An efficient framework using visual recognition for IoT based smart city surveillance

Kumar, Manish; Raju, Kota Solomon; Kumar, Dinesh; Goyal, Nitin; Verma, Sahil; Singh, Aman

doi:10.1007/s11042-020-10471-x

Cited by 37 publications

(16 citation statements)

References 54 publications

(36 reference statements)

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“…Then, we defined the artifacts and the design processes that would be used to build those artifacts. The literature was reviewed to [24,36,[41][42][43][44][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61] Lack of integration between reference architecture and metamodel in smart building context [24,45,60] Lack of real-life validation of reference architecture in smart building context [52,59] determine if the knowledge base provides support to the artifact design. The artifacts were then designed, and an evaluation method was proposed to test the designed artifacts.…”

Section: Methodsmentioning

confidence: 99%

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A Reference Architecture for IoT-Enabled Smart Buildings

2022

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The management and analytics of big data generated from IoT sensors deployed in smart buildings pose a real challenge in today’s world. Hence, there is a clear need for an IoT focused Integrated Big Data Management and Analytics framework to enable the near real-time autonomous control and management of smart buildings. The focus of this paper is on the development and evaluation of the reference architecture required to support such a framework. The applicability of the reference architecture is evaluated by taking into account various example scenarios for a smart building involving the management and analysis of near real-time IoT data from 1000 sensors. The results demonstrate that the reference architecture can guide the complex integration and orchestration of real-time IoT data management, analytics, and autonomous control of smart buildings, and that the architecture can be scaled up to address challenges for other smart environments.

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Section: Methodsmentioning

confidence: 99%

“…In [54], the authors present a technique for the facial recognition in smart cities. This paper lacks a reference architecture for smart buildings.…”

Section: Related Workmentioning

confidence: 99%

A Reference Architecture for IoT-Enabled Smart Buildings

2022

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“…Fingerprint-based biometric systems could be a reason to spread the virus and it is not a safe method of authentication. Other options for biometric authentication include face or Iris recognition but they also have certain limitations [4,5]. With the recommended use of face masks, it is impossible to adopt face recognition and iris recognition needs high user cooperation that is also impossible to implement [6].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

Wazirali¹,

Ahmed²

2022

Computer Systems Science and Engineering

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The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required feature is the point of concern. This paper has proposed adopted multiple features and emphasis on the periocular region. In the proposed approach, combination of local binary pattern (LBP), color histogram and features in frequency domain have been used with deep learning algorithms for classification. Hence, we extract three types of features for the classification of periocular regions for biometric. The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel. In order to extract the frequency domain features, the wavelet transformation is obtained. By learning from these features, a convolutional neural network (CNN) becomes able to discriminate the features and can provide better recognition results. The proposed approach achieved the highest accuracy rates with the lowest false person identification.

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“…Specialized devices called wearable health devices that continuously monitor the real-time body conditions of the patient have gained immense popularity [ 2 ]. Since it can be personalized and can be used for continuous monitoring of vital parameters of a person it has become one of the most propitious platforms [ 3 ]. To frequently check the actions of a person without any disturbance, wearable devices are worn by an individual [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

BBNSF: Blockchain-Based Novel Secure Framework Using RP2-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems

Kumar

Mukherjee²,

Verma

et al. 2022

Sensors

Self Cite

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The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.

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An efficient framework using visual recognition for IoT based smart city surveillance

Cited by 37 publications

References 54 publications

A Reference Architecture for IoT-Enabled Smart Buildings

A Reference Architecture for IoT-Enabled Smart Buildings

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

BBNSF: Blockchain-Based Novel Secure Framework Using RP2-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems

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