Video Sensor Security System in IoT Based on Edge Computing

Jin, Shaolei; Sun, Bin; Zhou, Yusun; Han, Hongtuo; Li, Qiang; Xu, Chengang; Jin, Xinyu

doi:10.1109/wcsp49889.2020.9299709

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Moreover, with the recent developments in deep learning and machine learning (ML) algorithms, it is also possible to process and analyze large datasets. Therefore, this study focuses on user authentication using human activities and artificial intelligence approaches [3].…”

Section: Introductionmentioning

confidence: 99%

Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

Alzahrani¹,

Alderaan²,

Alatawi³

et al. 2023

Computers, Materials &Amp; Continua

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Internet of Things (IoT) devices incorporate a large amount of data in several fields, including those of medicine, business, and engineering. User authentication is paramount in the IoT era to assure connected devices' security. However, traditional authentication methods and conventional biometrics-based authentication approaches such as face recognition, fingerprints, and password are vulnerable to various attacks, including smudge attacks, heat attacks, and shoulder surfing attacks. Behavioral biometrics is introduced by the powerful sensing capabilities of IoT devices such as smart wearables and smartphones, enabling continuous authentication. Artificial Intelligence (AI)-based approaches introduce a bright future in refining large amounts of homogeneous biometric data to provide innovative user authentication solutions. This paper presents a new continuous passive authentication approach capable of learning the signatures of IoT users utilizing smartphone sensors such as a gyroscope, magnetometer, and accelerometer to recognize users by their physical activities. This approach integrates the convolutional neural network (CNN) and recurrent neural network (RNN) models to learn signatures of human activities from different users. A series of experiments are conducted using the MotionSense dataset to validate the effectiveness of the proposed method. Our technique offers a competitive verification accuracy equal to 98.4%. We compared the proposed method with several conventional machine learning and CNN models and found that our proposed model achieves higher identification accuracy than the recently developed verification systems. The high accuracy achieved by the proposed method proves its effectiveness in recognizing IoT users passively through their physical activity patterns.

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Section: Introductionmentioning

confidence: 99%

Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

Alzahrani¹,

Alderaan²,

Alatawi³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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A Video Analytics System for Person Detection Combined with Edge Computing

et al. 2022

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Ensuring citizens’ safety and security has been identified as the number one priority for city authorities when it comes to the use of smart city technologies. Automatic understanding of the scene, and the associated provision of situational awareness for emergency situations, are able to efficiently contribute to such domains. In this study, a Video Analytics Edge Computing (VAEC) system is presented that performs real-time enhanced situation awareness for person detection in a video surveillance manner that is also able to share geolocated person detection alerts and other accompanied crucial information. The VAEC system adopts state-of-the-art object detection and tracking algorithms, and it is integrated with the proposed Distribute Edge Computing Internet of Things (DECIoT) platform. The aforementioned alerts and information are able to be shared, though the DECIoT, to smart city platforms utilizing proper middleware. To verify the utility and functionality of the VAEC system, extended experiments were performed (i) in several light conditions, (ii) using several camera sensors, and (iii) in several use cases, such as installed in fixed position of a building or mounted to a car. The results highlight the potential of VAEC system to be exploited by decision-makers or city authorities, providing enhanced situational awareness.

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A Smart Building Fire and Gas Leakage Alert System with Edge Computing and NG112 Emergency Call Capabilities

et al. 2022

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Nowadays, the transformations of cities into smart cities is a crucial factor in improving the living conditions of the inhabitants as well as addressing emergency situations under the concept of public safety and property loss. In this context, many sensing systems have been designed and developed that provide fire detection and gas leakage alerts. On the other hand, new technologies such edge computing have gained significant attention in recent years. Moreover, the development of recent intelligent applications in IoT aims to integrate several types of systems with automated next-generation emergency calls in case of a serious accident. Currently, there is a lack of studies that combine all the aforementioned technologies. The proposed smart building sensor system, SB112, combines a small-size multisensor-based (temperature, humidity, smoke, flame, CO, LPG, and CNG) scheme with an open-source edge computing framework and automated Next Generation (NG) 112 emergency call functionality. It involves crucial actors such as IoT devices, a Public Safety Answering Point (PSAP), the middleware of a smart city platform, and relevant operators in an end-to-end manner for real-world scenarios. To verify the utility and functionality of the proposed system, a representative end-to-end experiment was performed, publishing raw measurements from sensors as well as a fire alert in real time and with low latency (average latency of 32 ms) to the middleware of a smart city platform. Once the fire was detected, a fully automatic NG112 emergency call to a PSAP was performed. The proposed methodology highlights the potential of the SΒ112 system for exploitation by decision-makers or city authorities.

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Video Sensor Security System in IoT Based on Edge Computing

Cited by 4 publications

References 12 publications

Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

A Video Analytics System for Person Detection Combined with Edge Computing

A Smart Building Fire and Gas Leakage Alert System with Edge Computing and NG112 Emergency Call Capabilities

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