CrowdTracker: Optimized Urban Moving Object Tracking Using Mobile Crowd Sensing

Yao, Jing; Guo, Bin; Wang, Zhu; Li, Victor O. K.; Lam, Jacqueline C. K.; Yu, Zhiwen

doi:10.1109/jiot.2017.2762003

Cited by 51 publications

(31 citation statements)

References 45 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Lim

Luong

Hoang

et al. 2020

IEEE Commun. Surv. Tutorials

1,748

797

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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, e.g., for medical purposes and in vehicular networks. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. 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 legislations 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 and future research directions in FL.

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Section: Introductionmentioning

confidence: 99%

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Lim

Luong

Hoang

et al. 2020

IEEE Commun. Surv. Tutorials

1,748

797

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: A a General Frameworkmentioning

confidence: 99%

Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach

et al. 2020

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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. INDEX TERMS Federated learning, vehicular edge computing, model aggregation, contract theory.

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“…Guo et al [14][15] utilized the built-in cameras of smart devices to collect data of targets on the visual crowdsensing platform. CrowdTracker [11] is a target tracking system based on mobile crowdsensing, which recruits people to collaboratively take photos of the target. CrowdTracking [7] can rapidly locate a vehicle by using the photographing contexts and the road network, then estimate the vehicle's speed according to two successive localization results.…”

Section: B Target Trackingmentioning

confidence: 99%

Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones

Han

et al. 2020

IEEE Access

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Tracking moving objects in a city, such as suspicious vehicles or persons, is important for public safety management. Traditionally, target tracking is assisted by the pre-deployed stationary surveillance cameras, which are with insufficient coverage. In this work, we propose a different approach called Co-Tracking, a real-time target tracking system that leverages both citizens' mobile phones and stationary surveillance cameras to track moving objects collaboratively. Two key techniques are focused. Firstly, in order to accurately assign tracking tasks, we propose the Middle Query Location Prediction (MQLP) algorithm for predicting the target's location. Secondly, in order to efficiently utilizes these human/machine resources, we propose a heuristic algorithm, namely S-Maximum, to optimize the task allocation, including maximizing the number of completed tracking tasks and minimizing the number of mobile phones. Experimental results show that the proposed Co-Tracking system can effectively track moving objects with low incentive costs.

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CrowdTracker: Optimized Urban Moving Object Tracking Using Mobile Crowd Sensing

Cited by 51 publications

References 45 publications

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach

Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones

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