People-Centric Urban Sensing Andrew T. Campbell, * Shane B. Eisenman, † Nicholas D. Lane, * Emiliano Miluzzo, * Ronald A. Peterson * * Computer Science, Dartmouth College † Electrical Engineering, Columbia University Hanover, New Hampshire, USA New York, New York, USA {campbell,niclane,miluzzo,rapjr}@cs.dartmouth.edu shane@ee.columbia.edu ABSTRACT The vast majority of advances in sensor network research over the last five years have focused on the development of a series of small-scale (100s of nodes) testbeds and special- ized applications (e.g., environmental monitoring, etc.) that are built on low-powered sensor devices that self-organize to form application-specific multihop wireless networks. We believe that sensor networks have reached an important cross- roads in their development. The question we address in this paper is how to propel sensor networks from their small- scale application-specific network origins, into the commer- cial mainstream of people’s every day lives; the challenge being: how do we develop large-scale general-purpose sen- sor networks for the general public (e.g., consumers) capable of supporting a wide variety of applications in urban set- tings (e.g., enterprises, hospitals, recreational areas, towns, cities, and the metropolis). We propose MetroSense, a new people-centric paradigm for urban sensing at the edge of the Internet, at very large scale. We discuss a number of challenges, interactions and characteristics in urban sens- ing applications, and then present the MetroSense architec- ture which is based fundamentally on three design princi- ples: network symbiosis, asymmetric design, and localized interaction. The ability of MetroSense to scale to very large areas is based on the use of an opportunistic sensor network- ing approach. Opportunistic sensor networking leverages mobility-enabled interactions and provides coordination be- tween people-centric mobile sensors, static sensors and edge wireless access nodes in support of opportunistic sensing, op- portunistic tasking, and opportunistic data collection. We discuss architectural challenges including providing sensing coverage with sparse mobile sensors, how to hand off roles and responsibilities between sensors, improving network per- formance and connectivity using adaptive multihop, and im- portantly, providing security and privacy for people-centric sensors and data. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 200X ACM X-XXXXX-XX-X/XX/XX ...$5.00. Categories and Subject Descriptors C.2 [Computer-Communication Networks]: Network Architecture and Design General Terms Design, Experimentation Keywords Urban Sensing, People-centric, Mobile Wireless Sensor Net- works 1. INTRODUCTION To date, the bulk of work in the wireless sensor network space has focused on environmental, agricultural or indus- trial monitoring. Networks of static sensing elements are either physically placed or randomly distributed across a target area of interest, with a focus on application-specific deployments. A substantial body of literature exists ad- dressing a wide spectrum of issues in such sensor networks. Such networks are of utility and offer research challenges to scientists and engineers, but do not currently directly benefit the general population. Furthermore, humans are disengaged bystanders in the sensing and communication processes, passively waiting on the fringe of the network for data to appear. In this paper, we move away from the tradi- tional focus of wireless sensor networks and propose a new people-centric sensing paradigm for urban sensing at very large scale. While traditional sensor networks target remote and unattended deployments we target the urban setting, an environment possessing a rich diversity of lifestyles, ac- tivities, and thus potential applications. As our focus is on enabling human-centric applications, requirements on the architectural solution include the abil- ity to sense people and characteristics of their immediate surroundings, and the ability to sense data related to in- teractions between people and interactions between people and their surroundings. These requirements are made more challenging by human and vehicle mobility (e.g., cars, buses, bikes). Furthermore, the people-centric nature of the data collected implies a requirement for privacy beyond what is present in traditional wireless sensor network application targets such as forest microclimates. In addition, the urban environment presents a host of challenges absent, or only partially present in other sensing domains. The architec- tural solution must scale at least across a large metropoli- tan area, must be able to handle a diversity of hardware platforms, application heterogeneity, interactions between a