NOTE: The Center for Mobile Computing is no longer active, and this web site represents a historical view of its activities from 1996-2008. Although there is still mobile-computing research underway at Dartmouth, we no longer update this web site. Please contact David Kotz with any inquiries about the CMC.
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The CMC loosely categorizes its activities as Major Programs or Individual Projects. This page lists Major Programs, which generally correspond to a DARPA or DoD program, include multiple technical projects, and have their own Web site. A separate page lists Individual Projects below, many of which are done under the auspices of one or more of the major programs. This page lists only some CMC programs; we will expand our coverage of CMC activities in the near future.
D'Agents
A mobile agent is a program that can migrate under its own
control from machine to machine in a heterogeneous network.
Mobile agents allow some applications to make more effective use
of network resources by moving code to the network location of
the data, rather than pulling large volumes of intermediate
results back to the home machine. Mobile agents are particularly
attractive in wireless networks or other low-bandwidth,
unreliable network environments, and are best viewed as another
tool that programmers can use to develop the most effecient
distributed applications. The D'Agents program is a nine-year
effort that has developed a mobile-agent system, D'Agents, and
explored the performance, security, and applications of mobile
agents and other forms of mobile code.
Active Communications
The ActComm project seeks to provide soldiers with wearable
computers and wireless data communications, and on top of this
infrastructure, various mission-support capabilities. The ActComm
project is a Multi-disciplinay University Research Initiative
(MURI), funded by the Department of Defense and administered by
the Air Force Office of Scientific Research. ActComm participants
are Dartmouth College, Harvard University, RPI, the University of
Illinois, Lockheed Martin - ATL, and ALPHATECH. Technical
projects under ActComm include ad-hoc wireless routing
algorithms, network sensing and prediction techniques, and
applications of mobile code.
Control of Agent-Based Systems (CoABS)
The CMC's CoABS program is part of DARPA's CoABS program, which
has over forty participating institutions looking at different
aspects of agent-based systems and programming. The CMC has
focused on resource control and scheduling algorithms for mobile
agents, mobile-code performance and scalability, and
interoperability middleware for mobile-agent systems.
This page lists some of the CMC Projects. The dates associated with each project indicate when the project description was written, and where possible, we provide links to people or pages that have more recent information.
We expect that wearable, portable, and even embeddable medical sensors will enable long-term continuous medical monitoring for many purposes, such as patients with chronic medical conditions (such as the recently announced blood-sugar sensors for diabetics), people seeking to change behavior (e.g., losing weight, or quitting smoking), or athletes wishing to monitor their condition and performance. The resulting data may be used directly by the person, or shared with others: with a physician for treatment, with an insurance company for coverage, or by a trainer or coach. Such systems have huge potential benefit to the quality of healthcare and quality of life for many people.
Since the sensor data may be gathered through a patient's mobile device (such as a mobile phone), a wireless network, and the Internet, there are many opportunities for the sensor data to be tampered or otherwise inaccurate. How can we assess confidence in sensor data? How can we present that level of confidence, in context, with the sensor data? This project will develop methods to assess confidence in medical sensor data.
Funded by the Intel University Research Council.
Sensor networks will provide a foundation to protect and monitor our national infrastructure, including economically important businesses with global reach (e.g., stock markets), critical transport and industrial facilities, the enterprise, and the border. These tiny, low-cost wireless devices embed on-board sensing, are fully programmable, and can spontaneously form large sensor webs with thousands of distributed sensor devices. In this project, we will study, analyze, propose, deploy, and evaluate MetroSense, a radically different scalable secure sensor architecture and system capable of reliable real-time monitoring and data fusion for large-scale critical infrastructure, resources, and assets. MetroSense opportunistically leverages mobile sensors when available to deal with sparse coverage and communications when sensing. We plan to develop a campus-area sensing architecture based on three integrated components (sensing and communications, sensor security, and sensor fusion) and deploy the system incrementally across campus with the goal of using static and mobile sensors for reliable monitoring and data fusion of campus plant, spaces, and people flow. Results from this project will serve as a foundation for building secure sensor networks capable of monitoring large-scale critical infrastructure.
As a community resource, the CMC is building an archive with the capacity to store wireless trace data from many contributing locations, with the staff to develop better tools for collecting, anonymizing, and analyzing the data. This Community Resource for Archiving Wireless Data At Dartmouth, CRAWDAD, will work with community leaders to ensure that the archive meets the needs of the research community, work with the other leading centers that develop network tracing tools and metadata, and work with research organizations and corporations to ensure continuing support for the archive after NSF's funding ends.
Wireless mesh networks can be used to provide communication infrastructure for emergency response operations in areas with limited or damaged infrastructure. We imagine the formation of a wireless mesh of heterogeneous devices such as transceivers on ambulances, fire trucks and police cars. This mesh would support a network of Personal Digital Assistants (PDAs) on first responders and an ad hoc network of rapidly deployed micro-sensor devices. Monitoring of such a mesh network will be crucial to the success of first responder operations. Standard techniques for monitoring wired networks or even wireless infrastructure networks are unsuitable for a wireless mesh network with unpredictable links and resource-constrained devices. Our goal is to develop a wireless mesh monitoring system to detect and identify real-time problems and aid system administrators in making proactive as well as reactive management decisions.
We propose to develop a mesh monitoring system that can be used to generate real-time network topology maps, power maps and provide real-time data on network traffic and user locations to aid mission planners. The aim of this project is to present new ways to efficiently implement a real-time wireless monitoring system that assists in fault detection, repair and the automation of network management tasks. It may also be possible to use the monitored information about the state of the network to improve and optimize the performance of the mesh routing protocol. Some other contributions of this work will be in the use of error codes to recover information from corrupt or lost packets and to maximize utility of monitoring information sent over an unreliable channel. Our initial plan is to deploy a 15-node multi-radio mesh network and to monitor it using in-band channels as well as out-of-band channels (such as a wired backhaul or a separate wireless channel) for the traffic being monitored. Thus we can study the effectiveness of the monitoring system and its impact on the behavior of the mesh network. In parallel, we will simulate a mesh network to study scalability and other characteristics of the monitoring system.
Digital technology plays an increasing role in everyday life, and this trend is only accelerating. Consider daily life five years from now, in 2010: we will each be surrounded by far more digital devices, mediating far more activities in our work, home, and play; the boundary between cyberspace and physical space will fade as sensors and actuators allow computers to be aware of, and control, the physical environment; and the devices in our life become increasingly (and often invisibly) interconnected with each other and with the Internet. Today, typical home users struggle to maintain the security of their home computer, and have difficulty managing their privacy online. Tomorrow, these challenges may become unimaginably complex. This 18-month project studies, and begins to address, the security and privacy challenges involved in developing this world of Digital Living in 2010.
Specifically, this project focuses on the advent of sensor networks, and their applications in the home and work environment. Although sensor networks have been an active area of academic research, and are becoming commercially available for deployment in industrial settings, sensor networks will soon have many uses in enterprise and residential settings. People will live in spaces, or work with devices, that have embedded sensing capability. For people to accept this new technology into their lives, they must be able to have confidence that the systems work as expected, and do not pose unreasonable threats to personal privacy. This confidence results from a variety of technical and organizational mechanisms. This project delves into the sociological underpinnings of privacy and trust in digital living, into the technological foundations for secure and robust sensor networks, and into mechanisms for users to express control over information about their activity.
With the rise of Voice over wireless LAN (VoWLAN), any complete WiFi security solution must address denial of service attacks, such as kicking off other clients, consuming excessive bandwidth, or spoofing access points, to the detriment of legitimate clients. Even authorized clients may be able to sufficiently disrupt service quality to make the network ineffective for legitimate clients. Our approach provides a new foundation for wireless network security, able to dynamically measure, analyze and protect a WiFi network against existing and novel threats, including rogue clients and access points, with a focus on VoWLAN use cases. Our goal is to support thousands of APs and clients, quickly recognize most new attacks, and generate few false alarms.
The Automated Remote Triage and
Emergency Management Information System (ARTEMIS) is an
ongoing research effort at Dartmouth College's Institute for
Security Technology Studies that aims to provide real-time
physiological information to first responders and command
personnel in emergency/disaster situations. The prototype
system is capable of monitoring and assessing physiological
parameters of individuals, transmitting pertinent medical
data to and from multiple echelons of medical service
personnel, and providing filtered data for command and
control applications.
The system employs wireless
networking, portable computing devices, and reliable
messaging technology as a framework for information analysis,
information movement, and decision support capabilities.
Physiological status assessment is based on a medical model
that relies on input from humans and a pulse oximetry device.
Our physiological status determination methodology follows
NATO defined guidelines for remote triage and is implemented
using an approach based on fuzzy logic. The approach
described on this website can be used in both military and
civilian settings.
The long-term goal of the ARTEMIS project is to integrate advances in communications and analysis technologies into a remote triage system that can expedite and improve care of the wounded in small-to-large scale emergency situations. Our aim is to provide an unprecendented degree of medical situational awareness at all levels of the first-responder command heirarchy.
Many people who design, develop, or deploy wireless networks use simulations to evaluate the impact of their design decisions on the performance of the network. For these simulations to be effective, however, one must have a realistic model of device mobility. Currently available models of device mobility do not reflect the movement patterns of real users. Using the traces collected by access points (APs) on our campus, we aim to develop realistic mobility models.
We are interested in
developing models of both AP-association patterns and
physical user movements. The former presents how mobile users
roam from one AP to another, while the latter describes how
mobile users move in a physical space. To develop an
association model, we first extract the characteristics of
association patterns directly from the syslog messages
(available on this site). We then derive an association model
from these characteristics. To develop a physical-mobility
model, however, we first need to estimate the physical
location of users from the association patterns; this task is
not easy because a mobile device does not necessarily
associate with the geographically-closest AP. Our path
extractor estimates paths from AP-association patterns and
has been validated against GPS track data as shown in the
figure. These extracted paths are used for developing a
physical-mobility model.
In wireless networks users can move from one location to another location without losing their network connection. This flexibility of mobility introduces new challenges in quaranteeing quality of service (QoS) and in locating users and transfering data between them and the access points (APs). By predicting a user's next AP we can reduce the overhead of mobility management and make bandwidth reservations to guarantee the QoS.
Many prediction algorithms have been proposed, but most of them are evaluated by simulations using synthetic data. We have collected the association messages at our campus-wide wireless network. From the association messages, we extracted the mobility traces, and evaluated prediction methods using our real wireless mobility data. We found that low-order Markov predictors performed as well or better than the more complex and more space-consuming compression-based predictors.
Besides predicting the next AP, anticipating a user's handoff time is also important for applications such as bandwidth reservation, which needs to know when to reserve bandwidth. It is easier to estimate the handoff probability with a period of time than to predict the exact time. We developed such a time predictor and combined it with a location predictor to compute the probability that a user handoffs to a certain AP within a given period of time. We simulated several bandwidth reservation schemes using this location-and-time-integrated predictor with our real mobility data. The results show that both call-drop rate and call-block rate are reduced significantly.
Since our simulation indicates that with accurate location-and-time prediction the QoS of calls is improved, we would like to improve the performance of predictors. In the future, we will continue to collect wireless association data and investigate the characteristics of users mobility patterns. We believe these mobility characteristics will help us develop better predictors.
A wireless sensor network can extend the
sensory perception of people and robots far beyond their
normal range. Wireless sensors are also small computers. When
the sensors are used to detect danger they can perform
distributed computations to compute the safest path along
which a person or robot can be guided. Sensors that detect
their own network connectivity can be used to guide a robot
to repair holes in that connectivity. Sensors that detect a
fault in an industrial process can guide a robot or person to
the location of the fault for further inspection. Robots and
people can also store information in a sensor network which
can later be used for guidance, or by the sensor network
itself (for example by telling the sensors their GPS
coordinates.)
We have been exploring all these concepts in a large variety
of experiments. In the picture on the right, USC's AVATAR
autonomous flying robot is repairing the gaps in connectivity
in a sensor network. The sensor network computed the
locations of missing sensors, the robot queried the network
for the gap location, and then flew over the gap, dropping
new sensors to repair the network.
In the picture on the left, a crane robot at CMU is
interacting with a sensor network. The robot is controlled by
precision winches connected to the four cables attached to
the robot from the ceiling. This type of robot might be used
inside a factory to maintain sensors that monitor industrial
processes. The robot first broadcasts location messages while
moving in a precise pattern to localize the sensors. A radio
message was then broadcast to the sensor network and followed
a precise geographic path through the sensors. The robot then
queried the sensors to follow the same path as the radio
message.
We have also been looking at using maps of
sensed data to guide people and robots. The picture on the
left shows a temperature map as it varies over time in a room
where a large fire has been started. Guidance algorithms can
make use of such maps to bring people to safety, or to guide
firefighters to the danger.
A device we call a "flashlight", shown in the center of the
sensors in the picture below, can be carried by a person or
robot to find their way through an area based on the data
stored in the sensors or on the readings from the
sensors.
This project considers a distributed system that disseminates high-volume data streams to many simultaneous monitoring applications over a low-bandwidth network. For bandwidth efficiency, we propose a group-aware stream filtering approach, used together with multicasting, that exploits two overlooked, yet important, properties of monitoring applications: 1) many of them can tolerate some degree of ``slack'' in their data quality requirements, and 2)there may exist multiple subsets of the source data satisfying the quality needs of an application. We can thus choose the ``best alternative'' subset for each application to maximize the data overlap within the group to best benefit from multicasting. Here we provide a general framework for the group-aware filtering problem, which we prove is NP-hard. We introduce a suite of heuristics-based algorithms that ensure data quality (specifically, granularity and timeliness) while preserving bandwidth.
Our work exploits applications' semantics to better managing precious network resources. For evaluation, we integrate group-aware filtering with a general-purpose sensor data dissemination middleware system, Solar, developed at Dartmouth College. Our evaluation shows that quality-managed group-aware filtering is effective in trading CPU time for bandwidth savings, compared with self-interested stream filtering.
IEEE 802.11 Wireless Local Area Networks (WLANs) are now commonplace on many academic and corporate campuses. As “Wi-Fi” technology becomes ubiquitous, understanding trends in the usage of these networks becomes increasingly important for network deployment, management, and the development of new wireless and location-aware applications. We have been measuring various aspects of Dartmouth's campus-wide WLAN since the installation of the network in 2001. The extensive coverage of Dartmouth's WLAN allows us to study how the network is used by students, faculty and staff.
We employ a variety of methods to measure wireless network usage. We have deployed “sniffer” boxes (Linux PCs with multiple network interfaces) around the campus to observe the data packets that are transferred over the network; this enables us to measure wireless application usage. By using SNMP and syslog to monitor the access points we can measure user mobility patterns. We have also deployed wireless sniffers to measure the 2.4GHz and 5GHz frequency bands that are used by IEEE 802.11a/b/g networks; this allows us to measure wireless traffic that does not traverse the wired side of the Dartmouth network, and also lets us observe other wireless networks, such as ad-hoc networks or “rogue” access points. Finally, we are also investigating the use of psychological methodologies, such as the Experience Sampling Method, to ask the network users themselves about their experiences with the wireless network.
We have discovered that since the deployment of the network, usage has moved away from non-realtime applications such as the World Wide Web, with an increasing amount of streaming audio/video and peer-to-peer file transfers being conducted over the WLAN. Although Dartmouth has migrated to a Voice over IP telephone system, we have seen little wireless VoIP usage. We encourage other researchers to make use of the data that we have collected, and anonymised datasets are available on CRAWDAD.
In many emergency calls the presense of
deadly, invisible chemicals is first noticed when people
start coughing or falling ill. Even after the presence of a
toxin has been verified, unless visible it is difficult to
avoid exposure due to air motion. Networks of mobile chemical
sensors (sensors on robots) can provide a first warning of
nearby toxins, and tell us where they are, where they are
moving towards, and how to avoid them.
As part of ongoing work in medical and environmental sensors
for first responders, we devised a simulated air crash
scenario that involves a chemical leak. The crash throws some
debris into a nearby farmers field where a tank of anhydrous
ammonia used as fertilizer is present on a trailer attached
to a tractor. Anhydrous ammonia, when released into the
atmosphere, is a clear colorless gas, which remains near the
ground and drifts with the wind. It attacks the lungs and
breathing passages and is highly corrosive, causing damage
even in relatively small concentrations. It can be detected
with an appropriate sensor such as the Figaro TGS 826 Ammonia
sensor. Our experiments map the presence of an ammonia
cloud and guide a first responder to safety along the path of
least chemical concentration. The image on the right shows
such a danger map with the safest path computed by a network
of 38 Mica Mote sensors.
We are currently exploring the utility of mobile sensor
networks in warning, guidance, and sensing for search and
rescue missions in the difficult environments created by
disaster situations, such as the rubble pile from a destroyed
building shown to the left.
Fences on open ranges cost the cattle industry a lot of time and money to install and maintain. Herding cattle also involves much time and effort. A collaboration between Daniela Rus, the CSIRO Robotics Team in Australia and a USDA Ranch Management Research Animal Scientist was initiated at Dartmouth to consider the problem of monitoring and controlling the position of herd animals.
The goal is to apply the vast body of
theory in robotics and motion planning to virtual fences for
controlling animals and to integrate new technologies, such
as wireless adhoc networking, into a field where technology
has yet had little penetration. Similar to the "invisible
fence" products sold for fencing pets in the yard at home, a
virtual fence is a collar or tag worn by an animal which
tracks its location via GPS and applies a stimulus to the
animal to control its motion. Animals are not robots and
their unpredictable reactions mean that existing robotics
motion control solutions must be modified to take into
account the imprecise control before they can be useful.
The picture on the upper right shows a cow
wearing an early prototype of a Smart Collar during an
experiment. The picture on the lower left shows an automatic
path planner for herding animals around obstacles to a goal.
The picture on the lower right shows another early Smart
Collar prototype with a PDA, adhoc WiFi multihop networking,
GPS, and sound system for producing stimuli.
In pervasive computing, many
applications will be context-aware; that is, the applications
adapt their behaviors according to user's situation or
environment. We are examining the potential for making
authorization decisions (such as access to a medical
database) based on the context of the user making the
request. Such a context-sensitive authorization scheme is
necessary when mobile users (e.g., first responders) move
across multiple administrative domains where they are not
registered into the systems in advance. For example, in the
First Responder project, a granting decision could depend on
a requester's current location or medical conditions, which
is obtained from the information servers. The information
servers, in return, collect situational information from a
sensor network that covers an emergency area. Since context
information, such as the location of the user, might allow an
malicious party to infer the user's private information, we
particularly address the issue of protecting confidential
information involved in authorization policies to protect the
users' privacy.
We developed a secure logic-based
authorization system where authorization policies are
expressed as logical rules (i.e., Horn clauses). A request is
granted if the authorization server succeeds in constructing
a proof tree that derives the granting decision. Our
decentralized scheme does not need a universally trusted
central authorization server that maintains all the context
information; the authorization decisions are made by multiple
hosts, each of which only has partial knowledge about the
context information, in a peer-to-peer way. Our novel
distributed algorithm decomposes a proof tree into multiple
subproof trees produced by different hosts so that
confidentiality policies of each host are satisfied. We are
deploying our current implementation into an emergency
response system to evaluate the performance and scalability
of the system.
Analysis of the campus-wide wireless network and the impact
of VoIP
September 2003
David Kotz
In contrast, this code is part of our ongoing effort to use open source and TCPA to turn ordinary computers into "virtual" secure coprocessors---more powerful but less secure than their high-assurance cousins.
The Linux Enforcer Module is a Linux Security Module designed to help improve integrity of a computer running Linux. The Enforcer provides a subset of Tripwire-like functionality. It runs continuously and as each protected file is opened its SHA1 is calculated and compared to a previously stored value. More information about this project and a recent paper can be found at this Dartmouth Technical Report TR2003-17 as well as the Enforcer Sourceforge site
Mobile ad-hoc networks (MANETs) are infrastructure free networks of mobile nodes that communicate with each other wirelessly.Our goal is to utilize three-dimensional (3D) position information to provide more reliable as well as efficient routing. We thus describe extensions to various location aware routing algorithms to work in 3D. We propose a new hierarchical, zone-based 3D routing algorithm, based on GRID by Liao, Tseng and Sheu. Our new algorithm called "Hyper-GRID" is a hybrid algorithm that uses multipath routing in 3D.
We wish to implement a multipath algorithm similar to AOMDV in Hyper-GRID as we expect to see lower end-to-end delays, lower packet loss and reduced routing overhead by reducing the frequency of route discovery phases through use of a multipath routing strategy.
The Solar project is using the campus wireless network, as well as a location-tracking system developed by Versus Technologies and installed in the computer-science building, to investigate the potential for location-aware applications and for pervasive computing in general.Kotz and his students are developing a flexible and secure infrastructure to collect, process, and disseminate location and other contextual information to context-aware applications; prototyping location-aware and context-aware applications, both in a campus setting and in emergency-response scenarios.
Their Solar System is a software infrastructure that supports context collection, aggregation, and dissemination. Solar provides a small composition language, allowing applications to construct a graph of operators to compute desired context from appropriate sources. Solar implements a context-sensitive resource discovery mechanism to achieve flexibility, and improves the scalability by balanced distribution and reuse of operators.
Professor Cooley at Thayer School of Engineering and Newbury Networks have teamed to install Newbury's Locale Points within the Engineering School. Now, a user with a wireless-enabled TabletPC, notebook, of PDA is pushed web content depending on whether they are standing in the reception area, working in a computer lab, taking a class, etc. Thus, welcome information, how to get help, or class notes can be easily provided to the user.
An example project currently underway to take advantage of this location technology is Multimedia Techniques for Engineering Instruction, MTEI. With this system, course materials for specific courses, offered at specific times are wirelessly delivered to students in a given classroom. The MTEI system is also use for online quizzes for credit, and anonymous assessment to gauge whether the class is understanding a particular point or not. The latter results are displayed on the, "clue meter", a web-based gauge of student understanding.
We develop distributed algorithms for self-reconfiguring sensor networks that help direct an object (say, a soldier or a robot) through a dangerous region. The sensor network models the danger levels sensed across its area, representing the dangerous areas as obstacles. A protocol that combines the artificial potential field of the sensors with the object's goal location guides the moving object incrementally across the network to the goal, while maintaining the safest distance to the danger areas. To evaluate the performance of the algorithms, we have done many hardware experiments using a physical sensor network consisting of Berkeley's Mote sensors.
We develop online power-aware routing algorithms in large wireless ad-hoc networks for applications where the message sequence is not known. We seek to optimize the lifetime of the network. We develop a series of approximation algorithms to solve the problem, including the centralized max-min zPmin algorithm, hierarchical algorithm, and several distributed algorithms that can reduce the message broadcasts on each node. Our experiments show that the performance is quite good. We are also working on the MAC-layer protocols to conserve energy by putting nodes into sleeping mode.
Mobile sensor networks are a new form of sensor network in which the sensors are tethered to some moving equipment, such as wheels or flying objects. We study how the sensors can reconfigure themselves to achieve better network connectivity, message transmission, and other group behaviors. We developed algorithms to guarantee message delivery in a disconnected mobile sensor network by asking mobile sensors to move. Our next task is to develop algorithms for a mobile sensor network to explore a large area.
Our research focuses on how to integrate battlefield information systems in a dynamic information environment to support information exploitation. Our goal is to create greater semantic interoperability among sensors and information assets. Our wireless sensor platform currently uses a hybridization of WiFi, 900 Mhz Spread Spectrum, GPS, and Dartmouth designed MiniME GPS sensors. Sensor measurements include sound, temperature, and seismic vibrations. These measurements are combined with a variety of data fusion algorithms distributed across the network.
Two students are installing private APs in off-campus residences, and configuring them as repeaters. The goal is to expand the reach of the campus wireless network into private residences nearby. They route wireless traffic into the on-campus APs and thence to the campus backbone and the Internet. As a result we are getting valuable early experience with the realities of repeaters and mesh technologies.
Jon used ideas from economics to develop a market-based approach to the allocation of resources in a distributed system. In his approach, computations are mobile agents that need to jump from host to host to reach the resources they need. They must pay for the computation time they use at each host. The resulting market is an efficient mechanism for fair, distributed allocation of computational resources. In the fall Jon will be a professor in the Mathematics and Computer Science department at Colorado College.
Ammar developed a secure, scalable directory service for mobile users, and applied it to the mobile voice-over-IP application developed by Ayorkor. Chief among its goals was protecting the privacy of mobile users, so that a stalker cannot track the IP address (and thus the location) of a moving user. For his work, Ammar was awarded High Honors and shared the Kemeny Prize for Computing.
Ayorkor extended the H.323 telephony protocols so that a voice-over-IP conversation can continue even as the mobile user's computer roams from access point to access point, and from IP subnet to IP subnet, changing IP addresses. For her work, Ayorkor was awarded High Honors and shared the Kemeny Prize for Computing.
Arun implemented the first application for Guanling Chen's Solar system. His SmartReminder application reminds its user of upcoming appointments depending on the current location and the location of the next appointment. For his work, Arun was awarded High Honors and shared the Kemeny Prize for Computing.
Michael designed and built a collection of small sensor modules, each with a small processor and RF network link. When turned on, his modules quickly identify their neighbors in the ad-hoc wireless network and use a novel GPS-based routing algorithm to communicate their sensor readings to a central collection point.