Cellular Internet of Things (CIoT) has emerged as a promising technology to support applications that generate infrequent data. One requirement on these applications, often battery-powered devices, is low energy consumption to enable extended battery life. Narrowband IoT (NB-IoT) is a promising technology for IoT due to its low power consumption, which is essential for devices that need to run on battery power for extended periods. However, the current battery life of NB-IoT devices is only a few years, which is insufficient for many applications. This thesis investigates the impact of energy-saving mechanisms standardized by 3GPP on battery life of NB-IoT devices. The main research objective is to classify and analyze existing energy-saving solutions for CIoT and examine their limitations, to study the impact of standardized energy-saving mechanisms on the battery life of NB-IoT devices, both in isolation and combined, and to provide guidelines on how to configure NB-IoT devices to reduce energy consumption efficiently. The research aims to provide a deeper understanding of the effect of energy-saving mechanisms and best practices to balance energy efficiency and performance of NB-IoT devices. Applying the proposed solutions makes it possible to achieve a battery life of 10~years or more for CIoT devices.

The rapid rise of Cellular Internet of Things (CIoT) technology is expected to connect over 6 billion devices by 2029. Many of these devices, often deployed in remote, urban, or hard-to-reach areas, operate on limited battery resources and are expected to last up to 10 years. However, current battery limitations challenge the long-term operation required by many applications. Ensuring low energy consumption is therefore crucial for avoiding frequent recharging or battery replacements.

This thesis addresses the challenge of enhancing the energy efficiency of Narrow-Band Internet of Things (NB-IoT) devices by examining and optimizing the energy-saving mechanisms standardized by the 3rd Generation Partnership Project (3GPP). Specifically, the research classifies and evaluates existing energy-saving solutions for CIoT— particularly for NB-IoT—by identifying their limitations and studying the impact of mechanisms such as Discontinuous Reception (DRX), Release Assistance Indicator (RAI), Power Saving Mode (PSM), Early Data Transmission (EDT), and Preconfigured Uplink Resources (PUR) on battery life. While improved energy efficiency is essential, it often comes at the cost of increased latency. This thesis evaluates these effects on both energy consumption and latency, offering insights into the trade-offs between the two.

Based on these findings, we propose guidelines for configuring NB-IoT devices to achieve an optimal balance between energy efficiency and performance. A significant contribution of this research is the development of a machine learning-based optimization approach that dynamically adjusts configurations based on network conditions, such as signal quality, packet loss, and data transmission frequency. By integrating advanced energy-saving mechanisms with optimization techniques, this work deepens our understanding of the interplay between device configurations and battery life. Although energy-saving measures may reduce performance (e.g., increased latency or reduced throughput), further investigation into these trade-offs is essential. The proposed guidelines and strategies aim to extend NB-IoT devices’ battery life to 10 years or more, enhancing their usability across diverse CIoT deployments.

Den snabba utvecklingen av Cellular Internet of Things (CIoT)-teknologi förväntas koppla samman över 6 miljarder enheter till år 2029. Många av dessa enheter, som ofta placeras i avlägsna, urbana eller svårtillgängliga områden, drivs av begränsade batteriresurser och förväntas fungera i upp till 10 år. Dock utgör nuvarande batteribegränsningar en utmaning för långvarig drift i många applikationer. Därför är låg energiförbrukning avgörande för att undvika frekventa laddningar eller batteribyten.

Denna avhandling adresserar utmaningen att förbättra energieffektiviteten hos NB-IoT-enheter genom att undersöka och optimera de energibesparande mekanismer som standardiserats av 3rd Generation Partnership Project (3GPP). Specifikt klassificerar och utvärderar forskningen befintliga energibesparande lösningar för CIoT, särskilt för Narrowband Internet of Things (NB-IoT), genom att identifiera deras begränsningar samt studera effekterna av mekanismer såsom Discontinuous Reception (DRX), Release Assistance Indicator (RAI), Power Saving Mode (PSM), Early Data Transmission (EDT) och Pre-configured Uplink Resources (PUR) på batteritid. Förbättrad energieffektivitet kommer dock ofta till priset av ökad latens. Denna avhandling utvärderar dessa effekter på både energiförbrukning och latens och erbjuder insikter i de avvägningar som krävs.

Baserat på resultaten föreslås riktlinjer för att konfigurera NB-IoT-enheter så att en optimal balans mellan energieffektivitet och prestanda uppnås. Ett betydande bidrag från detta arbete är utvecklingen av en maskininlärningsbaserad optimeringsmetod som dynamiskt justerar konfigurationer beroende på nätverksförhållanden, såsom signalstyrka, paketförlust och dataöverföringsfrekvens. Genom att integrera avancerade energibesparande mekanismer med optimeringstekniker fördjupar detta arbete förståelsen för samspelet mellan enhetskonfigurationer och batteritid. Även om energibesparande åtgärder kan minska prestanda (t.ex. ökad latens eller reducerad genomströmning), krävs ytterligare undersökningar kring dessa avvägningar. De föreslagna riktlinjerna och strategierna syftar till att förlänga NB-IoT-enheternas batteritid till 10 år eller mer, vilket förbättrar deras användbarhet i olika CIoT-implementeringar.

The rapid rise of Cellular Internet of Things (CIoT) is connecting billions of devices worldwide, many of which must run on limited battery power for up to 10 years. Ensuring low energy consumption is vital to avoid frequent recharges or replacements. This thesis focuses on enhancing the energy efficiency of Narrow-Band IoT (NB-IoT) devices by optimizing 3GPP’s energy-saving mechanisms. We investigate Discontinuous Reception (DRX), Release Assistance Indicator (RAI), Power Saving Mode (PSM), Early Data Transmission (EDT), and Preconfigured Uplink Resources (PUR) to evaluate how each feature affects battery life and latency. Striking a balance between energy savings and performance is key. Our machine learning-based optimization approach dynamically adjusts configurations based on network conditions, offering valuable guidelines for extending battery life to 10+ years in diverse CIoT scenarios.