Detecting Pickpocket Suspects from Large-Scale Public Transit Records

Du, Bowen; Liu, Chuanren; Zhou, Wenjun; Hou, Zhenshan; Xiong, Hui

doi:10.1109/tkde.2018.2834909

Cited by 27 publications

(32 citation statements)

References 28 publications

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“…Several works are reported to predict next location from movement traces such as GPS log, check-in data or social network information [16], [17], [18]. There are challenging applications, namely, urban land-use classification from taxi-traces [19], categorizing users in an academic campus [20] or catching pick-pockets from large-scale transit records [21]. It is well known that human movement traces follow spatio-temporal regularity.…”

Section: Related Workmentioning

confidence: 99%

Mobi-IoST: Mobility-Aware Cloud-Fog-Edge-IoT Collaborative Framework for Time-Critical Applications

Ghosh

Mukherjee

Ghosh

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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The design of mobility-aware framework for edge/fog computing for IoT systems with back-end cloud is gaining research interest. In this paper, a mobility-driven cloud-fog-edge collaborative real-time framework, Mobi-IoST, has been proposed, which has IoT, Edge, Fog and Cloud layers and exploits the mobility dynamics of the moving agent. The IoT and edge devices are considered to be the moving agents in a 2-D space, typically over the road-network. The framework analyses the spatio-temporal mobility data (GPS logs) along with the other contextual information and deploys machine learning algorithm to predict the location of the moving agents (IoT and Edge devices) in real-time. The accumulated spatio-temporal traces from the moving agents are modelled using probabilistic graphical model. The major features of the proposed framework are: (i) hierarchical processing of the information using IoT-Edge-Fog-Cloud architecture to provide better QoS in real-time applications, (ii) uses mobility information for predicting next location of the agents to deliver processed information, and (iii) efficiently handles delay and energy consumption. The performance evaluations yield that the proposed mobility prediction algorithm has approximately 93% accuracy and reduced the delay and power by approximately 23-26% and 37-41% respectively than compared to the existing mobility-aware task delegation system.

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Section: Related Workmentioning

confidence: 99%

Mobi-IoST: Mobility-Aware Cloud-Fog-Edge-IoT Collaborative Framework for Time-Critical Applications

Ghosh

Mukherjee

Ghosh

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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“…How to separate the noises from a dataset is a challenging problem. In data mining, researchers use the density [67][68] or distribution [69][70][71] to eliminate noises or isolated points. Furthermore, clustering [72][73] and classification algorithms [74][75] provide functions to eliminate them.…”

Section: Dhblan Coefficientmentioning

confidence: 99%

Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

Cao

Bao-zhi

et al. 2019

IEEE Trans. Neural Netw. Learning Syst.

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“…For instance, constant, intermittent, reactive, and adaptive jamming schemes have been proposed under different system setups, in which Gaussian noise is artificially generated as the jamming signals to interfere with the targeted receivers [1]. To efficiently disrupt the communications, these existing schemes require 4 It has been shown in [6] that in public transit systems, rough user mobility profiles collected based on passengers' large-scale transit records are useful to catch malicious users such as pickpocket suspects; while the user mobility profiles extracted from mobile data here are even more accurate. As a result, the user mobility profiling is practically feasible in suspicious user identification.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, if the devices (such as smartphones like BlackBerry and iPhone) can automatically encrypt transmit data for users with the key pre-installed, the SIDs (managed by government agencies) can legally acquire such information from the device (such as smartphone) manufacturing company. 6 We refer interested readers to [1] for more details on higher-layer encryption and decryption.…”

Section: Proactive Eavesdropping Of Suspicious Communicationsmentioning

confidence: 99%

Surveillance and Intervention of Infrastructure-Free Mobile Communications: A New Wireless Security Paradigm

Duan

Zhang

2017

IEEE Wireless Commun.

137

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Conventional wireless security assumes wireless communications are rightful and aims to protect them against malicious eavesdropping and jamming attacks. However, emerging infrastructure-free mobile communication networks are likely to be illegally used (e.g., by criminals or terrorists) but difficult to be monitored, thus imposing new challenges on the public security. To tackle this issue, this article presents a paradigm shift of wireless security to the surveillance and intervention of infrastructure-free suspicious and malicious wireless communications, by exploiting legitimate eavesdropping and jamming jointly. In particular, proactive eavesdropping (via jamming) is proposed to intercept and decode information from suspicious communication links for the purpose of inferring their intentions and deciding further measures against them. Cognitive jamming (via eavesdropping) is also proposed so as to disrupt, disable, and even spoof the targeted malicious wireless communications to achieve various intervention tasks.

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Detecting Pickpocket Suspects from Large-Scale Public Transit Records

Cited by 27 publications

References 28 publications

Mobi-IoST: Mobility-Aware Cloud-Fog-Edge-IoT Collaborative Framework for Time-Critical Applications

Mobi-IoST: Mobility-Aware Cloud-Fog-Edge-IoT Collaborative Framework for Time-Critical Applications

Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

Surveillance and Intervention of Infrastructure-Free Mobile Communications: A New Wireless Security Paradigm

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