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.

Background

High professional competence is crucial for ambulance personnel, as expected by patients, relatives, and organizations. Prehospital advanced trauma and medical care demand exceptional competence. Specialized training programs i.e., AMLS, ATLS, and PHTLS are widely adopted internationally. Integrating “beyond-5G and 6G technologies” can significantly enhance realism, increase the number of simulated patient cases, and improve prehospital nursing education by providing real-time data and advanced communication capabilities. This integration supports the development of critical thinking and decision-making skills and ensures that ambulance personnel are well-prepared to handle a wide range of emergencies, ultimately improving patient outcomes and overall service efficiency. The aim of this study is to evaluate the impact of integrating high-fidelity simulation with “beyond-5G and 6G technologies” in prehospital nurse education.

Methods

Students will practically carry out multiple high fidelity simulation cases in a road ambulance. The cases are communicated and distributed from the learning site to the ambulance using “beyond-5G and 6G technologies”. Data are gathered using the Paramedic Global Rating Scale and System Usability Scale. Students’ experiences of reality and learning will also be explored in individual (n=15) interviews.

Results

The study is expected to demonstrate that integrating realistic high-fidelity simulation with “beyond-5G and 6G technologies” can improve clinical and decision-making skills in prehospital nursing students. The study is also expected to be able to relate effective simulation methods to different simulated scenarios and contribute to more effective prehospital nurse education.

Conclusions

High-fidelity simulation with “beyond-5G and 6G technologies” could be a valuable addition to future prehospital nurse education.

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.

Power efficiency is a growing concern in softwarized 5G gNBs due to increasing energy costs and carbon emissions. While SmartNICs offer low-power acceleration for packet processing, they struggle with complex operations, often requiring offloading to servers, which raises power usage. This study evaluates the performance and power consumption when dividing 5G gNB data plane tasks between SmartNICs and a DPDK-enabled host, comparing flow-based and function-based task allocation methods. We introduce an adaptive CPU power management strategy that adjusts CPU power states based on traffic. Results show that deploying five SmartNICs with function-based partitioning, which retains packet buffering on the host and offloads header decapsulation, insertion, and lookup-table operations to the SmartNICs, delivers a throughput of 109 MPPS, which is 173% more than a SmartNIC-only setup and reduces latency by 70% compared with a host-only setup. Adaptive power management lowers total power consumption by 12.5% in the optimal partitioning while preserving high throughput and low latency.

Power efficiency is crucial in softwarized 5G gNBs due to rising energy costs and CO_2 emissions from the networking infrastructure. SmartNICs have the potential to accelerate packet processing tasks. However, their limited resources and functionalities make handling complex tasks like retransmissions challenging. Offloading operations to server-class processors can improve scalability but also increase power consumption, warranting careful management. This study examines the performance and power consumption of splitting the processing of the 5G gNB between SmartNICs and DPDK-enabled servers. We implement an adaptive CPU power conserving strategy in the DPDK data plane that dynamically adjusts CPU power states using P-states and C-states based on traffic load. Our evaluation shows that when using five SmartNICs, we can achieve 95 MPPS, demonstrating that offloading can boost throughput by up to 40% for gNB header processing. Our adaptive power management limits CPU power consumption growth to a maximum of 11%, compared to 52% without such management, balancing performance and power efficiency effectively.

Deep Q-Learning is an important algorithm in the field of Reinforcement Learning for automated sequential decision making problems. It trains a neural network called the DQN to find an optimal policy. Training is highly unstable with high variance. A target network is used to mitigate these problems, but leads to longer training times and, high training data and very large memory requirements. In this paper, we present a two phase pre-trained online training procedure that eliminates the need for a target network. In the first - offline -  phase, the DQN is trained using expert actions. Unlike previous literature that tries to maximize the probability of picking the expert actions, we train to minimize the usual squared Bellman loss. Then, in the second - online - phase, it continues to train while interacting with an environment (simulator). We show, empirically, that the target network is eliminated; training variance is reduced; training is more stable; when the duration of pre-training is carefully chosen the rate of convergence (to an optimal policy) during the online training phase is faster; the quality of the final policy found is at least as good as the ones found using traditional methods.

This paper presents a novel online task scheduling strategy called SATS, designed for a hierarchical Mobile Edge Computing (MEC) architecture. SATS utilizes a Simulated Annealing-based method for scheduling tasks and demonstrates that Simulated Annealing can be a viable solution for online task scheduling, not just for offline task scheduling. However, the paper also emphasizes that the effectiveness of SATS depends on the precision of service request predictions. The paper evaluates three types of predictors: neutral, conservative, and optimistic. It concludes that when using a conservative predictor that overestimates the number of service requests, SATS performs the best in terms of higher acceptance rates and shorter processing times. In fact, when using a conservative predictor, SATS can offer an acceptance ratio that is only 5% lower than what it could have been if SATS had known the frequency of service request arrivals beforehand and deviates less than 20% from this acceptance ratio in all conducted experiments.

As the adoption of Fifth Generation (5G) systems increases, efforts towards Sixth Generation (6G) systems have already started across research, standardization, and stakeholder fora. 6G is expected to support applications with immersive capabilities, with specific use case requirements from different verticals playing a critical role in solution development. Unlike current solutions in the education vertical that uses immersive technologies such as Augmented/Virtual/eXtended Reality (AR/VR/XR), which rely on pre-recorded content and  lack engagement, 6G can enhance remote education by enabling real-time, AR/VR/XR-enriched interactions among students and instructors. This paper presents ongoing activities within the 6G-PATH EU project, towards the design, implementation, and testing of a 6G use case for healthcare personnel remote education/training, which aims to facilitate real-time, AR/VR/XR-enhanced interactions among healthcare trainees and instructors.

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.

The traditional method of data plane programming involves deploying a single P4 program to a single target. However, different targets have varying capabilities, functionalities, and support for various programming languages beyond P4. Therefore, disaggregating a single data plane program into multiple subprograms that run on different targets can allow us to leverage the strengths of each target, which becomes particularly important in the context of 5G, where some data plane processing functions, such as buffering and retransmission for RLC processing, cannot be effectively expressed in P4. This paper delves into the decomposition of a 5G gNB across a P4-programmable SmartNIC and an x86 server using DPDK-based processing, thus harnessing the strengths of each target. Our evaluation revealed that offloading certain processing to an x86 server can improve throughput by up to 50%, thanks to the scalability of DPDK applications' performance with the number of CPU cores. However, offloading does introduce a slight increase in latency, so the approach should be adjusted based on the specific needs and available resources.