Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.

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A survey of mobile phone sensing

An emerging category of devices at the edge of the Internet are consumer-centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensors. These devices will fuel the evolution of the Internet of Things as they feed sensor data to the Internet at a societal scale. In this article, we examine a category of applications that we term mobile crowdsensing, where individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest. We present a brief overview of existing mobile crowdsensing applications, explain their unique characteristics, illustrate various research challenges, and discuss possible solutions. Finally, we argue the need for a unified architecture and envision the requirements it must satisfy.

Mobile crowdsensing: current state and future challenges

A global center for commercial innovation, PARC, a Xerox company, works closely with enterprises, entrepreneurs, government program partners and other clients to discover, develop, and deliver new business opportunities. PARC was incorporated in 2002 as a wholly owned subsidiary of Xerox Corporation (NYSE: XRX).

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Named Data Networking (NDN) Project

Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

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Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing

Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes.

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VTrack: accurate, energy-aware road traffic delay estimation using mobile phones

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.

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PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

Center for Embedded Networked Sensing SensorBase.org - A Centralized Repository to Slog Sensor Network Data Nathan Yau, Gong Chen, Mark Hansen, Deborah Estrin Introduction: Sharing Sensor Network Data Sensor Data Logging Slog is a portmanteau of “sensor” and “log” and was coined to reflect the spirit of sharing of information represented by blogs. Blog user interfaces (e.g. Blogger, Wordpress) are very user- friendly; users can publish, delete, and set permissions on entries with little effort. We wish to create a similar interface and backend for slogging. Blogs have RSS generated on the fly for easy notification and syndication. Sensor Data Retrieval Once published, data or subsets of the data can be retrieved. Ability to search for data characteristics is vital. Similar to blogs, users should be able to subscribe to RSS data feeds on per project or per sensor basis to receive notification or summary statistics of data recently published. – Users should be notified when something out of the ordinary happens. Problem Description: Making many complex data types publishable and retrievable Different data formats such as numeric, text, images, video, and audio all stored and easily retrieved from one central repository Currently, much data exists as flat text files or floating in hundreds of folders on individual hard drives that is easily forgotten and cumbersome to share. Hence, the data is useless, and there is no point in collecting it; however if we can slog data to a centralized repository, and make it easily retrievable, data once again becomes useful. Proposed Solution: Slog data to one place and make it easy to retreive and use How Data is Slogged • To publish data, a user first creates a sensor network project, which is a bit like categories in blogs. • Data can be uploaded as XML or as comma-delimited text. Binary data can also be uploaded file-by-file. • Published data can be searched and retrieved via the user interface or via SOAP web services. Application Building on Top of SensorBase.org • Applications can easily be developed on top of SensorBase.org using SensorBase.org SOAP web services. • Data from different projects can easily be merged using simple database functionality. Future Work • Rule-based RSS notifications • Data visualization e.g. R, GIS, sparklines • More powerful data searching tools e.g. trends, thresholds, events UCLA – UCR – Caltech – USC – CSU – JPL – UC Merced

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SensorBase.org: A Centralized Repository to Slog Sensor Network Data (KNO 2)

Author(s): Gong Chen; Nathan Yau; Mark Hansen; Deborah Estrin | Abstract: Sensor networks generate a variety of data streams in different temporal and spatial resolutions. The data come as numbers text, images, and audio and are dynamically produced periodically and sporadically. How can we organize hundreds of thousands of such data streams? How can we make it easy for scientists and engineers to publish and share such data streams? In this paper, we present our solution, SensorBase.org. It is a web application that not only provides the user with the functionality of a traditional database management system, but also runs under the notion of a Web 2.0 data experience with a responsive user interface design and RSS data feed techniques. SensorBase.org also aims to be a data search engine to promote exploration. Like a web search engine, the user should be able to search for structures, or rather, “signals”, in the data using simple language queries. We provide a solution to a specific type of signal search problem and describe a search framework derived from the solution. A visualization component is in development to help people understand the data and to enhance the results provided by a data search engine. The development of SensorBase.org allows us, the statisticians, to influence “data practices”. People share data and add documentation within the guidelines of SensorBase.org which supports good experimental design and helps future modeling efforts. Last but not least, SensorBase.org provides a base for data integrity and data quality.

Sharing Sensor Network Data

Center for Embedded Networked Sensing TEOS: Terrestrial Ecology Observing Systems Overview of Embedded Networked Systems and EMISSARY Tools for Instrument In strument Management and Data Exploration Michael Hamilton, Phil Rundel, Eric Graham, Michael Allen, Deborah Estrin, Mark Hansen, Mike Taggart, Sean Askay, Richard Guy, Kevin Chang, Yeung Lam, Vanessa Rivera del Rio, Nathan Yau, Eric Yuen Embedded Networked Sensors for Ecological Research at the James Reserve Hardware, Software, Systems TEOS applications research group collaborates with many core CENS groups to design, develop, deploy, evaluate, and support Embedded Networked Sensors (ENS) and instrumented platforms for ecological research. Fixed and mobile arrays, instruments, and associated technologies for measurement are deployed, tested, and are being continually refined as the first wave of CENS systems at our James Reserve field site. Figure 1. On the left is an original CMS weather station that is now being used as a reference to compare newer technology hardware and firmware developed for the new ESS systems. Figure 4. At Deep Canyon, and at the James Reserve, energy- independent monitoring systems as well as towers supporting imagers and acoustic sensors are now adding to the TEOS data stream. Imagers located on these towers are publicly available for control and image capture from the internet. Figure 5. NIMS mobile nodes are receiving upgrades to increase their reliability and sustainability for data collection and experiment-specific applications, such as the ongoing soil sensing project in collaboration with the AMARSS investigations. Figure 2. Nest boxes with built-in cameras and micrometeorological sensors supply a constant data stream for analyzing nesting behavior and nesting location selection.. Figure 3. Fixed imagers, such as the Mosscam, provide high- throughput data streams. Years worth of daily (or more) images can now be analyzed for trends not sensed by other means. EMISSARY enables a user to interact in real- time with sensors and data streams real - Progress towards a new class of Biological Research Portal, EMISSARY is composed of the following tools: 1. Deploy, troubleshoot, and control static and mobile sensor networks. The two top figures are from (1) a NIMS control interface that allows a non-technical user control over node movement and camera position, and (2) DAS, a collection of tools accessible on the internet that helps users understand and deploy static sensor networks 2. Visualize and graph sensor data collected by those networks. The figure to the right represents a new class of web-based tools allowing the user more flexibility in zooming/panning through data and altering the details of graph formatting without reloading the page. Sensor measurement streams can also be stacked directly on top of image capture time series. 3. Analyze image data collected by those networks. Many tools have been developed for data analysis and interoperability is the key focus of EMISSARY. Most tools are web-based, like the image analyzer (first figure to the right) or are platform-independent code that can be compiled for any operating system (second figure to the right, Matlab-based analysis). 4. Explore all these data sets in an integrated, visual, 3-D system. Because Google Earth is designed for tight web-integration, tying it to our existing web-based graphing tools and sensor database has been relatively straightforward. Thus far, we have had great success using Google Earth as a top-level visual interface for TEOS systems. UCLA – UCR – Caltech – USC – CSU – JPL – UC Merced

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TER 0: TEOS: Terrestrial Ecology Observing Systems Overview of Embedded Networked Systems and EMISSARY Tools for Instrument Management and Data Exploration

Nathan Yau

Papers

PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

SensorBase.org: A Centralized Repository to Slog Sensor Network Data (KNO 2)

Sharing Sensor Network Data

TER 0: TEOS: Terrestrial Ecology Observing Systems Overview of Embedded Networked Systems and EMISSARY Tools for Instrument Management and Data Exploration