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., 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.

Network delays and user perceived latencies are of major importance in many applications in cellular networks. Delays can be measured with multiple approaches and at different protocol layers. This work involves a detailed examination of several delay metrics from a network, transport, and application perspective. The study explores base delay as well as latency under load, capturing also the effect of buffering. The examination is based on a comprehensive active measurement campaign performed in the networks of four Swedish operators. The results show that the delay captured by different metrics can vary significantly, with delay captured from the TCP three-way-handshake and adaptive ping measurements giving the most consistent results for base network delay in our measurements. As expected, when background traffic is introduced measured delay increases by an order of magnitude due to buffering in the network, highlighting the importance of also capturing latency under load when describing network performance. Finally, using an analytic model of flow completion time, we show that well-selected network measurements can provide a good prediction of higher layer delay performance.

The deployment of 5G networks, including 5G Non-Public Networks (5G-NPNs) for private use in several verticals, is rapidly taking place worldwide. However, deploying these networks in under-served areas, where there may be limited Internet access or wired backhauling capabilities, presents challenges. To address these challenges, there is a growing interest in using Low Earth Orbit (LEO) satellites, such as SpaceX's Starlink, which can provide high-throughput and low-latency Internet access via dense satellite constellations. In this paper, we present CARL-W, the Wireless module of the Communications Advanced Research Laboratory (CARL) at Karlstad University, which combines a 5G-NPN and a Starlink deployment. CARL-W serves as a platform for empirical analyses on both systems, thus contributing to the study of their possible integration. In particular, we outline the CARL-W experimentation framework and provide access to the CARL-W visualization and data exporting platform. We also open-source a 1-month Starlink dataset, facilitating further analyses of this relatively new technology.

Train-mounted aggregation routers that provide WiFi access to train passengers and bundle external communication over multiple cellular modems/links is an efficient way of providing communication services on trains. However, the characteristics of such systems have received limited attention in the literature. In this paper we address this gap by examining the communication characteristics of such systems based on a large data set gathered over six months from an operational Swedish railway system. We focus our examination on the relationship between per link throughput and train velocity. Using Levenberg- Marquardt non-linear regression a noticeable critical point is observed for an RS-SINR of around 12 dB. At this point the impact of increased train velocity on per link throughput changes from being negative to becoming positive. Using a machine learning approach we also explore the relative importance of several observed metrics in relation to per link throughput.