Millimeter-wave (mmWave) small base stations (SBSs) enhance indoor 5G heterogeneous network by delivering high-speed wireless access and offloading macro base station (MBS) traffic. Due to the limited range of mmWave signals, dense yet often noncontinuous SBS deployments are required, leading to energy inefficiencies—especially during low-traffic periods—because of high-power consumption. To address this, traffic-aware sleep strategies dynamically switch SBSs between active and sleep modes. However, most analytical models assume continuous coverage, limiting their real-world applicability. This article proposes an analytical model tailored to noncontinuous mmWave SBS deployments, evaluating three metrics: 1) the proportion of SBSs in sleep mode; 2) the traffic offloading ratio; and 3) the frequency of discovery broadcasts during sleep. Simulation results validate the model under varying deployment and traffic scenarios, demonstrating its value in supporting energy-efficient design for indoor 5G networks.

Detecting abnormal driving behavior is crucial for preventing traffic accidents, as they are responsible for a sig-nificant majority of incidents. However, existing methods for detection often come with high costs or execution restrictions. In this paper, we introduce EADD, an Edge-based Anomaly Detection platform for Driving behavior. EADD overcomes these limitations by detecting abnormal driving behavior without the need for additional sensors or restrictions. Additionally, EADD boasts low computational requirements and enables real-time detection on mobile devices like the Raspberry Pi 3 Model B.

Effective driver identification is one of critical aspects of Internet of Vehicles (IoV) applications, playing a pivotal role in various contexts, such as vehicle anti-theft, fleet management, personalized insurance, vehicle settings automation, digital forensics, and so on. In this article, we propose CPBW, a novel mechanism that combines change point detection and Bag-of-Words (BoW). The CPBW utilizes the smartphone triaxial accelerometer data to accurately identify drivers. The key innovation of CPBW lies in its exceptional efficiency within short time windows, significantly enhancing the real-time performance. The study adopts naturalistic driving studies, collecting the unrestricted real-world data to increase applicability. However, challenges arise from dynamic urban environments influencing driving behavior and the need to balance hardware costs, privacy concerns, and data reliability. In comparison to the previous methodologies, CPBW demonstrates a reduced time requirement for driver identification. Particularly, our proposed CPBW mechanism showcases impressive performance, achieving accuracy, precision, recall, and F1-score up to 98.1%, 98.1%, 98.1%, and 98.0%, respectively. As a result, CPBW markedly enhances the practicality of driver identification in real-world scenarios.

The 3rd Generation Partnership Project (3GPP) is standardizing the Low Earth Orbit satellite network (LEO-SN), positioning it as a next-generation network (NGN) technology. Unlike conventional terrestrial networks, such as 4G and 5G, LEO satellites’ high mobility prompts recurrent handoffs with user terminals (UTs). This leads to challenges like elevated signaling traffic, diminished bandwidth efficiency, and prolonged handoff times, potentially compromising Quality of Service (QoS). This research introduces and assesses the User Terminal-Controlled Handoff (UCHO) and User Terminal-Assisted Handoff (UAHO) strategies through a 3GPP-based simulation model to evaluate their performances, offering valuable perspectives for LEO-SN standardization.

Real-time population mobility pattern at a specific time is an essential indication for sudden events. With the high penetration ratio of mobile phones, the mobile network could serve as a sensor network to monitor the population distribution, and user positioning without extra cost because the distribution of mobile phones is approximately the same as the population distribution. The abnormal spatial-temporal transition of the population is one of the critical indicators for sudden events. In this article, using the log data obtained from the mobile networks, we propose an AI-based framework, anomaly detection for population distribution (ADPD), to detect abnormal population distribution in geo-space. Different from the previous works, to detect anomaly of a specific grid area, the ADPD uses only the population information in the grid, which makes the ADPD more practical in the actual situation. We investigate the performance of the ADPD by running experiments based on the log data of the mobile network obtained during an actual sudden event, the 2018 Hualien Earthquake in Taiwan. Our study shows that the ADPD can identify the abnormal population transition grids nearby the grids with sudden events.

Reliability and stability have been treated as the major requirements for the Business Support System (BSS) in telecommunications networks. It is crucial and essential for service providers to maintain good operating state of the BSS. In this article, we aim at system error prediction for a BSS, i.e., we predict occurrences of the abnormal state or behavior of the BSS. Because the occurrences of system errors are rare events in the BSS (i.e., the dataset of system status is highly imbalanced), it is highly challenging to use machine learning or deep learning algorithms to predict system error for the BSS. To address this challenge, we propose a machine learning-based framework for the system error prediction and a Frequency-based Feature Creation (FFC) algorithm to create new features to improve prediction. By adding the time-series information created by the existing features, the proposed FFC can amplify the effects of important features. Our experimental results show that the FFC significantly improves the prediction performance for the Random Forest algorithm.

Recently, more and more network operators have deployed cloud environment to implement network operations centers that monitor the status of their large-scale mobile or wireline networks. Typically, the cloud environment adopts container-based virtualization that uses Docker for container packaging with Kubernetes for multihost Docker container management. In such a container-based environment, it is important that the Kubernetes can dynamically monitor the resource requirements and/or usage of the running applications, and then adjust the resource provisioned to the managed containers accordingly. Currently, Kubernetes provides a naive dynamic resource-provisioning mechanism which only considers CPU utilization and thus is not effective. This paper aims at developing a generic platform to facilitate dynamic resource-provisioning based on Kubernetes. Our platform contains the following three features. First, our platform includes a comprehensive monitoring mechanism that integrates and provides the relatively complete system resource utilization and application QoS metrics to the resource-provisioning algorithm to make the better provisioning strategy. Second, our platform modularizes the operation of dynamic resource- provisioning operation so that the users can easily deploy a newly designed algorithm to replace an existing one in our platform. Third, the dynamic resource-provisioning operation in our platform is implemented as a control loop which can consequently be applied to all the running application following a user-defined time interval without other manual configuration.

The small cell technology is proposed to provide wireless transmission services in the indoor environment and offload the traffic from a macro cell. Because of the small coverage of a small cell, there are usually a large number of small cells deployed in the mobile network, and it is likely that there are no User Equipments (UEs) in a small cell. A small cell may be idle most of the time and waste energy. In this paper, we propose a Connection-Driven (CD) mechanism for energy saving of small cells, where a small cell in the sleep mode is woken up when there are UEs (that have ongoing dedicated bearers) within its service area. We propose an analytical model and simulation experiments to investigate the performance of the CD mechanism.