Search for: All records

The key to optimal occupant comfort as well as resource utilization in a smart building is to provide personalized control over smart appliances. Additionally, with an exponentially growing Internet-of-Things (IoT), reducing the need of frequent user attention and effort involving building management to control and manage an enormous number of smart devices becomes inevitable. One crucial step to enable occupant-specific personalized spaces in smart buildings is accurate identification of different occupants. In this paper, we introduce SolarWalk to show that small and unobtrusive indoor photovoltaic harvesters can identify occupants in smart home scenarios. The key observations are that i) photovoltaics are commonly used as a power source for many indoor energy-harvesting devices, ii) a PV cell's output voltage is perturbed differently when different persons pass in close range, creating an unique signature voltage trace, and iii) the voltage pattern can also determine the person' walking direction. SolarWalk identifies occupants in a smart home by training a classifier with their shadow voltage traces. SolarWalk achieves an average accuracy of 88% to identify five occupants in a home and on average 77% accurate to determine whether someone entered or exited the room. SolarWalk enables an accurate occupant identification system that is non-invasive, ubiquitous, and does not require dedicated hardware and rigorous installation. 

Commercial Internet of Things (IoT) deployments are mostly closed-source systems that offer little to no flexibility to modify the hardware and software of the end devices. Once deployed, retrofitting such systems to an upgraded functionality requires replacing all the devices, which can be extremely time and cost prohibitive. End users cannot generally leverage deployed infrastructure to add their own sensors or custom data. However, we observe that IoT systems sometimes report battery voltage information to the cloud, and batteries are often user-serviceable. This indicates that perturbing the battery voltage to encode customized information could be a minimally invasive method to retrofit existing IoT devices. 

Recent studies have shown that, experiencing the appropriate lighting environment in our day-to-day life is paramount, as different types of light sources impact our mental and physical health in many ways. Researchers have intercon-nected daylong exposure of natural and artificial lights with circadian health, sleep and productivity. That is why having a generalized system to monitor human light exposure and recommending lighting adjustments can be instrumental for maintaining a healthy lifestyle. At present methods for collecting daylong light exposure information and source identification contain certain limitations. Sensing devices are expensive and power consuming and methods of classifications are either inac-curate or possesses certain limitations. In addition, identifying the source of exposure is challenging for a couple of reasons. For example, spectral based classification can be inaccurate, as different sources share common spectral bands or same source can exhibit variation in spectrum. Also irregularities of sensed information in real world makes scenario complex for source identification. In this work, we are presenting a Low Power BLE enabled Color Sensing Board (LPCSB) for sensing background light parameters. Later, utilizing Machine learning and Neural Network based architectures, we try to pinpoint the prime source in the surrounding among four dissimilar types: Incandescent, LED, CFL and Sunlight. Our experimentation includes 27 distinct bulbs and sunlight data in various weather/time of the day/spaces. After tuning classifiers, we have investigated best parameter settings for indoor deployment and also analyzed robustness of each classifier in several imperfect situations. As observed performance degraded significantly after real world deployment, we include synthetic time series examples and filtered data in the training set for boosting accuracy. Result shows that our best model can detect the primary light source type in the surroundings with accuracy up to 99.30% in familiar and up to 90.25% in unfamiliar real world settings with enlarged training set, which is much elevated than earlier endeavors. 

Energy-harvesting designs typically include highly entangled app-lication-level and energy-management subsystems that span both hardware and software. This tight integration makes developing sophisticated energy-harvesting systems challenging, as developers have to consider both embedded system development and intermit-tent energy management simultaneously. Even when successful, solutions are often monolithic, produce suboptimal performance, and require substantial effort to translate to a new design. Instead, we propose a new energy-harvesting power management architecture, Altair that offloads all energy-management operations to the power supply itself while making the power supply programmable. Altair introduces an energy supervisor and a standard interface to enable an abstraction layer between the power supply hardware and the running application, making both replaceable and recon-figurable. To ensure minimal resource conflict on the application processor, while running resource-hungry optimization techniques in the supervisor, we implement the Altair design in a lower power microcontroller that runs in parallel with the application. We also develop a programmable power supply module and a software library for seamless application development with Altair. We evaluate the versatility of the proposed architecture across a spectrum of IoT devices and demonstrate the generality of the plat-form. We also design and implement an online energy-management technique using reinforcement learning on top of the platform and compare the performance against fixed duty-cycle baselines. Results indicate that sensors running the online energy-manager perform similar to continuously powered sensors, have a l0x higher event generation rate than the intermittently powered ones, 1.8-7x higher event detection accuracy, experience 50% fewer power failures, and are 44% more available than the sensors that maintain a constant duty-cycle. 

Algorithmic decisions made by machine learning models in high-stakes domains may have lasting impacts over time. However, naive applications of standard fairness criterion in static settings over temporal domains may lead to delayed and adverse effects. To understand the dynamics of performance disparity, we study a fairness problem in Markov decision processes (MDPs). Specifically, we propose return parity, a fairness notion that requires MDPs from different demographic groups that share the same state and action spaces to achieve approximately the same expected time-discounted rewards. We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs sharing the same state and action spaces into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies. Motivated by our decomposition theorem, we propose algorithms to mitigate return disparity via learning a shared group policy with state visitation distributional alignment using integral probability metrics. We conduct experiments to corroborate our results, showing that the proposed algorithm can successfully close the disparity gap while maintaining the performance of policies on two real-world recommender system benchmark datasets. 

Time-series data gathered from smart spaces hide user's personal information that may arise privacy concerns. However, these data are needed to enable desired services. In this paper, we propose a privacy preserving framework based on Generative Adversarial Networks (GAN) that supports sensor-based applications while preserving the user identity. Experiments with two datasets show that the proposed model can reduce the inference of the user's identity while inferring the occupancy with a high level of accuracy. 

Wireless sensing and the Internet of Things support real-time monitoring and data-driven control of the built environment, enabling more sustainable and responsive infrastructure. As buildings and physical structures tend to be large and complex, instrumenting them to support a wide range of applications often requires numerous sensors distributed over a large area. One impediment to this type of large-scale sensing is simply tracking where exactly devices are over time, as the physical infrastructure is updated and interacted with over time. Having low-cost but accurate localization for devices (instead of users) would enable scalable IoT network management, but current localization approaches do not provide a suitable tradeoff in terms of cost, energy, and accuracy for low power devices in unknown environments. 

While relying on energy harvesting to power Internet of Things (IoT) devices eliminates the maintenance burden of battery replacement, energy generation fluctuation constitutes a major source of uncertainty to design reliable self-powered IoT devices. To characterize spatial-temporal variability of energy harvesting, data acquisition campaigns are needed across the range of potential harvesting sources. In this work we present a dataset to characterize thermal energy sources in residential settings by measuring thermoelectric generator (TEG) operating conditions over 16 deployment locations for periods ranging from 19 to 53 days. We present our easy-to-use thermal energy measurement platform built from off-the-shelf component modules and a custom TEG interface circuit. We demonstrate how the collected measurements can inform the design of energy harvesting IoT devices by deriving the TEG's maximum power output and estimating the available energy at each harvesting location.