Kamin Whitehouse

The smart thermostat: using occupancy sensors to save energy in homes

Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption. In this paper, we demonstrate how to use cheap and simple sensing technology to automatically sense occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning off the homes HVAC system. We call this approach the smart thermostat. We evaluate this approach by deploying sensors in 8 homes and comparing the expected energy usage of our algorithm against existing approaches. We demonstrate that our approach will achieve a 28% energy saving on average, at a cost of approximately

Hood: a neighborhood abstraction for sensor networks

25 in sensors. In comparison, a commercially-available baseline approach that uses similar sensors saves only 6.8% energy on average, and actually increases energy consumption in 4 of the 8 households.

A practical evaluation of radio signal strength for ranging-based localization

This paper proposes a neighborhood programming abstraction for sensor networks, wherein a node can identify a subset of nodes around it by a variety of criteria and share state with those nodes. This abstraction allows developers to design distributed algorithms in terms of the neighborhood abstraction itself, instead of decomposing them into component parts such as messaging protocols, data caches, and neighbor lists. In those applications that are already neighborhood-based, this abstraction is shown to facilitate good application design and to reduce algorithmic complexity, inter-component coupling, and total lines of code. The abstraction as defined here has been successfully used to implement several complex applications and is shown to capture the essence of many more existing distributed sensor network algorithms.

Calibration as parameter estimation in sensor networks

Radio signal strength (RSS) is notorious for being a noisy signal that is difficult to use for ranging-based localization. In this study, we demonstrate that RSS can be used to localize a multi-hop sensor network, and we quantify the effects of various environmental factors on the resulting localization error. We achieve 4.1m error in a 49 node network deployed in a half-football field sized area, demonstrating that RSS localization can be a feasible alternative to solutions like GPS given the right conditions. However, we also show that this result is highly sensitive to subtle environmental factors such as the grass height, radio enclosure, and elevation of the nodes from the ground.

Trio: enabling sustainable and scalable outdoor wireless sensor network deployments

We describe an ad-hoc localization system for sensor networks and explain why traditional calibration methods are inadequate for this system. Building upon previous work, we frame calibration as a parameter estimation problem; we parameterize each device and choose the values of those parameters that optimize the overall system performance. This method reduces our average error from 74.6% without calibration to 10.1%. We propose ways to expand this technique to a method of autocalibration for localization as well as to other sensor network applications.

Exploiting the capture effect for collision detection and recovery

We present the philosophy, design, and initial evaluation of the Trio testbed, a new outdoor sensor network deployment that consists of 557 solar-powered motes, seven gateway nodes, and a root server. The testbed covers an area of approximately 50,000 square meters and was in continuous operation during the last four months of 2005. This new testbed in one of the largest solar-powered outdoor sensor networks ever constructed and it offers a unique platform on which both systems and application software can be tested safely at scale. The testbed is based on Trio, a new mote platform that provides sustainable operation, enables efficient in situ interaction, and supports fail-safe programming. The motivation behind this testbed was to evaluate robust multi-target tracking algorithms at scale. However, using the testbed has stressed the system software, networking protocols, and management tools in ways that have exposed subtle but serious weaknesses that were never discovered using indoor testbeds or smaller deployments. We have been iteratively improving our support software, with the eventual aim of creating a stable hardware-software platform for sustainable, scalable, and flexible testbed deployments

Semantic streams: a framework for composable semantic interpretation of sensor data

In this paper we evaluate a technique to detect and recover messages from packet collisions by exploiting the capture effect. It can differentiate between collisions and packet loss and can identify the nodes involved in the collisions. This information is provided at virtually no extra cost and can produce significant improvements in existing collision mediation schemes. We characterize this technique using controlled collision experiments and evaluate it in real world flooding experiments on a 36-node sensor network.

Clairvoyant: a comprehensive source-level debugger for wireless sensor networks

We present a framework called Semantic Streams that allows users to pose declarative queries over semantic interpretations of sensor data. For example, instead of querying raw magnetometer data, the user queries whether vehicles are cars or trucks; the system decides which sensor data and which operations to use to infer the type of vehicle. The user can also place constraints on values such as the the amount of energy consumed or the confidence with which the vehicles are classified. We demonstrate how this system can be used on a network of video, magnetometer, and infrared break beam sensors deployed in a parking garage with three simultaneous and independent users.

Marionette: using RPC for interactive development and debugging of wireless embedded networks

Wireless sensor network (WSN) applications are notoriously difficult to develop and debug. This paper describes Clairvoyant which is a comprehensive source-level debugger for wireless, embedded networks. With Clairvoyant, a developer can wirelessly connect to a sensor network and execute standard debugging commands including break, step, watch, and backtrace, as well as new commands that are specially designed for debugging WSNs. Clairvoyant attempts to minimize its effect on the program being debugged in terms of network load, memory footprint, execution speed, clock consistency, and flash lifetime.

The effects of ranging noise on multihop localization: an empirical study

A main challenge with developing applications for wireless embedded systems is the lack of visibility and control during execution of an application. In this paper, we present a tool suite called Marionette that provides the ability to call functions and to read or write variables on pre-compiled, embedded programs at run-time, without requiring the programmer to add any special code to the application. This rich interface facilitates interactive development and debugging at minimal cost to the node