Article

# CrowdTracker: Optimized Urban Moving Object Tracking Using Mobile Crowd Sensing

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## Abstract

This paper proposes CrowdTracker, a novel object tracking system based on mobile crowd sensing (MCS). Different from traditional video-based object tracking approaches, CrowdTracker recruits people to collaboratively take photos of the object to achieve object movement prediction and tracking. The optimization objective of CrowdTracker is to effectively track the moving object in real time and minimize the cost on user incentives. Specifically, the incentive is determined by the number of workers assigned and the total distance that workers move to complete the task. In order to achieve the objective, we propose the MPRE model for object movement prediction and two other algorithms for task allocation, namely, T-centric and P-centric. T-centric selects workers in a task-centric way, while P-centric allocates tasks in a people-centric manner. By analyzing a large number of historical vehicle trajectories, MPRE builds a model to predict the object's next position. In the predicted regions, CrowdTracker selects workers by utilizing T-centric or P-centric. We evaluate the algorithms over a large-scale real-world dataset. Experimental results indicate that CrowdTracker can effectively track the object with a low incentive cost.

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... However, the ML applications raise a privacy concern in the data collection for training purposes. In many ML applications (e.g., Crowdtracker [2], Waze Carpool [3], etc.), users are required to share their sensitive personal information (i.e., user location, user identity, user photos, etc.) to the server. Furthermore, uploading a massive amount of data throughout radio access links or the Internet to the cloud data centers is costly. ...
... Similar to our prior works [10], [20], we consider that the channel gain between the base station and the UE follows the exponential distribution with the mean g 0 (d 0 /d) 4 where g 0 = −40 dB, the reference distance d 0 = 1 m between BS and UEs. Here, the actual distance d between the UEs and the base station is uniformly generated between [2,50] m. Furthermore, the uplink system bandwidth B = 20 M Hz is shared amongst UEs, the Noise power spectral is 10 −10 W , and the transmit power of UEs and BS are 10 W and 40 W , respectively. ...
... For the local training model at UEs, we first set the training data size of UEs in each learning service following a uniform distribution in 10 − 20 M B. The maximum computation capacity (i.e., CPU frequency) at each UE is uniformly distributed between [1,2] GHz. The required CPU cycles c s to train one bit of data at the UE for each learning service is {50, 70, 90} cycles. ...
Article
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Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices. In this paper, we study a new model of multiple federated learning services at the multi-access edge computing server. Accordingly, the sharing of CPU resources among learning services at each mobile device for the local training process and allocating communication resources among mobile devices for exchanging learning information must be considered. Furthermore, the convergence performance of different learning services depends on the hyper-learning rate parameter that needs to be precisely decided. Towards this end, we propose a joint resource optimization and hyper-learning rate control problem, namely MS-FEDL, regarding the energy consumption of mobile devices and overall learning time. We design a centralized algorithm based on the block coordinate descent method and a decentralized JP-miADMM algorithm for solving the MS-FEDL problem. Different from the centralized approach, the decentralized approach requires many iterations to obtain but it allows each learning service to independently manage the local resource and learning process without revealing the learning service information. Our simulation results demonstrate the convergence performance of our proposed algorithms and the superior performance of our proposed algorithms compared to the heuristic strategy.
... In contrast, the network-based system involves hardware changes while designing the network with new equipment [13] [14]. Both the systems require a huge amount of investment, as the mobile travels to the particular destination [23] [24]. Normally, its future position is highly correlated with its personal mobility profile, the cells viewed currently, as well as the time spending in each position [15]. ...
... Whenever a sea lion identifies its target prey, it calls the other members to attack the prey. The arithmetical model of this behavior is determined in Eq. (22), (23), and (24). ...
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... In traditional machine learning approaches, neural network models are trained at a server or a data center. Thus, the centralized learning approaches typically require the raw data, e.g., photos and location information, collected by mobile devices to be centralized at the server [1]. The centralized learning approaches thus face big issues including privacy, long propagation delay, and backbone network burden [2]. ...
... Recently, federated learning as a decentralized machine learning approach has been proposed to address the above issues [3], [4]. In the federated learning, mobile devices, i.e., workers, are required to collaboratively train the neural network model of the model owner 1 . In particular, the model owner first transmits its global model to the workers. ...
... In traditional machine learning approaches, neural network models are trained at a server or a data center. Thus, the centralized learning approaches typically require the raw data, e.g., photos and location information, collected by mobile devices to be centralized at the server [1]. The centralized learning approaches thus face big issues including privacy, long propagation delay, and backbone network burden [2]. ...
... Recently, federated learning as a decentralized machine learning approach has been proposed to address the above issues [3], [4]. In the federated learning, mobile devices, i.e., workers, are required to collaboratively train the neural network model of the model owner 1 . In particular, the model owner first transmits its global model to the workers. ...
Preprint
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Federated learning allows mobile devices, i.e., workers, to use their local data to collaboratively train a global model required by the model owner. Federated learning thus addresses the privacy issues of traditional machine learning. However, federated learning faces the energy constraints of the workers and the high network resource cost due to the fact that a number of global model transmissions may be required to achieve the target accuracy. To address the energy constraint, a power beacon can be used that recharges energy to the workers. However, the model owner may need to pay an energy cost to the power beacon for the energy recharge. To address the high network resource cost, the model owner can use a WiFi channel, called default channel, for the global model transmissions. However, communication interruptions may occur due to the instability of the default channel quality. For this, special channels such as LTE channels can be used, but this incurs channel cost. As such, the problem of the model owner is to decide amounts of energy recharged to the workers and to choose channels used to transmit its global model to the workers to maximize the number of global model transmissions while minimizing the energy and channel costs. This is challenging for the model owner under the uncertainty of the channel, energy and mobility states of the workers. In this paper, we thus propose to employ the Deep Q-Network (DQN) that enables the model owner to find the optimal decisions on the energy and the channels without any a priori network knowledge. Simulation results show that the proposed DQN always achieves better performance compared to the conventional algorithms.
... Based on the historical access data, we propose a deep reinforcement learning method called PA-DDQN to maximize the POI coverage and matching degree for instant sensing and then instant actuation. For sensing locations in urban area, some studies adopt the method of equal-grid division [30] to divide them into small areas, and then allocate the tasks within each grid. This method surely can obtain the locally optimal result, but it also has some disadvantages. ...
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... Jing et al. proposed a semisupervised elastic net (SSEN) regression method to improve the "time-space intensive" assumption that a high enough video frame rate is required to capture the smoothness of the crowd on the time axis, by constructing a regular term based on the order information between an unlabeled sample and its neighbors in the time domain. ese are all penalized by unreasonable predicted changes [12]. Coşar argues that the real world is difficult to meet the requirements when considering issues such as data bandwidth, storage space, and actual hardware devices; it proposes an alternative semisupervised regression framework that uses information about the underlying population distribution geometry to perform transfer learning, while relaxing the requirements based on the "time-space intensive." ...
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In this paper, we analyze and calculate the crowd density in a tourist area utilizing video surveillance dynamic information analysis and divide the crowd counting and density estimation task into three stages. In this paper, novel scale perception module and inverse scale perception module are designed to further facilitate the mining of multiscale information by the counting model; the main function of the third stage is to generate the population distribution density map, which mainly consists of three columns of void convolution with different void rates and generates the final population distribution density map using the feature maps of different branch regressions. Also, the algorithm uses jump connections between the top convolution and the bottom void convolution layers to reduce the risk of network gradient disappearance and gradient explosion and optimizes the network parameters using an intermediate supervision strategy. The hierarchical density estimator uses a hierarchical strategy to mine semantic features and multiscale information in a coarse-to-fine manner, and this is used to solve the problem of scale variation and perspective distortion. Also, considering that the background noise affects the quality of the generated density map, the soft attention mechanism is integrated into the model to stretch the distance between the foreground and background to further improve the quality of the density map. Also, inspired by multitask learning, this paper embeds an auxiliary count classifier in the count model to perform the count classification auxiliary task and to increase the model’s ability to express semantic information. Numerous experimental results demonstrate the effectiveness and feasibility of the proposed algorithm in solving the problems of scale variation and perspective distortion.
... On the other hand, some other popular methods utilized direct human participation instead of drones. CrowdTracker is one such method that recruits people to collaboratively take photographs of the surroundings to determine object movement prediction and tracking [10]. Depending on the location, budget, urgency the agency might have to select a specific approach from the list of available options. ...
Conference Paper
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Crowdsourcing is an efficient and scalable way for the service providers (SP) to provide a broad range of services with a low initial service setup cost. With its growing popularity, people are continuously introducing new crowdsourcing-based applications and searching for suitable crowd platforms for the deployment of those applications as a service. Consequently, a contributor participates in multiple platforms from different SPs and concurrently executes various tasks in a time interval. As the number of SP increases, managing and monitoring each contributor separately becomes a primary challenge for each SP. Moreover, upgrading a service, ensuring service scalability, and migration is tough for any crowd-based service provider. Frequently , a service provider has difficulty finding the appropriate contributors that will satisfy their service specifications. Besides, as a contributor, it is also hard to decide whether or not they should participate in a task offered by an SP. Therefore, in this paper, we introduced CrowdPick, a middleware that provides a crowdsourced platform for the people who want to provide a crowdsourcing-based service. Each SP contacts CrowdPick for initiating any service chooses an appropriate application package from its repository and specifies its service requirements. Instead of joining individually to each SP, the contributor needs to register into CrowdPick. All the tasks associated with services are scheduled and monitored by CrowdPick, and the CrowdPick decides the corresponding monetary incentives. Along with providing a detailed cost-benefit analysis of an SP and a contributor, we describe the service level agreement (SLA) policies. We simulate our model to show that implementing CrowdPick in real life is viable, and beneficial for both an SP and a contributor.
... Note that cameras are multifarious, not only include those for traffic and public surveillance, but also those of citizens' smart mobile phones, thus it is reasonable to assume that these cameras are distributed densely enough and with long-term recording capability (e.g., videos or images stored with time and location stamp). If that's not a reality, it will be in the near future, because the concept of collaborative sensing [16][17][18][19] is emerging, which can greatly enhance the urban sensing ability by the combination of both stationary infrastructures and mobile phones. Since the quantity of cameras can be up to several millions, and their records are last from the witness moment to current moment, the challenge is "how can we find the car with minimal number of spatiotemporal searches?" ...
Preprint
Tracking a car or a person in a city is crucial for urban safety management. How can we complete the task with minimal number of spatiotemporal searches from massive camera records? This paper proposes a strategy named IHMs (Intermediate Searching at Heuristic Moments): each step we figure out which moment is the best to search according to a heuristic indicator, then at that moment search locations one by one in descending order of predicted appearing probabilities, until a search hits; iterate this step until we get the object's current location. Five searching strategies are compared in experiments, and IHMs is validated to be most efficient, which can save up to 1/3 total costs. This result provides an evidence that "searching at intermediate moments can save cost".
... We take image classification as a typical AI application in VEC. DNN-based image classification has been widely used in autopilot and interactive navigation for ICV, as well as object tracking and event detection in ITS [17], [18]. To obtain high accuracy and efficiency of model aggregation, the central server should evaluate the image quality and computation capability of vehicular clients, and select the ''fine'' models from vehicular clients. ...
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Federated learning is a newly emerged distributed machine learning paradigm, where the clients are allowed to individually train local deep neural network (DNN) models with local data and then jointly aggregate a global DNN model at the central server. Vehicular edge computing (VEC) aims at exploiting the computation and communication resources at the edge of vehicular networks. Federated learning in VEC is promising to meet the ever-increasing demands of artificial intelligence (AI) applications in intelligent connected vehicles (ICV). Considering image classification as a typical AI application in VEC, the diversity of image quality and computation capability in vehicular clients potentially affects the accuracy and efficiency of federated learning. Accordingly, we propose a selective model aggregation approach, where “fine” local DNN models are selected and sent to the central server by evaluating the local image quality and computation capability. Regarding the implementation of model selection, the central server is not aware of the image quality and computation capability in the vehicular clients, whose privacy is protected under such a federated learning framework. To overcome this information asymmetry, we employ two-dimension contract theory as a distributed framework to facilitate the interactions between the central server and vehicular clients. The formulated problem is then transformed into a tractable problem through successively relaxing and simplifying the constraints, and eventually solved by a greedy algorithm. Using two datasets, i.e., MNIST and BelgiumTSC, our selective model aggregation approach is demonstrated to outperform the original federated averaging (FedAvg) approach in terms of accuracy and efficiency. Meanwhile, our approach also achieves higher utility at the central server compared with the baseline approaches.
... Wang et al. propose a hybrid predictive model by combining both the regularity and conformity of human mobility to boost the predictive performance based on heterogeneous mobility datasets [12]. Jing et al. propose a movement prediction model to predict the object's next position by analyzing historical vehicle trajectories [13]. Besides, Markov chain based prediction approach has been used to trajectory domains and proved to be effective. ...
Article
Excavating human’s temporal and spatial regularities hidden in trajectory data and predicting users’ mobility patterns are conducive to providing proactive smart services for people. Combining Markov transition and tensor theories to improve the prediction performance has proved to be effective. However, existing state-of-the-art multivariate multi-order Markov model neglects the mutual influence among different users. In practical trajectory system, people’s mobility patterns are influenced by their social relationships. Therefore, this paper focuses on proposing a novel multi-user multivariate multi-order Markov model and a multi-modal user mobility pattern prediction approach. First, we construct two concrete Markov trajectory transition models based on the single-user multivariate multi-order Markov model. Then, we propose a multi-user multivariate multi-order Markov model including the influence model of multiple users and the multi-user Markov trajectory transition model. Afterwards, two unified product based power methods are developed to calculate the stationary joint eigentensor (SJE) for single-user and multi-user multivariate multi-order Markov models. Furthermore, an SJE based multi-modal prediction approach is proposed to realize precise mobility pattern prediction. Finally, we conduct a series of experiments based on real-world GPS trajectory dataset to verify the performance of the proposed approaches. Experimental results demonstrate that the proposed multi-user multivariate multi-order Markov based multi-modal prediction approach can improve the trajectory prediction accuracy by highest up to 31.10 percentage points compared with the Z-eigen based approach.
... In the traditional cloud-centric approach, data collected by mobile devices is uploaded and processed centrally in a cloudbased server or data center. In particular, data collected by IoT devices and smartphones such as measurements [5], photos [6], videos [7], and location information [8] are aggregated at the data center [9]. Thereafter, the data is used to provide insights or produce effective inference models. ...
Preprint
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In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional Machine Learning (ML) approaches require the data to be centralized in a cloud server. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislation and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges, open issues and future research directions in FL.
... This way increases the cost of system and affects the availability negatively. In [13], the authors develop a crowdsensing-based object tracking system (CrowdTracker) for the purpose of tracking vehicles on road. Visual Crowd Sensing (VCS) is applied to the tracking system which utilizes participants' contributed photos with a lower incentive cost. ...
Article
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As the widespread of mobile devices in recent years, mobile crowdsensing (MCS) has become a powerful mechanism to produce knowledge by collecting the individual contributed sensor data. In this paper, we aim to solve the target tracking problem through mobile crowdsensing. The traditional tracking method tends to rely on photos or videos provided by pre-deployed monitors, which may consume much power resources. Different from the traditional tracking method, the Tracking Approach through Mobile Crowdsensing (TAMC) proposed in this paper utilizes the wireless communication of mobile users to collect and contribute the valuable information about the target’s whereabouts. Specifically, whenever the mobile users witness the target person, they will take photos of the target person and report the location and time of witnessing the target to the platform. Due to the fact that mobile users communicate with the platform only when they witness the target, the crowdsensing network composed of mobile users can be seen as a green network. In this way, the visited location history and corresponding time sequence of the target are available through the reports of mobile users. Once a new report is uploaded to the platform, the location history is updated. Then, according to the latest report, we apply a tree-based location prediction model named XGBoost, which is a scalable machine learning system, to predict the next place to be visited by the target. Finally, we conduct extensive experiments on a large-scale real-world dataset, namely, Gowalla check-in dataset. The experimental results show that compared with the baseline methods, the tracking approach can predict the next places accurately.
... CrowdTracker recruits people to collaboratively take photos to achieve object tracking. By analyzing a large number of historical vehicles' trajectories, CrowdTracker builds a model to predict the objects next position [19]. In contrast, we exploit the geometrical relationships among the location of the participant, the picture shooting direction, and the road network to locate the vehicle. ...
Article
Traditionally, vehicle tracking is accomplished using pre-deployed video camera networks, which relies on stationary cameras and searches for the target vehicle from videos. In this work, we develop CrowdTracking, i.e., a crowd tracking system that people can collaboratively keep track of the moving vehicle by taking photos, especially in areas where video cameras are deficient. In other words, the underlying support of CrowdTracking is mobile crowdsensing. Several novel ideas underpin CrowdTracking. First, the vehicle can be rapidly localized by using both photographing contexts (including the location and the shooting direction) of the photographer and the road network. Second, the moving speed of the vehicle can be estimated according to two localization results and the trajectory will be predicted. As a result, through continuously collecting photos of the moving vehicle on different roads, the vehicle can be tracked and localized almost in real time. Through precisely localizing the specified vehicle, two optimization objectives are met: 1) maximizing the tracking coverage to the vehicle’s actual trajectory; 2) minimizing the number of participants who are assigned vehicle-tracking tasks. We evaluate the localization method with a real dataset and report about 6 meters error. We also evaluate the vehicle-tracking method of CrowdTracking using a synthetic data set, and experimental results validate its effectiveness and efficiency.
... The use of IoT for public transport has largely been limited to routing, navigation, safety and tracking [9][10][11]. On the other hand, wireless sensor networks to determine seat occupancy have been used in auditoriums, cinema halls and concerts [12]. ...
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Chapter
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Chapter
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Tracking a suspicious car or a person in a city efficiently is crucial in urban safety management. But how can we complete the task with the minimal number of spatiotemporal searches when massive camera records are involved? To this end, this study proposes a strategy named intermediate searching at heuristic moments (IHMs). At each step, we determine which moment is the best one for the search based on a heuristic indicator. Then, at that moment, locations are searched one by one in descending order of predicted appearing probabilities until a search hit is obtained. We iterate this step until we derive the object’s current location. Five searching strategies are compared via experiments. Among these strategies, the IHMs strategy is validated as the most efficient. IHMs can save up to 1/3 of the total cost. This result provides evidence that “searching at intermediate moments can save cost.”
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Consumer-centric mobile devices, such as smartphones, are an emerging category of devices at the edge of the Internet. Leveraging volunteers and their mobiles as a (sensing) data collection outlet is known as Mobile Crowd Sensing (MCS) and poses interesting challenges, with particular regard to the management of sensing resource contributors, dealing with their subscription, random and unpredictable join and leave, and node churn. To facilitate and expedite the (commercial) exploitation of this trend, in this paper we propose to adopt a service-oriented approach to cope with MCS application deployment into a sensing Cloud infrastructure, decoupling the MCS application domain from the infrastructure one. To this purpose we provide the building blocks for implementing such a novel take on MCS, which from a Cloud layering perspective can be identified as a platform service, i.e., an MCS as a service (MCSaaS). A prototype implementation that serves as a blueprint and a proof-of-concept of the proposed framework is presented, while an evaluation of the effectiveness of the MCSaaS paradigm has been provided using suitable mobility-related use cases for a validation of the concept, as well as a modeling approach through the adoption of generalized stochastic Petri nets.
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Location-based social networks (LBSNs) provide people with an interface to share their locations and write reviews about interesting places of attraction. The shared locations form the crowdsourced digital footprints, in which each user has many connections to many locations, indicating user preference to locations. In this paper, we propose an approach for personalized travel package recommendation to help users make travel plans. The approach utilizes data collected from LBSNs to model users and locations, and it determines users’ preferred destinations using collaborative filtering approaches. Recommendations are generated by jointly considering user preference and spatiotemporal constraints. A heuristic search-based travel route planning algorithm was designed to generate travel packages. We developed a prototype system, which obtains users’ travel demands from mobile client and generates travel packages containing multiple points of interest and their visiting sequence. Experimental results suggest that the proposed approach shows promise with respect to improving recommendation accuracy and diversity.
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By using increasingly popular smartphones, participatory sensing systems can collect comprehensive sensory data to retrieve context-aware information for different applications (or sensing tasks). However, new challenges arise when selecting the most appropriate participants when considering their different incentive requirements, associated sensing capabilities, and uncontrollable mobility, to best satisfy the quality-of-information (QoI) requirements of multiple concurrent tasks with different budget constraints. This paper proposes a multitask-oriented participant selection strategy called “DPS,” which is used to tackle the aforementioned challenges, where three key design elements are proposed. First is the QoI satisfaction metric, where the required QoI metrics of the collected data are quantified in terms of data granularity and quantity. Second is the multitask-orientated QoI optimization problem for participant selection, where task budgets are treated as the constraint, and the goal is to select a minimum subset of participants to best provide the QoI satisfaction metrics for all tasks. The optimization problem is then converted to a nonlinear knapsack problem and is solved by our proposed dynamic participant selection (DPS) strategy. Third is how to compute the expected amount of collected data by all (candidate) participants, where a probability-based movement model is proposed to facilitate such computation. Real and extensive trace-based simulations show that, given the same budget, the proposed participant selection strategy can achieve far better QoI satisfactions for all tasks than selecting participants randomly or through the reversed-auction-based approaches.
Conference Paper
Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arrive, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a non-negative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
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This paper presents iSee, a crowdsourced approach to detecting and localizing events in outdoor environments. Upon spotting an event, an iSee user only needs to swipe on her smartphone's touchscreen in the direction of the event. These swiping directions are often inaccurate and so are the compass measurements. Moreover, the swipes do not encode any notion of how far the event is located from the user, neither is the GPS location of the user accurate. Furthermore, multiple events may occur simultaneously and users do not explicitly indicate which events they are swiping towards. Nonetheless, as more users start contributing data, we show that our proposed system is able to quickly detect and estimate the locations of the events. We have implemented iSee on Android phones and have experimented in real-world settings by planting virtual "events" in our campus and asking volunteers to swipe on seeing one. Results show that iSee performs appreciably better than established triangulation and clustering-based approaches, in terms of localization accuracy, detection coverage, and robustness to sensor noise.
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Mobile crowd sensing is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous largescale applications. Human involvement is one of the most important features, and human mobility offers unprecedented opportunities for both sensing coverage and data transmission. In this article, we investigate the opportunistic characteristics of human mobility from the perspectives of both sensing and transmission, and discuss how to exploit these opportunities to collect data efficiently and effectively. We also outline various open issues brought by human involvement in this emerging research area.
Conference Paper
Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.
Conference Paper
We investigate opportunistic routing, centering on the recommendation of ideal diversions on trips to a primary destination when an unplanned waypoint, such as a rest stop or a refueling station, is desired. In the general case, an automated routing assistant may not know the driver's final destination and may need to consider probabilities over destinations in identifying the ideal waypoint along with the revised route that includes the waypoint. We consider general principles of opportunistic routing and present the results of several studies with a corpus of real-world trips. Then, we describe how we can compute the expected value of asking a user about the primary destination so as to remove uncertainly about the goal and show how this measure can guide an automated system's engagements with users when making recommendations for navigation and analogous settings in ubiquitous computing.
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Mobile Crowd Sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private profiles to the crowdsourcer when they arrive, and the crowdsourcer aims at selecting a subset of users before a specified deadline for minimizing the total payment while a specific number of tasks can be completed.We design three online mechanisms, Homo-OMZ, Hetero-OMZ and Hetero-OMG, all of which can satisfy the computational efficiency, individual rationality, cost-truthfulness, and consumer sovereignty. The Homo-OMZ mechanism is applicable to the homogeneous user model and can satisfy the social efficiency but not constant frugality. The Hetero-OMZ and Hetero-OMG mechanisms are applicable to both the homogeneous and heterogeneous user models, and can satisfy the constant frugality. Besides, the Hetero-OMG mechanism can also satisfy the time-truthfulness. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
Conference Paper
It has been estimated that traffic congestion costs the world economy hundreds of billions of dollars each year, increases pollution, and has a negative impact on the overall quality of life in metropolitan areas. A significant part of congestion in urban areas is due to vehicles searching for on-street parking. Detailed and accurate on-street parking maps can help drivers easily locate areas with large numbers of legal parking spaces and thus relieve congestion. In this paper, we address the problem of mapping street parking spaces using vehicles' preinstalled parking sensors. In particular, we focus on identifying legal parking spaces from crowdsourced data, whereas earlier work has largely assumed that such maps of legal spaces are given. We demonstrate that crowdsensing data from vehicle parking sensors can be used to classify on-street areas into legal/illegal parking spaces. Based on more than 2 million data points collected in Highland Park, NJ and downtown Brooklyn, NY areas, we show that on-street parking maps can be estimated with an accuracy of ~90% using proposed weighted occupancy rate thresholding algorithm.
Conference Paper
Compared to the other types of sensor networks, the wireless camera sensor networks can offer much more comprehensive and accurate information in mobile target tracking applications. We propose a dynamic node collaboration scheme for mobile target tracking in wireless camera sensor networks. Unlike the traditional sensing models, we develop a nonlinear localization-oriented sensing model for camera sensors by taking the perspective projection and the observation noises into account. Based on our sensing model, we apply the sequential Monte Carlo (SMC) technique to estimate the belief state of the target location. In order to implement the SMC based tracking mechanism efficiently, we propose a dynamic node collaboration scheme, which can balance the tradeoff between the quality of tracking and the network cost. Our scheme deploys the dynamic cluster architecture which mainly includes the following two components. First, we design a scheme to elect the cluster heads during the tracking process. Second, we develop an optimization-based algorithm to select an optimal subset of camera sensors as the cluster members for estimating the target location cooperatively. Also conducted is a set of extensive simulations to validate and evaluate our proposed schemes.
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The detection and extraction of target blobs is implemented by an adaptive Gaussian mixture background modeling. Its computational complexity can be greatly reduced because of frame reconstruction and the flexibility of Gaussian mixture components. The unscented Kalman filter is used to dynamically predict the target position and estimate the tracking uncertainty. The target association combining homography transformation with hue histogram matching implements the identity of the same target in different views. For the same target, the confidence measure based on the target size and the tracking uncertainty is defined to achieve optimal node selection. And then the new camera node continues the target tracking and the next node selection process.
Conference Paper
Destination prediction is an essential task for many emerging location based applications such as recommending sightseeing places and targeted advertising based on destination. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, existing techniques using this approach suffer from the “data sparsity problem”, i.e., the available historical trajectories is far from being able to cover all possible trajectories. This problem considerably limits the number of query trajectories that can obtain predicted destinations. We propose a novel method named Sub-Trajectory Synthesis (SubSyn) algorithm to address the data sparsity problem. SubSyn algorithm first decomposes historical trajectories into sub-trajectories comprising two neighbouring locations, and then connects the sub-trajectories into “synthesised” trajectories. The number of query trajectories that can have predicted destinations is exponentially increased by this means. Experiments based on real datasets show that SubSyn algorithm can predict destinations for up to ten times more query trajectories than a baseline algorithm while the SubSyn prediction algorithm runs over two orders of magnitude faster than the baseline algorithm. In this paper, we also consider the privacy protection issue in case an adversary uses SubSyn algorithm to derive sensitive location information of users. We propose an efficient algorithm to select a minimum number of locations a user has to hide on her trajectory in order to avoid privacy leak. Experiments also validate the high efficiency of the privacy protection algorithm.
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This article investigates how and to what extent the power of collective although imprecise intelligence can be employed in smart cities. The main visionary goal is to automate the organization of spontaneous and impromptu collaborations of large groups of people participating in collective actions (i.e., participAct), such as in the notable case of urban crowdsensing. In a crowdsensing environment, people or their mobile devices act as both sensors that collect urban data and actuators that take actions in the city, possibly upon request. Managing the crowdsensing process is a challenging task spanning several socio-technical issues: from the characterization of the regions under control to the quantification of the sensing density needed to obtain a certain accuracy; from the evaluation of a good balance between sensing accuracy and resource usage (number of people involved, network bandwidth, battery usage, etc.) to the selection of good incentives for people to participAct (monetary, social, etc.). To tackle these problems, this article proposes a crowdsensing platform with three main original technical aspects: an innovative geo-social model to profile users along different variables, such as time, location, social interaction, service usage, and human activities; a matching algorithm to autonomously choose people to involve in participActions and to quantify the performance of their sensing; and a new Android-based platform to collect sensing data from smart phones, automatically or with user help, and to deliver sensing/actuation tasks to users.
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Barrier coverage is an important issue in wireless sensor network. In wireless camera sensor networks, the cameras take the images or videos of target objects, the position and angle of camera sensor impact on the sense range. Therefore, the barrier coverage problem in camera sensor network is different from scalar sensor network. In this paper, based on the definition of full-view coverage, we focus on the Minimum Camera Barrier Coverage Problem (MCBCP) in wireless camera sensor networks in which the camera sensors are deployed randomly in a target field. Firstly, we partition the target field into disjoint subregions which are full-view-covered regions or not-full-view-covered regions. Then we model the full-view-covered regions and their relationship as a weighted directed graph. Based on the graph, we propose an algorithm to find a feasible solution for the MCBCP problem. We also proved the correctness of the solution for the MCBCP problem. Furthermore, we propose an optimal algorithm for the MCBCP problem. Finally, simulation results demonstrate that our algorithm outperforms the existing algorithm.
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This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through an urban community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user's destination and mode of transportation. To achieve efficient inference, we apply Rao–Blackwellized particle filters at multiple levels of the model hierarchy. Locations such as bus stops and parking lots, where the user frequently changes mode of transportation, are learned from GPS data logs without manual labeling of training data. We experimentally demonstrate how to accurately detect novel behavior or user errors (e.g. taking a wrong bus) by explicitly modeling activities in the context of the user's historical data. Finally, we discuss an application called “Opportunity Knocks” that employs our techniques to help cognitively-impaired people use public transportation safely.
Conference Paper
The use of centralized, real-time position tracking is proliferating in the areas of logistics and public transportation. Real-time positions can be used to provide up-to-date information to a variety of users, and they can also be accumulated for uses in subsequent data analyses. In particular, historical data in combination with real-time data may be used to predict the future travel times of vehicles more accurately, thus improving the experience of the users who rely on such information. We propose a Nearest-Neighbor Trajectory (NNT) technique that identifies the historical trajectory that is the most similar to the current, partial trajectory of a vehicle. The historical trajectory is then used for predicting the future movement of the vehicle. The paper's specific contributions are two-fold. First, we define distance measures and a notion of nearest neighbor that are specific to trajectories of vehicles that travel along known routes. In empirical studies with real data from buses, we evaluate how well the proposed distance functions are capable of predicting future vehicle movements. Second, we propose a main-memory index structure that enables incremental similarity search and that is capable of supporting varying-length nearest neighbor queries.