Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors These sensors include GPS sensors, vision sensors (ie, cameras), audio sensors (ie, microphones), light sensors, temperature sensors, direction sensors (ie, magnetic compasses), and acceleration sensors (ie, accelerometers) The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals We then used the resulting training data to induce a predictive model for activity recognition This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (eg, sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise

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Activity recognition using cell phone accelerometers

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

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.

https://www.cs.dartmouth.edu/%7Esensorlab/pubs/cenceme_sensys08.pdf

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

A smart phone senses audio, imagery, and/or other stimulus from a user's environment, and acts autonomously to fulfill inferred or anticipated user desires. In one aspect, the detailed technology concerns phone-based cognition of a scene viewed by the phone's camera. The image processing tasks applied to the scene can be selected from among various alternatives by reference to resource costs, resource constraints, other stimulus information (e.g., audio), task substitutability, etc. The phone can apply more or less resources to an image processing task depending on how successfully the task is proceeding, or based on the user's apparent interest in the task. In some arrangements, data may be referred to the cloud for analysis, or for gleaning. Cognition, and identification of appropriate device response(s), can be aided by collateral information, such as context. A great number of other features and arrangements are also detailed.

Intuitive computing methods and systems

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 Project's 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.

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The Rise of People-Centric Sensing

Future mobile sensing systems are being designed using 802.15.4 low-power short-range radios for a diverse set of devices from embedded mobile motes to sensor-enabled cellphones in support, for example, of people-centric sensing applications. However, there is little known about the use of 802.15.4 in mobile sensor settings nor its impact on the performance of future communication architectures. We present a set of initial results from a simple yet systematic set of benchmark experiments that offer a number of important insights into the radio characteristics of mobile 802.15.4 person-to-person communication. Our results show that the body factor - that is to say, the human body and where sensors are located on the body (e.g., on the chest, foot, in the pocket) - has a significant effect on the performance of the communications system. While this phenomenon has been discussed in the context of other radios (e.g., cellular, WiFi, UWB) its impact on 802.15.4 based mobile sensor networks is not understood. Other findings that also serve to limit the communication performance include the effective contact times between mobile nodes, and, what we term the zero bandwidth crossing, which is a product of mobility and the body factor. This paper presents a set of initial findings and insights on this topic, and importantly, we consider the impact of these findings on the design of future communication architectures for mobile sensing.

/pdf/radio-characterization-of-802-15-4-and-its-impact-on-the-57k6ve4469.pdf

Radio characterization of 802.15.4 and its impact on the design of mobile sensor networks

Social networking sites such as Facebook and MySpace connect millions of people worldwide through a range of features including fairly static profile information, such as job history and likes/dislikes, and more dynamic content like what people are doing and how people are feeling at various points throughout the day. This dynamic content is updated manually and represented using plain text (e.g., “Meeting new friends at the gym”). While this sort of input provides the ultimate flexibility, the requirement for manual input places a barrier between a person’s dynamic status and its representation on a users profile page. As a result, the minutiae that provide texture to our daily lives is filtered from a person’s online self, and as a result friends are less connected. The CenceMe system [5] transparently makes useful inferences from data gathered from sensors embedded in mobile phones, and exports the resulting “sensing presence” to social network applications. CenceMe fills the gap left between manual status updates by automatically updating activity and location presence information to a person’s social network profile page. The system currently supports Symbianbased Nokia phones and the Apple iPhone, and integrates with Facebook profiles.

Transforming the social networking experience with sensing presence from mobile phones

We present a new task dissemination scheme called multicast deluge (MDeluge). MDeluge can disseminate tasks into a subset of the sensor network. MDeluge uses a tree which is formed when the source node broadcasts the code version message. The source node keeps the tasks and sends it based on the requests. MDeluge supports newly emerging wireless sensor network architectures with layered structure and mobile sensor nodes. We developed a prototype implementation of MDeluge on TelosB motes. Assuming a grid structured wireless sensor network, we present an analysis of various message costs as well as the overall cost of data messages. Experimentation with the prototype and simulation of MDeluge are done to show the MDeluge performance in static sensor network. Simulation also shows how the performance will be impacted by various parameters in a sensor network with mobile sensor nodes.

Xiao Zheng

Papers

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

The Rise of People-Centric Sensing

Radio characterization of 802.15.4 and its impact on the design of mobile sensor networks

Transforming the social networking experience with sensing presence from mobile phones

Task dissemination with multicast deluge in sensor networks