Pervasive Reflective Sensing with Visible light and Beyond

Abstract

Considering that the IoT is giving rise to new sensing infrastructures with stringent requirements, we are posed with new trade-offs. On one hand, there is a need to develop systems that have a low cost, complexity, and ecological footprint, while being privacy-aware. On the other hand, despite these constraints, there is a need to still maintain a high accuracy.

Since deploying a large number of precise but expensive sensors is not an option, the research community is investigating alternatives that require either deploying low-cost sensors or re-purposing existing sensors for other applications. For example, instead of using cameras for indoor monitoring, which infringes privacy, a new generation of low-cost mmWave radars are used for that purpose; and instead of deploying hospitals in remote areas, researchers are re-purposing smartphone cameras to perform health checks such as blood pressure. The main challenge solved by these studies is achieving good accuracy with low-cost or re-purposed sensors. This thesis follows that same line of research: expanding the pervasiveness of reflective sensing systems in the Visible Light, Infrared, and Microwave spectra. To contribute to tackling that challenge, we need to answer the following research question: What design alternatives are available to approach the performance of high-end sensors with either low-cost or re-purposed sensors?

This thesis argues that the above research question can be explored through different options. In conventional approaches, the gap is mainly filled by high-end sensors purposely designed for the required task and some methods on top of the sensor to perform data processing. If, on the other hand, the system relies on low-cost or re-purposed sensors, the sensing gap is exposed.

The sensing gap can be investigated at two levels. The first level is to solely quantify the gap, without trying to bridge it. From a research perspective, such an approach allows exposing the magnitude of the problem raised by using low-power or re-purposed sensors. The second level is to bridge the gap in Chapter 2, 3, and 4. For this approach, we identify three alternatives.

The first alternative is to enhance the methodology in Chapter 2. For scenarios where the sensor is re-purposed, an enhanced methodology could overcome the low quality of the received signals. Advanced signal processing and machine learning techniques are particularly valuable for this alternative.

The second alternative is to enhance the re-purposed sensor in Chapter 3. The best example of this alternative is the use of smartphones for health-related applications. Several studies show that optimizing different camera parameters or performing minor physical modifications to the microphone can allow checking auditory or cardiovascular issues.

The third alternative is to enhance the object in Chapter 4. Given that reflective sensing is fundamentally determined by the properties of the object's external surface, for some applications, it may be possible to perform minor modifications to the object's surface to facilitate sensing.