Jiakang Lu

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

Flash Flooding: Exploiting the Capture Effect for Rapid Flooding in Wireless 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.

The hitchhiker's guide to successful residential sensing deployments

We present the Flash flooding protocol for rapid network flooding in wireless sensor networks. Traditional flooding protocols can be very slow because of neighborhood contention: nodes cannot propagate the flood until neighboring nodes have finished their transmissions. The Flash flooding protocol avoids this problem by allowing concurrent transmissions among neighboring nodes. It relies on the capture effect to ensure that each node receives the flood from at least one of its neighbors, and introduces new techniques to either recover from or prevent too many concurrent transmissions. We evaluate the Flash flooding protocol on both a 48-node wireless sensor network testbed and in a trace-based simulator. Our results indicate that the Flash flooding protocol can reduce latency by as much as 80%, achieving flooding latencies near the theoretical lower bound without sacrificing coverage, reliability or power consumption.

Using simple light sensors to achieve smart daylight harvesting

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.

Stream feeds: an abstraction for the world wide sensor web

Lighting is the largest single energy consumer in commercial buildings. In this paper, we demonstrate how to improve the effectiveness of daylight harvesting with a single light sensor on each window. Our system automatically infers the window orientation and the cloudiness levels of the current sky to predict the incoming daylight and set window transparency accordingly. We evaluate our system with ten weeks of empirical data traces collected from windows around an office building and compare our approach with non-predictive feedback control. Experimental results show that our scheme can infer the orientation of a window to within ±7° of the actual orientation and improve energy savings by 10% over existing approaches without sacrificing user comfort.

SunCast: fine-grained prediction of natural sunlight levels for improved daylight harvesting

RFIDs, cell phones, and sensor nodes produce streams of sensor data that help computers monitor, react to, and affect the changing status of the physical world. Our goal in this paper is to allow these data streams to be first-class citizens on the World Wide Web. We present a new Web primitive called stream feeds that extend traditional XML feeds such as blogs and Podcasts to accommodate the large size, high frequency, and real-time nature of sensor streams. We demonstrate that our extensions improve the scalability and efficiency over the traditional model for Web feeds such as blogs and Podcasts, particularly when feeds are being used for in-network data fusion.

Towards Occupancy-Driven Heating and Cooling

Daylight harvesting is the use of natural sunlight to reduce the need for artificial lighting in buildings. The key challenge of daylight harvesting is to provide stable indoor lighting levels even though natural sunlight is not a stable light source. In this paper, we present a new technique called SunCast that improves lighting stability by predicting changes in future sunlight levels. The system has two parts: 1) it learns predictable sunlight patterns due to trees, nearby buildings, or other environmental factors, and 2) it controls the window transparency based on a quadratic optimization over predicted sunlight levels. To evaluate the system, we record daylight levels at 39 different windows for up to 12 weeks at a time, and apply our control algorithm on the data traces. Our results indicate that SunCast can reduce glare by 59% over a baseline approach with only a marginal increase in artificial lighting energy.

Exploiting the capture effect for low-latency flooding in wireless sensor networks

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.

MetroNet: Case Study for Collaborative Data Sharing on the World Wide Web

In this paper, we present the Flash flooding protocol that exploits the capture effect to produce low-latency network floods. The capture effect is the ability of some radios to correctly receive one of several concurrently transmitted messages, even if the received strengths of the two messages are almost the same. We exploit this phenomenon in a network flooding scenario by allowing nodes to propagate the flooding message concurrently, thus reducing delays due to neighborhood contention. Our experimental results indicate that Flash can reduce latency by 75-80 percent without sacrificing flooding reliability or coverage.

Smart Blueprints: How Simple Sensors Can Collaboratively Map Out Their Own Locations in the Home

We demonstrate MetroNet, which is an application that illustrates collaborative data sharing on the Wide Wide Web. MetroNet has two parts. First, our sensors gather data about pedestrian foot traffic in front of and into stores, which is made available online to shopkeepers. Second, this sensor data can be made public, at the discretion of the shopkeeper, for use by city planners, other shopkeepers, or residents of the city. MetroNet is an application through which we study fundamental problems of sharing data on the Web, such as search, data fusion, and privacy.