Our projects
Read more about the project in our paper published in MobiSys '09. [manuscript]
Read more about the project in our white paper published in EUROSSC '07 and system evaluation pubished in Sensys '08. [EUROSSC '07 manuscript] [SenSys '08 manuscript]
We have built MetroTrack, a fully distributed tracking system based on off-the-shelf mobile phones capable of tracking mobile targets through collaboration among local sensing devices that track and predict the future location of a target using an efficient distributed Kalman-Consensus filtering algorithm. MetroTrack is implemented on Nokia N80 and N95 phones and as a proof-of-conceptwe show that MetroTrack is effective at tracking a mobile noise source in an outdoor urban environment. Simulation results indicate that MetroTrack is robust in the presence of different mobility models and mobile density.
More information on this project online soon.
In a people-centric opportunistic sensor network (OSN), mobile sensors may receive queries both on behalf of applications running locally on the mobile device and applications running remotely on back end infrastructure (e.g., Internet). For instance, a sensor-enabled mobile phone may be requested to gather data about particulate matter in the air for a certain area of the city. These queries minimally specify a sensor type to be sampled and the rate and duration of the sampling, and may also include conditions under which it is preferable that the sampling take place. These sampling conditions, i.e., the sampling context, may include things like location, position on the body/clothing, and the activity of the person carrying the sensor. Given the dynamism of a mobile sensor’s context in the people-centric paradigm, it may often be the case that the mobile sensor can not itself meet the required sampling context, and in fact, due to device heterogeneity, may not even possess the requested sensor type on-board. This means either the query is dropped/rejected, or the fidelity of the captured data (with respect to what the application wants) is reduced. Further, a given query may explicitly require inputs from more than one sensor.
To address these issues in mobile people-centric sensor networks we propose Quintet, a method for orchestrating neighborhood sensors to increase sensing fidelity through opportunistic in-situ sensor sharing. With Quintet, an underqualified node, in terms of sensor type or sampling context, can temporarily borrow the sensor of a qualified node in an effort to satisfy an application query. We describe a sensor sharing protocol focused on local interactions between mobile sensors in the field. The protocol uses lightweight solicitation/reply messaging exchanges between a primary queried node and potential sharing nodes it encounters as it moves in the field to feed a distributed context and resource aware matching algorithm that makes sensor sharing decisions. So far, we have implemented and evaluated our techniques in a human pedestrian sensor network testbed using both mote-class devices and mobile phones. We demonstrate that sensor sharing provides a viable method to improve sensing fidelity and delay in answering application queries, and also facilitates a new class of applications that require collaborative sensing.
More information on this project online soon.
Virtual world simulators like Second Life represent the latest and most
successful frontier of online services for entertainment and business. People
have virtual lives in these worlds using personal avatars. Bridging real life
and these virtual worlds together is challenging, but enables new application
scenarios for these systems. Existing work focuses on the representation of
objects inside virtual worlds. We believe that the overall experience
will be improved if we can reproduce not only inanimate and passive objects like
buildings and beer steins but also the subjects in these virtual
environments.
In order to have a seamless integration between the physical and virtual worlds, and to maximize ease of integration and scale of adoption, the system should rely on devices that people already use every day. We argue that the sensors embedded in commercial mobile phones can be used to infer real-world activities, that in turn can be reproduced in virtual settings. We are investigating the challenges related to the implementation of such a system both from algorithmic and a systematic points of view. We have started by implementing Second Life with CenceMe, a platform to infer activity and more general context information from sensor-equipped phones.
Read more about the project in our short paper published in HotEmNets'08. [manuscript]
Opportunistic people-centric mobile-sensing model introduces a new security
challenge in the design of mobile systems: protecting the privacy of
participants while allowing their devices to reliably contribute high-quality
data to these large-scale applications. AnonySense is a privacy-aware
architecture for realizing pervasive applications based on collaborative,
opportunistic sensing by personal mobile devices. AnonySense allows
applications to submit sensing tasks that will be distributed across anonymous
participating mobile devices, later receiving verified, yet anonymized, sensor
data reports back from the field, thus providing the first secure
implementation of this participatory sensing model. Our papers describe the
underlying threat model and trust model, and show how AnonySense provides the
desired security properties. We evaluated our prototype implementation through
experiments that indicate the feasibility of this approach, and through
demonstration applications. Our experiments show that our implementation is
efficient, and how our location-blurring feature can provide statistical
k-anonymity.
Read more about the project here.
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 of, for example, 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.
which is a product of mobility and the body factor.
Read more in our project report published in EWSN '08. [manuscript]
There is substantial interest in the cycling community in collecting data
quantifying various aspects of the cycling experience. Existing commercial
products have begun to integrate data from multiple local sensors (including
biometric sensors and a GPS receiver) on a single user display, and even
provide map software. A limitation of the currently available products is
the inability to share data with other riders in real-time. Further, often
real-time performance analysis of locally collected data is limited to local
display of simple statistics like min, max and mean over the entire trip. When
the road terrain is highly non-uniform and uphills can last a long time,
comparing current speed against a trip-wide average loses significance.
With the BikeNet application, we work to address these limitations by allowing cyclists to share information about themselves and the paths they mutually traverse for real-time display. In BikeNet, information sharing occurs via short range radios, and can be direct (i.e., bike-to-bike) or indirect through neutral third-party entities called Checkpoints. Checkpoints are virtual storage and aggregation devices that are placed along roads and trails frequented by cyclists; they store location-specific performance data on per-cyclist and aggregate bases.
Read more in our project report published in SENSYS '07. [manuscript]
Skiers are interested in knowing current trail conditions (e.g., ice, bare
spots, congestion) when at the base of the mountain in order to determine which
lift to use to get to the desired trail head at the top of the mountain. Resort
managers are interested in learning skier flow statistics to estimate wear on
lift to use to get to the desired trail head at the top of the mountain. Resort
managers are interested in learning skier flow statistics to estimate wear on
the terrain in order to enact preventative maintenance (e.g., close trail, make
artificial snow). Safety/emergency personnel are interested in tracking skiers'
location and speed in case of accidents (e.g., fall off trail, avalanche), and
also to prevent accidents by speed policing. Skiers may be interested in
tracking their own location or the location of their friends on the mountain, as
well as avoiding lifts with long queues.
We are inspired by the resemblance of a ski resort trail map to a static sensor
network data dissemination tree; many trails with heads at the top of the
mountain funnel towards a small number of lift entry/collection points at the
base of the mountain. In the SkiScape, ski lifts provide
a continuous supply of data mules (skiers) to the trails at no cost to the
sensing/communication infrastructure. Static sensors, mounted on light poles,
sense data about the adjacent trail area; mobile sensors, mounted on skiers, can
collect data in their locality as the skier traverses the mountain. Skiers
opportunistically collect/carry data of interest as they travel along the trails
to the data sinks at the base. In this way we leverage a sparse deployment of
both static and mobile sensors to give a more complete picture over time of the
field of interest at lower cost than would be required with a fully static
deployment.
Read more about the idea in the poster abstract from SENSYS '06.
[manuscript]
[poster]
We have started to deploy Sensor Access Points (SAPs) - Aruba AP-70 WiFi APs
and with one or more Tmote Invents - across Dartmouth Campus. Currently we
have complete coverage of the Computer Science building and plan on covering
Thayer School of Engineering next and then experiment with mobile sensors and
their interaction with these SAPs to develop out MetroSense. Collectively this
infrastructure presents a general purpose, open programming environment for
people-centric applications. The idea is to "parasitically" use existing APs to
build out our campus area SAP infrastructure. SAPs seamlessly support standard
WiFi access, ground-truth sensing (since SAPs have sensors), and sensor comms
(802.15.4) and run the Open-WRT Linux distribution on the AP and TinyOS on the
mote.
Read our architecture white paper published in WICON '06. [manuscript]