Exploiting Radio Irregularity in Wireless Networks for Automated People Counting

Wireless devices exist almost everywhere in our daily life. Wireless communications, which is an integral part of wireless devices, suffers from radio irregularity – a phenomenon referring to radio waves being selectively absorbed, reflected or scattered by objects in their paths, e.g., human bodies that comprises liquid, bone and flesh. Radio irregularity is often treated as a major challenge for wireless communication. However, we aim to take advantage of the phenomenon of radio irregularity to provide a cost-effective approach for automated people counting. People counting is extensively used for intelligence-gathering to be used in forecasting, resource allocation and safety-related applications like crowd control. Existing people counting techniques use light, infrared, or thermal energy for human movement detection. However there have major limitations, for example the visible light camera and infrared sensors do not penetrate smoke or obstacles such as wall and furniture. Also, a large deployment of these devices is costly owing to the use of specialized sensors. We propose an automated people counting system using the radio irregularity phenomenon of existing wireless infrastructure with minimal additional hardware and installation costs. This thesis presents an experimental study to demonstrate how radio signal fluctuations arising from radio irregularity can be used to provide a simple low-cost alternative to dedicated sensing systems for indoor automated people counting. Firstly, we study the effect on received signal strength with human motion interference on radio signals. Then we propose and evaluate the performance of three approaches, namely, overcomplete dictionary based pattern recognition (OCPR) approach, probability density approach and standard deviation approach. With high accuracy of motion detection, we then focus on the design of automated people counting system using the proposed detection approach. To differentiate the number of people, we apply discriminant analysis which is a statistical method to perform classification based on independent variables. We validated the proposed people counting system by conducting experiments under both controlled and uncontrolled environments and show that we are able to achieve high accuracy in counting up to five people in groups with no specific formation

An Echo State Network based pedestrian counting system using wireless sensor networks

A pedestrian counter has a lot of applications like effective resource utilization, planning of service activities and ensuring safety and convenience. The design and implementation of a new intelligent pedestrian counter is presented in this paper. The counter is publicly usable, low cost, easily deployable and scalable. We used off-the-shelf components for our design and the overall cost is less than 200 euro. The counter works in distributed mode and has wireless communication facilities. The hardware platform consists of PIR sensor units that detect the pedestrian movements, the wireless sensor nodes that handle the sensor data acquisition and transmission and the base station computer that process the data. We have trained an echo state network and this recurrent neural network functions as the brain for the the pedestrian counter. A system model was constructed using Simulink for generating the training data for the neural network. A modular layered software framework was designed for processing the pedestrian counter data. The echo state network successfully learned the various motion patterns and the pedestrian counter gave a reasonably good performance of 80.4%. To improve the performance further, redesigning the systems using low cost Active IR distance sensor is suggested