Occupancy Estimation Using Low-Cost Wi-Fi Sniffers

Real-time measurements on the occupancy status of indoor and outdoor spaces can be exploited in many scenarios (HVAC and lighting system control, building energy optimization, allocation and reservation of spaces, etc.). Traditional systems for occupancy estimation rely on environmental sensors (CO2, temperature, humidity) or video cameras. In this paper, we depart from such traditional approaches and propose a novel occupancy estimation system which is based on the capture of Wi-Fi management packets from users' devices. The system, implemented on a low-cost ESP8266 microcontroller, leverages a supervised learning model to adapt to different spaces and transmits occupancy information through the MQTT protocol to a web-based dashboard. Experimental results demonstrate the validity of the proposed solution in four different indoor university spaces.Comment: Submitted to Balkancom 201

Fade Depth Prediction Using Human Presence for Real Life WSN Deployment

Current problem in real life WSN deployment is determining fade depth in indoor propagation scenario for link power budget analysis using (fade margin parameter). Due to the fact that human presence impacts the performance of wireless networks, this paper proposes a statistical approach for shadow fading prediction using various real life parameters. Considered parameters within this paper include statistically mapped human presence and the number of people through time compared to the received signal strength. This paper proposes an empirical model fade depth prediction model derived from a comprehensive set of measured data in indoor propagation scenario. It is shown that the measured fade depth has high correlations with the number of people in non-line-of-sight condition, giving a solid foundation for the fade depth prediction model. In line-of-sight conditions this correlations is significantly lower. By using the proposed model in real life deployment scenarios of WSNs, the data loss and power consumption can be reduced by the means of intelligently planning and designing Wireless Sensor Network

Online real-time crowd behavior detection in video sequences

Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach

Crowd Counting Through Walls Using WiFi

Counting the number of people inside a building, from outside and without entering the building, is crucial for many applications. In this paper, we are interested in counting the total number of people walking inside a building (or in general behind walls), using readily-deployable WiFi transceivers that are installed outside the building, and only based on WiFi RSSI measurements. The key observation of the paper is that the inter-event times, corresponding to the dip events of the received signal, are fairly robust to the attenuation through walls (for instance as compared to the exact dip values). We then propose a methodology that can extract the total number of people from the inter-event times. More specifically, we first show how to characterize the wireless received power measurements as a superposition of renewal-type processes. By borrowing theories from the renewal-process literature, we then show how the probability mass function of the inter-event times carries vital information on the number of people. We validate our framework with 44 experiments in five different areas on our campus (3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and receiver installed outside of the building, and for up to and including 20 people. Our experiments further include areas with different wall materials, such as concrete, plaster, and wood, to validate the robustness of the proposed approach. Overall, our results show that our approach can estimate the total number of people behind the walls with a high accuracy while minimizing the need for prior calibrations.Comment: 10 pages, 14 figure

An inexpensive and continuous radon progeny detector for indoor air-quality monitoring

A silicon photodiode-based inexpensive detector working as a counter and spectrometer for alpha particles has been conceived, designed, constructed and analyzed in depth. Monte Carlo simulations by means of MCNPX ver. 2.7.0 code have been carried out to select the most suitable sensitive element for the intended applications. The detecting unit has been coupled to an Arduino board and tested for low-rate alpha-particle counting and spectroscopy. Results demonstrate a maximum count rate of 4000 s-1, an energy resolution corresponding to a full width at half maximum of 160 keV over the entire energy range of measured alpha (namely 4 ÷ 6.5 MeV), and the sensitive element’s intrinsic efficiency of about 100%. Being the detector capable of distinguishing alpha energy associated to decays of radon daughters, its applications include 222Rn progeny monitoring. The air sampling system has been realized by a volumetric micro-pump forcing the air-flow through a millipore filter. By knowing the air-flow rate processed and the corresponding alpha energy spectrum measured, the concentrations of 218Po, 214Po and 210Po are determined. The potential alpha energy concentration-in-air is inferred, and effective dose evaluated. Calibration and testing measurements have been carried out by comparing the obtained results to the outputs of professional and expensive radon progeny monitor. The detector capability of “following” radon progeny concentration-in-air vs. time has been demonstrated. The device studied here can be configured as a prototype for an inexpensive radon progeny sensor to be potentially suitable for indoor air-monitoring in residential buildings, evaluating people’s exposures to radon and initiating corrective actions (e.g., mechanical ventilation) if necessary