The deployment of Cellular Internet of Things (CIoT)is expected to reach over six billion devices by 2030. Many ofthese devices will be located in remote areas where replacingor recharging their batteries would be difficult and expensive.Therefore, it is crucial to configure these devices for efficientenergy use to avoid frequent battery replacements or recharging.However, optimizing the energy consumption of CIoT devices, con-sidering their applications and operating environmental conditions,presents a complex challenge. In response to this challenge, wepropose the Gradient-Boosted Learning Optimization for BatteryEfficiency (GLOBE) framework for dynamic configuration ofNarrowband Internet of Things (NB-IoT) devices. GLOBE adjuststhe radio layer of NB-IoT devices based on data transmissionpatterns and network conditions, enabling swift and automatedreconfiguration. Our results demonstrate that GLOBE reducesenergy consumption by 30% to 75% compared to baselineconfigurations, offering significant benefits for both networkoperators and end devices by improving energy efficiency.

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.

Cellular Internet of Things (IoT) offers extensive connectivity today and is poised for further growth in the 5G era, especially after the upcoming sunsetting of 2G and 3G networks. It facilitates crucial IoT applications, such as smart metering to reduce energy consumption, smart logistics to enhance distribution efficiency, and smart environmental monitoring to address urban pollution. To support this expansion, leading mobile operators, global vendors, and developers are deploying NB-IoT networks as part of their long-term 5G IoT strategies. A key goal of NB-IoT is to optimize the battery life of IoT devices. While NB-IoT includes several power-saving features, the cell selection and re-selection processes result in significant energy consumption. We conducted a measurement campaign across three locations in two countries, Norway and Sweden, to investigate this issue based on an NB-IoT commercial network. Our findings reveal that cell re-selection frequently occurs even when the IoT device is stationary. Additionally, the reliance on Reference Signal Received Power (RSRP) for cell selection often leads to oscillations between the nearby cells or prolonged attach procedure. To address this challenge, we propose a cell reselection framework that considers RSRP while also considering historical information on transmission reliability. Evaluations of our proposed framework demonstrate energy savings of over 50% compared to legacy RSRP-based cell selection methods.

Deploying Cellular Internet of Things (CIoT) devices in urban and remote areas faces significant energy efficiency challenges. This is especially true for Narrowband IoT (NB-IoT) devices, which are expected to function on a single charge for up to 10 years while transmitting small amounts of data daily. The 3rd Generation Partnership Project (3GPP) has introduced energy-saving mechanisms in Releases 13 to 16, including Early Data Transmission (EDT) and Preconfigured Uplink Resources (PURs). These mechanisms extend battery life and reduce latency by enabling data transmission without an active Radio Resource Control (RRC) connection or Random Access Procedure (RAP). This paper examines these mechanisms using the LENA-NB simulator in the ns-3 environment, which is a comprehensive framework for studying various aspects of NB-IoT. The LENA-NB has been extended with PURs, and our analysis shows that PURs significantly enhance battery life and latency efficiency, particularly in high-density environments. Compared to the default RAP method, PURs reduce energy consumption by more than 2.5 times and increases battery life by 1.6 times. Additionally, PURs achieve latency reductions of 2.5 - 3.5 times. The improvements with PURs are most notable for packets up to 125 bytes. Our findings highlight PURs' potential to enable more efficient and effective CIoT deployments across various scenarios. This study represents a detailed analysis of latency and energy consumption in a simulated environment, advancing the understanding of PURs' benefits.

The Cellular Internet of Things (CIoT) has seen significant growth in recent years. With the deployment of 5G, it has become essential to reduce the power consumption of these devices for long-term sustainability. The upcoming 6G cellular network introduces the concept of zero-energy CIoT devices, which do not require batteries or manual charging. This paper focuses on these devices, providing insight into their feasibility and practical implementation. The paper examines how CIoT devices use simultaneous wireless information and power transfer, beamforming, and backscatter communication techniques. It also analyzes the potential use of energy harvesting and power management in zero-energy CIoT devices. Furthermore, the paper explores how low-power transceivers can lower energy usage while maintaining dependable communication functions.

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 Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energy-saving solutions for CIoT, e.g., the configuration of particular parameter values and software modifications of transport- or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.

Cellular Internet of Things (CIoT) is a Low-Power Wide-Area Network (LPWAN) technology. It aims for cheap, lowcomplexity IoT devices that enable large-scale deployments and wide-area coverage. Moreover, to make large-scale deployments of CIoT devices in remote and hard-to-access locations possible, a long device battery life is one of the main objectives of these devices. To this end, 3GPP has defined several energysaving mechanisms for CIoT technologies, not least for the Narrow-Band Internet of Things (NB-IoT) technology, one of the major CIoT technologies. Examples of mechanisms defined include CONNECTED-mode DRX (cDRX), Release Assistance Indicator (RAI), and Power Saving Mode (PSM). This paper considers the impact of the essential energy-saving mechanisms on minimizing the energy consumption of NB-IoT devices, especially the cDRX and RAI mechanisms. The paper uses a purpose-built NB-IoT simulator that has been tested in terms of its built-in energy-saving mechanisms and validated with realworld NB-IoT measurements. The simulated results show that it is possible to save 70%-90% in energy consumption by enabling the cDRX and RAI. In fact, the results suggest that a battery life of 10 years is only achievable provided the cDRX, RAI, and PSM energy-saving mechanisms are correctly configured and used

With the aim to improve road safety services in critical situations, vehicle trajectory and future location prediction are important tasks. An infinite set of possible future trajectories can exit depending on the current state of vehicle motion. In this paper, we present a multimodel-based Extended Kalman Filter (EKF), which is able to predict a set of possible scenarios for vehicle future location. Five different EKF models are proposed in which the current state of a vehicle exists, particularly, a vehicle at intersection or on a curve path. EKF with Interacting Multiple Model framework is explored combinedly for mathematical model creation and probability calculation for that model to be selected for prediction. Three different parameters are considered to create a state vector matrix, which includes vehicle position, velocity, and distance of the vehicle from the intersection. Future location of a vehicle is then used by the software-defined networking controller to further enhance the safety and packet delivery services by the process of flow rule installation intelligently to that specific area only. This way of flow rule installation keeps the controller away from irrelevant areas to install rules, hence, reduces the network overhead exponentially. Proposed models are created and tested in MATLAB with real-time global positioning system logs from Jeju, South Korea.

In this paper, we study the energy consumptionof Narrowband IoT devices. The paper suggests that key tosaving energy for NB-IoT devices is the usage of full Discontinuous Reception (DRX), including the use of connected-mode DRX (cDRX): In some cases, cDRX reduced the energy consumption over a 10-year period with as much as 50%. However, the paper also suggests that tunable parameters, such as the inactivity timer, do have a significant impact. On the basis of our findings, guidelines are provided on how to tune the NB-IoT device so that it meets the target of the 3GPP, i.e., a 5-Wh battery should last for at least 10 years. It is further evident from our results that the energy consumption is largely dependent on the intensity and burstiness of the traffic, and thus could be significantly reduced if data is sent in bursts with less intensity,irrespective of cDRX support.