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In this paper passive Infrared sensor “PIR” based security system is introduced. With this sensor we can save power, and effective management at low cost and requires small memory space.

Security is of enormous importance in this modern age. The crime rate increases exponentially, and traditional home surveillance systems are expensive and limited in intelligent capabilities, such as real-time detection, instant alert, and prompt reporting. As a result of the rapid evolution of cutting technologies and devices, such as Raspberry Pi and several enabled APIs, the limitation, as mentioned, has become a thing of the past. This paper implements an Intrusion Detection System (IDS) using a Raspberry Pi processor that serves as a controller and stations at a home environment. The choice of Raspberry Pi 4 is considered, which is the core of the system; it does all processing between all components (i.e., Passive Infrared sensor, Web camera, Light Emitting Diode) connected to it. The intelligent device is integrated with the Telegram bot via its API. The API with the appropriate Telegram framework is used as an instant messenger generator and notification system. The designed system achieved promising results, as it accurately identified known people, and all unidentified people were flagged by sending a Telegram text message, which included the captured image of the unknown intruder. The overall accuracy rate achieved was 84 per cent, and the accuracy rate for known people was 86 per cent.

Electricity has the major resource consumption among many resources in the country. Also, it is the most misused resource, so control of consumption needs to be taken so that cost is maintained and future generation is benefited. The system proposed here represents a low cost and efficient classroom system which controls appliances like lights and fans using NodeMcu module and relays and adapters with the available LAN or Wi-Fi in the University, so that devices can be remotely controlled using the web. This also uses low-cost sensor which detects the presence of humans switching on the lights when necessary and off if humans are not detected. The simple and effective sensor called Passive Infrared Sensor (PIR sensor) will be used in each classroom. Human existence is sensed by the sensor through heat radiations which are produced by humans. Then the input is given as the signal through simple web application is created. The procedure to operate the devices which manually followed to operate the devices is however neglected, therefore, installing this type of equipment, and a web application will surely reduce the wastage of electricity in classrooms by remote-operating devices.

Funder: Cardiff University, Funder: Project CASE partner ‐ Eco‐explore Community Interest Company, Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioral ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.Here, we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof-especially to humidity and salt spray. The camera was paired with a passive infrared sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviors and interspecific interactions of two sympatric species of storm-petrel (Monteiro's storm-petrel Hydrobates monteiroi and Madeiran storm-petrel Hydrobates castro) during their respective breeding seasons.Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30-s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.Here, we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently, the camera-build methods and scripts could be applied easily to many different species that also utilize cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviors and interspecies interactions.

In this paper passive Infrared sensor “PIR” based security system is introduced. With this sensor we can save power, and effective management at low cost and requires small memory space.

Detecting count of human beings accurately in a closed indoor environment is crucial in diverse application areas including search and rescue, surveillance, customer analytics, abnormal event detection, human gait characterization, congestion analysis, and many more. Moreover, it has significant importance in preventing any intrusion in a secured indoor space such as a bank vault. Sensor-based technologies (for example camera, Passive Infrared sensor (PIR), etc.) are becoming more popular day by day to ensure enhanced security in such closed indoor setting. However, sensors used in these technologies have to be deployed in visible places. Thus, there exist possibilities of damaging the sensors by intruders. Therefore, this paper proposes a novel methodology to detect human count in such closed indoor setting, which can be deployed in any hidden place being oblivious to intruders. Here, we perform human counting based on different environmental gaseous parameters (Carbon Dioxide, Liquefied Petroleum Gas or LPG, Nitrogen Dioxide, and Sulfur Dioxide) and two weather parameters (temperature and humidity). We conduct numerous experiments under closed controlled settings and perform the counting task using different machine learning algorithms such as Bagging, RandomForest, IBK, and J48. We also propose a novel Deep Neural Network based framework for this task and evaluate its performance on the collected data sets. Using this method, we achieve 95% accuracy in detecting the number of humans present even in the unseen test case scenario.

In this paper, an IoT based fire-fighting and affected area monitoring robot is proposed. This fire-fighting robot can be used as a supplementary to the firefighters in critical situations. To function this robot, a flame sensor, a gas sensor, an IR sensor, and a temperature humidity sensor has been used. The flame sensor is used to detect the fireplace at the same time as the gas sensor informs about the presence of flammable gases, the Passive Infrared Sensor confirms the presence of a human, and the temperature humidity sensor sends statistics about the temperature and humidity of the locality. The robot can run in both manual control system and autonomic control system. This paper discusses the detail and top working condition of a fire-fighting robot and recapitulates an IoT based communication system to monitor the fire affected area using Wi-Fi and also discusses the elaborate functions of each module and the implementation of the system. All the data are sent to the cloud server for further investigation. The proposed firefighting robot has been used for many experiments and proper evaluation has been done based on its performance. It has an excellent performance to extinguish the fire in an emergency situation.

Agriculture is the primary source of food supply for all countries in the world. The issue of agriculture has always been hampering the growth and development of the country. To further improve this condition, we need to deploy smart and precision agriculture by revamping the old methods of agriculture. Hence, the paper aims at making agriculture smart and precise by using Internet of things (IoT) technologies. This paper proposes a system design for an IoT-based agriculture monitoring system. It also suggests a cloud-based mobile application for easy monitoring and analyzing the farms at the farmer’s point of view. The proposed work deals with real-time observation by efficiently using the most reliable sensors. The main features of this works are the integration of various agriculture-related sensors such as moisture, temperature, and humidity with the Raspberry Pi and a mobile application. It also stores the recorded data on a ThinkSpeak cloud for future prediction and improving the quality of the crop. This work integrates a passive infrared sensor (PIR) to monitor the intrusion of animals or human interference. The live monitoring of the sensor’s values using cloud computing and a mobile-based application was also analyzed. The proposed system prototype was successfully implemented, executed, and tested. It is a low cost, easy to use for the farmers to efficiently monitor their farms and to improve the crop quality by reducing the wastage of energy and resources.

In today's world, all the countries are facing threats from the intruder. This is challenging the internal security of the nation. Hence, military applications require locating the intruders in the border areas. In this application, tracking of target, processing the data and analysis of the fetched data plays a major role. The proposed system requires tracking of target in the secured area and analysing the data received. Sensors play a major role in detecting the intruder. In this paper, the proposed, designed and implemented a system which makes use of Passive Infrared Sensor(PIR) to detect the intruder and surrounding environment has been discussed. The information gathered from the sensor network is sent to the base station for processing using Long Range(LoRa) Technology. The implementation results justifies the importance of the methodology used. The performance results justifies effectiveness of the proposed system.

In this paper, a tool for the simulation of home environment capable of representing human moving patterns, related to Activities of Daily Living (ADL), and modelling Passive Infrared (PIR) sensor networks is presented. PIR sensors have been chosen because they are non-intrusive, contactless and low-cost. The tool has been programmed in MATLAB and provides a graphic interface from which the developer can change key simulation parameters. It makes it possible to load and visualise the 2D map of the home environment used for simulation as well as add, customise and place ceiling or wall mounted PIR sensors, and regulate users’ trajectory parameters, such as walking speed, step length or path efficiency. The simulator is useful for quickly generating synthetic data to train machine learning (ML) algorithms able to recognize user behavior, without the necessity to perform long acquisition periods. In order to demonstrate its applicability, the tool has been used to create normal and wandering trajectories and their related sensor activations. These data were employed to develop a ML algorithm able to detect overnight wandering, a common behaviour in patients with dementia. The results show that a Decision Tree (DT) algorithm is reliable for the purpose of distinguishing normal trajectories from the wandering ones detected by PIR sensor activations, obtaining an accuracy level of over 95% using a cross-validation approach.

The goal of this paper is to localize a resident in indoor environments by using motion information from distributed environmental sensors and body activity information from wearable sensors. The passive infrared sensor nodes distributed in a home provide binary information about human motion in their field of views, while the wearable inertial measurement unit sensor node collects motion data that can be used in body activity recognition, walking velocity, and heading estimation. Basic human activities such as sitting, sleeping, standing, and walking are recognized. We proposed a particle filter-based sensor fusion algorithm that takes advantage of the human location/activity correlation in indoor environments to increase the localization accuracy. Experiments were conducted in a mock apartment testbed. We used the ground truth data obtained from a motion capture system to evaluate the results. Note to Practitioners —This paper is motivated by a goal to realize various home automation applications through human localization. For example, with the knowledge of human location, lighting, and air conditioning systems can be more efficiently managed, or a home service robot can provide better service to the resident. There currently lacks an accurate human localization method in indoor environments. The theoretical framework proposed in this paper aims to combine simple environmental sensors (passive infrared sensors) and a single wearable motion sensor to accurately track human locations. This method reduces the intrusiveness to the human, while keeping the cost of the infrastructure low. The theoretical framework has been tested and evaluated in a mock apartment environment as a proof of concept. For deployment into real homes, we need to conduct more realistic tests and thorough evaluation of the system. It is also necessary to reduce the size and weight of the wearable motion sensor and simplify the installation of the environmental sensors.

This paper aims to provide the most efficient and automatic Power Management System with no human intervention in hotels and lodges. A passive infrared (PIR) sensor is used to detect the presence of people in the room by receiving the infrared (IR) rays emitted from the human body. The PIR sensor is connected to the microcontroller to which the relay is also connected. When the presence/motion of people has detected the relay triggers and power supply to the room is switched ‘on’. And when any motion has not been detected for 5 minutes or more the main power supply to the room is switched ‘off’. This can prevent unwanted usage of power in hotels /lodges when no one is in the room. The hotel management can also remotely monitor the availability of people in the room by monitoring the output of the Sensors through IoT.

Internet of Things is the next big thing, as almost everything developed now has an extensive use of data which is then used to get the daily statistics and usage of every individual. The work mainly consists of constructing a screen where the parking space will be shown, and a camera module will be set up, and PIR (Passive Infrared Sensor) will be at the entrance to detect the entrance of a car or any vehicle eligible to park at the lot. The vehicle will be scanned for its registration number in to provide a check whether the vehicle is registered to park or not. This also acts as the security of the parking lot. Moreover, a viable sensor will be placed at each parking slot through which the vacancy of each parking slot will be shown to determine the exact spot available to the user. In order to surpass the project completion, we will be using Raspberry Pi 3 with camera module mounted on it and by using Tensorflow, Node-Red we would be able to identify the car and the license number and also infrared sensor to detect the parking availability which would be displayed on the screen.

Manual irrigation is still widely used in agricultural field using traditional drip and can watering. However, traditional irrigation systems are inefficient and inexact, leading to either insufficient or excessive watering. Moreover, it is difficult for farmers to predict suitable quantities at the appropriate time. Manual monitoring of the crop field may also lead to human error and is potentially risky for rural areas. Farmers may also not be aware of intrusions if they are not on location. Therefore, this project is designed to develop a smart monitoring and automated irrigation system to provide not only efficient water consumption based on specific conditions, but also enables real-time monitoring of the environment. Furthermore, this system prevents damage to plants and reduces the likelihood of plant theft. This system uses NodeMCU ESP32 as a microcontroller that collects environmental data such as humidity, temperature, soil moisture levels from sensors. The NodeMCU is integrated with a relay and RTC module to irrigate plants at specific times and is also equipped with a passive infrared sensor to detect intruders near the crop-field. Upon detection, an ESP32 camera is used to automatically capture the current conditions and farmers will be subsequently notified. Warnings are also sent to farmers upon detection of unwanted circumstances such as extreme temperature, which could prevent instances of open burning. The utility of the developed prototype is evident in the way it automatically irrigates the crop field without human intervention. Farmers may monitor and manually control the irrigation process using an attached Android application. Additionally, they may manually activate a buzzer warn off any potential malicious actors.

The development of embedded systems has been demonstrated in a reliable solution for a motion detection system, so motion detection is one of the key technologies in surveillance. Focuses on the use wherein detecting A Passive Infrared sensor (PIR sensor) for unauthorised activity on low for multisensory computing system captures an image "Raspberry Pi," to improve motion detection. The System is suitable for the small personal area of intelligent home monitoring system assisted motion, the car park entrance whenever the PIR sensor and camera detect motion to capture an image by storing a raspberry PI module, then sent to the email server. Internet of Things (IOT) applications for informed and view the image movement occurs via email server via the Internet; therefore, the System provides the IOT to use an innovative method to detect theft. A motion sensor uses one or multiple technologies to detect movement in an area. When a sensor detects motion, it sends a signal to the security system's control panel, which connects to the monitoring center. Some units combine motion sensors and light sensors, but their photocells don't turn the lights on when the sun goes down. When paired with motion detectors, photocells keep lights off during daylight hours. Also, adjust these fixtures to allow the motion sensors to work all-day.

Green technology a technology to mitigate the effects of human activity on the environment and power efficiency. The scope of this project is only for office or classroom application. The issue of the system in the classroom is the high consumption of energy due to the nonstop operation of the system even though there is no one in the classroom. The main objective of this project is to design. The main objective of this project is to design an autonomous lighting system that can minimize the electricity usage and improve its productivity on human life. The methodology of the system is by using sensors to detect the presence of human and control the intensity of the lamp. Three sensors which are the Infrared (IR), the passive infrared sensor (PIR) and light sensor work separately, but it operates simultaneously under a few conditions. This system can measure and analyze the brightness of light according to the surrounding exposure. The system is designed for easy installation. The outcome of the system is showing the potential application in real time.

In this paper, a PIR based security system which saves the power consumption and the memory space of the recording system has been proposed. Passive Infrared Radiation (PIR) sensor detects the change in infrared radiation of warm blooded moving object in its feasible detection range. Once the system gets adapted to the environment in first few initial seconds, then any change in the level of infrared radiations surrounding him will pass on the signal to microcontroller of the system that will in turn trigger the alarm for immediate saviour action and switches on the camera through relay to start recording the motion and corresponding activities of the intruder an save it in the system for later surveillance and an evidence against some criminal activity which might happen or for any breakage of the law. A software installed in the system will highlight the intruder face and help camera to focus on his motion specifically and guide his own motion according to his movements and an email message will be sent to the owner of the shop to alert him about the situation in addition to detecting the webcam connection and starting and saving the video simultaneously. Once the intruder moves out of detection range of the sensor, the webcam and light gets turn OFF. The software repeats the process. Thereby saving the power consumption and the memory space of the recording system as the webcam will only get turned ON when PIR sensors detects someone. As a result, the system starts recording only when the webcam is turned ON, hence saving memory space.

A wearable sensor capable of detecting the presence of humans within a front-facing 90-degree sector of varying radius is demonstrated herein. The system offers extensive applicability across a variety of scenarios where detecting the parameters of human interaction, including separation distance and duration, is of value. Sensing is accomplished using an ultrasonic distance and passive infrared sensor. This design improves upon previous approaches presented in the literature by eliminating privacy concerns associated with audio and video capture, and also relaxing the requirement that both interacting individuals be in possession of dedicated hardware. A KNN classifier is developed using data obtained from a designed indoor experiment intended to demonstrate system robustness across geometries consistent with those observed in the target application. Employing a set of only three features, an overall accuracy rate of 94.2% is realized for detecting human interactions occurring within a 90-degree sector of three-foot radius.

Design and implement an energy-efficient smart camera mote architecture to be used as surveillance device for assisted living. Add the Passive Infrared Sensor (PIR) to WVSN, PIR detect the human or animal’s moving, then it triggers the camera to wake up. The image captured will be grayscale processing by the central processing unit. Camera sensor nodes transmit a grayscale image over wireless channel to master control station. It offers reduced complexity, response time, and power consumption over conventional solutions. By experimental results from the test illustrate that performance of the designed wireless image sensor is better than the exiting ones in the market in terms of received signal strength intensity (RSSI) and packet rate ratio (PRR) with respect to the distance. This scheme is less complicated than other wireless video sensor surveillance techniques, allowing resource-constrained video sensors to operate more reliably and longer.

This paper proposes an ultrasonic sensor based intelligent automatic door system which improves the performance of conventional door systems by adding more intelligent functions such that it offers more convenience to passersby and reduces power loss. The conventional automatic door systems employed passive and active infrared sensors for detecting objects and human bodies. But, they have problems such as power loss in door closing, not sensing fast approaching objects, and safety. The proposed automatic door system with ultrasonic sensors prevents unnecessary door closings to save the power and senses fast approaching objects to open the door at proper time, and improves safety. Thus, the proposed system improves the performance of the conventional systems in terms of operation, economy, and safety.

Detection of human activities is a set of techniques that can be used in wide range of applications, including smart homes and healthcare. In this paper we focus on activity detection in a smart home environment, more specifically on detecting entrances to a room and exits from a room in a home or office space. This information can be used in applications that control HVAC (heating, ventilation, and air conditioning) and lighting systems, or in Ambient Assisted Living (AAL) applications which monitor the people's wellbeing. In our approach we use data from two simple sensors, passive infrared sensor (PIR) which monitors presence and hall effect sensor which monitors whether the door is opened or closed. This installation is non-intrusive and quite simple because the sensor node to which sensors are connected is battery powered, and no additional work to ensure power supply needs to be performed. Two approaches for activity detection are proposed, first based on a sliding window, and the other based on artificial neural network (ANN). The algorithms are tested on a dataset collected in our laboratory environment.

This project describes hardware design and implementation of low-cost smart alarm clock based on Arduino platform, which uses passive infrared sensor (PIR) to detect sleep states of users. Sleep is not just a passive process. People can achieve different states during the night, which are known as Hypnagogia (state from wakefulness to sleep), NREM (non-rapid eye movement), REM (rapid eye movement), Hypnexagogium (awakening state) and dreaming. The main goal for this developed smart alarm clock is to detect these states and adjust alarm time to the best possible moment, when people are in awaking state or in light sleep. Awaking in these states is quite better and people feel much more refreshed. Hardware of this developed alarm clock is composed from LCD LED display, real-time (RTC) clock unit, temperature and humidity sensor, photosensitive module for detection of daytime, touch sensor and WiFi module for time synchronization from NTP (Network Time Protocol) servers. This Smart alarm clock could be used for better and more effective awakening for users.

In this paper, we use GSM mobile network that is the most reliable and matured in this day to achieve community alarm system. And it will directly convey the alarm news in short message or telephone at manager's mobile phone. The design includes hardware and software parts. Its hardware uses MCU AT89C52 series as the core, and controls three alarm parts, they are infrared monitoring alarm system, alarm displayed on LCD, and GSM module. The system adopted passive infrared sensor to detect, and it turned the traditional network security and anti-theft window into the invisible network, so as to timely handle accidents. When the accident happened in the community, the infrared sensor module sends out the alarm signal to the micro-controller, single chip microcomputer receives the signal through the GSM module to send the message immediately to the manager, and then managers to take immediate measures, the site security alarm and remote alarm can be achieved simultaneously.

In recent years, wireless sensor networks offer cost-effective solutions to various surveillance and tracking applications with the developments in sensor techniques. In many of these applications, sensor nodes that are equipped with directional sensors and they operate autonomously in unattended environments. The deployment strategy of directional sensor nodes is critical to improve target detection and tracking accuracy. In this paper, deployment of passive infrared motion (PIR) sensors is analyzed in terms of coverage issue. A PIR Sensor Deployment (PSD) problem is addressed using deployment schemes that are based on computational geometry. Finally, the performance of deployment schemes is evaluated in a Java based simulation environment.

In order to achieve sustainability, steps must be taken to reduce energy consumption. In particular, heating, cooling, and ventilation systems, which account for 42% of the energy consumed by US buildings in 2010 [8], must be made more efficient. In this paper, we demonstrate ThermoSense, a new system for estimating occupancy. Using this system we are able to condition rooms based on usage. Rather than fully conditioning empty or partially filled spaces, we can control ventilation based on near real-time estimates of occupancy and temperature using conditioning schedules learned from occupant usage patterns. ThermoSense uses a novel multisensor node that utilizes a low-cost, low-power thermal sensor array along with a passive infrared sensor. By using a novel processing pipeline and sensor fusion, we show that our system is able measure occupancy with a RMSE of only a0.35 persons. By conditioning spaces based on occupancy, we show that we can save 25% energy annually while maintaining room temperature effectiveness.

Our project is a remotely controlled robot capable of surveying disaster situations or act as a security countermeasure. It utilizes a stationary network of passive infrared sensor nodes interconnected through a multi-hop Zigbee network. The sensors are motion sensitive and using regional localization can be used for identifying the location of survivors or intruders based on the situation. The robot is controlled via a Wi-fi link which streams real time video back to the base station. The main processor is a TI Sitara AM335x ARM processor. It also acts as relay for the sensor data. Each node consists of three passive infrared detection circuits each covering a sector of 120 degrees and connects via the TI CC2530 ZNP chip. The raw PIR data is signal conditioned using an LM324 Op-amp. The nodes can be deployed easily due to their compact size. Their low power consumption and low cost makes them ideal for remote areas and can be deployed in large numbers.

Mobile object localization and tracking means that a multitude of sensor nodes collaborate with each other to determine the locations and tracks of target objects in a specified area. It is one of the most significant research directions in WSN. This paper implements a system named MOLTS (Mobile Object Localization and Tracking System Based on Wireless Sensor Networks), which applies abundant sound and passive infrared sensors to make localization and tracking complementarily. The fusion of two kinds of sensors can enhance accuracy of the system significantly, and expands the application range. Moreover, we propose a new localization algorithm named FER-NLS based on ER-NLS. FER-NLS is more accurate than ER-NLS in localization and tracking. The evaluation platform of MOLTS consisted of 16 sound and passive infrared sensor nodes verifies accuracy and efficiency of the system. In addition, we equipped testbed boards designed by ourselves with MOLTS to make evaluations, and the results show that MOLTS is with high stability and reliability.

Nowadays, almost all the countries are facing threats from terrorists and intruders from their border areas, challenging the internal security of the country in those areas. So many civilian and military applications require locating an intruder in a secured area. Target tracking, data processing and analysis play a major role in this type of applications. The proposed system is to develop a centralized computer application that needs to identify moving objects in a specific area using sensors. The system will be basically designed to detect human intruders. The objective is to design and implement an object tracking system using a wireless sensor network. This application is able to detect and track objects, and report direction and speed of the intruder to a central base station. The human intruder is detected using a passive infrared (PIR) sensor. The sensor is connected to a MICAz sensor node. The PIR sensor is able to detect the humans and provide information about the direction of the movement. The gathered information from the sensor network is to be given to the base station for processing. The proposed system provides an environment for easy deployment and which does not require any existing infrastructure or constant monitoring by humans.

We consider the problem of tracking an intruder in a plane region by using a wireless sensor network comprising motes equipped with passive infrared (PIR) sensors deployed over the region. An input-output model for the PIR sensor and a method to estimate the angular speed of the target from the sensor output are proposed. With the measurement model so obtained, we study the centralized and decentralized tracking performance using the extended Kalman filter.

McQ developed for the U.S. Army Research Laboratory (ARL) a very low-cost iScout® sensor system for detecting people in buildings and caves after military clearing operations to prevent their reuse by adversaries. The mission applications have expanded to include typical field operations such as Force Protection and facility security. To meet a broader mission capability, McQ significantly enhanced the performance of the iScout® Unattended Ground Sensor (UGS) system. The enhanced performance includes improvements to the seismic, acoustic, magnetic, and passive infrared sensor processing algorithms and multimodal fusion to improve target classification. Additional features are a new radio frequency (RF) network architecture, built-in global positioning system (GPS) for automatic sensor position reporting, a new rugged watertight case, and an extremely low power consumption electronics design. McQ will describe these enhancements and present data characterizing the performance of the enhanced iScout® sensors.

This paper presents the method for the detection and localization of moving targets in passive infrared (PIR) sensor networks in both indoor and outdoor settings. It reports our design and implementation of PIR sensor network, especially, we proposed a detection algorithm, which uses adaptive threshold with constant false alarm rate; and developed a localization algorithm using direction search in the grid space of the network. The experimental results have shown that our PIR sensor network can detect and locate the moving targets with reasonable accuracy.

In this paper, we propose a distributed system consists of heart-rate, passive infrared, and audio sensors for sleep condition inference. We apply machine learning methods to infer the sleep-awake condition during the time a user spends on the bed. This sleep-awake information would be useful for estimating critical factors including sleep latency, sleep duration, and habitual sleep efficiency related to sleep quality measurement. Our experimental results show that the proposed approach could be a good alternative to the traditional motion sensor Actigraph, with competitive performance on the sleep-related activity monitoring. Furthermore, the distributed computation nature of our system also makes it favorable for practical health-care applications