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

Understanding the WiFi usage of university students

In this work, we analyze the use of a WiFi network deployed in a large-scale technical university. To this extent, we leverage three weeks of WiFi traffic data logs and characterize the spatio-temporal correlation of the traffic at different granularities (each individual access point, groups of access points, entire network). The spatial correlation of traffic across nearby access points is also assessed. Then, we search for distinctive fingerprints left on the WiFi traffic by different situations/conditions; namely, we answer the following questions: Do students attending a lecture use the wireless network in a different way than students not attending a lecture?, and Is there any difference in the usage of the wireless network during architecture or engineering classes? A supervised learning approach based on Quadratic Discriminant Analysis (QDA) is used to classify empty vs. occupied rooms and engineering vs. architecture lectures using only WiFi traffic logs with promising results

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

Proximity Graphs for Crowd Movement Sensors

Sensors are now common, they span over different applications, different purposes and some over large geospatial areas. Most data produced by these sensors needs to be linked to the physical location of the sensor itself. By using the location of a sensor we can construct (mathematically) proximity graphs that have the sensors as nodes. These graphs have a wide variety of applications including visualization, packet routing, and spatial data analysis. We consider a sensor network that measures detections of WiFi packets transmitted by devices, such as smartphones. One important feature of sensors is given by the range in which they can gather data. Algorithms that build proximity graphs do not take this radius into account. We present an approach to building proximity graph that takes sensor position and radius as input. Our goal is to construct a graph that contains edges between pairs of sensors that are correlated to crowd movements, reflecting paths that individuals are likely to take. Because we are considering crowd movement, it gives us the unique opportunity to construct graphs that show the connections between sensors using consecutive detections of the same device. We show that our approach is better than ones that are based on the positioning of sensors only

Understanding social behaviors in the indoor environment: a complex network approach

Being able to monitor and analyze human interactions in the indoor environment over time has many architectural applications from spatial planning to post occupancy evaluation. In this paper, we present our interdisciplinary approach that interprets human-spatial interactions as a complex network. We combine methods and techniques from sensor networks, signal processing, data mining, network theory, and information visualization to form a novel framework that facilitates versatile investigations. We will demonstrate the framework with a real-world case study: we have collected and analyzed human-spatial interaction data from a workshop scenario where multiple design projects were conducted within a shared studio space

Filters for Wi-Fi Generated Crowd Movement Data

Cities represent large groups of people that share a common infrastructure, common social groups and/or common interests. With the development of new technologies current cities aim to become what is known as smart cities, in which all the small details of these large constructs are controlled to better improve the quality of life of its inhabitants. One of the important gears that powers a city is given by traffic, be it vehicular or pedestrian. As such traffic is closely related to all other activities that take place inside of a city. Understanding traffic is still a difficult process as we have to be able to not only measure it in the sense of how many people are using a particular path but also in analyzing where people are going and when, while still maintaining individual privacy. And all this has to be done at a scale that would cover most if not all individuals in a city. With the high increase in smartphones adoption we can reliably assume that a large part of the population in cities are carrying with them, at all times, at least one Wi-Fi enabled device. Because Wi-Fi devices are regularly transmitting signals we can rely on these devices to detect individual's movements unobtrusively without identifying or tracking any particular individual. Special sensors that monitor Wi-Fi frequencies can be placed around a city to gather data that can later be used to identify patterns in the traffic flows. We present a set of filters that can be used to minimize the amount of data needed for processing and without negatively impacting the result or the information that can be extracted from this data. Part of the filters we present can be deployed at the sensor level, making the entire system more scalable, while a different part can be executed before data processing thus enabling real time information extraction and a broader temporal and spatial range for data analysis. Some of these filters are particular to Wi-Fi but some of them can be applied to any detection system

Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies

In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.info:eu-repo/semantics/publishedVersio

Smart Geographic object: Toward a new understanding of GIS Technology in Ubiquitous Computing

One of the fundamental aspects of ubiquitous computing is the instrumentation of the real world by smart devices. This instrumentation constitutes an opportunity to rethink the interactions between human beings and their environment on the one hand, and between the components of this environment on the other. In this paper we discuss what this understanding of ubiquitous computing can bring to geographic science and particularly to GIS technology. Our main idea is the instrumentation of the geographic environment through the instrumentation of geographic objects composing it. And then investigate how this instrumentation can meet the current limitations of GIS technology, and offers a new stage of rapprochement between the earth and its abstraction. As result, the current research work proposes a new concept we named Smart Geographic Object SGO. The latter is a convergence point between the smart objects and geographic objects, two concepts appertaining respectively to