Introduction: This paper presented an analysis of the energy consumption and responsiveness of MicroPython in an embedded system. The purpose of this study was to understand the energy consumption and response time of a MicroPython based system to optimize its overall performance and efficiency. Two research questions had been formulated to concretize the purpose of this thesis: [RQ1] How does the energy consumption of a MicroPython based embedded system compare to that of a C-based embedded system for tasks utilizing low power modes? [RQ2] What is the wake-up response time of MicroPython for low power modes when receiving external and internal interrupts, and how does it compare to an established language like C on an embedded system? Method: To answer the research questions and achieve the purpose, an experimental study was conducted. The energy consumption of the MicroPython based system was analyzed under different scenarios. The time it took for MicroPython to respond to an interrupt request from a sleeping state was also measured. The data collected from the experiment was analyzed to determine the level of energy consumption and responsiveness of MicroPython in an embedded system. Results: The results indicated that C was generally more energy efficient and responsive than MicroPython for tasks utilizing low power modes for the Deepsleep mode. Although MicroPython proved to have shorter response times for the Lightsleep low power mode. For energy consumption, C was more stable in the measurements while MicroPython reached both lower minimum currents and higher maximum currents. Conclusions: In conclusion, this study found that while MicroPython could achieve lower power levels than C in both low power modes tested, it reached higher current levels upon waking up. Despite this, MicroPython could still be a choice for applications that spend longer durations in low power modes, as this could offset the increased current spikes during wake-up. Response times for MicroPython were faster than C in the Lightsleep internal interrupt case, but MicroPython exhibited significantly longer response times in the Deepsleep mode due to the system resetting and restarting the interpreter. Keywords: Embedded systems, Energy consumption, Interrupt requests, Low power modes, MicroPython, Responsiveness.

The importance of energy efficiency has grown alongside awareness of climate change due to the rapid increase of greenhouse gases. With the increasing trend regarding mobile subscribers, it is necessary to prevent an expansion of energy consumption via mobile networks. In this thesis, the energy optimization of the new radio access technology called 5G NR utilizing different sleep states to put base stations to sleep when they are not transmitting data is discussed. Energy savings and file latency with heterogeneous and super dense urban scenarios was evaluated through simulations with different network deployments. An updated power model has been proposed and the sensitivity of the new power model was analyzed by adjusting wake-up time and sleep factors. This showed that careful implementation is necessary when adjusting these parameter settings, although in most cases it did not change the end results by much. Since 5G NR has more potential in energy optimization compared to the previous generation mobile network 4G LTE, up to 4 sleep states was implemented on the NR base stations and one idle mode on LTE base stations. To mitigate unnecessary sleep, deactivation timers are used which decides when to put base stations to sleep. Without deactivation timers, the delay could increase significantly, while with deactivation timers the delay increase would only be a few percent. Up to 42.5% energy could be saved with LTE-NR non-standalone deployment and 72.7% energy with NR standalone deployment compared to LTE standalone deployment, while minimally impacting the delay on file by 1%.