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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. 

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. 

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. 

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. 

The emergence of radio frequency (RF) dependent device-free indoor occupancy detection has seen slow acceptance due to its high fragility. Experimentation shows that an RF-dependent occupancy detector initially performs well in the room to be sensed. However, once the physical arrangement of objects changes in the room, the performance of the classifier degrades significantly. To address this issue, we propose BLECS, a Bluetooth-dependent indoor occupancy detection system which can adapt itself in the dynamic environment. BLECS uses a reinforcement learning approach to predict the occupancy of an indoor environment and updates its decision policy by interacting with existing IoT devices and sensors in the room. We tested this system in five different rooms for 520 hours in total, involving four occupants. Results show that, BLECS achieves 21.4% performance improvement in a dynamic environment compared to the state-of-the-art supervised learning algorithm with an average F1 score of 86.52%. This system can also predict occupancy with a maximum 89.23% F1 score in a completely unknown environment with no initial trained model.