Accurately detecting passenger traffic flow in public transportation, e.g., for the purpose of identifying congested stations, and detecting anomalies in that flow, are both important for enhancing passenger satisfaction and safety. However, current methods for traffic flow detection and prediction train their models in offline modes using potentially outdated data, which can’t be refreshed with new data. In this paper, we introduce a two-step, continuously updating framework aimed at real-time prediction of anomalies within transportation networks, which we name Adaptive Time-Aware Anomaly Detection Approach (ATADA). Specifically, the first step filters out regular traffic patterns and balances the data set, while the second step employs a sequence-to-sequence attention model, a type of deep learning model, to further detect the traffic anomalies. We propose a dynamic online time-aware learning mechanism which enables our models to continuously train on incoming data and to adapt predictive strategies based on the most recent traffic patterns. The proposed method is validated using real subway AFC data from Suzhou, China and Hangzhou, China. Experimental results demonstrate that our framework significantly improves efficiency while maintaining high accuracy in real-time traffic anomaly detection.

Mobility pattern recognition is a central aspect of transportation and data mining research. Despite the development of various machine learning techniques for this problem, most existing methods face challenges such as reliance on handcrafted features (e.g., user has to specify a feature such as “travel time”) or issues with data imbalance (e.g., fewer older travelers than commuters). In this paper, we introduce a novel Data Balancing Generative Adversarial Network (DBGAN), which is a specifically designed attention mechanism-based GAN model to address these challenges in mobility pattern recognition. DBGAN captures both static (e.g., travel locations) and dynamic (e.g., travel times) features of different passenger groups, and avoids using handcrafted features that may result in information loss, based on a sequence-to-image embedding method. Our model is then applied to overcome the data imbalance issue and perform mobility pattern recognition. We evaluate the proposed method on real-world public transportation smart card data from Suzhou, China, and focus on recognizing two different passenger groups: older people and students. The results of our experiments demonstrate that DBGAN is able to accurately identify the different passenger groups in the data, with the detected mobility patterns being consistent with the ground truth. These results highlight the effectiveness of DBGAN in overcoming data imbalance in mobility pattern recognition, and demonstrate its potential for wider use in transportation and data mining applications.

Estimating human poses from a video is at the foundation of many visual intelligent systems. Various convolutional neural networks have been proposed, achieving state-of-the-art performance on different image datasets. However, most existing approaches are image based, which deliver unreliable estimations on videos since they fail to model temporal consistency across video frames. Recently, another line of work leverages temporal cues for multi-frame person pose estimation, yet still in an instance-unaware fashion, disregarding the specific traits of different instances (persons) or different joints. In this paper, we propose a novel approach to learn specific keypoint motion representations for each person, termed Personalized Motion-Aware Network (PMAN). In the PMAN, we devise three components: (i) an Instance-Sensitive Extractor that adaptively computes the spatial features according to human physical characteristics; (ii) a Keypoint Motion Encoder that separately generates convolution kernels with fine-grained keypoint motion encoding; (iii) a Motion Driven Decoder that parses multi-frame spatial features of the same person to provide precise human pose estimations. Extensive experiments on PoseTrack2017 and PoseTrack2018 datasets demonstrate that our approach greatly improves the performance of multi-frame human pose estimation. It is worth mentioning that our approach surpasses the state-of-the-art method by +1.7 mAP and achieves 82.9 mAP on PoseTrack2017 dataset.

Zero-shot learning (ZSL) addresses the novel object recognition problem by leveraging semantic embedding to transfer knowledge from seen categories to unseen categories. Generative ZSL models synthesize the visual features of unseen classes and convert ZSL task into a classical supervised learning problem. These generative ZSL models are trained by using the seen classes. Although promising progress has been achieved in the ZSL and generalized zero-shot learning (GZSL) tasks. The existing approaches still suffer from a strong bias problem between unseen and seen classes, where unseen objects in the target domain tend to be recognized as seen classes in the source domain. To deal with the problem, we propose a novel named semantic consistent Wasserstein generative adversarial network (scWGAN), which uses a semantic reconstructor to reconstruct semantic embeddings from generated visual features by incorporating a novel Semantic Consistent Loss noted L rec . The Semantic Consistent Loss guides our proposed scWGAN to generate visual features that mirror the semantic relationships between seen and unseen classes. We also introduce a visual classifier to constrain visual feature generator. Extensive experiments show that the proposed approach is superior to previous state-of-the-art works under both traditional ZSL and challenging GZSL settings on six popular data sets AWA1, AWA2, CUB, APY, and SUN.

Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. Specifically, we first transform the zero-risk values in labels to fit the training network. Then, we propose the Differential Time-varying Graph neural network (DTGN) to capture the immediate changes of traffic status and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and region selection schemes to highlight citywide most-likely accident subregions, bridging the gap between biased risk values and sporadic accident distribution. Extensive experiments on two real-world datasets demonstrate the effectiveness and scalability of our RiskOracle framework.

Crowd flow forecasting plays an important role in risk assessment and public safety. It is a difficult task due to complex spatial-temporal dependencies as well as missing values in data. A number of models are proposed to predict crowd flow on city-scale, yet the missing pattern in city complex environment is seldomly considered. We propose a crowd flow forecasting model, Imputed Spatial-Temporal Convolution network(ISTC) to accurately predict the crowd flow in large complex buildings. ISTC uses convolution layers, whose structures are configured by graphs, to model the spatial-temporal correlations. Meanwhile ISTC adds imputation layers to handle the missing data. We demonstrate our model on several real data sets collected from sensors in a large six-floor commercial complex building. The results show that ISTC outperforms the baseline methods and is capable of handling data with as much as 40% missing data.

Urban housing price is widely accepted as an economic indicator of both business and research interest in urban computing. In this work, we propose an effective and fine-grained model for urban subregion housing price predictions. Compared to existing works, our proposal improves the forecasting granularity from city-level to mile-level in spite of data sparsity and complex factors. The fine-grained housing price forecasting has the potential to support a broad scope of applications, ranging from urban planning to housing market recommendations. To achieve that, in this paper, we propose a novel integrated framework, FTD_DenseNet, which incorporates more social and economic features and makes full use of all-level spatiotemporal features. Specifically, the Kalman Filter-based future expection is firstly involved as an influence factor in our model. Extensive empirical studies on real data show the effectiveness of our proposals.

Recent advances in video surveillance systems enable a new paradigm for intelligent urban traffic management systems. Since surveillance cameras are usually sparsely located to cover key regions of the road under surveillance, it is a big challenge to perform a complete real-time traffic pattern analysis based on incomplete sparse surveillance information. As a result, existing works mostly focus on predicting traffic volumes with historical records available at a particular location and may not provide a complete picture of real-time traffic patterns. To this end, in this paper, we go beyond existing works and tackle the challenges of traffic flow analysis from three perspectives. First, we train the transition probabilities to capture vehicles' movement patterns. The transition probabilities are trained from third-party vehicle GPS data, and thus can work in the area even if there is no camera. Second, we exploit the Multivariate Normal Distribution model together with the transferred probabilities to estimate the unobserved traffic patterns. Third, we propose an algorithm for real-time traffic inference with surveillance as a complement source of information. Finally, experiments on real-world data show the effectiveness of our approach.

Forecasting the inflow and outflow of crowds at metro station, immediately controlling the number of people entering at some special times and places to avoid the occurrence of malignant events for public safety, is of great significance to subway stations management and very challenging as it is affected by many complex elements, such as inter region station flows, major events or activities, and weather. We propose a approach based on deep residual learning, called ST-DRN, to discern the pattern of spatial and temporal and integrally predict the inflow and outflow of crowds in each subway station of a city. We propose an end-to-end structure of ST-DRN based on distinct attributes of spatio-temporal data. More specifically, we apply the residual neural network framework to model the temporal nearby, day, and week properties of crowd in subway station. For each feature, we design a branch of residual convolutional units, each of which handles the spatial properties of subway crowd. ST-DRN learns to dynamically summation the output of the three residual neural networks, assigning different weights to each branch. The summation is also further combined with external elements, such as weather, holiday and workdays or weekends, to forecast the final traffic flow of crowds in each station. Evaluations on the automatic fare collection (AFC) system subway record data in Suzhou demonstrate that we proposed ST-DRN outperforms than three prominent baseline methods.

Due to the sparse distribution of video surveillance cameras and low installation rate of dash-mounted online telematics systems, tracking precise trajectories of urban private vehicles is a challenging task. Previous studies on vehicle tracking are mostly concerned with recovering trajectories with low- sampling rate GPS coordinates or captured surveillance information by identifying road traffic patterns from this information. Nevertheless, to the best of our knowledge, none of them have considered using the vehicle encounter information to enhance the sampling rate as well as the time-varying and in-group characteristics of vehicle traffic patterns, let alone to achieve vehicle tracking. With this insight, we divide all vehicles into clusters with a Canopy and K- means combined algorithm for different time periods and use an exponential distribution to approximate the time that vehicles from one cluster encounter a fixed Wi-Fi hotspot in the future during a given time period and at a specific location. Based on these preliminary results, we propose a novel approach to select the optimal data packet transmission scheme between encountered vehicles to transfer vehicle encounter information to the server as much and quickly as possible, and then calculate the trajectories of all private vehicles accurately. We evaluate our solution via real-world private vehicles and road surveillance system datasets. Experimental results demonstrate that our approach outperforms other solutions in terms of the accuracy ratio of vehicle tracking.

In this paper, we present GDA, a generalized decision aggregation framework that integrates information from distributed sensor nodes for decision making in a resource efficient manner. Different from traditional approaches, our proposed GDA framework is able to not only estimate the reliability of each sensor, but also take advantage of its confidence information, and thus achieves higher decision accuracy. Targeting generalized problem domains, our framework can naturally handle the scenarios where different sensor nodes observe different sets of events whose numbers of possible classes may also be different. GDA also makes no assumption about the availability level of ground truth label information, while being able to take advantage of any if present. For these reasons, our approach can be applied to a much broader spectrum of sensing scenarios. In this paper, we also propose two extensions of the GDA framework, i.e., incremental GDA (I-GDA) and parallel GDA (P-GDA) to deal with streaming and large-scale data. The advantages of our proposed methods are demonstrated through both theoretic analysis and extensive experiments.

Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for vehicles remains a challenging task. To the best of our knowledge, none of the previous research considered using on-road taxicabs as mobile video surveillance cameras and road traffic flow patterns, therefore not suitable for recovering trajectories of vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose an approach to calculate possible location and time distribution of the vehicle, select the taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.

The research of electric vehicles (EVs) has gained more and more attention in recent years in both industry and academia, and new registrations of EVs increase rapidly, while the long delay at the convenient but crowded charging stations may discourage many drivers from switching to EVs. To address the problems, we propose a novel dynamic pricing policy that allows charging stations to adjust their service fees in real time based on the load at the stations. In our work, the selection of drivers is modeled by a new dissatisfaction function with multiple variables, which can be easily validated and improved by real applications, and our solution is evaluated from the real-world e-charge dataset. To the best of our knowledge, this is the first work that considers dynamic service fees among various charging stations for load balancing and reduction of queueing delay. This makes our work more realistic and beneficial.

Due to the sparse distribution of road video surveillance cameras, precise trajectory tracking for hit-and-run vehicles remains a challenging task. Previous research on vehicle trajectory recovery mostly focuses on recovering trajectory with low-sampling-rate GPS coordinates by retrieving road traffic flow patterns from collected GPS information. However, to the best of our knowledge, none of them considered using on-road taxicabs as mobile video surveillance cameras as well as the time-varying characteristics of vehicle traveling and road traffic flow patterns, therefore not suitable for recovering trajectories of hit-and-run vehicles. With this insight, we model the travel time-cost of a road segment during various time periods precisely with LNDs (Logarithmic Normal Distributions), then use LSNDs (Log Skew Normal Distributions) to approximate the time-cost of an urban trip during various time periods. We propose a novel approach to calculate possible location and time distribution of the hit-and-run vehicle in parallel, select the optimal taxicab to verify the distribution by uploading and checking video clips of this taxicab, finally refine the restoring trajectory in a parallel and recursive manner. We evaluate our solution on real-world taxicab and road surveillance system datasets. Experimental results demonstrate that our approach outperforms alternative solutions in terms of accuracy ratio of vehicle tracking.

As the scale of electric vehicles (EVs) and data centers continues to expand, grid regulation utilizing EVs and data centers has attracted wide interest. However, all existing work only considers partial combination which does not leverage their functionalities to the full extent. In this paper, we present the design and performance evaluation of a novel system, which integrate the servers, UPS, Plug-in Electric Vehicles (PEVs) and Battery Swapping Stations (BSS) to realize a holistic and optimized smart grid regulation solution. With our proposed dynamic priority mechanism, the control tasks are allocated in a flexible and reasonable way. In addition, we design a novel control strategy to minimize the whole cost of the system. Numerical results demonstrate that the proposed system outperforms alternative solutions in terms of grid regulation capacity and cost.

In urban road systems, it is a challenging task to investigate traffic status of all intersections due to the scarce distribution of road surveillance cameras. Previous research mostly focuses on how to use historical data of camera-equipped intersections to infer their future traffic statuses. However, as far as we know, there does not exist an effective algorithm to infer the real-time traffic statuses of those camera- free intersections by using the traffic information from some other road video cameras in urban road system. In this paper, we first study the spatial- temporal variation characteristics of urban traffic flows from a macroscopic view, including turning ratio models and traveling time models of individual road segments. And then we build a novel traffic impact tree model to calculate the real-time traffic volume for specific camera-free intersections. We evaluate our solutions on real-world taxicab and road surveillance system data-set. The experimental results show that our proposed method outperforms alternative solutions in terms of the accuracy of the reckoned future traffic flow.

Several models have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). The AGR approach significantly accelerates graph-based learning by exploring a set of anchors. However, when a dataset becomes much larger, AGR still faces a big graph which brings dramatically increasing computational costs. To overcome this issue, we propose a novel Hierarchical Anchor Graph Regularization (HAGR) approach by exploring multiple-layer anchors with a pyramid-style structure. In HAGR, the labels of datapoints are inferred from the coarsest anchors layer by layer in a coarse-to-fine manner. The label smoothness regularization is performed on all datapoints, and we demonstrate that the optimization process only involves a small-size reduced Laplacian matrix. We also introduce a fast approach to construct our hierarchical anchor graph based on an approximate nearest neighbor search technique. Experiments on million-scale datasets demonstrate the effectiveness and efficiency of the proposed HAGR approach over existing methods. Results show that the HAGR approach is even able to achieve a good performance within 3 minutes in an 8-million-example classification task.

This paper explores efficient 3G/4G budget utilization in mobile sensing applications. Distinct from previous research work that either relied on limited WiFi access points or assumed the availability of unlimited 3G/4G communication capability, we offer a more practical mobile sensing system that leverages potential 3G/4G budgets that participants contribute at will, and uses it efficiently customized for the needs of multiple mobile sensing applications with heterogeneous sensitivity to environmental changes. We address the challenge that the information of data generation and WiFi encounters is not a priori knowledge, and propose an online decision making algorithm that takes advantage of participants' historical data. Three typical mobile sensing applications, vehicular application, mobile health and video sharing application are explored. Experimental results demonstrate that our proposed algorithms lead to significantly better system performance compared to alternative solutions for both applications.

Due to high mobility and intermittent connections in vehicular networks, reliable and efficient vehicular communication is a challenging task. Previous research on Vehicle-to-Vehicle (V2V) communication mostly focuses on achieving reliable transmissions from a given source to a given destination by mining moving patterns of taxicabs. However, to the best of our knowledge, none of them considered the habit-driven regularities of individual taxicabs as well as the urban-layout-driven time-varying regularities of crowds of taxicabs synthetically. With this insight, we model both individual and holistic driving patterns by Markov Chain models, then devise a new method to predict possible driving routes for every single taxicab. In addition, we design a new method to evaluate the probability that a single taxicab retrieves information of a specific road segment while it drives through another road segment during a given time period, and also to quantify the expected probability that a single taxicab obtains the information of a given road segment in the near future. With such information, our solution enables the selection of the optimal data packet transmission scheme. We evaluate our solution on a real-world taxicab dataset. Experimental results demonstrate that our approach outperforms alternative solutions in terms of diffusion speed and success ratio of data retrieval.

Recent advances in vehicular networks, GPS and smartphone technologies have changed the paradigm of intelligent taxicab systems. Indeed, taxicab trajectories and online calling information have enabled us to provide more efficient and personalized services. However, existing approaches are not sufficient in exploiting cooperative scheduling techniques and utilizing real time calling information. To this end, in this paper, we model the time-varying regularities of traffic flows, activity ratios of passengers, and unoccupied taxicabs of road segments by mining statistical data on taxicab trajectories. Along this line, we propose a novel approach to calculate the expected revenue of possible routes for individual taxicabs while considering the influence of others, and at the same time, advance a dynamic taxicab scheduling mechanism with online taxicab calling information. Finally, we evaluate our algorithm on real-world taxicab data. Experimental results demonstrate that our approach outperforms existing alternative solutions in terms of average revenue of taxi drivers.

Due to high mobility and intermittent connections in vehicular networks, reliable and efficient Vehicle-to-Vehicle (V2V) communication is a challenging task. The Named Data Networking (NDN) paradigm is recently being applied to achieve efficient V2V communication, however, proactive V2V information diffusion conflicts with the receiver-initiated nature of NDN. This paper bridges this gap by exploiting hierarchical data names to achieve efficient and proactive V2V information diffusion. We first identify a popular subgroup of vehicles, then select them as the diffusion seeds with 3G/4G capability, while others are only equipped with short-range V2V communication. We also design a namespace-based method to optimize data transmission when vehicles are close, in order to maximize the information distribution across geographical space. We evaluate our solution via a real-world taxicab dataset. Experimental results demonstrate that our approach significantly outperforms state-of-the-art solutions in terms of diffusion speed and success rate of data retrieval.

Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context ofGreenGPS; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in[1], however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5 over the fastest, 11.2 percent over the shortest, and 8.4 percent over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.

This paper explores direct phone-to-phone communication (via WiFi interface) among vehicles to support participatory sensing applications. Sensing data usually contains location, speed, and fuel consumption of the car, and has a long time delay between collected and transferred to the server. Direct communication among phones aboard is important in reducing data transfer delay time and sharing participatory sensing information in an inexpensive manner. We design a practical and optimized communication mechanism for direct phone-to-phone data transfer among phones aboard that strategically enables phone-to-phone and/or phone-to-WiFiAP communications by optimally toggling the phones between the normal client and the hotspot modes. We take advantage of the WiFi hotspot functionality on smartphones, and hence require neither involvement of participants nor changes to existing wireless infrastructure and protocols. An analytical model is established to optimize toggling between client and hotspot modes for optimal system efficiency. We fully implement this system on off-the-shelf Google Galaxy Nexus and Nexus S phones. Through a 35-vehicle two-month deployment study, as well as simulation experiments using the real-world T-drive 9,211-taxicab dataset, we show that our solution significantly reduces data transfer delay time and maintains over 80 percent system efficiency under varying system parameters.

This paper presents experiences with eNav, a smartphone-based vehicular GPS navigation system that has an energy-saving location sensing mode capable of drastically reducing navigation energy needs. Traditional navigation systems sample the phone's GPS at a fixed rate (usually around 1Hz), regardless of factors such as current vehicle speed and distance from the next navigation waypoint. This practice results in a large energy consumption and unnecessarily reduces the attainable length of a navigation session, if the phone is left unplugged. The paper investigates two questions. First, would drivers be willing to sacrifice some of the affordances of modern navigation systems in order to prolong battery life? Second, how much energy could be saved using straightforward alternative localization mechanisms, applied to complement GPS for vehicular navigation? According to a survey we conducted of 500 drivers, as much as 91% of drivers said they would like to have a vehicular navigation application with an energy saving mode. To meet this need, eNav exploits on-board accelerometers for approximate location sensing when the vehicle is sufficiently far from the next navigation waypoint (or is stopped). A user test-study of eNav shows that it results in roughly the same user experience as standard GPS navigation systems, while reducing navigation energy consumption by almost 80%. We conclude that drivers find an energy-saving mode on phone-based vehicular navigation applications desirable, even at the expense of some loss of functionality, and that significant savings can be achieved using straightforward location sensing mechanisms that avoid frequent GPS sampling.

Despite the clear benefits of electric vehicles (EVs) in terms of reducing greenhouse gas emissions and traditional energy consumptions, the popularization of EVs remains a challenge in the short run. When considering electric taxis, urban planners must face the additional issue of providing battery swapping services. While previous studies focused on planning battery swapping stations for private EVs, we investigate ways of supporting the upgrade of an entire urban taxi system, with demands differing both in scale and nature. With this insight, we analyze the historical sensing data of taxi routes, and evaluate the battery swapping demand profile, as well as the driving time between positions in the road network. Based on these inputs, we propose a method to calculate an optimized battery swapping station scheme. Our strategies are then evaluated via a real world 366-day, 3,976-taxi dataset. The results show that compared to uniform deployment, our planning scheme reduces the average time-cost by 67.2%.

As the size of smartphone touchscreens becomes larger and larger in recent years, operability with single hand is getting worse especially for female users. We envision that user experience can be significantly improved if smartphones are able to detect the current operating hand and adjust the UI subsequently. In this paper, we propose a novel scheme that leverages user-generated touchscreen traces to recognize current operating hand accurately, with the help of a supervised classifier constructed from twelve different kinds of touchscreen trace features. As opposed to existing solutions that all require users to select the current operating hand or dominant hand manually, our scheme follows a more convenient and practical manner, and allows users to change operating hand frequently without any harm to user experience. We conduct a series of real-world experiments on Samsung Galaxy S4 smartphones, and evaluation results demonstrate that our proposed approach achieves 94.1% accuracy when deciding with a single trace only, and the false positive rate is as low as 2.6%.

Online shopping has been emerging in the past years and significantly affected conventional shopping styles, but many people still favor retail stores for reasons such as social lives and trying dresses. When they find interested goods, usually hope to obtain more information of them from online shopping for price comparison and so forth. However, state of the art solutions either have users frequently search them manually via smart phone apps, which results in a negative effect on shopping experience; or force users to delay the purchase after checking information late at home. In this demo, we present a novel system that can unobtrusively assist shopping in retail stores in a real time manner. It leverages the camera, voice control, and Internet capabilities on smart glasses, and allows users to conduct price match in real time to improve shopping experience. We describe the overall system design and demonstrate the system using Google Glass and Google Nexus 5 phone.

In this article we present SmartRoad, a crowd-sourced road sensing system that detects and identifies traffic regulators, traffic lights, and stop signs, in particular. As an alternative to expensive road surveys, SmartRoad works on participatory sensing data collected from GPS sensors from in-vehicle smartphones. The resulting traffic regulator information can be used for many assisted-driving or navigation systems. In order to achieve accurate detection and identification under realistic and practical settings, SmartRoad automatically adapts to different application requirements by (i) intelligently choosing the most appropriate information representation and transmission schemes, and (ii) dynamically evolving its core detection and identification engines to effectively take advantage of any external ground truth information or manual label opportunity. We implemented SmartRoad on a vehicular smartphone test bed, and deployed it on 35 external volunteer users’ vehicles for two months. Experiment results show that SmartRoad can robustly, effectively, and efficiently carry out the detection and identification tasks.

Many applications in wireless sensor networks require communication performance that is both consistent and of high quality. Unfortunately, performance of current network protocols can vary significantly because of various interferences and environmental changes. Current protocols estimate link quality based on the reception of probe packets over a short time period. This method is neither efficient nor accurate enough to capture the dramatic variations of link quality. Therefore, we propose a link metric called competence that characterizes links over a longer period of time. We combine competence with current short-term estimations in routing algorithm designs. To further improve network performance, we have designed a distributed route maintenance framework based on feedback control solutions. This framework allows every link along an end-to-end (E2E) path to adjust its link protocol parameters, such as transmission power and number of retransmissions, to ensure specified E2E reliability and latency under dynamic link qualities. Our solutions are evaluated in both extensive simulations and real system experiments. In real system evaluations with 48 T-Motes, our overall solution improves E2E packet delivery ratio over existing solutions by up to 40% while reducing transmission energy consumption by up to 22%. Importantly, our solution also achieves more stable and better transient performance than current approaches.

This paper complements the large body of social sensing literature by developing means for augmenting sensing data with inference results that "fill-in" missing pieces. Unlike trend-extrapolation methods, we focus on prediction in disaster scenarios where disruptive trend changes occur. A set of prediction heuristics (and a standard trend extrapolation algorithm) are compared that use either predominantly-spatial or predominantly-temporal correlations for data extrapolation purposes. The evaluation shows that none of them do well consistently. This is because monitored system state, in the aftermath of disasters, alternates between periods of relative calm and periods of disruptive change (e.g., aftershocks). A good prediction algorithm, therefore, needs to intelligently combine time-based data extrapolation during periods of calm, and spatial data extrapolation during periods of change. The paper develops such an algorithm. The algorithm is tested using data collected during the New York City crisis in the aftermath of Hurricane Sandy in November 2012. Results show that consistently good predictions are achieved. The work is unique in addressing the bi-modal nature of damage propagation in complex systems subjected to stress, and offers a simple solution to the problem.

This paper describes the exploitation of hierarchical data names to achieve information-utility maximizing data collection in social sensing applications. We describe a novel transport abstraction, called the information funnel. It encapsulates a data collection protocol for social sensing that maximizes a measure of delivered information utility, that is the minimized data redundancy, by diversifying the data objects to be collected. The abstraction leverages named-data networking, a communication paradigm where data objects are named instead of hosts. We argue that this paradigm is especially suited for utility-maximizing transport in resource constrained environments, because hierarchical data names give rise to a notion of distance between named objects that is a function of only the topology of the name tree. This distance, in turn, can expose similarities between named objects that can be leveraged for minimizing redundancy among objects transmitted over bottlenecks, thereby maximizing their aggregate utility. With a proper hierarchical name space design, our protocol prioritizes transmission of data objects over bottlenecks to maximize information utility, with very weak assumptions on the utility function. This prioritization is achieved merely by comparing data name prefixes, without knowing application-level name semantics, which makes it generalizable across a wide range of applications. Evaluation results show that the information funnel improves the utility of the collected data objects compared to other lossy protocols.

We present the design, implementation, and evaluation of Centaur, an application-level user-assisted message dissemination solution for Online Social Networks (OSN). Characteristics of OSNs make their message dissemination distinct from scenarios like multicast streaming and P2P file sharing. First, updates issued by each user are sporadic and the “online” follower set is highly dynamic. Hence, it is unnecessarily expensive to maintain always-alive multicast topologies. Second, the key advantage of OSNs over traditional media is realtime update, which would be greatly shadowed if it takes long to construct well-shaped dissemination structures. Therefore, in contrast to the multitude of prior multicast solutions, Centaur constructs location-aware dissemination trees locally for each incoming message. We implement a prototype with Cirrus and evaluate it with Twitter data. Experiment results show that Centaur achieves 98% delivery ratio and few seconds of delay with only around one tenth server traffic compared to centralized solutions used in many current OSNs.

Memory management systems have significantly affected the overall performance of modern multi-core smartphone systems. Android, as one of the most popular smartphone operating systems, adopts a global buddy system with the FCFS (first come, first served) principle for memory allocation, and releases requests to manage external fragmentations and maintain the memory allocation efficiency. However, extensive experimental study on thread behaviors indicates that memory external fragmentation is no longer the crucial bottleneck in most Android applications. Specifically, a thread usually allocates or releases memory in bursts, resulting in serious memory locks and inefficient memory allocation. Furthermore, the pattern of such bursting behaviors varies throughout the life cycle of a thread. The conventional FCFS policy of Android buddy system fails to adapt to such variations and thus suffers from performance degradation. In this paper, we propose a novel memory management framework, called Memory Management Based on Thread Behaviors (MMBTB), for multi-core smartphone systems. It adapts to various thread behaviors through targeted optimizations to provide efficient memory allocation. The efficiency and effectiveness of this new memory management scheme on multicore architecture is proved by a theoretical emulation model. Our experimental studies on the real Android system show that MMBTB can improve the efficiency of memory allocation by 12%-20%, confirming the theoretical analysis results.

This paper explores direct phone-to-phone communication (via WiFi interface) among vehicles to support mobile sensing applications. Direct communication among drivers' phones is important in improving data collection efficiency and sharing participatory sensing information in an inexpensive manner. We design a practical and optimized communication mechanism for direct phone-to-phone data transfer among drivers' phones that strategically enables phone-to-phone and/or phone-to-WiFiAP communications by optimally toggles the phone between the normal client and the hotspot modes. We take advantage of the WiFi hotspot functionality on smartphones, and hence require neither involvement of participants nor changes to existing wireless infrastructure and protocols. An analytical model is established to optimize toggling between client and hotspot modes for optimal system efficiency. We fully implement this system on off-the-shelf Google Galaxy Nexus and Nexus S phones. Through a 35-vehicle 2-month deployment study, as well as simulation experiments using the real-world T-drive 9,211-taxicab dataset, we show that our solution significantly reduces data transfer delay time and maintains over 80% efficiency under varying system parameters. We even achieve 90% for parameter settings of the latest smartphones.

People are using social media to generate, share, and communicate information with each other. Finding actionable insights from such big data has attracted a lot of research attentions on, for example, finding targeted user groups based on their historical on-line activities. However, existing ma- chine learning algorithms fail to keep up with the increasing large data volume. In this paper, we develop a scalable regression-based algorithm called distributed iterative shrinkage-thresholding algorithm (DISTA) that can identify potential users. Our experiments conducted on Facebook data containing billions of users and associated activities show that DISTA with feature selection not only enables on-line audience-targeted approach for precise marketing but also performs efficiently on parallel computers.

In this paper, we present GDA, a generalized decision aggregation framework that integrates information from distributed sensor nodes for decision making in a resource efficient manner. Traditional approaches that target similar problems only take as input the discrete label information from individual sensors that observe the same events. Different from them, our proposed GDA framework is able to take advantage of the confidence information of each sensor about its decision, and thus achieves higher decision accuracy. Targeting generalized problem domains, our framework can naturally handle the scenarios where different sensor nodes observe different sets of events whose numbers of possible classes may also be different. GDA also makes no assumption about the availability level of ground truth label information, while being able to take advantage of any if present. For these reasons, our approach can be applied to a much broader spectrum of sensing scenarios. The advantages of our proposed framework are demonstrated through both theoretic analysis and extensive experiments.

The primary goal of breadcrumb trail sensor networks is to transmit in real-time users' physiological parameters that measure life-critical functions to an incident commander through reliable multihop communication. In applications using breadcrumb solutions, there are often many users working together, and this creates a well-known body shadowing effect (BSE). In this article, we first measure the characteristics of body shadowing for 2.4GHz sensor nodes. Our empirical results show that the body shadowing effect leads to severe packet loss and consequently very poor real-time performance. Then we develop a novel Intentional Forwarding solution. This solution accurately detects the shadowing mode and enables selected neighbors to forward data packets. Experimental results from a fully implemented testbed demonstrate that Intentional Forwarding is able to improve the end-to-end average packet delivery ratio (PDR) from 58% to 93% and worst-case PDR from 45% to 85%, and is able to meet soft real-time requirements even under severe body shadowing problems.

Breadcrumb systems (BCS) aid first responders by communicating their physiological parameters to remotely located base stations. In this paper, we describe the design, implementation, and evaluation of an automatic and robust multi-user breadcrumb system for indoor first response applications. Our solution includes a breadcrumb dispenser with a link estimator that is used to decide when to deploy breadcrumbs to maintain reliable wireless connectivity. The solution includes accounting for realities of buildings and dispensing such as the height difference between where the dispenser is worn and the floor where the dispensed nodes are found. We also include adaptive power management to maintain link quality over time. Moreover, we propose UF, a distributed cooperative deployment algorithm, to achieve longer breadcrumb chain lengths while maintaining fairness and high system reliability via selecting appropriate benefit and cost functions. We deployed and evaluated our system in real buildings with several different first responder mobility patterns. Experimental results from our study show that compared to the state of the art solution , our breadcrumb system achieves 200 percent link redundancy with only 23 percent additional deployed nodes. Our deployed breadcrumb chain can achieve 90 percent PRR when one node fails in the chain. In addition, by applying the UF coordination algorithm, the system can maintain connectivity for up to 87 percent longer distances than baseline greedy coordination approach while maintaining 96 percent packet delivery ratio.

In this paper, we introduce Minerva; an information-centric programming paradigm and toolkit for social sensing. The toolkit is geared for smartphone applications whose main objective is to collect and share information about the physical world. Information-centric programming refers to a publish-subscribe paradigm that maximizes the amount of information delivered. Unlike a traditional publish-subscribe system where publishers are assumed to have independent content, Minerva is geared for social sensing applications where different sources (participants sharing sensor data) often overlap in information they share. For example, through lack of coordination, they might collect redundant pictures of the same scene or redundant speed measurements of the same street. The main contribution of Minerva, therefore, lies in a data prioritization scheme that maximizes information delivery from publishers to subscribers by reducing redundancy, taking into account the non-independent nature of content. The algorithm is implemented on Android phones on top of the recently introduced named data networking framework. Evaluation results from both two smartphone-based experiments and a large-scale real data driven simulation demonstrate that the prioritization algorithm outperforms other candidates in terms of information coverage.

This paper explores efficient 3G budget utilization in mobile participatory sensing applications. 1 Distinct from previous research work that either rely on limited WiFi access points or assume the availability of unlimited 3G communication capability, we offer a more practical participatory sensing system that leverages potential 3G budgets that participants contribute at will, and uses it efficiently customized for the needs of multiple participatory sensing applications with heterogeneous sensitivity to environmental changes. We address the challenge that the information of data generation and WiFi encounters is not a priori knowledge, and propose an online decision making algorithm that takes advantage of participants' historical data. We also develop a heuristic algorithm to consume less energy and reduce the storage overhead while maintaining efficient 3G budget utilization. Experimental results from a 30-participant deployment demonstrate that, even when the budget is as small as 2.5% of a popular data plan, these two algorithms achieve higher utility of uploaded data compared to the baseline solution, especially, they increase the utility of received data by 151.4% and 137.8% for those sensitive applications.

In this paper we present SmartRoad, a crowd-sourced sensing system that detects and identifies traffic regulators, traffic lights and stop signs in particular. As an alternative to expensive road surveys, SmartRoad works on participatory sensing data collected from GPS sensors from in-vehicle smartphones. The resulting traffic regulator information can be used for many assisted-driving or navigation systems. We implement SmartRoad on a vehicular smartphone testbed, and deploy on 35 external volunteer users' vehicles for two months. Experiment results show that SmartRoad can robustly, effectively and efficiently carry out its detection and identification tasks without consuming excessive communication energy/bandwidth or requiring too much ground truth information.

This paper develops and evaluates algorithms for exploiting physical constraints to improve the reliability of social sensing. Social sensing refers to applications where a group of sources (e.g., individuals and their mobile devices) volunteer to collect observations about the physical world. A key challenge in social sensing is that the reliability of sources and their devices is generally unknown, which makes it non-trivial to assess the correctness of collected observations. To solve this problem, the paper adopts a cyber-physical approach, where assessment of correctness of individual observations is aided by knowledge of physical constraints on both sources and observed variables to compensate for the lack of information on source reliability. We cast the problem as one of maximum likelihood estimation. The goal is to jointly estimate both (i) the latent physical state of the observed environment, and (ii) the inferred reliability of individual sources such that they are maximally consistent with both provenance information (who claimed what) and physical constraints. We evaluate the new framework through a real-world social sensing application. The results demonstrate significant performance gains in estimation accuracy of both source reliability and observation correctness.

In this demo, a learning system, called Metis, is presented that extrapolates missing pieces in participatory sensing data. The work addresses the challenge of incomplete coverage in participatory sensing applications, where lack of complete control over participant mobility and sensing patterns may create coverage gaps in space and in time. Metis learns the underlying spatiotemporal patterns of the measured phenomenon from available incomplete observations, and uses these patterns to infer missing data. We describe the overall system design and demonstrate the system using data collected during the New York City gas crisis in the aftermath of Hurricane Sandy.

The primary goal of breadcrumb trail sensor networks is to transmit in real-time users' physiological parameters that measure life critical functions to an incident commander through reliable multihop communication. In applications using breadcrumb solutions, there are often many users working together, and this creates a well-known body shadowing effect (BSE). In this paper, we first measure the characteristics of body shadowing for 2.4 GHz sensor nodes. Our empirical results show that the body shadowing effect leads to severe packet loss, and consequently very poor real-time performance. Then we develop a novel Intentional Forwarding solution. This solution accurately detects the shadowing mode and enables selected neighbors to forward data packets. Experimental results from a fully implemented testbed demonstrate that Intentional Forwarding is able to improve the end-to-end average packet delivery ratio (PDR) from 58% to 93% and worst-case PDR from 45% to 85%, and meet soft real-time requirements even under severe body shadowing problems.

Previous localization solutions in wireless sensor networks mainly focus on using various techniques to estimate node positions. In this paper, we argue that quantifying the uncertainty of these estimates is equally important in practice. By using the quantitative uncertainty of measurements and estimates, we can derive more accurate estimates by better fusing the measurements, provide confidence information for confidence-based applications, and know how to select the best anchor nodes so as to minimize the total mean square errors of the whole network. This paper quantifies the estimation uncertainty as an error covariance matrix, and presents an efficient incremental centralized algorithm---INOVA and a decentralized algorithm---OSE-COV for calculating the error covariance matrix. Furthermore, we present how to use the error covariance matrix to infer the confidence region of each node's estimate, and provide an optimal strategy for the anchor selection problem. Extensive simulation results show that INOVA significantly improves the computation efficiency when the network changes dynamically; the confidence region inference is accurate when the measurement number to node number ratio is more than 2; and the optimal anchor selection strategy reduces the total mean square error by four times as much as the variation-based algorithm in best case.

Breadcrumb systems (BCS) aid first responders by communicating their physiological parameters to remotely located base stations. However, state-of-the-art research only focuses on deploying breadcrumb systems on the assumption of uncoordinated users, which is inefficient. In this paper, we present the first design, implementation, and evaluation of reliable multiuser breadcrumb systems (MUBCS) which exploits efficient and automatic coordination among system users to achieve better utilization of limited breadcrumbs. We propose UF, a distributed cooperative deployment algorithm, to achieve longer breadcrumb chain length while maintaining fairness and high system reliability via selecting appropriate benefit and cost functions. UF also requires no prior assumptions about users' mobility models, making the design practical for real applications. We deployed and evaluated our system in real buildings with several different first responder mobility patterns. Experimental results indicate that this approach can maintain connectivity for up to 87% longer distances than baseline greedy coordination approach while maintaining 96% packet delivery ratio.

Breadcrumb systems (BCS) have been proposed to aid firefighters inside buildings by communicating their physiological parameters to base stations outside the buildings. In this paper, we describe the design, implementation and evaluation of an automatic and robust breadcrumb system for firefighter applications. Our solution includes a breadcrumb dispenser with an optimized link estimator that is used to decide when to deploy breadcrumbs to maintain reliable wireless connectivity. The solution includes accounting for realities of buildings and dispensing such as the height difference between where the dispenser is worn and the floor where the dispensed nodes are found. We also include adaptive power management to maintain link quality over time.

timization under storage constraint for wireless sensor networks with a mobile sink node. The problem is particularly challenging since we need to consider both mobility scheme and storage con- straint. Previous works suggest to use a simple single-hop routing model in which source nodes can only communication with the sink node directly in those mobile networks, However, we notice that this statement is unsuitable for sensor networks with storage constraint because we prove it is a NP-complete problem under single-hop routing model by reducing the Traveling Salesman Problem (TSP) to it in polynomial time. Hence, we try a different way. First we analyze this problem and give a lifetime upperbound, so whether this upperbound is tight is what we concern mostly. Thus, we first construct a 2-approximation O(n2) algorithm to solve the TSP problem, then a novel data diffusion mechanism is built to achieve this upperbound. We prove that under some reasonable assumptions, our algorithm can output this optimal lifetime.

optimization under coverage and connectivity requirements for sensor networks where different targets need to be monitored by different types of sensors running at possibly different sampling rates as well as different initial energy reserve. The problem is particularly challenging since we need to consider both connectiv- ity requirement and so-called target Q-coverage requirement, i.e., different targets may require different sensing quality in terms of the number of transducers, sampling rate, etc. First we formulate this NP-complete lifetime optimization problem, which is general and allows unprecedented diversity in coverage requirements, communication ranges, and sensing ranges. Our approach is based on column generation, where a column corresponds to a feasible solution; our idea is to find a column with steepest ascent in lifetime, based on which we iteratively search for the maximum lifetime solution. To speed up the convergence rate, we generate an initial solution through a novel random selection algorithm. Through extensive simulations, we systematically study the effect of target priorities, communication ranges, and sensing ranges on the lifetime.

Environmental wireless sensor network (EWSN) systems are deployed in potentially harsh and remote environments where inevitable node and communication failures must be tolerated. LUSTER---Light Under Shrub Thicket for Environmental Research---is a system that meets the challenges of EWSNs using a hierarchical architecture that includes distributed reliable storage, delay-tolerant networking, and deployment time validation techniques.