Tamim I. Sookoor

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

The hitchhiker's guide to successful residential sensing deployments

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.

MacroLab: a vector-based macroprogramming framework for cyber-physical systems

Homes are rich with information about peoples energy consumption, medical health, and personal or family functions. In this paper, we present our experiences deploying large-scale residential sensing systems in over 20 homes. Deploying small-scale systems in homes can be deceptively easy, but in our deployments we encountered a phase transition in which deployment effort increases dramatically as residential deployments scale up in terms of 1) the number of nodes, 2) the length of time, and 3) the number of houses. In this paper, we distill our experiences down to a set of guidelines and design principles to help future deployments avoid the potential pitfalls of large-scale sensing in homes.

Macrodebugging: global views of distributed program execution

We present a macroprogramming framework called MacroLab that offers a vector programming abstraction similar to Matlab for Cyber-Physical Systems (CPSs). The user writes a single program for the entire network using Matlab-like operations such as addition, find, and max. The framework executes these operations across the network in a distributed fashion, a centralized fashion, or something between the two - whichever is most efficient for the target deployment. We call this approach deployment-specific code decomposition (DSCD). MacroLab programs can be executed on mote-class hardware such as the Telos [24] motes. Our results indicate that MacroLab introduces almost no additional overhead in terms of message cost, power consumption, memory footprint, or CPU cycles over TinyOS programs.

RoomZoner: occupancy-based room-level zoning of a centralized HVAC system

Creating and debugging programs for wireless embedded networks (WENs) is notoriously difficult. Macroprogramming is an emerging technology that aims to address this problem by providing high-level programming abstractions. We present MDB, the first system to support the debugging of macroprograms. MDB allows the user to set break-points and step through a macroprogram using a source-level debugging interface similar to GDB, a process we call macrodebugging. A key challenge of MDB is to step through a macroprogram in sequential order even though it executes on the network in a distributed, asynchronous manner. Besides allowing the user to view distributed state, MDB also provides the ability to search for bugs over the entire history of distributed states. Finally, MDB allows the user to make hypothetical changes to a macroprogram and to see the effect on distributed state without the need to redeploy, execute, and test the new code. We show that macrodebugging is both easy and efficient: MDB consumes few system resources and requires few user commands to find the cause of bugs. We also provide a lightweight version of MDB called MDB Lite that can be used during the deployment phase to reduce resource consumption while still eliminating the possibility of heisenbugs: changes in the manifestation of bugs caused by enabling or disabling the debugger.

Towards Occupancy-Driven Heating and Cooling

Cyber-Physical Systems (CPSs) combine low-power radios with embedded processors in order to provide high-resolution sensing and actuation over a geographic area. This revolution has begun to deliver a new generation of engineering systems and scientific breakthroughs. One area in which CPSs can have a significant impact is in energy conservation through the intelligent control of systems. In this paper we present a CPS that enables a centralized Heating, Ventilation, and Air Conditioning (HVAC) system to be retrofitted to enable room-level conditioning of a residence. This is a compelling application due to residential HVAC systems accounting for over 15% of all U.S. energy usage, making it one of the nations largest energy consumers. Also, it has all the aspects of a complex CPS: it uses sensors to detect room occupancy and indoor climate, it actuates hardware, and it requires complex control algorithms in order to maximize energy savings without damaging the HVAC equipment or discomforting the occupants. With an implementation using commercial off-the-shelf (COTS) components and a simple control algorithm we demonstrate an almost 15% energy saving in a residence over its existing centralized thermostat. With this demonstration, we pose a challenge to control theorists with the CPS community to refine our approach which could lead to even greater energy savings.

Modeling building thermal response to HVAC zoning

HVAC systems are eventually needed to maintain comfort for occupants, and as a result sensing and leveraging user context information is critical for the energy-efficient operation of buildings. This article describes an occupancy-driven HVAC control framework for more effective heating and cooling management.

Programming cyber-physical systems with MacroLab

HVAC systems account for 38% of building energy usage. Studies have indicated at least 5-15% waste due to unoccupied spaces being conditioned. Our goal is to minimize this waste by retrofitting HVAC systems to enable room-level zoning where each room is conditioned individually based on its occupancy. This will allow only occupied rooms to be conditioned while saving the energy used to condition unoccupied rooms. In order to achieve this goal, the effect of opening or closing air vent registers on room temperatures has to be predicted. Making such a prediction is complicated by the fact that weather has a larger effect on room temperatures than the settings of air vent registers, making it hard to isolate the influence of the HVAC system. We present a technique for dynamically estimating the heat load due to weather on room temperatures and subtracting it out in order to predict the effect of the HVAC system more directly.

A modular and extensible macroprogramming compiler

We demonstrate MacroLab, which is a macroprogramming framework that offers a vector programming abstraction similar to Matlab for cyber-physical systems (CPSs). The user writes a single program for an entire network using Matlab like operations such as addition, find, and max. The framework executes these operations across the network in a distributed fashion, a centralized fashion, or something between the two - whichever is most efficient for the target deployment. We call this approach deployment-specific code decomposition (DSCD). The MacroLab programming framework will facilitate the easy development of applications for CPSs by domain experts such as scientists and engineers with almost no additional overhead to the nodes in terms of message cost, power consumption, memory footprint, or CPU cycles over TinyOS programs.

Macrodebugging with MDB

Translation of a macroprogram to node-level microprograms is a complex and challenging task for a compiler. Developing of a robust macroprogramming compiler framework poses a unique challenge because users need to create optimized function decompositions, in a modular way, such that they can be composed with other compiler functionality such as optimization and debugging. This differs from most compilers because they typically employ a fixed set of code optimizations for a particular target platform. We present a macroprogramming compiler for MacroLab which has a modular architecture that provides users with a two layer interface: one to tackle user created optimized function decompositions and another to handle low-level compiler modifications. Our results show that the introduction of new decompositions and compiler optimizations can be accomplished with a small number of lines of code and typically written in a matter of hours.