Building Occupancy Monitoring Based on Microcontroller and PIR Sensors

Transcription

1 Building Occupancy Monitoring Based on Microcontroller and PIR Sensors Emmanuel Hitiyise, Etienne Ntagwirumugara, William Habarurema, Winfred Ingabire and Geoffrey Gasore Department of Electrical and Electronics Engineering, University of Rwanda-College of Science and Technology, School of Engineering, P O BOX 3900 Kigali-Rwanda. Abstract Following different events that are happening in these days such as earthquakes, terrorist attacks, or accidental building collapse there are growing concerns about the exact knowledge about people located in a certain building at any given moment especially in commercial or event building such that if any incident happened actions should be taken to rescue victims or track criminals with sureness, This paper discussed a method for identifying the number of people occupying a certain building, the number is updated at every moment a person enters or exits a building by using two PIR sensors and Arduino microcontroller and a display unit. We tested our system with both, Proteus software for sensors simulations as well as with real hardware, it means that if there are many entrances, each one of them will require just two PIR Sensors plus a microcontroller and an LCD display, and all will be working in similar way as explained in our algorithm in the next parts of this paper. It exhibits an improvement about the accuracy of building occupancy detection while keeping low cost compared to other methods. In our conclusion, we also suggested an approach to deal with wider entrance challenge. AN OVERVIEW ON SOME PREVIOUS OCCUPANCY SYSTEMS Owing to such benefits of occupancy detection/monitoring, several approaches have been proposed in the literature by taking into account the use of different devices, assumptions and goals. These approaches have certain disadvantages with respect to accuracy cost in the sense that they will increase the cost and inaccuracy. For example a camera can not specify the number of people who frequented a certain building, in addition it is more expensive than PIR sensors Different challenges linked with Occupancy : Keywords: PIR Sensor, Microcontroller, Occupancy, People tracks INTRODUCTION In today s modern building, everything is moving from traditional ways of life to smarter ones, whereby an increasing number of activities are being carried out for humanity with less frustration than it was in the past, all this because of rapid evolution of facilitating electrical and electronics and computing devices. For example energy consumption in building can be automatically supplied to parts, building or equipment requiring it at a certain moment and switched off automatically and intelligently when not needed by this building part or equipment. A typical case is where a building is occupied by people or is completely empty, thus respectively supply energy for lights or disconnect it. In addition to energy issues, real-time occupancy tracking investigated in this paper may also help rescuing survivors in case of emergency response applications[1] Figure 1: Occupancy monitoring challenges [2] Occupancy Detection: This scenario indicates if a space is occupied or not at a given time. It simply implies a yes or no answer which does not tell how many people exist if the space/building is occupied. Occupancy Counting: The goal of this problem is to determine the total number of people in a building at a given time. There are two versions of this problem: First, counting all the people in the whole building. Second, counting people Occupancy Tracking: This problem can be considered as the superset of the all of the above problems. It not only detects people, but also counts, locates, and tracks them. The solutions to this problem can utilize the well-known user localization algorithms that run on the network side rather than the user devices

2 Occupancy Event/Behavior Recognition: This problem is mostly related with the activities of the users once they are detected at certain locations. the number of people inside the building. This is achieved by using two PIR sensors, one microcontroller and a display unit. WiFi-BASED OCCUPANCY MONITORING WEAKNESS WiFi APs (Access Points) have been used extensively for indoor localization in the past [3] [6]. These works, however, focus on individual user localization, assuming that an individual carries a wireless device and in most cases an app on the user s device is needed, this implies that in case some users do not carry wireless devices and or do not have app installed in their devices this method is inaccurate. For the WiFi based monitoring systems the inaccuracies are mostly attributed to aggressive power management by smartphones which stops their WiFi connections temporarily the authors show that the proposed approach infers occupancy correctly for 86% of the time. [7] Figure 2: Ingoing (left) and Outgoing (right) movement of people in a building CAMERA-BASED MONITORING CHALLENGES When counting people directly using camera, it may be challenging due to partial/complete object obstruction [8] and difficulty to locate and analyze the window(s) having a person (or people) [8]. Tracking moving trajectories may help to overcome the obstruction problem, but it has to deal with the complexity of different motion paths observed by different parts of a moving body and intersecting paths of multiple people [8] SENSOR NETWORK BASED OCCUPANCY MONITORING Dodier et al. [9] proposed a Bayesian belief network comprising of three PIR sensors and a telephone sensor to probabilistically infer occupancy. Occupied state of individual offices/rooms was modeled with a Markov chain. Their system had a detection accuracy of 76%, but was unable to count the number of occupants. Meyn et al. [10] improved occupancy detection accuracy by using a sensor network comprising CO2 sensors, digital video cameras, and PIR detectors, as well as historic building utilization data for occupancy estimation at the building level. The system used a receding-horizon convex optimization algorithm to infer occupancy numbers which is an extension of Kalman filter. Their system detection accuracy reached 89%. However, it was not able to estimate occupancy numbers at the room level. OUR PROPOSED ALGORITHM FOR OCCUPANCY MONITORING SYSTEM PIR (Passive Infrared Sensor) detects human presence, it detects the amount of change in infrared rays that occurs when a person whose temperature is different from the surroundings, thus it is well suited to detecting the motion of people by their body temperature, Our method consists of detecting people entering or exiting a building, then update Figure 3: Block diagram of our proposed system The fine-tuning of detection area is very important in order to focus only on a person located very near to the entrance, as shown below in figure 5, whereby in our design, the output pin of Sensor_Out or Out_Sensor (see figure 5) is connected to Pin A1 of arduino microcontroller and the output pin of Sensor_In connected to PinA0. But for simulation purpose we used switches (available in Proteus software) to describe those sensors. Our idea behind this arrangement (figure 4) implies two cases as follows: Case 1: when a person enters the building, he/she will first be detected by the Sensor_Out facing the outside (as in Figure 2) and this status is memorized for a certain short time till this moving person intercept the Sensor_In or In_Sensor,thus as soon as this Sensor_In triggered, the combination of the Sensor_Out triggered a moment ago and Sensor_In triggered at this right moment describes a person entering the building thus incrementing the number of people inside the building as in figure 4 (left half) 10415

3 Figure 4: Our flowchart for people entering or exiting the building Case 1: when a person enters the building, he/she will first be detected by the Sensor_Out facing the outside (as in Figure 2) and this status is memorized for a certain short time till this moving person intercept the Sensor_In or In_Sensor,thus as soon as this Sensor_In triggered, the combination of the Sensor_Out triggered a moment ago and Sensor_In triggered at this right moment describes a person entering the building thus incrementing the number of people inside the building as in figure 4 (left half) OCCUPANCY MONITORING SYSTEM DESIGN The key elements in our design are two HC-SR501 PIR sensors for one entrance. They detect temperature differences, and suited to detecting the motion of people by their body temperature they have three pins and we follow the pin-out as shown in Figure 8 to make connection for Proteus simulator we used in this work. The arduino platform subunit we used is based on Atmel MCU (ATmega328) with specification in figure 6, compatible for C / C + + language. And we programmed the MCU to control our system according to figure 4 above. Finally we need an LCD display to show the number of people inside the building all these are shown in figure 9. Figure 5: Detection area of a PIR sensor fine-tuning to increase precision on one person [11] Case 2: when a person exits the building, he/she will first be detected by the Sensor_In facing the inside (as in Figure 2) and this status is memorized for a certain short time( that can be also fine-tuned ) till the moving person intercept the Sensor _Out then as soon as this Sensor_In triggered, the combination of the Sensor_In triggered a moment ago and Sensor_Out triggered now results/describes in a person leaving the building thus decrementing the number of people inside the building as in Figure 4(right half). Figure 6 Arduino Technical specifications [12] 10416

4 SIMULATION OF RESULTS These results in figure 8 were obtained according to our algorithm with arduino IDE as follows: From figure 9, the results are explained in Table1. These results were obtained according to our algorithm with arduino IDE as follows: From figure 9 Once Switch at A1(PIR Sensor_Out) Pressed FIRST, Switch at A0(PIR Sensor_In) Pressed NEXT, then Number of people increases As in Figure 8 a) Switch at A0(PIR Sensor_In) Pressed FIRST Switch at A1(PIR Sensor_Out) Pressed NEXT Number of people decreases As in Figure 8 b) Figure 7: HC-SR501 PIR Sensor pin-out [11] Figure 8: Arduino IDE simulation results a) people entering b) people exiting the building Table 1: Explanation for results in figure 8 and 9 First triggering Second triggering Results Switch at A1(PIR Sensor_Out) Pressed FIRST Switch at A0(PIR Sensor_In) Pressed FIRST Switch at A0(PIR Sensor_In) NOT Pressed Switch at A0(PIR Sensor_In) Pressed NEXT Switch at A1(PIR Sensor_Out) Pressed NEXT Switch at A1(PIR Sensor_Out) NOT Pressed Number of people increases As in Figure 8 a) Number of people decreases As in Figure 8 b) Number of people remain the same 10417

5 Figure 9: Proteus simulation displaying the number of people inside the building Figure 10: Equipment used and experiment set up In this experiment we replaced the switches with sensors in back to back orientations so that PIR Sensor_Out was looking to the entrance PIR Sensor_In looking inside the building and the results are the same as described above in simulation. CONCLUSION In this paper we investigated how to monitor people occupying a certain building at a given moment and we have been successful as the results above show, as we have also explored some other previous works we realized that in case the building entrance is too wide there may occur some errors due to for example people entering in parallel, two or more people intercepting the sensors at the same time. Our future work will be dealing with this issue and we will try to minimize errors as far as possible, by for example combining sensors and camera or using many sensors to monitor many (assumed people tracks or slots) across very wide entrances so that each individual among those entering in parallel can be monitored, together with our findings above this paper may also serve as a basis for other researchers who are investigating building occupancy

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