Existing tools for debugging battery-free applications are limited to specific architectures or require code changes of the Device Under Test (DUT) to function. These tools also cannot measure the efficiency of the application designed for battery-free systems. Currently, there is a lack of independent broad comparisons of intermittent systems. Our work, therefore, evaluates state-of-the-art frameworks and their artifacts and finds shortcomings in reproducibility and their performance. To overcome these shortcomings, we introduce DIPS+, a multi-platform debugger and measurement platform for intermittent systems with ARM and MSP430 support. DIPS+ introduces new methods to analyse applications for battery-free systems. One method finds the minimum energy budget required for forward progress, crucial for determining the minimal capacitor size for intermittent systems. Furthermore, DIPS+ offers functions to perform automatic profiling tests, like code start-up time, which gives valuable insights into the system's efficiency. DIPS+ achieves significant improvements in debugging performance, with 11 times faster connection time and reduced code execution by 157 times on the DUT before full reconnecting after intermittency occurs. The evaluation of selected frameworks reveals substantial overheads caused by the additional overhead of saving and restoring of system's state. This causes certain benchmarks to take up to 110 times longer to complete than their uninstrumented counterparts. These findings raise concerns about the viability of task-based approaches as an effective solution for managing intermittency in battery-free Internet of Things devices.

This report details the software part of the development process of the eNose technology. The technology is posed by Momo Medical. Momo Medical is a start-up company located in Delft, it provides and develops non-intrusive monitoring systems in the nursing sector. The project is the next step in an already existing product: BedSense. BedSense enables nurses to check for among others decubritus, whether the patient is out of bed, and even if the patient has passed away. The finished project will be able to detect solid stool and hence, when integrated into the system of BedSense, will greatly assist the nurses.

The eNose technology is able to detect solid bowel movement using its gas sensing abilities. It consists of gas sensors that detect the relevant gasses that are related to feces. These sensor values are then fed into an algorithm that is able to interpret them and detect defecation. Besides it includes a communication system that handles the internal and external communication. Finally, the technology supports Over The Air (OTA) updates which allows to update the firmware of the devices remotely.

The final prototype functions accurately in certain restrooms and can be regarded as a proof of concept.However more work and data is needed in order to make the eNose work in various environments, with possible integration of machine learning analysis, as it has showed great potential.

The project is executed in two groups, hardware and software. This report contains only the software part of the process. It includes the development of the detection algorithm. And the developmentof a communication and OTA programming system.