc. Whereas these methods have their own drawbacks. In this article, an automated HPO method based on parallel genetic algorithm (PGA) is proposed. According to the process of PGA, this article divided HPO into several stages, including population initialization, fitness function, tournament selection, crossover operators, mutation operators, subgroup exchange, and end of evolution. Then, the proposed HPO method is implemented in LSTM models and tested on two different time-series datasets collected by real-world IoT sensors. By comparing our proposed method with other mainstream HPO methods in different datasets, it is proved that our HPO method based on PGA shows a better performance on both time costs and prediction results.

Myriads of web applications in the Big Data era demand an effective measure of similarity based on billion-scale network structures, e.g., collaborative filtering. Recently, CoSimRank has been devised as a promising graph-theoretic similarity model, which iteratively captures the notion that “two distinct nodes are evaluated as similar if they are connected with similar nodes”. However, the existing CoSimRank model for assessing similarities may either yield unsatisfactory results or rather cost-inhibitive, rendering it impractical in massive graphs. In this paper, we propose CoSimHeat, a novel scalable graph-theoretic similarity model based on heat diffusion. Specifically, we first formulate CoSimHeat model by taking advantage of heat diffusion to emulate the activities of similarity propagations on the Web. Then, we show that the similarities produced by CoSimHeat are more satisfactory than those from CoSimRank families since CoSimHeat fulfils four axioms that an ideal similarity model should satisfy while circumventing the “dead-loop” problem of CoSimRank. Next, we propose a fast algorithm to substantially accelerate CoSimHeat computations on billion-sized graphs, with guarantees of accuracy. Our experiments on various datasets validate that CoSimHeat achieves higher accuracy and is order-of-magnitude faster than state-of-the-art competitors.

Human–Machine Interaction (HMI) in Intelligent and Connected Vehicles (ICVs) has drawn great attention in recent years due to its potentially significant positive impacts on the automotive revolution and travel experience. In this paper, we conduct an in-depth review of HMI in ICVs. Firstly, research and application development status are pointed out through the discussion on the cutting-edge technology classification, achievements, and challenges of the HMI technologies in ICVs, including recognition technology, multi-dimensional human vehicle interface, and emerging in-vehicle intelligent units. Then, the human factors issues of ICVs are discussed from three aspects: ICV acceptance, interaction quality of ICVs, and user experience of ICVs. Besides, based on the interaction technology and the mapping of the above issues, we conducted a visual analysis of the literature to realize the reflective thinking of the current HMI in ICVs. Finally, the challenges of HMI technology in ICVs are summarized. Moreover, the promising future opportunities are proposed from three aspects: utility optimization, experience reconfiguration, and value acquisition, to gaining insight into advanced and pleasant HMI in ICVs.

The high demand for ubiquitous availability of reliable parking spaces in cities faces challenges on timely information sharing and low-cost infrastructure deployment. In this paper, we propose a mobile crowdsensing system, namely ParkHop, to aggregate on-street and roadside parking space information through sensorless sensing, and disseminate this information to urban drivers in a resilient manner. ParkHop targets special social groups that have stable work routines to serve as crowd workers. We propose a crowdsensing algorithm employing a joint estimator to process crowdsensed data, and evaluate the reliability of crowd workers based on the veracity of their answers to a series of control questions. In addition, the specific worker selection method to speed up the crowdsensing process and incentive scheme to achieve fair reward distribution have been carefully designed in ParkHop. Our system disseminates the availability of parking spaces and their up-to-date price information to drivers with on-demand needs via a publish-subscribe messaging pattern. The efficacy of ParkHop for aggregation and dissemination of parking space information has been evaluated in both real-world tests and simulations. Our results show the system is robust and agile enough to cope with different crowdsensing scenarios.

Vehicle trajectory collection usually faces challenges such as inaccurate and incomplete trajectory data, mainly due to missing trajectories caused by Global Navigation Satellite System (GNSS) outages. In this paper, a novel ensemble transfer regression framework is proposed for urban environments with transfer learning as the primary solution for constructing a fine-grained trajectory dataset during GNSS outages. First, GNSS and motion information are fused for the training process. Then, a regression-to-classification (R2C) process is employed to implement incremental training to adapt to dynamically changing environments. Third, to account for GNSS outages, transfer learning is integrated to construct a data filtering strategy that minimizes negative sample weights during the current scenario. Finally, a more accurate classification-type loss function for ensemble learning is designed to obtain the ensemble transfer regression model. We utilize real-world datasets to verify the accuracy of the comparative methods and the proposed framework in trajectory interpolation prediction. The experimental results show that our framework is significantly superior to the comparative methods.

Due to the blooming of Internet of Things (IoT), heterogeneous IoT mobile devices emerge to connect the network infrastructure. Traditional mobile access system faces several challenges arising from these IoT devices: 1) centralized controllers are distant from the end devices, 2) inefficient access control of heterogeneous IoT devices, and 3) insufficient authentication and monitoring for IoT devices. In order to tackle the challenges from IoT devices on mobile access control and scalable access monitoring, we present LEDGE, an agile and secured software-defined edge computing system for resilient access management of mobile IoT. In a nutshell, our LEDGE is a synergy of an efficient location authentication method to secure communication between each IoT mobile device and access point (AP) pair, an optimal AP assignment scheme to satisfy IoT flow requests, a Personal AP protocol for scalable access, and a deep learning model for anomaly detection. We prototype our system, and realistic testbed experiments demonstrate that LEDGE could achieve promising results in mobile IoT.

The time series data generated by massive sensors in Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, stability) on the real-time analysis and decision making for different IoT applications. In this paper, we design, implement and evaluate EdgeLSTM, a unified data-driven system to enhance IoT computing at the network edge. The EdgeLSTM leverages the grid long short-term memory (Grid LSTM) to provide an agile solution for both deep and sequential computation, therefore can address important features such as large-scale, variety, time dependency and real time in IoT data. Our system exploits the advantages of Grid LSTM network and extends it with a multiclass support vector machine by rigorous regularization and optimization approaches, which not only has strong prediction capability of time series data, but also achieves fine-grained multiple classification through the predictive error. We deploy the EdgeLSTM into four IoT applications, including data prediction, anomaly detection, network maintenance and mobility management by extensive experiments. Our evaluation results of real-world time series data with different short-term and long-term time dependency from these typical IoT applications show that our EdgeLSTM system can guarantee robust performance in IoT computing.

The data generated by millions of sensors in the industrial Internet of Things (IIoT) are extremely dynamic, heterogeneous, and large scale and pose great challenges on the real-time analysis and decision making for anomaly detection in the IIoT. In this article, we propose a long short-term memory (LSTM)-Gauss-NBayes method, which is a synergy of the long short-term memory neural network (LSTM-NN) and the Gaussian Bayes model for outlier detection in the IIoT. In a nutshell, the LSTM-NN builds a model on normal time series. It detects outliers by utilizing the predictive error for the Gaussian Naive Bayes model. Our method exploits advantages of both LSTM and Gaussian Naive Bayes models, which not only has strong prediction capability of LSTM for future time point data, but also achieves an excellent classification performance of the Gaussian Naive Bayes model through the predictive error. We evaluate our approaches on three real-life datasets that involve both long-term and short-term time dependence. Empirical studies demonstrate that our proposed techniques outperform the best-known competitors, which is a preferable choice for detecting anomalies.

In order to provide a robust estimate of vehicle position in all environments, especially, in challenging urban areas where GPS signals are blocked, a fusion framework based on sparse Gaussian-Wigner prediction (SG-WP) is proposed. This new approach combines the advantages of both the random matrix theory and the sparse property to provide enhanced vehicle localization capabilities. In this method, measurement noises are assumed to be non-Gaussian distributed, and a generalized error distribution is adopted as an approximation to non-Gaussian densities. To ensure the robustness and the stability of the proposed approach, road-test experiments in various scenarios, including free, partial, and complete GPS outages, were performed based on the geometric dilution of precision metric. During complete outages, the SG-WP fuses all available INS measurements to improve the vehicle position prediction, whereas in free outages, only GPS information is processed. Besides, information from both GPS and INS are taken as inputs during partial outages, and the slide window is then introduced to regulate the flow data. The experimental comparison with the existing prediction methods reveals that the proposed method can achieve accurate and reliable positioning for land vehicles in all considered environments when the measurement noises are Gaussian or non-Gaussian distributed.

Understanding the travel behavior of private cars will generate promising solutions on addressing urban problems such as alleviating traffic congestion and improving transport services. In this paper, we focus on investigating the individual travel patterns of private car users based on a large-scale private car trajectory dataset. To achieve this goal, we first analyze the stop-and-wait information from the private car trajectory data and utilize DBSCAN method to implement clustering with the aim at identifying the frequently-visit places (FVPs). After that, we leverage Markov chain to study the spatial-temporal transition characteristics when private cars travel among their FVPs. Finally yet importantly, we design the concept of spatial-temporal entropy rate and conduct a quantitative study for measuring the regularity of each individual private car's mobility. We validate the proposed methodology based on a real-world dataset including 25,564 private cars driving during one month in China. Extensive experiments demonstrate that the proposed method outperforms the existing methods in terms of the accuracy on measuring the mobility behavior. Moreover, we observe that, on one side, the travel pattern is easier to mine from the private car users with fewer FVPs, on the other side, there are also a small number of users whose FVPs are large, while their mobility are relatively regular. Our work is the first effort to explore individual travel patterns of private car users via studying private car trajectory big data, thereby being able to provide new insight into the research of human travel activities, traffic management and urban planning.

In many parts of the world, passengers traveling on underground metro systems do not enjoy uninterrupted Internet connectivity. This results in passenger frustration since during such trips the access of online social media services is a highly popular activity. Being the world's oldest underground metro system, London's underground is a typical transportation environment, where the Internet connectivity is often not available during journeys which predominantly take place underground along sub-surface and deep-level track lines. To alleviate the absence of continuous connectivity, we designed DeepOpp, a context-aware mobile system that facilitates offline access to online social media content. The DeepOpp operates efficiently due to its opportunistic approach: it executes content prefetching and caching operations when adequate urban 3G or WiFi signal is detected. The functionality of DeepOpp includes the crowdsourcing of measurements of signal characteristics (strength, bandwidth availability, and latency) which are subsequently used in predicting mobile network signal coverage and initiating data prefetching operations. During data prefetching, an optimization scheme selectively specifies the social media content to be cached based on current network conditions and device storage availability. We implemented DeepOpp as an Android application which we trialled during real trips on the London underground. Our evaluations show that the DeepOpp offers significant reduction when compared with existing approaches in terms of power usage and the volume of data downloaded. Even though we only tested DeepOpp in the London underground metro system, its feature set makes it readily applicable in similar underground metro systems (in cities like New York, Paris, and Shanghai) as well as in situations, where mobile device users suffer from significant connectivity interruptions.

The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g., WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions (assigning one controller per partition) and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g., 67.21 percent throughput improvement, 72.99 percent reduced delay, and 69.59 percent jitter improvements, compared with alternative SDN systems.

The scarcity of parking spaces in cities leads to a high demand for timely information about their availability. In this paper, we propose a crowdsensed parking system, namely ParkCrowd, to aggregate on-street and roadside parking space information reliably, and to disseminate this information to drivers in a timely manner. Our system not only collects and disseminates basic information, such as parking hours and price, but also provides drivers with information on the real time and future availability of parking spaces based on aggregated crowd knowledge. To improve the reliability of the information being disseminated, we dynamically evaluate the knowledge of crowd workers based on the veracity of their answers to a series of location-dependent point of interest control questions. We propose a logistic regression-based method to evaluate the reliability of crowd knowledge for real-time parking space information. In addition, a joint probabilistic estimator is employed to infer the future availability of parking spaces based on crowdsensed knowledge. Moreover, to incentivise wider participation of crowd workers, a reliability-based incentivisation method is proposed to reward workers according to their reliability and expertise levels. The efficacy of ParkCrowd for aggregation and the dissemination of parking space information has been evaluated in both real-world tests and simulations. Our results show that the ParkCrowd system is able to accurately identify the reliability level of the crowdsensed information, estimate the potential availability of parking spaces with high accuracy, and be successful in encouraging the participation of more reliable crowd workers by offering them higher monetary rewards.

Mobile peer to peer (P2P) networks offer a huge potential for distributed mobile P2P crowd services (MPCS), which enable data and computational tasks to be offloaded and executed directly between mobile devices. Similar to centralised mobile crowd services, such as mobile crowdsensing, incentivisation mechanisms are core to encouraging mobile users to participate in MPCS systems. However, due to the impact of task execution failures and unreliable behaviours of mobile users (particularly task requesters), it is a daunting task to design and implement an incentivisation mechanism to cater for the needs of MPCS systems. In this paper, we propose a fault-tolerant incentivisation mechanism (FTIM) for MPCS systems. With conditional payment strategies, FTIM is proven to accommodate the requirements of two important application scenarios by achieving mechanism properties such as incentive compatibility, economic efficiency, individual rationality, and weak budget balance. Moreover, to tackle the practical challenges in implementing FTIM in the real world, we design a MPCSTo-ken smart contract to facilitate its service auction, task execution and payment settlement process. We implement the MPCSToken contract on Ethereum blockchain. Both real-world experiment and simulation results show that the system is cost effective for deployments and improves the overall mobile users' utility by exploring the opportunities offered by MPCS.

With the pervasive usage of IoT devices to collect healthcare data from human body, the real-time need on data acquirement and analysis for effective diagnosis and feedbacks becomes an challenging issue in practice. We propose a hybrid architecture, called EdgeCNN, that balances the capability of edge and cloud computing to address this issue for agile learning of healthcare data from IoT devices. Specifically, deep learning is customized as the inference method running on edge devices, making real-time analysis and diagnosis closer to the IoT data source. This can significantly reduce learning latency and network I/O, ease the pressure on the cloud platform for large user groups and massive data, and drastically decrease the cost to build and maintain cloud platforms. To verify the feasibility of EdgeCNN, we design a set of streamlined diagnosis model and learning algorithm for edge computing based on convolutional neural network (CNN), facilitating EdgeCNN to identify and infer electrocardiograms in real-time, as a specific healthcare application using smart devices on the edge. Our experimental results show that under the premise of ensuring the accuracy, EdgeCNN has significant advantages in diagnosis delay, network I/O, application usability and resource cost, in comparison with the architecture solely based on the cloud computing. Another important benefit from EdgeCNN is that it can effectively protect the privacy of user data from IoT devices.

Estimating communication latency is a challenging task in the applications of industrial Internet of things (IIoT). Mobile convergecast, as a many-to-one communication pattern, has been recently explored in mobile sensor networks for IIoT, where sensor nodes are usually in mobile status, and report the sensed data regularly or randomly to one or more stationary sinks through the multihop routing path. As convergecast becomes increasingly relevant for industrial sensing and monitoring, a critical part of empowering information aggregation is to maintain consistent transmission. Path duration is one important component of end-to-end delay for communications along the path. In this paper, a probabilistic model for mobile convergecast has been proposed and evaluated to capture path duration times, by considering parameters including network models, sensor network scope, and mobility patterns of network elements. Through experiments, it has been verified that the proposed model can provide a feasible analysis of end-to-end delays in industrial networks implementing convergecast.

Accessing online social media content on underground metro systems is a challenge due to the fact that passengers often lose connectivity for large parts of their commute. As the oldest metro system in the world, the London underground represents a typical transportation network with intermittent Internet connectivity. To deal with disruption in connectivity along the sub-surface and deep-level underground lines on the London underground, we have designed a context-aware mobile system called DeepOpp that enables efficient offline access to online social media by prefetching and caching content opportunistically when signal availability is detected. DeepOpp can measure, crowdsource and predict signal characteristics such as strength, bandwidth and latency; it can use these predictions of mobile network signal to activate prefetching, and then employ an optimization routine to determine which social content should be cached in the system given real-time network conditions and device capacities. DeepOpp has been implemented as an Android application and tested on the London Underground; it shows significant improvement over existing approaches, e.g. reducing the amount of power needed to prefetch social media items by 2.5 times. While we use the London Underground to test our system, it is equally applicable in New York, Paris, Madrid, Shanghai, or any other urban underground metro system, or indeed in any situation in which users experience long breaks in connectivity.

We present ADDSEN middleware as a holistic solution for Adaptive Data processing and dissemination for Drone swarms in urban SENsing. To efficiently process sensed data in the middleware, we have proposed a cyber-physical sensing framework using partially ordered knowledge sharing for distributed knowledge management in drone swarms. A reinforcement learning dissemination strategy is implemented in the framework. ADDSEN uses online learning techniques to adaptively balance the broadcast rate and knowledge loss rate periodically. The learned broadcast rate is adapted by executing state transitions during the process of online learning. A strategy function guides state transitions, incorporating a set of variables to reflect changes in link status. In addition, we design a cooperative dissemination method for the task of balancing storage and energy allocation in drone swarms. We implemented ADDSEN in our cyber-physical sensing framework, and evaluation results show that it can achieve both maximal adaptive data processing and dissemination performance, presenting better results than other commonly used dissemination protocols such as periodic, uniform and neighbor protocols in both single-swarm and multi-swarm cases.

Due to spectrum sharing, device-to-device (D2D) communications underlaying cellular networks enhance system capacity significantly that benefits services of local area. On the other hand, network coding enables highly efficient cooperation, which increases the system capacity through code-and-forward mechanism. It is a challenging problem that how to allocate resource in the network coding aided cooperative D2D communications. In this paper, we first design a network coding aided cooperative diversity scheme for D2D communication, and derive the system transmission rate with the consideration of interference. Then, we formulate the problem of joint spectrum resource allocation and relay selection as an overlapping coalition formation game, where one relay is able to serve multiple coalitions to increase the system capacity. For each coalition, maximum bipartite graph matching model is established to select the optimal relay to achieve the maximum transmission rate. Finally, to solve the formulated game problem, we propose a distributed algorithm based on switch operations with low computation complexity. Extensive numerical results demonstrate that our solution increases the system transmission rate by about 30-40 percent without bringing extra computation complexity, compared with other state-of-the-art schemes.

For mobile networks diverse usage scenarios have different capability requirements on connection density and user experienced data rate, and modeling such capability diversity is crucial to the strategy evaluation in addressing the problem of high traffic load and scalability of network resources. Therefore, it is necessary to build a capability model in two dimensions of connection density and user experienced data rate. This paper aims at addressing this challenge based on an investigation of network capability in large-scale urban environment. First, our statistical study shows that the spatial distribution of these two parameters can be accurately fitted by log-normal mixture model. Second, we find that only six basic capability patterns exist among the 9,000 cellular base stations. Their connections with the urban functions of geographical locations are also explored in our work. Based on these two discoveries, we build a network capability model which can generate synthetic base stations with diverse connection density and user experienced data rate. We believe that this flexible and powerful model can help telecommunication operators to design and standardize mobile network in the future.

Using a mobile phone for fine-grained indoor localization remains an open problem. Low-complexity approaches without infrastructure could not achieve accurate and reliable results due to various restrictions. Accurate solutions relying on dense anchor nodes are inconvenient and cumbersome in deployment. The anchor blockage problem would further reduce the effective coverages. In this paper, we investigate the problems associated with improving indoor localization of a mobile phone via opportunistic anchor sensing, a new sensing paradigm leveraging multiple anchors without minimum number or constellation requirement. One key motivation is that the location results could be improved by exploring more data types rather than deploying more anchor nodes. To enable this high scalability and accuracy design, we leverage low-coupling hybrid ranging by our low cost anchor nodes with centimeter-level relative distance estimation. Activity pattern extracted in local smartphone is utilized for accurate displacement and direction estimation. Finer localization resolution could be achieved with sufficient anchor access. We introduce the delay-constraint robust semidefinite programming in trilateration calculation with the potential of centimeter-level location resolution. We conduct extensive experiments in various scenarios. Compared with other approaches, opportunistic sensing could improve the location accuracy, scalability as well as robustness under various anchor accessibilities.

Open WiFi access points (APs) are demonstrating that they can provide opportunistic data services to moving vehicles. We present CrowdWiFi, a novel system to look up roadside WiFi APs located outdoors or inside buildings. CrowdWiFi consists of two components: online compressive sensing (CS) and offline crowdsourcing. Online CS presents an efficient framework for the coarse-grained estimation of nearby APs along the driving route, where received signal strength (RSS) values are recorded at runtime, and the number and location of the APs are recovered immediately based on limited RSS readings and adaptive CS operations. Offline crowdsourcing assigns the online CS tasks to crowd-vehicles and aggregates answers on a bipartite graphical model. Crowd-server also iteratively infers the reliability of each crowd-vehicle from the aggregated sensing results, and then refines the estimation of the APs using weighted centroid processing. Extensive simulation results and real testbed experiments confirm that CrowdWiFi can successfully reduce the computation cost and energy consumption of roadside WiFi lookup, while maintaining satisfactory localization accuracy.

Understanding and forecasting mobile traffic of large scale cellular networks is extremely valuable for service providers to control and manage the explosive mobile data, such as network planning, load balancing, and data pricing mechanisms. This paper targets at extracting and modeling traffic patterns of 9,000 cellular towers deployed in a metropolitan city. To achieve this goal, we design, implement, and evaluate a time series analysis approach that is able to decompose large scale mobile traffic into regularity and randomness components. Then, we use time series prediction to forecast the traffic patterns based on the regularity components. Our study verifies the effectiveness of our utilized time series decomposition method, and shows the geographical distribution of the regularity and randomness component. Moreover, we reveal that high predictability of the regularity component can be achieved, and demonstrate that the prediction of randomness component of mobile traffic data is impossible.

The growing of Internet of Things (IoT) devices has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist. Managing the multinetworks for mobile IoT access is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick configuration of network devices, but its application in multinetworks with frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for ubiquitous flow control and mobility management in multinetworks. UbiFlow adopts distributed controllers to divide urban-scale SDN into different geographic partitions. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on the per-device requirement and whole-partition capability. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance in each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks.

Energy savings optimization becomes one of the major concerns in the wireless sensor network (WSN) routing protocol design, due to the fact that most sensor nodes are equipped with the limited nonrechargeable battery power. In this paper, we focus on minimizing energy consumption and maximizing network lifetime for data relay in one-dimensional (1-D) queue network. Following the principle of opportunistic routing theory, multihop relay decision to optimize the network energy efficiency is made based on the differences among sensor nodes, in terms of both their distance to sink and the residual energy of each other. Specifically, an Energy Saving via Opportunistic Routing (ENS_OR) algorithm is designed to ensure minimum power cost during data relay and protect the nodes with relatively low residual energy. Extensive simulations and real testbed results show that the proposed solution ENS_OR can significantly improve the network performance on energy saving and wireless connectivity in comparison with other existing WSN routing schemes.

Roadside units (RSUs) are public and personal wireless access points that can provide communications with infrastructure in ad hoc vehicular networks. We present CLOCS (Counting and Localization using Online Compressive Sensing), a novel system to retrieve both the number and locations of RSUs through war-driving. CLOCS employs online compressive sensing (CS), where received signal strength (RSS) values are recorded at runtime, and the number and location of RSUs are recovered immediately based on limited RSS readings. CLOCS also uses fine retrieval based on an expectation maximization method along the driving route. Extensive simulation results and experiments in a real testbed deployed in the campus of the University of California, Irvine, confirm that CLOCS can successfully reduce the number of measurements for RSU recovery, while maintaining satisfactory counting and localization accuracy. In addition, data dissemination, time cost, and effects of different mobile scenarios using CLOCS are analyzed, and the impact of CLOCS on network connectivity is studied using Microsoft VanLan traces.

Wireless mesh networks (WMNs) suffer from scalability, performance degradation and service disruption issues due to overwhelming multi-hop co-channel interference, unscrupulous channel utilization and inherent network mobility. We propose a set of efficient distributed dynamic channel access scheduling protocols, called DDCS, based on distributed clustering and dynamic Latin squares for WMN systems with multi-radio multi-channel (MRMC) communication capabilities. DDCS uses nodal interference information to form cliques for inter-cluster and intra-cluster structures in WMNs, and then applies Latin squares to map the clique-based clustering results to radios and channels for wireless communication purposes. Afterwards, DDCS again applies Latin squares to schedule the channel access amongst nodes within each cluster in a collision-free manner. A coexistence mechanism for DDCS and widely used IEEE 802.11 DCF is also addressed in this paper, so that our MRMC access protocol DDCS can be effectively applied in large-scale WMNs. The evaluation results show that DDCS achieves much better performance than existing IEEE 802.11 standards and other multi-channel access control protocols.

WiFi access points, mesh routers, wireless sensors and any other wireless routers along the road can serve as roadside unit (RSU), and these RSUs can provide infrastructural supports for wireless access and data dissemination in cyber-transportation systems. We present a hybrid routing scheme in vehicular networks for inter-vehicle, vehicle-to-roadside and inter-roadside data dissemination in urban hybrid networks. First, a location-based crowdsensing framework, including online sensing and offline crowdsourcing, is proposed to retrieve the number and location of available RSU resources. Then, we combine RSU resources and ad hoc solutions to design a routing switch mechanism, which can guarantee quality of data dissemination under various network connectivity and deployment configurations. The performance of our hybrid data dissemination scheme is evaluated using both simulation and real testbed experiments.

In this paper, we present CrowdWiFi, a novel vehicular middleware to identify and localize roadside WiFi APs that are located outside or inside buildings. Our work is motivated by the recent surge in availability of open WiFi access points (APs) that are enabling opportunistic data services to moving vehicles. Two key elements of CrowdWiFi that provide vehicles with opportunistic WiFi access include (a) an online compressive sensing component and (b) an offline crowdsourcing module. Online compressive sensing (CS) techniques are primarily used to for the coarse-grained estimation of nearby APs along the driving route; here, the received signal strength (RSS) values are recorded at runtime, and the number and locations of APs are recovered immediately based on limited RSS readings. The offline crowdsourcing mechanism assigns the online CS tasks to crowd-vehicles and aggregates answers using a bipartite graphical model. This offline crowdsourcing executes at a crowd-server that iteratively infers the reliability of each crowd-vehicle from the aggregated sensing results and refines the estimation of APs using weighted centroid processing. Extensive simulation results and real testbed experiments confirm that CrowdWiFi can successfully reduce the number of measurements needed for AP recovery, while maintaining satisfactory counting and localization accuracy. In addition, the impact of CrowdWiFi middleware on WiFi handoff and data transmission applications is examined.

It is a challenge to design efficient routing protocols for vehicular ad hoc networks (VANETs) because of their highly dynamic properties. We address the vehicular communication problem in urban hybrid networks and present a hybrid routing scheme for data dissemination in VANETs. Location-based crowdsourcing of nearby roadside units (RSUs) has been applied to the infrastructural support of inter-vehicle, vehicle-to-roadside, and inter-roadside communications in hybrid VANETs. The combination of RSU resources and ad hoc networks involves an online probabilistic RSU retrieval algorithm that uses coarse- and fine-grained localization to estimate the number and location of available RSUs; a network coding based multicast routing for dense VANETs using maximum distance separation (MDS) code and local topology information from the forwarding set to achieve robust communication and max-flow min-cut data dissemination; an application of opportunistic routing, using a carry-and-forward scheme to solve the forwarding disconnection problem in sparse VANETs; and a routing switch mechanism to guarantee quality of service (QoS) under various network connectivity and deployment configurations. The performance of our hybrid routing scheme is evaluated using both simulations and real testbed experiments.

In wireless sensor networks (WSNs), localization has many important applications, among which wireless sensor retrieval bears special importance for cost saving, data analysis and security purposes. Localization for sensor retrieval is especially challenging due to the fact that the number and locations of these sensors are both unknown. In this paper, we propose two probabilistic localization algorithms that iteratively identify the locations of multiple wireless sensors in WSNs, one of which calculates location information offline, and the other online. In both algorithms, we implement a two-step localization process — the first step is called Grid-LEGMM (grid location estimation based on the Gaussian mixture model), a coarse-grain location search using grids by choosing the proper number and locations of the wireless sensors that maximize a likelihood estimation, and the second step is called EM-LEGMM (expectation maximization based on the Gaussian mixture model), which uses the EM-method to refine the results of Grid-LEGMM. An additional step in the online localization algorithm is a credit-based filtering mechanism that removes spurious sensor locations. The performance of both offline and online localization algorithms are analyzed using the Cramer–Rao lower bound (CRLB), and evaluated using simulations and real testbed experiments.

Localization has many important applications in wireless sensor networks. A variety of wireless technologies, such as acoustic, infrared, and ultra-wide band (UWB) media have been applied for localization purposes. This paper consists of two parts. The first part presents new UWB-based communication protocols for received signal strength (RSS) information collection, namely, a robust UWB coding method called U-BOTH (UWB based on Orthogonal Variable Spreading Factor and Time Hopping), an ALOHA-type channel access method and a message exchange protocol to collect location information. The second part presents the localization algorithm, which is applied in coal mine environments. The localization algorithm first derives the corresponding UWB path loss model, then applies the maximum likelihood estimation (MLE) method to compute the distances to the reference sensors using the RSS information, and to estimate the coordinate of the moving sensor using least squares (LS) method. The performance of the system is validated using theoretic analysis and simulations. Results show that U-BOTH transmission technique can effectively reduce the bit error rate under the path loss model, and the corresponding ranging and localization algorithms can accurately compute object locations in coal mine environments.

Localization has many important applications in wireless sensor networks (WSNs). A variety of technologies, such as acoustic, infrared. and UWB (ultra-wide band) media have been utilized for localization purposes. In this paper, we propose a helistic, buttom-up design of a UWB-based communication architecture and related protocols for localization in WSNs. A new UWB coding method, called U-BOTH (UWB ased on Orthogonal Variable Spreading Factor and Time Hopping), is utilized for minimum interference communication, and an ALOHA-type channel access method and a message exchange protocol are used to collect distance information in WSNs. We derive the corresponding UWB path loss model in order to apply the maximum likelihood estimation (MLE) method to compute the distances between neighbor nodes using the RSSI information. Then, we propose NMDS-MLE (Non-metric Multidimensional Scaling and Maximum Likelihood Estimation) localization algorithms based on the two types of distance information: estimated distance and Euclidean distance. The performance of the system is validated using theoretic analysis and simulations.

The traditional networking builds on layered protocol architecture to isolate the complexities in different layers. It has been realized that real-life wireless sensor networks (WSNs) must be considered holistically across different layers for optimum performance. We consider a special case of WSNs that is deployed in underground tunnels. Underground communications present unique signal propagation characteristics due to the geographic and geological features, which in turn impact the underground network deployment and multi-hop routing patterns. We propose an efficient routing algorithm, called BRIT (bounce routing in tunnels), for underground WSNs, and evaluate BRIT against the bottomline AODV in terms of network throughput, packet loss rate, stability and latencies using simulations. The contributions of the paper include a hybrid signal propagation model in three dimentional underground tunnels, an assortment of sensor deployment strategies in tunnels, an integrated routing metric (forwarding speed), and a route suppression mechanism.