Ruoxi Jia

PresenceSense: zero-training algorithm for individual presence detection based on power monitoring

Non-intrusive presence detection of individuals in commercial buildings is much easier to implement than intrusive methods such as passive infrared, acoustic sensors, and camera. Individual power consumption, while providing useful feedback and motivation for energy saving, can be used as a valuable source for presence detection. We conduct pilot experiments in an office setting to collect individual presence data by ultrasonic sensors, acceleration sensors, and WiFi access points, in addition to the individual power monitoring data. PresenceSense (PS), a semi-supervised learning algorithm based on power measurement that trains itself with only unlabeled data, is proposed, analyzed and evaluated in the study. Without any labeling efforts, which are usually tedious and time consuming, PresenceSense outperforms popular models whose parameters are optimized over a large training set. The results are interpreted and potential applications of PresenceSense on other data sources are discussed. The significance of this study attaches to space security, occupancy behavior modeling, and energy saving of plug loads.

Environmental sensing by wearable device for indoor activity and location estimation

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant-carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.

Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence

Occupancy detection for buildings is crucial to improving energy efficiency, user comfort, and space utility. However, existing methods require dedicated system setup, continuous calibration, and frequent maintenance. With the instrumentation of electricity meters in millions of homes and offices, however, power measurement presents a unique opportunity for a non-intrusive and cost-effective way to detect occupant presence. This study develops solutions to the problems when no data or limited data is available for training, as motivated by difficulties in ground truth collection. Experimental evaluations on data from both residential and commercial buildings indicate that the proposed methods for binary occupancy detection are nearly as accurate as models learned with sufficient data, with accuracies of approximately 78 to 93 percent for residences and 90 percent for offices. This study shows that power usage contains valuable and sensitive user information, demonstrating a virtual occupancy sensing approach with minimal system calibration and setup.

Privacy-enhanced architecture for occupancy-based HVAC Control

Large-scale sensing and actuation infrastructures have allowed buildings to achieve significant energy savings; at the same time, these technologies introduce significant privacy risks that must be addressed. In this paper, we present a framework for modeling the trade-off between improved control performance and increased privacy risks due to occupancy sensing. More specifically, we consider occupancy-based HVAC control as the control objective and the location traces of individual occupants as the private variables. Previous studies have shown that individual location information can be inferred from occupancy measurements. To ensure privacy, we design an architecture that distorts the occupancy data in order to hide individual occupant location information while maintaining HVAC performance. Using mutual information between the individuals location trace and the reported occupancy measurement as a privacy metric, we are able to optimally design a scheme to minimize privacy risk subject to a control performance guarantee. We evaluate our framework using real-world occupancy data: first, we verify that our privacy metric accurately assesses the adversarys ability to infer private variables from the distorted sensor measurements; then, we show that control performance is maintained through simulations of building operations using these distorted occupancy readings.

MapSentinel: Can the Knowledge of Space Use Improve Indoor Tracking Further?

Estimating an occupant’s location is arguably the most fundamental sensing task in smart buildings. The applications for fine-grained, responsive building operations require the location sensing systems to provide location estimates in real time, also known as indoor tracking. Existing indoor tracking systems require occupants to carry specialized devices or install programs on their smartphone to collect inertial sensing data. In this paper, we propose MapSentinel, which performs non-intrusive location sensing based on WiFi access points and ultrasonic sensors. MapSentinel combines the noisy sensor readings with the floormap information to estimate locations. One key observation supporting our work is that occupants exhibit distinctive motion characteristics at different locations on the floormap, e.g., constrained motion along the corridor or in the cubicle zones, and free movement in the open space. While extensive research has been performed on using a floormap as a tool to obtain correct walking trajectories without wall-crossings, there have been few attempts to incorporate the knowledge of space use available from the floormap into the location estimation. This paper argues that the knowledge of space use as an additional information source presents new opportunities for indoor tracking. The fusion of heterogeneous information is theoretically formulated within the Factor Graph framework, and the Context-Augmented Particle Filtering algorithm is developed to efficiently solve real-time walking trajectories. Our evaluation in a large office space shows that the MapSentinel can achieve accuracy improvement of 31.3% compared with the purely WiFi-based tracking system.

Occupancy modelling in shared spaces of buildings: a queueing approach

This paper describes the development of a queueing model for the simulation of occupancy patterns in shared spaces of buildings. Specifically, occupancy is modelled via an infinite-server queue with time-varying arrival and departure rates. In order to better capture the abrupt changes in occupancy, we also present an algorithm that efficiently learns the locally homogeneous intervals from the data and estimates the model parameters separately on each learned interval. Evaluated on the real-world occupancy data, the model has proved its capability of realistically reproducing the variations of occupancy, as well as the key properties, such as peak occupancy time, first arrival and last departure times, and occupied duration. We also compare our model with several occupancy models in the previous work, and show that our model is preferable in terms of simple structure, agile construction, minimal effort of manual calibration and the ability to reflect the occupancy patterns truthfully.

A fully unsupervised non-intrusive load monitoring framework

Monitoring an individual electrical loads energy usage is of great significance in energy-efficient buildings as it underlies the sophisticated load control and energy optimization strategies. Non-intrusive load monitoring (NILM) provides an economical tool to access per-load power consumption without deploying fine-grained, large-scale smart meters. However, existing NILM approaches require training data to be collected by sub-metering individual appliances as well as the prior knowledge about the number of appliances attached to the meter, which are expensive or unlikely to obtain in practice. In this paper, we propose a fully unsupervised NILM framework based on Non-parametric Factorial Hidden Markov Models, in which per-load power consumptions are disaggregated from the composite signal with minimum prerequisite. We develop an efficient inference algorithm to detect the number of appliances from data and disaggregate the power signal simultaneously. We also propose a criterion, Generalized State Prediction Accuracy, to properly evaluate the overall performance for methods targeting at both appliance number detection and load disaggregation. We evaluate our framework by comparing against other multi-tasking schemes, and the results show that our framework compares favorably to prior work in both disaggregation accuracy and computational overhead.

Poster Abstract: MapSentinel: Map-Aided Non-intrusive Indoor Tracking in Sensor-Rich Environments

Estimating an occupants location is arguably the most fundamental sensing task in smart buildings. Existing indoor tracking systems require occupants to carry specialized devices or install programs on their smartphones to collect inertial sensing data. In this paper, we propose MapSentinel, which performs non-intrusive location sensing based on WiFi access points and ultrasonic sensors. MapSentinel also combines the noisy sensor readings with the floormap information. Instead of using floormap merely to conduct sanity check of walking trajectories, we exploit the motion characteristics of occupants available from the floormap to enhance our location estimation. The fusion of heterogeneous information is theoretically formulated within the Factor Graph framework and the inference algorithm based on Particle Filtering is developed to efficiently solve real-time walking trajectories. Our evaluation in a large office space shows that the MapSentinel can achieve significant accuracy improvements compared with the purely WiFi-based tracking system.