Passive Infrared Sensor Research Articles

Cost-Efficient Automated Intrusion Detection and Reporting System for Homes in Nigeria

Substantial investments are made to mitigate the persistent threat to lives and assets posed by various forms of intrusion. Automated security systems have emerged as crucial tools for safeguarding homes against intrusions in recent times. A major advantage of this is its independent ability to report human activities around homes without direct observation. However, the cost of implementing the system is not pocket-friendly to an average Nigerian given the prevailing economic situation. This research therefore seeks to develop and implement an intrusion detection and reporting system using low cost materials while maintaining a balance between quality and cost at the same time. The system architecture employs the passive infrared sensor (PIR) for motion detection, the ultrasonic sensor measures intruders’ location from the home entrance while the NodeMCU ESP8266 microcontroller is responsible for coordinating the reception and relaying of signals within the system. The system was implemented with varying distances of human presence from the security device. Reports of potential intrusion alerts were obtained within reasonable time frames (between 11 seconds and 49 seconds) on the homeowner’s mobile phone via the Blynk application. This performance demonstrates the reliability of the system for home security. The system is equally cost-efficient relative to most similar state-of-the art IoT based home security systems considered in this work. Our work therefore contributes to knowledge by proposing an affordable home security solution for a low budget Nigerian user.

Development of a novel LED-IoT photoreactor for enhanced removal of carbamazepine waste driven by solar energy

This study introduces an innovative LED-IoT photoreactor, representing a significant advancement in response to the demand for sustainable water purification. The integration of LED-IoT installations addresses the challenge of intermittent sunlight availability, employing LEDs with a spectrum mimicking natural sunlight. Passive Infra-Red (PIR) sensors and Internet of things (IoT) technology ensure consistent radiation intensity, with the LED deactivating in ample sunlight and activating in its absence. Utilizing a visible light-absorbing photocatalyst developed through sol-gel synthesis and mild-temperature calcination, this research demonstrates a remarkable carbamazepine removal efficiency exceeding 95% under LED-IoT system illumination, compared to less than 90% efficiency with sunlight alone, within a 6-h exposure period. Moreover, the designed photocatalytic system achieves over 60% mineralization of carbamazepine after 12 h. Notably, the photocatalyst demonstrated excellent stability with no performance loss during five further cycles. Furthermore, integration with renewable energy sources facilitated continuous operation beyond daylight hours, enhancing the system's applicability in real-world water treatment scenarios. A notable application of the LED-IoT system at an operating sewage treatment plant showed nearly 80% efficiency in carbamazepine removal from sewage in the secondary settling tank after 6 h of irradiation, coupled with nearly 40% mineralization efficiency. Additionally, physicochemical analyses such as XPS and STA-FTIR confirm that the carbamazepine photooxidation process does not affect the surface of the photocatalyst, showing no adsorption for degradation products.

Deep Weighted Fusion Learning (DWFL)-based multi-sensor fusion model for accurate building occupancy detection

With the advancement of artificial intelligence, the dominance of deep learning (DL) models over ordinary machine learning (ML) algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition. With the growing demands for power savings, building energy loss reduction could benefit from DL techniques. For buildings/rooms with the varying number of occupants, heating, ventilation, and air conditioning (HVAC) systems are often found in operations without much necessity. To reduce the building’s energy loss, accurate occupancy detection/prediction (ODP) results could be used to control the proper operations of HVACs. However, ODP is a challenging issue due to multiple reasons, such as improper selection/deployment of sensors, inefficient learning algorithms for pattern recognition, varying room conditions, etc. To overcome the above challenges, we propose a DL-based framework, i.e., Deep Weighted Fusion Learning (DWFL), to detect and predict occupancy counts with optimal multi-sensor fusion structure. DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor. Such weight assignment considers different room conditions and the pros/cons of each type of sensor. To evaluate DWFL model in terms of occupancy prediction accuracy, we have set up an experimental testbed with low-cost cameras, carbon dioxide (CO2), and passive infrared (PIR) sensors. Among the recently proposed occupancy detection models, DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88% accuracy in occupancy count estimation (Xue et al., 2019). Another deep learning-based model MI-PIR achieved 91% accuracy on raw analog data from PIR sensors (Andrews et al., 2020). Our research outcome is 94%. Therefore, the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.

Novel occupancy detection method based on convolutional neural network model using PIR sensor and smart meter data

Occupancy information, especially the number of occupants, can effectively guide the operation of building systems. Capturing real-time and accurate occupancy information is critical. Passive infrared (PIR) sensors and smart meters have been deployed extensively. To improve the occupancy detection accuracy, this study proposes an innovative occupant number detection approach involving a temporal sequential analysis of occupancy-related data using data fusion from PIR sensors and smart meters with a convolutional neural network (CNN) model. A university office was investigated as a case study. PIR sensors and smart meters were adopted to collect data. And CNN model was established with both various data types and historical data as multi-dimensional model inputs. The model outperformed than traditional ANN models with an improved accuracy by 26.8%. The proposed occupancy detection model was applied to optimize the control strategy of an outdoor air system, which helped to provide comfortable built environment and realize energy savings.

Human Detection for the Betterment of Security System Using PIR Sensor and BLYNK App

Abstract: The Human Detection System (HDS) presented in this project utilizes Passive Infrared (PIR) sensors, Node MCU microcontroller, and the Blynk IoT app to detect human presence in a designated area, providing real-time monitoring and instant alerts. Additionally, an ESP32 module camera is integrated into the system to visually confirm detected events. Upon sensor activation, the ESP32 camera captures footage of the area, allowing users to verify the validity of the alert via a provided link accessible on mobile or laptop devices. This comprehensive approach enhances security measures by combining both sensor-based detection and visual confirmation capabilities, offering an effective solution for surveillance applications.

Home Automation System

The world is rapidly moving towards automation as people have less time to handle all the work that needs to be done. Automation is an easy solution to control any product or machine to suit our needs. This article presents the development and implementation of a new home automation system unit using Arduino Nano as the main processing unit. The system integrates multiple sensors such as passive infrared (PIR) sensors for motion detection, gas sensors for monitoring gas issues, temperature sensors for detecting temperature changes, and light sensors for monitoring ambient light, as well as Bluetooth modules for seamless wireless communication and monitoring capabilities. The primary objective of this research is to create a smart and flexible home automation solution that enhances comfort, security, and energy efficiency.

IOT BASED VEHICLE THEFT DETECTION AND ENGINE LOOKING SYSTEM

The advancement of smart home technology has revolutionized traditional security measures, offering more efficient and convenient solutions for safeguarding homes and properties. In this project, we propose a smart door lock system that integrates Passive Infrared (PIR) motion detection, a solenoid-based locking mechanism, and an ESP32 Cam module for intruder capturing and remote monitoring. The core components of the system include a PIR sensor, which detects motion in the vicinity of the door, and a solenoid actuator, which controls the locking and unlocking of the door. The ESP32 Cam module serves as the central processing unit, responsible for coordinating the operation of the components and capturing images or videos of intruders. Upon detecting motion, the PIR sensor triggers the ESP32 Cam module to activate the solenoid, thereby locking the door to prevent unauthorized access. Simultaneously, the ESP32 Cam captures images or videos of the intruder using its built-in camera and stores the evidence locally or transmits it to a designated mobile device. Keywords: Biometric access control, Motion detection, Intruder detection, PIR sensor, ESP32 Cam, Smartphone app control, Solenoid actuator

Farmland Intrusion Detection using Internet of Things and Computer Vision Techniques

Farmland security in Nigeria is still a major challenge and existing methods such as building brick fences around the farmland, installing electric fences, setting up deterrent plants with spikey branches or those that have displeasing scents are no longer suitable for farmland security. This paper presents an IoT based farmland intrusion detection model using sensors and computer vision techniques. Passive Infrared (PIR) sensors and camera sensors are mounted in strategic positions on the farm. The PIR sensor senses motion by the radiation of body heat and sends a message to the raspberry pi to trigger the camera to take a picture of the scene. An improved Faster Region Based Convolutional Neural Network is developed and used for object detection and One-shot learning algorithm for face recognition in the case of a person. At the end of the detection and recognition stage, details of intrusion are sent to the farm owner through text message and email notification. The raspberry pi also turns on the wade off system to divert an intruding animal away. The model achieved an improved accuracy of 92.5% compared to previous methods and effectively controlled illegal entry into a farmland.

An Automatic Sliding Door: Leveraging Locally Sourced Materials for Innovation

A door is an essential component of a building. It provides security and access to the different parts of a building. In the modern era, when people want everyday life to be completely automated so that work can be done easily in a short amount of time without wasting energy, automatic doors have continued to be fashionable. In addition to being effective and efficient as automation continues to develop rapidly, automatic doors are essential to curbing the transmission of infectious diseases like Ebola and COVID-19. While automatic doors are available in Nigeria, they are imported, thereby shrinking the nation’s foreign exchange. Not only that, but they also cost more when imported compared to when they are manufactured locally. In this work, therefore, a cost-effective automatic sliding door was developed using locally sourced materials from Nigeria. The door uses a passive infrared sensor (PIR) to detect a human or an object in a doorway and automatically opens and closes the door by signalling a microcontroller to activate a DC motor, which drives a belt pulley mechanism. The conceptual design of the system was designed and simulated using SolidWorks. Based on the design, calculations were made to determine the appropriate materials to source. The DC motor model was simulated using MATLAB SIMULINK as a physical model to observe how it would behave in the presence of noise, and the PID controller was tuned to give optimal performance using MATLAB Auto tuning. Some materials were locally purchased, while others were constructed through standard workshop methods. A cost-saving advantage of 95.77% and 89.13% were achieved when compared to imported equivalent types from the USA and China, respectively. The siding door was assembled and subjected to a functionality test, and it was observed that the automatic sliding door successfully detects humans and objects and actuates the motor to open and close the door after the object is out of the set sensing range of 50cm. This work is a step forward in boosting local innovation in Nigeria.

Intelligent passive infrared sensor based on learning vector quantization for human detection

Passive Infrared (PIR) Sensors have been used widely in human detection indoors nowadays due to their low cost and range. However, traditional PIR sensors may get fault detection, especially when the human is in a static pose. To overcome this limitation, a Machine Learning (ML)-based PIR sensor is proposed in this work for detection accuracy enhancement. The Learning Vector Quantization (LVQ) approach is used to be easily implemented in the embedded device (which requires a low computational complexity) to provide a real-time response. The experimental scenarios to create the datasets are conducted in two distinct locations for training and testing purposes. In each location, participants performed a series of different activities and left the room unoccupied. Data is collected via a PIR sensor and then wireless transmitted to a computer for training and testing. In the test set, the presence of humans with an accuracy of 89.25 % is obtained using the proposed LVQ algorithm prediction. Finally, the LVQ is implemented on an embedded device based on Xtensa Dual-Core 32-bit LX6 CPU to form an intelligent PIR (iPIR)-based LVQ sensor, this novel iPIR sensor then is evaluated and tested with a remarkable result.

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches.

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

A Calibration Method for Optimizing Detection of Species Using Camera Traps

Camera traps have become a common tool in research, monitoring, and conservation practice. Placing camera traps to detect wildlife accurately requires an understanding of how they detect animals. Importantly, how passive infrared sensors (PIR) detect radiant heat, the subsequent detection zone, heat signatures of target species, background temperature effects and appropriate height and orientation of the animal’s trajectory in relation to the camera trap are critical. We describe a calibration approach we developed to optimise detection of wildlife using a watermark overlay that delineates the detection zone of the camera trap in relation to the passage of travel of the target species.

IMANoBAS: An Improved Multi-Mode Alert Notification IoT-based Anti-Burglar Defense System

Burglary involves forced or unauthorized entry, which leads to damage or loss of property having monetary or emotional value and, more severely, puts lives at risk. The dire need for the safety of lives and properties has attracted so much research on burglary alert system using Internet of Things (IoT) technology. Most of the research focused on alerting the users of burglary attempts using any or a combination of two notification methods: SMS, call, and email. This study emphasizes three-mode notification that combines SMS, call, and email using the application of IoT technology in a burglary alert system, which uses a Passive Infrared (PIR) sensor for burglar detection to ensure that Homeowners or authorized personnel get alerts in events of imminent attempt to break-ins. The study also details the sensor integration with its supporting components, such as the central hub or microcontroller, buzzer, LED, and network interface in the development of the system. The software was developed to facilitate seamless integration with the hardware, ensuring timely and accurate event detection and subsequent alert generation using Arduino IDE programming language, a framework based on the C++ language. The system effected the 3-mode notification to ensure that users get notification in case of an imminent break-in since the failure of the three modes simultaneously is extremely rare. The system’s performance based on its responsiveness on the 3-mode notifications was evaluated, and an average of 83.56% responsiveness was obtained, indicating an acceptable response time.

Development of an Advanced Robotic System with Line Tracking and Sanitization in Healthcare Industry

COVID-19 is highly transmittable, especially for frontline healthcare workers. Far-UVC light at 222 nm wavelength effectively inactivates the virus and is safe for human skin and eyes. To reduce the contact between healthcare workers and infected patients while sanitizing the area, this work introduces UVood, an advanced robotic system with automated line tracking and sanitizer to enhance the safety of frontline healthcare workers during the COVID-19 pandemic. Utilizing far-UVC light, the system achieves effective sanitization without harm to humans. Controlled by an Arduino Mega microcontroller and incorporating infrared (IR) and passive infrared (PIR) sensors, UVood allows precise navigation and continuous disinfection. This prototype also integrates a touchscreen as a user interface for user control. Through the functionality verification, the developed proof-of-concept prototype has successfully demonstrated its great potential in healthcare automation and infection prevention.

RoboCoV Cleaner: An Indoor Autonomous UV-C Disinfection Robot with Advanced Dual-Safety Systems.

In the face of today's ever-evolving global health landscape and ambient assisted living (AAL), marked by the persistent emergence of novel viruses and diseases that impact vulnerable categories and individual safety, the need for innovative disinfection solutions has surged to unprecedented levels. In pursuit of advancing the field of autonomous UV-C disinfection robotics, we conducted two comprehensive state-of-the-art analyses: the first one in the literature and the second one in existing commercial disinfection robots to identify current challenges. Of all of the challenges, we consider the most outstanding ones to be safeguarding humans and animals and understanding the surroundings while operating the disinfection process challenges that we will address in this article. While UV-C lamps have demonstrated their effectiveness in sterilizing air and surfaces, the field of autonomous UV-C disinfection robotics represents a critical domain that requires advancement, particularly in safeguarding the wellbeing of humans and animals during operation. Operating UV-C disinfection robots in close proximity to humans or animals introduces inherent risks, and existing disinfection robots often fall short in incorporating advanced safety systems. In response to these challenges, we propose the RoboCoV Cleaner-an indoor autonomous UV-C disinfection robot equipped with an advanced dual and redundant safety system. This novel approach incorporates multiple passive infrared (PIR) sensors and AI object detection on a 360-degree camera. Under our test, the dual-redundant system reached more than 90% when detecting humans with high accuracy using the AI system 99% up to 30 m away in a university hallway (different light conditions) combined with the PIR system (with lower accuracy). The PIR system was proved to be a redundant system for uninterrupted operation during communication challenges, ensuring continuous sensor information collection with a swift response time of 50 ms (image processing within 200 ms). It empowers the robot to detect and react to human presence, even under challenging conditions, such as when individuals wear masks, in complete darkness, under UV light, or in environments with blurred visual conditions. In our test, the detection system performed outstandingly well with up to 99% detection rate of humans. Beyond safety features, the RoboCoV Cleaner can identify objects in its surroundings. This capability empowers the robot to discern objects affected by UV-C light, enabling it to apply specialized rules for targeted disinfection. The proposed system exhibits a wide range of capabilities beyond its core purpose of disinfection, making it suitable for healthcare facilities, universities, conference venues, and hospitals. Its implementation has the ability to improve significantly human safety and protect people. By showcasing the RoboCoV Cleaner's safety-first approach and adaptability, we aim to set a new benchmark for UV-C disinfection robots, promoting clean and secure environments while protecting vulnerable people, even in challenging scenarios.

Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands

With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency, security, responsiveness, and automation. This study aims to develop and assess IoT-based home security systems utilizing passive infrared (PIR) sensors to improve user interface, security, and automation controls using voice commands and buttons across different communication protocols. The proposed system incorporates controls for lighting and intrusion monitoring, as well as assessing both the functionality of voice commands and the precision of intruder detection via the PIR sensors. Intelligent light control and PIR intruder detection with a variable delay time for response detection are unified into the research methodology. The test outcomes examine the average effective response time in-depth, revealing performance distinctions among wireless fidelity (Wi-Fi) and fourth- and fifth-generation mobile connections. The outcomes illustrate the reliability of voice-activated light control via Google Assistant, with response accuracy rates of 83 percent for Thai voice commands and 91.50 percent for English voice commands. Moreover, the Blynk mobile application provided exceptional precision regarding operating light-button commands. The PIR motion detectors have a one hundred percent detection accuracy, and a 2.5 s delay is advised for PIR detection. Extended PIR detection delays result in prolonged system response times. This study examines the intricacies of response times across various environmental conditions, considering different degrees of mobile communication quality. This study ultimately advances the field by developing an IoT system prepared for efficient integration into everyday life, holding the potential to provide improved convenience, time-saving effectiveness, cost-efficiency, and enhanced home security protocols.

Sensing the Unseen – Using CO2 as a Key Indicator for Occupancy Detection in Smart Collaboration Spaces

The Internet of Things (IoT) has received significant attention recently due to its potential to build more innovative solutions to real-world problems. This study examines how IoT sensors can be integrated with Enterprise Information Systems (EIS) to measure the utilization of enclosed workspaces to create Smart Collaboration Spaces (SCS). These spaces are enriched by real-time data on occupancy and air quality measurements to optimize work and study conditions. Using Carbon Dioxide (CO2) sensors and passive infrared (PIR) sensors makes it possible to detect human presence in the room with an accuracy of over 90%. The generated data is processed and visualized in a workspace occupancy information system, accessible via a web interface. The findings suggest that SCS, through integration into EIS, can significantly improve resource utilization and enhance employee experience in organizations by saving time spent searching for an available workspace. Integrating IoT technologies with EIS holds considerable potential for developing smarter and more efficient work organization solutions in the future.

IOT Pada Purwarupa Sistem Keamanan Pintu Kantor Dengan Sensor RFID dan PIR Berbasis Telegram

The proposed office door security system combines RFID (Radio-Frequency Identification) Sensor and PIR (Passive Infrared) Sensor technology with Telegram-based communication. The design of this system requires RFID and PIR sensors to be formed in order to produce a system design that is made to meet the needs of system users. The aim of this system is to increase the security level of access to office space by combining identification of access owners via RFID cards and motion detection using PIR sensors. Communication through the Telegram platform is used to provide real-time notifications to access owners when there is activity at the office door. The control system for this device is controlled by the Esp32 Microcontroller, using 4 inputs, namely Rfid, PIR, Keypad and telegram sensors, as well as 5 outputs, namely Selenid Doorlock, Buzzer, LCD, Relay and telegram. By using the esp32 microcontroller to process data and the telegram application for IoT as a link between devices and smartphones. With 2 sensors as logic inputs for the esp32 microcontroller, the number of outputs that occur is 5, namely in the form of a sound from a buzzer as a warning, and a PIR as a sensor to open the door from the inside. When opening the RFID with a card tag, then the selenoid doorlock will open. Overall, the design of this office door security system has the potential to improve security and monitoring in the office space. By combining the technology of RFID sensors, PIR sensors and Telegram-based communication, this system enables accurate identification, real-time motion detection and fast interaction between the security system and the access owner. So that Telegram is successful in sending notifications if someone enters, that is, they will appear. "User Name Open Door: according to the registered UID", but if it fails "Unregistered User Failed Login Attempt" The LCD will also issue an output if someone has registered their RFID tag or not

Smart ATM Security and Alert System with Real-Time Monitoring

The growing adoption of automated teller machines (ATMs) worldwide, ensuring the security of ATM transactions and protecting customer data has become a critical concern for the banking industry. In this research paper, we proposed an Internet of Things (IoT) based ATM security system using the NodeMCU ESP8266 module, PIR (Passive Infrared) sensor, LCD display with I2C interface, and the Telegram app. The proposed system utilizes the NodeMCU ESP8266 module, which is a low-cost Wi-Fi enabled microcontroller, as the main controller for the security system. The PIR sensor is used to detect motion or presence near the ATM, which can potentially indicate unauthorized access. The LCD display with I2C interface is used to provide real-time status information about the ATM, such as Motion Detected or NO Motion. When the PIR sensor detects any suspicious activity near the ATM, the NodeMCU ESP8266 module sends a notification to the ATM owner or security personnel via the Telegram app. The notification includes details about the detected activity, allowing the owner or security personnel to take immediate action. The real- time alerts enable prompt response to potential security breaches and help prevent unauthorized access to the ATM.

Improving Indoor Occupancy Detection Accuracy of the SLEEPIR Sensor Using LSTM Models

We recently developed a synchronized low energy electronically chopped passive infrared (SLEEPIR) sensor node to detect stationary and moving occupants. It uses a liquid crystal shutter to modulate the infrared signal received by a traditional passive infrared (PIR) sensor and thus enables its capability to detect stationary occupants. However, the detection accuracy of the SLEEPIR sensor can be easily influenced by infrared environmental disturbances. To address this problem, in this paper, we propose two Long Short-Term Memory (LSTM) models to filter infrared environmental disturbance, named baseline LSTM (Base.LSTM) and statistical LSTM (Stat.LSTM). They use the sensor node raw output and statistical features as their respective input. For comparison, we propose two other models: the occupancy state switch detection (SSD) algorithm that directly uses a predetermined threshold voltage value to classify the occupancy state and its status change; and the multi-layer perception (MLP) classifier with statistical feature inputs (Stat.ML). To validate their detection performance, we designed two testing scenarios in different environment settings: (i) Daily occupancy tests and (ii) EDGE case tests. The first scenario intends to restore complex real-life environmental situations as much as possible in the lab and apartment rooms. The second scenario aims to verify their detection accuracy under different environmental temperatures. This scenario also considers different occupancy postures, such as lying down. Experimental results show that the detection accuracy of both LSTM models (> 95%) in both testing scenarios outperforms that of the SSD (around 82% to 94%) and the Stat.ML(around 80% to 90%).