The Rise of People-Centric Sensing
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The Rise of People-Centric Sensing A. T. Campbell∗, S. B. Eisenman†, N. D. Lane∗, E. Miluzzo∗, R. A. Peterson∗ H. Lu∗, X. Zheng∗, M. Musolesi∗, K. Fodor∗, G.-S. Ahn† †Columbia University, New York, NY, USA. ∗Dartmouth College, Hanover, NH, USA. Abstract limited embedded devices that communicate via low- power low-bandwidth radio. A natural first application People-centric sensing is poised to radically change of these networks of custom devices was solving rel- the way we see the world. Technological advances atively small-scale specialized problems in the scien- in sensing, computation, storage, and communica- tific and industrial domains, such as forest monitoring tions will turn the near ubiquitous mobile phone into and preventative maintenance. While these problems a global mobile sensing device that enables myriad and applications remain important, the recent minia- new personal, social, and public sensing applications. turization and subsequent introduction of sensors into People-centric sensing will help drive this trend by en- popular consumer electronics like mobile phones (e.g., abling a different way to sense, learn, visualize and Apple iPhone), PDAs (e.g., Nokia N810), and mp3 share information about ourselves, friends, communi- players (e.g., Nike+iPod) has opened the door to a new ties, the way we live and the world we live in. People- world of application possibilities. With wireless sen- centric sensing juxtaposes the traditional view of mesh sor platforms in the hands of the masses, and with the sensor networks where people are passive data con- proper architectural support, wireless sensor networks sumers that simply interact at the network periph- can be leveraged to address urban-scale problems or ery with physically embedded static sensor webs, with provide global information access (i.e., public sensing one where people carry mobile sensing elements (i.e., applications). At the same time, people as individuals, sensor-enabled mobile phones), enabling opportunis- or in social or special interest groups, can apply these tic sensing coverage, and thus represent a key archi- new sensing networks to applications with a more per- tectural component of the system. In this article, we sonal focus. We see a continuing push in this direction discuss our vision for people-centric sensing, the chal- and the advent of a new era of people-centric sensing. lenges it brings, and the ongoing development of a In a people-centric sensing system, humans, rather number of social sensing applications as part of the than trees or machines, are the focal point of sensing, MetroSense Project. and the visualization of sensor-based information is for the benefit of common citizens and their friends, rather than domain scientists or plant engineers. Addition- 1 Introduction ally, it is the aggregate mobility of the humans them- selves that enables both sensing coverage of large pub- The evolution of sensing, computing and communi- lic spaces over time, and allows an individual, as a cus- cation technology over the past few years has brought todian of the sensing device, to collect very targeted in- us to a tipping point in the field of wireless sensor net- formation about his or her daily life patterns and inter- working. A decade ago, research prototype hardware actions. We say the sensing coverage of spaces, events, began to emerge facilitating the genesis of wireless and human interactions is opportunistic in a people- sensor networks as they exist today - small resource- centric system since the system architecture has no point of control over the human mobility patterns and environment that enable the collection of sensor data actions that facilitate this coverage. While this lack on a large and heterogeneous scale, and establishes a of control can translate to gaps in sensing coverage, central repository for sharing the collected sensor data. the alternative of a world-wide web of static sensors SenseWeb [5], a project sponsored by Microsoft Re- is clearly not tenable in terms of monetary cost, scala- search, provides shared sensing resources and sensor bility, or management. Further, by having the sensing querying and data collection mechanisms to develop devices carried by people (e.g., mobile phones, iPods, sensing applications. In both projects, participating etc.), a people-centric sensing system creates a sym- universities develop their own applications and share biotic relationship between itself and the communities the collected data to facilitate research on data anal- and individuals it serves. ysis and mining, visualization, and machine learning. In this article, we describe our vision of people- The Urban UCLA Sensing initiative [13] has a vision centric sensing, and the architectural support we are of equipping users to compose a sensor-based record- developing in the MetroSense Project [2] to realize ing of their experiences and environment by leverag- this vision. People-centric sensing (see Figure 1) gives ing sensors embedded in mobile devices and integrat- rise to a host of new applications that can be classified ing existing public outlets of urban information (e.g., into three main groups: (i) personal sensing, those fo- weather, traffic, air quality). Urban Sensing is explor- cused on personal monitoring and archiving; (ii) social ing how these individual stories of everyday life can sensing, those where information is shared within so- be coordinated to document the urban environment, as cial and special interest groups; and (iii) public sens- well as be fused with other sensed data about the city ing, those where data is shared with everyone for the and fed back into the physical, collective experience greater public good (e.g., entertainment, community in urban public spaces. The Intel-sponsored Urban At- action). Each of these application foci comes with mospheres project [14] is also using sensors to explore its own challenges in terms of how data is best sam- the human condition. The MIT Cartel project [4] pro- pled, understood (e.g., via mining, inference), visual- vides a mobile communications infrastructure based ized and shared with others. We present a number of on car-mounted communication platforms exploiting prototype applications we are developing in the Met- open WiFi access points in a city, and provides ur- roSense Project that cover these sensing scenarios, in- ban sensing information such as traffic conditions. The cluding a discussion of how people can best learn in- Harvard/BBN CitySense project [11] provides a static formation of interest from the raw data, represent that sensor mesh offering similar types of urban sensing information in a meaningful way and share that infor- data feeds. mation, as appropriate. 3 The MetroSense Vision 2 Background The MetroSense conception of a people-centric People-centric sensing sits at the nexus of several sensing system is based on a three stage Sense, research disciplines, including sensor networking, per- Learn, Share framework. In the sense stage, Met- vasive computing, mobile computing, machine learn- roSense leverages mobility-enabled interactions be- ing, human-computer interfacing, and social network- tween human-carried mobile sensors (e.g., mobile ing. Significant research contributions made within phones, personal medical sensing devices), static sen- each discipline have facilitated the rise of people- sors embedded in the civic infrastructure (e.g., vehicle- centric sensing, and research focusing on synthesiz- based sensing networks, home medical sensing net- ing these contributions is now emerging [1]. In the works), and edge wireless access nodes providing a following, we highlight current projects related to our gateway to the Internet, to support: (i) the delivery of people-centric sensing initiative. SensorPlanet [15] application requests to the mobile devices, (ii) the sam- is a Nokia-initiated global research framework for pling of sensors specified by the request, and (iii) the mobile-device-centric wireless sensor networks. Sen- delivery of sampled data back to the application. Ap- sorPlanet provides hardware platforms and a research plication functions, including generating requests, data Figure 1. People-centric sensing applications can be thought of as having a personal, social, or public focus. analysis and visualization logic, may be installed di- and decrease the time it takes to learn these models. In rectly on the mobile device, may run on remote servers the share stage, the learned information is visualized (e.g., a web application) but communicate with the by the individual and optionally shared. For example, mobile device via wireless gateway nodes (e.g., GPRS sharing is possible within social groups (e.g., class- gateway, WiFi access point), or may be split between room, sports team, Facebook, MySpace), or within a the mobile device and these servers. An application global community (e.g., Second Life). Each of the sampling request specifies at least one required sensor stages in the Sense, Learn, Share framework are fur- type (e.g., an accelerometer), and the required sam- ther discussed in the following. pling context, that is, the set of conditions that must be met for the sampling to take place (e.g., time of day, 4 Sense: Exploit Mobility, Be Opportunistic location, sensor orientation). In the learn stage, we an- alyze the sensed data using both simple statistical mea- sures and more involved machine learning techniques From an infrastructural point of view, we ob- to extract higher-level meaning. The choices of what serve that sensing, computing, and communication re- data analysis techniques to apply, and what data fea- sources are already widely deployed, in the form of tures to analyze are made to best match the availabil- end user electronics, enterprise and public radio ac- ity and characteristics of the sensed data (e.g., noisi- cess networks, and Internet backhaul. Therefore, there ness, incompleteness), and the target application visu- is no longer a need to deploy specialized custom-built alization. In addition, we leverage the people-centric hardware to enable collection and transport of people- nature of the system by using social connections be- centric sensor data.