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.

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 Deep Q Network (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.

The lack of consideration for application delay requirements in standard loss-based congestion control algorithms (CCAs) has motivated the proposal of several alternative CCAs. As such, Copa is one of the most recent and promising CCAs, and it has attracted attention from both academia andindustry. The delay performance of Copa is governed by amostly static latency-throughput tradeoff parameter, δ. However,a static δ parameter makes it difficult for Copa to achieve consistent delay and throughput over a range of bottleneck bandwidths. In particular, the coexistence of 4G and 5G networks and the wide range of bandwidths experienced in NG-RANs can result in inconsistent CCA performance. To this end, we propose a modification to Copa, Copa-D, that dynamically tunes δ to achieve a consistent delay performance. We evaluate the modification over emulated fixed, 4G, and 5G bottlenecks. The results show that Copa-D achieves consistent delay with minimal impact on throughput in fixed capacity bottlenecks. Copa-D also allows a more intuitive way of specifying the latency-throughput tradeoff and achieves more accurate and predictable delay invariable cellular bottleneck.

Tactile Internet is an Internet network that combines ultra-low latency with extremely high availability and reliability. Since traditional protocols, such as UDP and TCP, cannot support this operation, other transport protocols are required to meet the stringent requirements of the Tactile Internet. This paper evaluates the implementation of the Multi-connection Tactile Internet Protocol (MTIP), a multi-connectivity transport protocol for the Tactile Internet.MTIP uses application and network status information to select network paths intelligently and, in so doing, to improve reliability and latency. The paper studies how different configurations of the MTIP algorithm impact its path selection and the effect on lost and late packets. This evaluation is performed in an emulated environment and in a 4G/5G lab to evaluate the protocol in diverse scenarios. The results show a direct trade-off between higher reliability requirements and the number of duplicate packets.

The Internet of Things (IoT) has emerged as a fundamental cornerstone in the digitalization of industry and society. Still, IoT devices’ limited processing and memory capacities pose a problem for conducting complex and time-sensitive computations such as AI-based shop floor monitoring or personalized health tracking on these devices, and offloading to the cloud is not an option due to excessive delays. Edge computing has recently appeared to address the requirements of these IoT applications. This paper formulates the scheduling of tasks between IoT devices, edge servers, and the cloud in a three-layer Mobile Edge Computing (MEC) architecture as a Mixed- Integer Linear Programming (MILP) problem. The paper proposes a simulated annealing-based task scheduling technique and demonstrates that it schedules tasks almost as time-efficient as if the MILP problem had been solved with a mixed integer programming optimization package; however, at a fraction of the cost in terms of CPU, memory, and network resources. Also, the paper demonstrates that the proposed task scheduling technique compares favorably in terms of efficiency, resource consumption, and timeliness with previously proposed techniques based on heuristics, including genetic programming.

This deliverable presents the third and final cycle of trials and experimentation activities executed over 5GENESIS facilities. The document is the continuation of deliverables D6.1 and D6.2, in the sense that it captures tests carried out over the evolved infrastructures hosting 5GENESIS facilities following the methodology defined in the previous editions of this deliverable. The tests reported in this document focus on i) the final 5G infrastructure deployments that includes radio and core elements mostly in Stand-Alone (SA) deployment configurations based on commercial and open implementations, and ii) the various use cases/applications, some of them also involving field trials. Most of the tests described herein, especially the generic/lab ones are performed using the Open5GENESIS experimentation suite.