Tracking Human Mobility using WiFi signals

We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80\% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking

Vibration Alert Bracelet for Notification of the Visually and Hearing Impaired

This paper presents the prototype of an electronic vibration bracelet designed to help the visually and hearing impaired to receive and send emergency alerts. The bracelet has two basic functions. The first function is to receive a wireless signal and respond with a vibration to alert the user. The second function is implemented by pushing one button of the bracelet to send an emergency signal. We report testing on a prototype system formed by a mobile application and two bracelets. The bracelets and the application form a complete system intended to be used in retirement apartment communities. However, the system is flexible and could be expanded to add new features or to serve as a research platform for gait analysis and location services. The medical and professional potential of the proposed system is that it offers a simple, modular, and cost-effective alternative to all the existing medical devices with similar functionality currently on the market. The proposed system has an educational potential as well: it can be used as a starting point for capstone projects and demonstration purposes in schools to attract students to STEM disciplines

Inferring Person-to-person Proximity Using WiFi Signals

Today's societies are enveloped in an ever-growing telecommunication infrastructure. This infrastructure offers important opportunities for sensing and recording a multitude of human behaviors. Human mobility patterns are a prominent example of such a behavior which has been studied based on cell phone towers, Bluetooth beacons, and WiFi networks as proxies for location. However, while mobility is an important aspect of human behavior, understanding complex social systems requires studying not only the movement of individuals, but also their interactions. Sensing social interactions on a large scale is a technical challenge and many commonly used approaches---including RFID badges or Bluetooth scanning---offer only limited scalability. Here we show that it is possible, in a scalable and robust way, to accurately infer person-to-person physical proximity from the lists of WiFi access points measured by smartphones carried by the two individuals. Based on a longitudinal dataset of approximately 800 participants with ground-truth interactions collected over a year, we show that our model performs better than the current state-of-the-art. Our results demonstrate the value of WiFi signals in social sensing as well as potential threats to privacy that they imply

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

Effectiveness of design codes for life cycle energy optimisation

The built environment is materially inefficient, with structural material wastage in the order of 50% being common. As operational energy consumption in buildings falls, due to continued tightening of regulations and improvements in the efficiency of energy generation and distribution, present inefficiencies in embodied energy use become increasingly significant in the calculation of whole life energy use. The status quo cannot continue if we are to meet carbon emissions reduction targets. We must now tackle embodied energy as vigorously as we have tackled operational energy in buildings in the past.Current design methods are poorly suited to controlling material inefficiency in design, which arises as a risk mitigation strategy against unknown loads and uncertain human responses to these loads. Prescriptive codes are intended to result in buildings capable of providing certain levels of performance. These performance levels are often based on small tests, and the actual performance of individual building designs is rarely fully assessed after construction. A new approach is required to drive the minimisation of embodied energy (lightweighting) through the collection of performance data on both structures and their occupants.This paper uses an industry facing survey to explore for the first time the potential use of performance measurement to create new drivers for lighter and more usable designs. The use of ubiquitous structural, human, and environmental sensing, combined with automated data fusion, data interpretation, and knowledge generation is now required to ensure that future generations of building designs are lightweight, lower-carbon, cheaper, and healthier

Effectiveness of design codes for life cycle energy optimisation

The built environment is materially inefficient, with structural material wastage in the order of 50% being common. As operational energy consumption in buildings falls, due to continued tightening of regulations and improvements in the efficiency of energy generation and distribution, present inefficiencies in embodied energy use become increasingly significant in the calculation of whole life energy use. The status quo cannot continue if we are to meet carbon emissions reduction targets. We must now tackle embodied energy as vigorously as we have tackled operational energy in buildings in the past.Current design methods are poorly suited to controlling material inefficiency in design, which arises as a risk mitigation strategy against unknown loads and uncertain human responses to these loads. Prescriptive codes are intended to result in buildings capable of providing certain levels of performance. These performance levels are often based on small tests, and the actual performance of individual building designs is rarely fully assessed after construction. A new approach is required to drive the minimisation of embodied energy (lightweighting) through the collection of performance data on both structures and their occupants.This paper uses an industry facing survey to explore for the first time the potential use of performance measurement to create new drivers for lighter and more usable designs. The use of ubiquitous structural, human, and environmental sensing, combined with automated data fusion, data interpretation, and knowledge generation is now required to ensure that future generations of building designs are lightweight, lower-carbon, cheaper, and healthier

Quantifying Surveillance in the Networked Age: Node-based Intrusions and Group Privacy

From the "right to be left alone" to the "right to selective disclosure", privacy has long been thought as the control individuals have over the information they share and reveal about themselves. However, in a world that is more connected than ever, the choices of the people we interact with increasingly affect our privacy. This forces us to rethink our definition of privacy. We here formalize and study, as local and global node- and edge-observability, Bloustein's concept of group privacy. We prove edge-observability to be independent of the graph structure, while node-observability depends only on the degree distribution of the graph. We show on synthetic datasets that, for attacks spanning several hops such as those implemented by social networks and current US laws, the presence of hubs increases node-observability while a high clustering coefficient decreases it, at fixed density. We then study the edge-observability of a large real-world mobile phone dataset over a month and show that, even under the restricted two-hops rule, compromising as little as 1% of the nodes leads to observing up to 46% of all communications in the network. More worrisome, we also show that on average 36\% of each person's communications would be locally edge-observable under the same rule. Finally, we use real sensing data to show how people living in cities are vulnerable to distributed node-observability attacks. Using a smartphone app to compromise 1\% of the population, an attacker could monitor the location of more than half of London's population. Taken together, our results show that the current individual-centric approach to privacy and data protection does not encompass the realities of modern life. This makes us---as a society---vulnerable to large-scale surveillance attacks which we need to develop protections against

Tracking and locating property on university campuses

Applied project submitted to the Department of Computer Science, Ashesi University College, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2017The problem of property crime exists on university campuses worldwide. There is also difficulty in monitoring and determining the location of items. Organizations have sought to rely on tracking devices to curb this issue. However, these devices are either too expensive, require high power consumption or are bulky and cannot be used to track small devices. In this project, an affordable and portable system is developed that monitors devices and alerts users once their items cross boundaries (geo fences).Ashesi University Colleg

Enhancing shopping experiences in smart retailing

The retailing market has undergone a paradigm-shift in the last decades, departing from its traditional form of shopping in brick-and-mortar stores towards online shopping and the establishment of shopping malls. As a result, “small” independent retailers operating in urban environments have suffered a substantial reduction of their turnover. This situation could be presumably reversed if retailers were to establish business “alliances” targeting economies of scale and engage themselves in providing innovative digital services. The SMARTBUY ecosystem realizes the concept of a “distributed shopping mall”, which allows retailers to join forces and unite in a large commercial coalition that generates added value for both retailers and customers. Along this line, the SMARTBUY ecosystem offers several novel features: (i) inventory management of centralized products and services, (ii) geo-located marketing of products and services, (iii) location-based search for products offered by neighboring retailers, and (iv) personalized recommendations for purchasing products derived by an innovative recommendation system. SMARTBUY materializes a blended retailing paradigm which combines the benefits of online shopping with the attractiveness of traditional shopping in brick-and-mortar stores. This article provides an overview of the main architectural components and functional aspects of the SMARTBUY ecosystem. Then, it reports the main findings derived from a 12 months-long pilot execution of SMARTBUY across four European cities and discusses the key technology acceptance factors when deploying alike business alliances