Ronald A. Peterson

Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.

The Rise of People-Centric Sensing

Technological advances in sensing, computation, storage, and communications will turn the near-ubiquitous mobile phone into a global mobile sensing device. People-centric sensing will help drive this trend by enabling a different way to sense, learn, visualize, and share information about ourselves, friends, communities, the way we live, and the world we live in. It juxtaposes the traditional view of mesh sensor networks with one in which people, carrying mobile devices, enable opportunistic sensing coverage. In the MetroSense Projects vision of people-centric sensing, users are the key architectural system component, enabling a host of new application areas such as personal, public, and social sensing.

People-centric urban sensing

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 specialized 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 crossroads 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 commercial mainstream of peoples every day lives; the challenge being: how do we develop large-scale general-purpose sensor networks for the general public (e.g., consumers) capable of supporting a wide variety of applications in urban settings (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 sensing applications, and then present the MetroSense architecture which is based fundamentally on three design principles: 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 networking approach. Opportunistic sensor networking leverages mobility-enabled interactions and provides coordination between people-centric mobile sensors, static sensors and edge wireless access nodes in support of opportunistic sensing, opportunistic 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 performance and connectivity using adaptive multihop, and importantly, providing security and privacy for people-centric sensors and data.

The BikeNet mobile sensing system for cyclist experience mapping

We describe our experiences deploying BikeNet, an extensible mobile sensing system for cyclist experience mapping leveraging opportunistic sensor networking principles and techniques. BikeNet represents a multifaceted sensing system and explores personal, bicycle, and environmental sensing using dynamically role-assigned bike area networking based on customized Moteiv Tmote Invent motes and sensor-enabled Nokia N80 mobile phones. We investigate real-time and delay-tolerant uploading of data via a number of sensor access points (SAPs) to a networked repository. Among bicycles that rendezvous en route we explore inter-bicycle networking via data muling. The repository provides a cyclist with data archival, retrieval, and visualization services. BikeNet promotes the social networking of the cycling community through the provision of a web portal that facilitates back end sharing of real-time and archived cycling-related data from the repository. We present: a description and prototype implementation of the system architecture, an evaluation of sensing and inference that quantifies cyclist performance and the cyclist environment; a report on networking performance in an environment characterized by bicycle mobility and human unpredictability; and a description of BikeNet system user interfaces. Visit [4] to see how the BikeNet system visualizes a users rides.

BikeNet: A mobile sensing system for cyclist experience mapping

We present BikeNet, a mobile sensing system for mapping the cyclist experience. Built leveraging the MetroSense architecture to provide insight into the real-world challenges of people-centric sensing, BikeNet uses a number of sensors embedded into a cyclists bicycle to gather quantitative data about the cyclists rides. BikeNet uses a dual-mode operation for data collection, using opportunistically encountered wireless access points in a delay-tolerant fashion by default, and leveraging the cellular data channel of the cyclists mobile phone for real-time communication as required. BikeNet also provides a Web-based portal for each cyclist to access various representations of her data, and to allow for the sharing of cycling-related data (for example, favorite cycling routes) within cycling interest groups, and data of more general interest (for example, pollution data) with the broader community. We present: a description and prototype implementation of the system architecture based on customized Moteiv Tmote Invent motes and sensor-enabled Nokia N80 mobile phones; an evaluation of sensing and inference that quantifies cyclist performance and the cyclist environment; a report on networking performance in an environment characterized by bicycle mobility and human unpredictability; and a description of BikeNet system user interfaces.

Autonomous deployment and repair of a sensor network using an unmanned aerial vehicle

We describe a sensor network deployment method using autonomous flying robots. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data we collected from field trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for a second, repair, pass to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth.).

Distributed Search and Rescue with Robot and Sensor Teams

We develop a network of distributed mobile sensor systems as a solution to the emergency response problem. The mobile sensors are inside a building and they form a connected ad-hoc network. We discuss cooperative localization algorithms for these nodes. The sensors collect temperature data and run a distributed algorithm to assemble a temperature gradient. The mobile nodes are controlled to navigate using this temperature gradient. We also discuss how such networks can assist human users to find an exit. We have conducted an experiment to at a facility used to train firefighters to understand the environment and to test component technology. Results from experiments at this facility as well as simulations are presented here.

From Robots to Animals: Virtual Fences for Controlling Cattle

We consider the problem of monitoring and controlling the position of herd animals, and view animals as networked agents with natural mobility but not strictly controllable. By exploiting knowledge of individual and herd behavior we would like to apply a vast body of theory in robotics and motion planning to achieving the constrained motion of a herd. In this paper we describe the concept of a virtual fence which applies a stimulus to an animal as a function of its pose with respect to the fenceline. Multiple fence lines can define a region, and the fences can be static or dynamic. The fence algorithm is implemented by a small position-aware computer device worn by the animal, which we refer to as a Smart Collar. We describe a herd-animal simulator, the Smart Collar hardware and algorithms for tracking and controlling animals as well as the results of on-farm experiments with up to ten Smart Collars.

Mobile-Agent versus Client/Server Performance: Scalability in an Information-Retrieval Task

Building applications with mobile agents often reduces the bandwidth required for the application, and improves performance. The cost is increased server workload. There are, however, few studies of the scalability of mobile-agent systems. We present scalability experiments that compare four mobile-agent platforms with a traditional client/server approach. The four mobile-agent platforms have similar behavior, but their absolute performance varies with underlying implementation choices. Our experiments demonstrate the complex interaction between environmental, application, and system parameters.

Localization and Navigation Assisted by Networked Cooperating Sensors and Robots

In this paper we discuss how a network of sensors and robots can cooperate to solve important robotics problems such as localization and navigation. We use a robot to localize sensor nodes, and we then use these localized nodes to navigate robots and humans through the sensorized space. We explore these novel ideas with results from two large-scale sensor network and robot experiments involving 50 motes, two types of flying robot: an autonomous helicopter and a large indoor cable array robot, and a human-network interface. We present the distributed algorithms for localization, geographic routing, path definition and incremental navigation. We also describe how a human can be guided using a simple hand-held device that interfaces to this same environmental infrastructure.