Poster Abstract: Smart Urban Surveillance Using Fog Computing

Chen, Ning; Yu, Chen; Song, Sejun; Huang, Chin‐Tser; Ye, Xinyue

doi:10.1109/sec.2016.25

Cited by 60 publications

(26 citation statements)

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“…However, subject to the sparsity of mobile data, it is challenging to develop an effective location prediction model for an individual suspect using the existing algorithms [60][61][62][63]. In this paper, we propose a novel CMoB model to address this issue based on the …”

Section: Discussionmentioning

confidence: 99%

Prediction of Suspect Location Based on Spatiotemporal Semantics

Duan

et al. 2017

IJGI

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Abstract:The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data sparsity. This paper proposes a novel location prediction model called CMoB (Crime Multi-order Bayes model) based on the spatiotemporal semantics to enhance the prediction performance. In particular, the model groups suspects with similar spatiotemporal semantics as one target suspect. Then, their mobility data are applied to estimate Markov transition probabilities of unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by integrating the total transition probabilities, which are derived from the multi-order property of the Markov transition matrix, into a Bayesian-based formula, it is able to realize multi-step location prediction for the individual suspect. Experiments with the mobility dataset covering 210 suspects and their 18,754 location records from January to June 2012 in Wuhan City show that the proposed CMoB model significantly outperforms state-of-the-art algorithms for suspect location prediction in the context of data sparsity.

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Section: Discussionmentioning

confidence: 99%

Prediction of Suspect Location Based on Spatiotemporal Semantics

Duan

et al. 2017

IJGI

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“…Cloud computing can overcome that issue by exploiting fog devices that are between the cloud and the end users, to process the data. Ning Chen et al [31] exploit the concept of fog computing to support an urban traffic monitoring system. In Huang's et al [32] work the concept of vehicular fog computing is explained along with its potential and the challenges arising with it.…”

Section: Past Related Workmentioning

confidence: 99%

A Low-Cost Vehicular Traffic Monitoring System Using Fog Computing

et al. 2020

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With the rapid increase of vehicles in use worldwide, the need for efficient traffic monitoring systems has arisen. This work proposes a low-cost vehicular traffic monitoring system using IoT devices and fog computing. The system is based on a three-tiered architecture which is composed of (i) the mobile tracking system that records the positions of the vehicles using GPS technologies; (ii) the information gathering system which gathers all the data collected by the mobile tracking system; and (iii) the fog devices that process the data collected and extract the information needed. The system is tested in the town of Corfu during a period of increased tourism when the traffic is considered to be relatively dense. The mobile tracking system devices are placed on taxis and with the help of professional taxi drivers the accuracy of the data collected is evaluated. The system is able to record the movement of the vehicles accurately using its own independent data. The results can be remotely accessed by utilizing fog and cloud computing infrastructure established to process the data and upload it on a server. The system is used to give a better understanding of the speed variance in the center of the town during different dates and hours. In conclusion the system presented in this study can be utilized to monitor the traffic and provide vital information about its behavior in relation to time.

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“…The approach is computationally more intensive for edge devices, therefore, a fog node was used to perform a second pass on ENF estimation on the audio recordings with more robust extraction by eliminating the false alarms produced by the edge devices. The discussion of the edge-fog-cloud hierarchy is beyond the scope of this paper—interested readers may find the architecture description in our related publications [33,34,35]. Along with robust audio-based ENF extraction, video-based ENF extraction module could be added.…”

Section: Detecting Malicious Frame Injection Attacks Using Enf Sigmentioning

confidence: 99%

Detecting Malicious False Frame Injection Attacks on Surveillance Systems at the Edge Using Electrical Network Frequency Signals

Nagothu

Chen

Blasch

et al. 2019

Sensors

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Over the past few years, the importance of video surveillance in securing national critical infrastructure has significantly increased, with applications including the detection of failures and anomalies. Accompanied by the proliferation of video is the increasing number of attacks against surveillance systems. Among the attacks, False Frame Injection (FFI) attacks that replay video frames from a previous recording to mask the live feed has the highest impact. While many attempts have been made to detect FFI frames using features from the video feeds, video analysis is computationally too intensive to be deployed on-site for real-time false frame detection. In this paper, we investigated the feasibility of FFI attacks on compromised surveillance systems at the edge and propose an effective technique to detect the injected false video and audio frames by monitoring the surveillance feed using the embedded Electrical Network Frequency (ENF) signals. An ENF operates at a nominal frequency of 60 Hz/50 Hz based on its geographical location and maintains a stable value across the entire power grid interconnection with minor fluctuations. For surveillance system video/audio recordings connected to the power grid, the ENF signals are embedded. The time-varying nature of the ENF component was used as a forensic application for authenticating the surveillance feed. The paper highlights the ENF signal collection from a power grid creating a reference database and ENF extraction from the recordings using conventional short-time Fourier Transform and spectrum detection for robust ENF signal analysis in the presence of noise and interference caused in different harmonics. The experimental results demonstrated the effectiveness of ENF signal detection and/or abnormalities for FFI attacks.

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Poster Abstract: Smart Urban Surveillance Using Fog Computing

Cited by 60 publications

References 1 publication

Prediction of Suspect Location Based on Spatiotemporal Semantics

Prediction of Suspect Location Based on Spatiotemporal Semantics

A Low-Cost Vehicular Traffic Monitoring System Using Fog Computing

Detecting Malicious False Frame Injection Attacks on Surveillance Systems at the Edge Using Electrical Network Frequency Signals

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