A smart lock system using Wi-Fi security

Kassem, Abdallah; Murr, Sami El; Jamous, Georges; Saad, Elie; Geagea, Marybelle

doi:10.1109/actea.2016.7560143

Cited by 45 publications

(15 citation statements)

References 5 publications

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“…A mobile application provides the user with all controls to monitor the locking system as he/she desires. It stores all the digital keys and the user can also create and send temporary keys to guests or any trusted ones [3]. This system has an easy installation process and with its operation, by minimum complexity and strong feasibility, it provides for better locking system prospects [3].…”

Section: Wi-fimentioning

confidence: 99%

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Smart Lock Systems: An Overview

Celestine¹

2020

IJCA

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Section: Wi-fimentioning

confidence: 99%

“…It is, however, possible to easily misplace keys than to lose a phone hence, the smart lock system uses this fact to its advantage. Moreover, to ensure high-end security while using smartphones, the standard AES encryption can be used which is hard to penetrate [3].…”

Section: Introductionmentioning

confidence: 99%

Smart Lock Systems: An Overview

Celestine¹

2020

IJCA

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“…There are methods proposed in the literature that use different wireless communication standards like ZigBee, RFID [4], Bluetooth [7] and Wi-Fi [9] to exchange data and signaling between components. These systems offer a way to simply open the door either remotely or by connecting to the LAN.…”

Section: Existing Systemsmentioning

confidence: 99%

IoT and Face Recognition based Remote Access System

Bairolu¹,

Sheth²,

Hazra³

et al. 2019

Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th Edition

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There are many security systems available in the market and in thesis, which are stringent and can make things worse in the hour of need as there are many situations which demand customized authentication. These systems either require the visitors to provide details of authenticity, for instance, ID cards, passwords, etc. or require someone to explicitly be near the entrance to manually check the details. There are even some existing security systems which become cumbersome for the owners by seeking permission time and again even when the visitor is a frequent one. This paper, therefore is focused on providing a smart solution, without compromising security, by employing a system which will not only grant access to authorized visitors, but will also allow some expected visitors to walk through the doors and restrict the rest even when there is no one physically present near the entrance or anywhere near the concerned area to manually check on the visitors. This system will therefore solicit permission from the owner only when the visitor is unknown. This remote access system therefore uses face recognition, which is a biometric artificial intelligence based technology, and Internet of Things (IoT) to give access to the known visitors when it recognizes them successfully or to give access to the expected visitors by sending an e-mail to the owner with a picture of the visitor, to let the owner decide whether or not to give access to the visitor even when the owner isn't anywhere near the entrance. For face recognition, histogram of oriented gradient has been used as it requires lower computational time and lower processing power. To allow the owner to remotely send controlling commands to the raspberry pi, Flask has been used to host a webpage designed solely for the stated purpose, along with serveo that allows remote port forwarding. The system also uses an IoT API (ThingSpeak) to upload the name of the authorized visitors and their time of arrival.

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“…To carry out three different levels of security, this research will implement a smart lock [11]. Multilevel Security will be carried out in forms of a combination of password, Wi-Fi connection, and device buttons implemented on the smart lock [12].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model

Putrada

Ramadhan

Abdurohman

2020

KINETIK

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Context-Aware Security demands a security system such as a Smart Door Lock to be flexible in determining security levels. The context can be in various forms; a person’s activity in the house is one of them and is proposed in this research. Several learning methods, such as Naïve Bayes, have been used previously to provide context-aware security systems, using related attributes. However conventional learning methods cannot be implemented directly to a Context-Aware system if the attribute of the learning process is low level. In the proposed system, attributes are in forms of movement data obtained from a PIR Sensor Network. Movement data is considered low level because it is not related directly to the desired context, which is activity. To solve the problem, the research proposes a hierarchical learning method, namely Hierarchical Hidden Markov Model (HHMM). HHMM will first transform the movement data into activity data through the first hierarchy, hence obtaining high level attributes through Activity Recognition. The second hierarchy will determine the security level through the activity pattern. To prove the success rate of the proposed method a comparison is made between HHMM, Naïve Bayes, and HMM. Through experiments created in a limited area with real sensed activity, the results show that HHMM provides a higher F1-Measure than Naïve Bayes and HMM in determining the desired context in the proposed system. Besides that, the accuracies obtained respectively are 88% compared to 75% and 82%.

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A smart lock system using Wi-Fi security

Cited by 45 publications

References 5 publications

Smart Lock Systems: An Overview

Smart Lock Systems: An Overview

IoT and Face Recognition based Remote Access System

Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model

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