Challenges for social sensing using WiFi signals
25 Jun 2012-pp 17-21
TL;DR: This paper reviews existing work on using WiFi for social sensing and outlines challenges that have to be addressed to utilize WiFi at large forsocial sensing.
Abstract: Smartphones are an attractive option for social sensing due to their widespread market penetration rate and advanced sensing capabilities. Enabling social sensing on smartphones would require techniques that can accurately detect and characterize physical proximity, an important prerequisite for the capture of more complex social phenomena. One of the most promising modalities for this purpose is WiFi, as it works both indoors and outdoors, and as WiFi signal environment tends to contain significant spatial variation. A challenge in using WiFi, however, is that the signals are affected by many noise sources, such as fast fading, body attenuation, hardware differences and varying access points densities. In this paper we review existing work on using WiFi for social sensing and outline challenges that have to be addressed to utilize WiFi at large for social sensing.
Citations
More filters
[...]
TL;DR: This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study.
Abstract: This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.
320 citations
Posted Content•
[...]
28 Jan 2014
Abstract: This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.
266 citations
[...]
TL;DR: This survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI, and presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.
Abstract: With the high demand for wireless data traffic, WiFi networks have experienced very rapid growth, because they provide high throughput and are easy to deploy. Recently, Channel State Information (CSI) measured by WiFi networks is widely used for different sensing purposes. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI. Different WiFi sensing algorithms and signal processing techniques have their own advantages and limitations and are suitable for different WiFi sensing applications. The survey groups CSI-based WiFi sensing applications into three categories, detection, recognition, and estimation, depending on whether the outputs are binary/multi-class classifications or numerical values. With the development and deployment of new WiFi technologies, there will be more WiFi sensing opportunities wherein the targets may go beyond from humans to environments, animals, and objects. The survey highlights three challenges for WiFi sensing: robustness and generalization, privacy and security, and coexistence of WiFi sensing and networking. Finally, the survey presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors, for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.
174 citations
Cites background from "Challenges for social sensing using..."
[...]
[...]
TL;DR: Applying the Bluetooth signal strength parameter to distinguish between observations, it is demonstrated that weak links, compared to strong links, have a lower probability of being observed at later times, while such links—on average—also have lower link-weights and probability of sharing an online friendship.
Abstract: Understanding how people interact and socialize is important in many contexts from disease control to urban planning. Datasets that capture this specific aspect of human life have increased in size and availability over the last few years. We have yet to understand, however, to what extent such electronic datasets may serve as a valid proxy for real life social interactions. For an observational dataset, gathered using mobile phones, we analyze the problem of identifying transient and non-important links, as well as how to highlight important social interactions. Applying the Bluetooth signal strength parameter to distinguish between observations, we demonstrate that weak links, compared to strong links, have a lower probability of being observed at later times, while such links—on average—also have lower link-weights and probability of sharing an online friendship. Further, the role of link-strength is investigated in relation to social network properties.
79 citations
[...]
TL;DR: The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high.
Abstract: Rich-sensor smart phones have made possible the recent birth of the mobile sensing research area as part of ubiquitous sensing which integrates other areas such as wireless sensor networks and web sensing There are several types of mobile sensing: individual, participatory, opportunistic, crowd, social, etc The object of sensing can be people-centered or environment-centered The sensing domain can be home, urban, vehicular… Currently there are barriers that limit the social acceptance of mobile sensing systems Examples of social barriers are privacy concerns, restrictive laws in some countries and the absence of economic incentives that might encourage people to participate in a sensing campaign Several technical barriers are phone energy savings and the variety of sensors and software for their management Some existing surveys partially tackle the topic of mobile sensing systems Published papers theoretically or partially solve the above barriers We complete the above surveys with new works, review the barriers of mobile sensing systems and propose some ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving for any type of mobile sensing system, and propose several realistic research challenges The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high
75 citations
Additional excerpts
[...]
References
More filters
[...]
TL;DR: The ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms is demonstrated.
Abstract: We introduce a system for sensing complex social systems with data collected from 100 mobile phones over the course of 9 months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms.
2,823 citations
"Challenges for social sensing using..." refers background in this paper
[...]
[...]
[...]
TL;DR: It is demonstrated that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns that allow the prediction of individual-level outcomes such as job satisfaction.
Abstract: Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, self-reports of physical proximity deviate from mobile phone records depending on the recency and salience of the interactions. We also demonstrate that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns. These behavioral patterns, in turn, allow the prediction of individual-level outcomes such as job satisfaction.
1,832 citations
"Challenges for social sensing using..." refers background in this paper
[...]
[...]
TL;DR: Lee Sailer as mentioned in this paper argued that a measurement whose accuracy is completely unknown has no use whatever and that a serious obstacle in the use of replications for increasing accuracy is the tendency to get closely agreeing repetitions for irrelevant reasons.
Abstract: Lee Sailer Department of Anthropology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260 "A measurement whose accuracy is completely unknown has no use whatever" [Wilson ( 1 07. p. 232)]. "A serious obstacle in the use of replications for increasing accuracy is the tendency to get closely agreeing repetitions for irrelevant reasons" [Wilson (107. p. 253)1. "My people don't lie to me" (Anonymous Anthropologist).
822 citations
"Challenges for social sensing using..." refers background in this paper
[...]
[...]
TL;DR: The system is sufficiently robust to enable a variety of location-aware applications without requiring special-purpose hardware or complicated training and calibration procedures, and can be adapted to work with previously unknown user hardware.
Abstract: We demonstrate a system built using probabilistic techniques that allows for remarkably accurate localization across our entire office building using nothing more than the built-in signal intensity meter supplied by standard 802.11 cards. While prior systems have required significant investments of human labor to build a detailed signal map, we can train our system by spending less than one minute per office or region, walking around with a laptop and recording the observed signal intensities of our building's unmodified base stations. We actually collected over two minutes of data per office or region, about 28 man-hours of effort. Using less than half of this data to train the localizer, we can localize a user to the precise, correct location in over 95% of our attempts, across the entire building. Even in the most pathological cases, we almost never localize a user any more distant than to the neighboring office. A user can obtain this level of accuracy with only two or three signal intensity measurements, allowing for a high frame rate of localization results. Furthermore, with a brief calibration period, our system can be adapted to work with previously unknown user hardware. We present results demonstrating the robustness of our system against a variety of untrained time-varying phenomena, including the presence or absence of people in the building across the day. Our system is sufficiently robust to enable a variety of location-aware applications without requiring special-purpose hardware or complicated training and calibration procedures.
754 citations
[...]
TL;DR: It is shown how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
Abstract: Today's mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way.de the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale experiments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies using traditional methods. In particular, we show how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
479 citations
"Challenges for social sensing using..." refers background in this paper
[...]
Related Papers (5)
[...]
[...]
[...]