IoT Based Home Security System

Face detection entails the technology that identifies the human face in digital images. Face detection can be used in various applications. One of the major areas of application of face recognition is in security systems. We know that a smart security system is the need of the hour and has become a necessity in today’s time. It is the basic need of humans to be able to protect one’s home and belongings. The security system needs to be smart and efficient. Hence, it avoids intruders and protects your possessions by minimizing break-ins. Installing a home security system helps in protecting your home and valuables from burglars. The security system developed has a camera module that will give access to only authenticated persons to unlock the main door of the house using facial recognition technology. This discrete system is developed using an electronic board called as Raspberry Pi can detect intruders as well as send off an alarm to nearby police stations using SMS or Email. Keywords: Home security; Face Recognition; Internet of Things (IoT); RaspberryPi Microcontroller; Control; Smartphones Cite this Article Ayush Yadav, Shaam Garg, Shivam Jamloki, Aanchal Sadana, Kusum Tharani. Home Security using Facial Recognition. Current Trends in Signal Processing. 2020; 10(2): 5–11p.

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Home automation system has achieved an enormous reputation in recent years, as day-to-day life is getting simpler due to the speedy growth of modern technology. Various implemented systems have been offered in the sphere of Home Automation but have apparently failed to provide cost-effective solutions for the same. In this paper, we provide a low-cost smart Home Automation and Security System (HASS) using Wireless Fidelity (Wi-Fi), and Bluetooth. These materialize the concept of internetworking of smart devices. A Wireless Sensor Network (WSN) based on Wi-Fi is considered to monitor and control the smart environment, safety as well as electrical parameters of a smart interconnected home. The Android application-based Graphical User Interface (GUI) on a Smartphone is used to give facility to users for seamless control over the devices in a smart home. It may also consist of home security for example access control and alarm systems. When connected to the Internet, home devices are an important element of the Internet of Things (IoT). Home security includes both the security hardware placed on a property and individuals' personal security practices. Security devices consist of a door locking, alert alarm, lighting, motion detectors, and security camera system. Additionally, several special features include such as ensuring windows are closed, each door is locked, all alarms are activated, and extra keys are not secret outside.

Internet of things and Automation systems have taken over the world at present times. Home security has always been a field of concern among mass people. Making home security systems safer, cheaper and easy to understand is a challenge that many engineers around the world are striving to achieve in developing countries. Considering the economic condition of people of a developing country like Bangladesh, we propose an IoT based smart home security and automation system named `Facebook messenger Chatbot' which is made using Raspberry PI as the central processor. It will enable its users to use it free of cost as Facebook messenger app is free in Bangladesh. People can use it from home or any remote location and can control digital home appliances like fans, lights etc. Smart door locking system, face recognition, gas leakage detection, temperature detection and control, humidity detection, fire detection, which has been done through collecting data from different sensors and passing them to Raspberry PI and then PI passes these to the BOT through a REST API, are the additional features that make our security system safer, effective and easier to use compared to other existing approaches.

The rapid expansion of Internet of Things (IoT) quickly evolves towards a connected network of AI-enabled, smart, multi-sensory devices that generate, consume and exchange enormous amounts of data. Stimulated by the omnipresent cloud-services and ever widening data bandwidth they aspire to control every aspect of our lives from transportation to our health, significantly increasing our reliance on the web-based services and their security. Already millions of cyber security alerts are generated every day and trigger increasingly costly investigations of the Security Operations Centres (SOC). In order to make their operation more efficient security warnings need to be reliably detected and classified based on various levels of severity, scale of potential damage or an ability to defend. We have responded to this challenge in the context of IEEE BigData Cup 2019 focused on predicting cyber security threats that require attention based on detailed logs of network activity leading to the security alert. We have developed a hybrid supervised learning ensemble model combining several state-of-the-art Extreme Gradient Boosting algorithms. Specifically, xGBoost and LightGBM model versions have been built on separate sets of features extracted from the raw logs of network events preceding the alerts and then synergically aggregated. Models' diversity imposed by algorithmic differences, complementary feature subsets, and individually optimized hyperparameters, combined with robust stratified cross-validation scheme resulted with the best true alerts detection rate yielding the AUC score in excess of 0.93, that outperformed all other 248 competing teams.

As the Internet of Things (IoT) continues to grow, efficient and reliable communication methods are essential for enabling widespread connectivity, particularly in remote and resource-constrained environments. LoRa, with its low power consumption and long-range capabilities, has become a cornerstone of IoT communication. The LoRaWAN protocol extends this functionality by enabling devices to transmit data globally through internet-connected gateways. These gateways aggregate data packets from nearby devices and forward them to centralized servers.However, traditional LoRaWAN gateways face significant limitations. They are often cost-prohibitive, require constant internet connectivity, and lack advanced data processing capabilities, making them unsuitable for deployment in areas with unreliable or no cellular coverage. These limitations hinder the adoption of LoRaWAN in scenarios such as environmental monitoring and rural IoT networks.To address these challenges, we developed a low-cost, energy-efficient edge-computing LoRaWAN gateway using embedded systems such as Raspberry Pi 0. Unlike conventional gateways, these edge-computing gateways locally process incoming data, enabling intelligent features such as optimized transmission, data buffering during network outages, and adaptive communication strategies. Additionally, these gateways can be configured to operate independently of cellular networks by utilizing satellite connectivity, further enhancing their usability in remote or off-grid applications.Preliminary testing demonstrates that these gateways consume up to one-third of the power required by traditional gateways while maintaining reliable data transmission. This substantial reduction in power consumption extends operational lifespans and reduces deployment costs. The ability to process and optimize data locally also improves network efficiency, ensuring timely and reliable communication even under challenging conditions.This innovation provides a scalable, cost-effective solution for IoT connectivity in remote and underserved regions. By addressing the limitations of conventional gateways, these edge-computing gateways enhance the feasibility of deploying IoT networks in scenarios where traditional infrastructure is impractical or unavailable. The broader implications of this work include improved access to IoT technologies for applications such as environmental monitoring and agriculture, ultimately expanding the reach and impact of IoT systems worldwide.

This project presents a Home Automation Security System that integrates face detection and recognition with RFID technology using a Raspberry Pi. The system aims to enhance security and access control in homes by automating the process of user authentication through facial recognition and RFID tags. The system utilizes a Raspberry Pi as the central control unit, a camera module for face detection and recognition, and an RFID reader to scan RFID tags for additional access control. The system functions by scanning an RFID tag, and upon detection of a valid tag, it activates the camera to capture the face of the person requesting access. The captured image is then processed using OpenCV and face_recognition libraries in Python to match the face with a pre-stored database of authorized individuals. If both the RFID tag and face match the stored data, the system grants access by performing automation actions such as unlocking doors or turning on lights. Keywords- Raspberry Pi, Pi Camera, Face Detection, Face Recognition, RFID Technology, RFID Tag Verification.

- In today’s digital landscape, the protection of sensitive and confidential documents has become critically important to prevent unauthorized access and potential data breaches. This project presents a Smart Document Protection System that leverages dual biometric authentication and IoT-enabled security alert mechanisms, ensuring a secure and intelligent approach to document protection. The core of the system is built around an Arduino Uno which manages the hardware-side authentication processes. The security system implements a two-step fingerprint authentication and a secure password check, allowing only verified users to gain access to the protected documents. Upon successful biometric and password verification, the system automatically unlocks an electronic door lock, granting access to the stored documents. In the case of an incorrect password or unauthorized access attempt, the system triggers a Python-based script to capture an image using the laptop's camera and sends a real-time alert with the photo to the concerned authority via Telegram messaging SMS notification. Key Words: Arduino Ide Software, Telegram API, Arduino C language, Real-time Security Alerts.

The widespread placement of sensors and devices in the Internet of Things (IoT) poses a significant challenge to spectral efficiency. Since its conception, the IoT has faced concerns about spectrum shortages. The application of Cognitive Radio (CR) technology is considered as a possible solution. CR poses challenges in the IoT, including short response times, efficient spectrum recognition in low signal-to-noise ratios. In this paper, Leveraging Global Channel State Information in Cognitive Radio IoT Networks: An Integrated Binarized Simplicial Convolutional Neural Network and Red-Fox Optimization Algorithm for Optimal Spectrum Sensing (CR-IoT-BSCN-RFOA) is proposed. Initially, data is gathered from RADIOML2016.10b dataset. Then the collected data is given into the feature extraction phase by using Refined Linear Chirplet Transform (RLCT) for extracting features, like spectrum holes detection, signal entropy, power spectral density, SNR, adjacent user range, received signal strength index, bandwidth, fading fact. The Binarized Simplicial Convolutional Neural Network (BSCN) is used to predict optimal primary user. The Red-Fox Optimization Algorithm (RFOA) is proposed to optimize the weight parameters of BSCN. Finally, the performance of the proposed CR-IoT-BSCN-RFOA method provides 27.10%, 29.22% and 28.36% higher accuracy when compared with existing techniques: Fuzzy ELM-dependent optimal spectrum sensing in CR-Internet of Things network (OSS-CR-IoT), Multi-Antenna Spectrum Sensing along Alpha-Stable Noise for Cognitive Radio-aided IoT (MAS-CR-IoT) and Adversarial Attacking and Defensing Modulation Recognition with DL in Cognitive Radio-assisted IoT (ADMR-DL-IoT) methods respectively.

In today’s life with the influence and vast usage of Internet of Things (IoT), a surveillance system become an essential needs (no longer a luxury facility) to home residents, buildings and other important premises. This project deals with web-based and mobile app surveillance system using Raspberry Pi and its supporting components i.e., Pi Camera, PIR motion sensor, Ultrasonic sensor, web-based & mobile application. This proposed solution can be implemented over the internet using any computer and mobile devices from anywhere and anytime. The use of Raspberry Pi has given the ability to operate and control the motion detectors, distance of the intruders and video cameras for remote sensing and surveillance. The cameras automatically stream live video and the raspberry pi device will send an alert via email and SMS to the facility owners’ computer or mobile devices. It is considered as a cost-effective solution, customizable and easy to implement by the home residents outside their home residents in comparison with other commercial surveillance system products such as CCTV, IP Camera, etc.

This study is a qualitative study with a descriptive approach, namely describing each main object used in this study, including the Internet of Things, Smart Room Customization, and Service Quality. The data used in this study are secondary data that researchers obtain from various credible sources such as books, scientific articles, credible websites, and various other research sources. The data obtained are analyzed by stages of data collection, data reduction, and drawing conclusions. The result in this aticle show. the Internet of Things can create smart Room Customization. Smart Room Customization is built using the concept of the Internet of Things (IoT), a concept where an object has the ability to receive and send data over a network without requiring human interaction to a computer. The Internet of Things (IoT) is a concept that aims to expand the benefits of continuously connected internet connectivity. Kaur, Singh, Khairwal, & Jain conducted a study on the Home Automation and Security System with the GSM communication method and Android Application, the system can control equipment and help protect thieves because there is an alarm and can send signals to the owner using Android [. Giorgos Sfikas also conducted a study Creating a Smart Room using an IoT approach, a study that explains the concept of building a Smart Room using Arduino and a Bluetooth module to control electronic equipment. In the end, the benefits that the researcher mentioned above serve to facilitate customers in accessing, utilizing facilities, and other things that can facilitate customers to travel and stay in an integrated place and hotel. The convenience that the researcher mentioned above is one form of improving the quality of tourism services.

This research paper talks about the development of an innovative IoT-based smart door lock that also uses facial recognition, Internet of Things (IoT) integration, and the ESP32-CAM microcontroller to increase home security. As locks continue to evolve with more new features, the use of IoT is increasing and enhancing the security and usability. ESP32-CAM was selected for this project because of its built-in Wi-Fi connectivity and camera, which render it exceptionally suited for creating a smart lock system that is able to not only recognize the saved faces but also alert the user if there is an intruder. When compared to other microcontrollers like Raspberry Pi, the ESP32- CAM offers many more advantages, including enhanced security features, a userfriendly interface, and a lower cost, and much more in initial price. This paper also compares and talks about already existing facial recognition-based door locks, providing a detailed analysis of their plus points and limitations to understand the current state of the technology. The research also provides a detailed breakdown including the system's development process, selection of components for the device, and compilation of hardware components for the implementation of the model, The paper also includes the algorithms and user interface design. In this paper, you’ll see how each component works, its usage, and how each component works together to ensure reliable and accurate facial recognition. Also, model testing was done to get the device's performance in a number of different conditions and usage areas. The results of these tests show that ESP32-CAM based smart door lock is a good and powerful solution and it is also cost-effective, offering good amount of savings compared to other smart lock systems in the market. This paper highlights how the project talks about some of the common security problems associated with the old locking system. By offering a more accessible and reliable alternative, this smart lock system has the potential to revolutionize home security. The integration of advanced facial recognition technology with IoT makes the home door lock system a more secure and user-friendly approach to home access control.

Congestive heart failure detection using random forest classifier

Abandoned object detection is used to identify the unattended, strange object for a specific time period. Abandoned object detection plays a vital role in the places where there is high security alerts e.g. terrorist hits. Human feature Detection is done to differentiate between humans from arbitrary objects. It is implemented for the real time situation and capturing of live streaming videos. In previous methodologies only security alarms were used to alert the user. But proposed system checks whether the detected abandoned object is human being or static object like bag. The image of abandoned object is sent to the security guard through E-Mail and Android Application. This system is divided into three parts as follows: 1. Abandoned Object Detection 2. Human feature Detection 3. Security Alert System.

Learning about sensor technology and actuator early is important as a step towards knowing and introducing of advanced technologies based on Internet of Things (IoT). The difficulties are how to learn sensor technology and move the actuator with accessing General Purpose Input Output (GPIO) of Raspberry Pi 3 Platforms using programming language syntax which often confusing and difficult to understand. To help people learning IoT by using Raspberry Pi 3 with an interesting Android apps, we believe that this learning module can integrate about the ease and attractiveness of IoT System Editor based on Android apps. This research create a mobile programming apps based on Android which people can build IoT project easily with GUI without program and middleware based on Raspberry Pi to connect between apps and hardware with especially task to manage data communication, data flow, and device driver. Hopefully new developer can develop the IoT application easily by using Android mobile visual programming that combined with Raspberry Pi 3 platform.