Search Results (14)

Fifth-generation Vehicle-to-Everything (V2X) networks have ushered in a new set of challenges that negatively affect seamless connectivity, specifically owing to high user equipment (UE) mobility and high density. As UE accelerates, there are frequent transitions from one cell to another, and handovers (HOs) are triggered by network performance metrics, including latency, higher energy consumption, and greater packet loss. Traditional HO mechanisms fail to handle such network conditions, requiring the development of Intelligent HO Decisions for V2X (IHD-V2X). By leveraging Q-Learning, the intelligent mechanism seamlessly adapts to real-time network congestion and varying UE speeds, thereby resulting in efficient handover decisions. Based on the results, IHD-V2X significantly outperforms the other mechanisms in high-density and high-mobility networks. This results in a reduction of 73% in unnecessary handover operations, and an 18% reduction in effective energy consumption. On the other hand, it improved handover success rates by 80% from the necessary handover and lowered packet loss for high mobility UE by 73%. The latency was kept at a minimum of 22% for application-specific requirements. The proposed intelligent approach is particularly effective for high-mobility situations and ultra-dense networks, where excessive handovers can degrade user experience. Full article

In this study, we introduce a decentralized architecture aimed at enhancing the efficiency of precise point positioning real-time kinematics (PPP-RTK) in large-scale networks with a common pivot station. Initially, we partition the extensive network into multiple smaller subnetworks (SNs), each with a common pivot station. The augmentation parameters for each SN are then computed using the precise orbit corrections and ionosphere-weighted constraints. However, directly applying the estimated augmentation parameters to users across subnetworks poses challenges due to inter-subnetwork discontinuities. These discontinuities arise from variations in the network configurations and the time correlation of the Kalman filters, despite the use of the same pivot station. To address this, common augmentation parameters, such as the satellite clocks and phase biases from each SN, are integrated into a unified set of parameters and broadcast to users. The aligned common augmentation parameters are then fed back into each SN, and the Kalman filter is re-updated to mitigate the inter-subnetwork discontinuities. The proposed architecture offers a reduced computational burden compared to the centralized PPP-RTK architecture, which handles a full-scale network simultaneously. Unlike previous research on decentralized PPP-RTK, the use of a common pivot station ensures a consistent basis for the common augmentation parameters. This approach enables seamless user positioning during transitions between SNs, eliminating the need to reset the user navigation filter during handover operations and simplifying the integration process. To evaluate the effectiveness of our proposed architecture, we gather dual-frequency global positioning system (GPS) observation data from over 40 continuously observed reference stations (CORSs) in Korea. These data are then partitioned into four SNs, each sharing a common pivot station. Subsequently, we compare the static positioning error and processing time of our proposed architecture with those of the centralized architecture. Additionally, the mitigation performance of the inter-network discontinuities is shown. Full article

In high-mobility non-terrestrial networks (NTN), the reference signal received power (RSRP)-based handover (RBH) mechanism is often unsuitable due to its limitations in handling dynamic satellite movements. RSRP, a key metric in cellular networks, measures the received power of reference signals from a base station or satellite and is widely used for handover decision-making. However, in NTN environments, the high mobility of satellites causes frequent RSRP fluctuations, making RBH ineffective in managing handovers, often leading to excessive ping-pong handovers and a high handover failure rate. To address this challenge, we propose an innovative approach called location-based handover with particle filter and reinforcement learning (LBH-PRL). This approach integrates a particle filter to estimate the distance between user equipment (UE) and NTN satellites, combined with reinforcement learning (RL), to dynamically adjust hysteresis, time-to-trigger (TTT), and handover decisions to better adapt to the mobility characteristics of NTN. Unlike the location-based handover (LBH) approach, LBH-PRL introduces adaptive parameter tuning based on environmental dynamics, significantly improving handover decision-making robustness and adaptability, thereby reducing unnecessary handovers. Simulation results demonstrate that the proposed LBH-PRL approach significantly outperforms conventional LBH and RBH mechanisms in key performance metrics, including reducing the average number of handovers, lowering the ping-pong rate, and minimizing the handover failure rate. These improvements highlight the effectiveness of LBH-PRL in enhancing handover efficiency and service continuity in NTN environments, providing a robust solution for intelligent mobility management in high-mobility NTN scenarios. Full article

The deployment of Augmented Reality (AR) is a necessity as an enabling technology for intelligent transportation systems (ITSs), with the potential to boost the implementation of Vehicle-to-Everything (V2X) networks while improving driver experience and increasing driving safety to fulfill AR functionality requirements. In this regard, V2X networks must maintain a high quality of service AR functionality, which is more challenging because of the nature of 5G V2X networks. Moreover, the execution of diverse traffic requirements with varying degrees of service quality is essential for seamless connectivity, which is accomplished by introducing efficient handover (HO) techniques. However, existing methods are still limited to basic services, including conversional, video streaming, and general traffic services. In this study, a Multiple Criteria Decision-Making (MCDM) technique is envisioned to address the handover issues posed by high-speed vehicles connected to ultra-high-density (UDN) heterogeneous networks. Compared with existing methods, the proposed HO mechanism handles high mobility in dense 5G V2X environments by performing a holistic evaluation of network conditions and addressing connection context requirements while using cutting-edge applications such as AR. The simulation results show a reduction in handover delays, failures, and ping-pong, with 84% prevention of unnecessary handovers. Full article

Due to the rapid development of smart vehicles, such as self-driving cars, the demand for mobile data traffic by vehicle users has increased so much that base stations cannot handle it, causing delays in content provision. The burden on the base station can be alleviated through roadside units (RSUs) to distribute the demand. However, outage zones, which fall outside the communication range of RSUs, still exist due to their high deployment cost. Existing schemes for covering outage zones have only considered single-hop precaching vehicles to provide precached content, which is insufficient to reduce outage zones effectively. Therefore, we propose a scheme to reduce outage zones by maximizing the amount of precached content using multi-hop precaching vehicles. The proposed scheme optimally selects precaching vehicles through a numerical model that calculates the amount of precached content. It enhances the process of multi-hop precaching by comparing the connection time of vehicles with the dark area time in the outage zone. To prevent excessive overheads due to frequent precaching vehicle handovers, the proposed scheme limits the selection to vehicles with a longer communication time, based on a precaching restriction indicator in the multi-hop precaching vehicle selection process. The simulation results show that our scheme outperforms representative schemes based on single-hop precaching. Full article

The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of next-generation vehicular networks. Full article

The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN’s identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay. Full article

The internet’s future architecture, known as Named Data Networking (NDN), is a creative way to offer content-based services. NDN is more appropriate for content distribution because of its special characteristics, such as naming conventions for packets and methods for in-network caching. Mobility is one of the main study areas for this innovative internet architecture. The software-defined networking (SDN) method, which is employed to provide mobility management in NDN, is one of the feasible strategies. Decoupling the network control plane from the data plane creates an improved programmable platform and makes it possible for outside applications to specify how a network behaves. The SDN is a straightforward and scalable network due to its key characteristics, including programmability, flexibility, and decentralized control. To address the problem of consumer mobility, we proposed an efficient SDPCACM (software-defined proactive caching architecture for consumer mobility) in NDN that extends the SDN model to allow mobility control for the NDN architecture (NDNA), through which the MC (mobile consumer) receives the data proactively after handover while the MC is moving. When an MC is watching a real-time video in a state of mobility and changing their position from one attachment point to another, the controllers in the SDN preserve the network layout and topology as well as link metrics to transfer updated routes with the occurrence of the handoff or handover scenario, and through the proactive caching mechanism, the previous access router proactively sends the desired packets to the new connected routers. Furthermore, the intra-domain and inter-domain handover processing situations in the SDPCACM for NDNA are described here in detail. Moreover, we conduct a simulation of the proposed SDPCACM for NDN that offers an illustrative methodology and parameter configuration for virtual machines (VMs), OpenFlow switches, and an ODL controller. The simulation result demonstrates that the proposed scheme has significant improvements in terms of CPU usage, reduced delay time, jitter, throughput, and packet loss ratio. Full article

Handling faults in a running cellular network can impair the performance and dissatisfy the end users. It is important to design an automatic self-healing procedure to not only detect the active faults, but also to diagnosis them automatically. Although fault detection has been well studied in the literature, fewer studies have targeted the more complicated task of diagnosing. Our presented method aims to tackle fault detection and diagnosis using two sets of data collected by the network: performance support system data and drive test data. Although performance support system data is collected automatically by the network, drive test data are collected manually in three mode call scenarios: short, long and idle. The short call can identify faults in a call setup, the long call is designed to identify handover failures and call interruption, and, finally, the idle mode is designed to understand the characteristics of the standard signal in the network. We have applied unsupervised learning, along with various classified algorithms, on performance support system data. Congestion and failures in TCH assignments are a few examples of the detected and diagnosed faults with our method. In addition, we present a framework to identify the need for handovers. The Silhouette coefficient is used to evaluate the quality of the unsupervised learning approach. We achieved an accuracy of 96.86% with the dynamic neural network method. Full article

Ubiquitous and seamless indoor-outdoor (I/O) localization is the primary objective for gaining more user satisfaction and sustaining the prosperity of the location-based services (LBS) market. Regular users, on the other hand, may be unaware of the impact of activating multiple localization sources on localization performance and energy consumption, or may lack experience deciding when to enable or disable localization sources in different environments. Consequently, an automatic handover mechanism that can handle these decisions on a user’s behalf can appreciably improve user satisfaction. This study introduces an enhanced I/O environmental awareness service that provides an automated handover mechanism for seamless navigation based on multi-sensory navigation integration schemes. Moreover, the proposed service utilizes low-power consumption sensor (LPCS) indicators to execute continuous detection tasks and invoke GNSS in confusion scenarios, and transition intervals to make the most firm decision on the credibility of the LPCS-triggered transition and compensate for indicator thresholds. In this manner, GNSS are used for short intervals that help reduce detection latency and power consumption. Consequently, the proposed service guarantees accurate and reliable I/O detection while preserving low power consumption. Leveraging the proposed service as an automated handover helped realize seamless indoor-outdoor localization with less switching latency, using an integrated solution based on extended Kalman filter. Furthermore, the proposed energy-efficient service was utilized to confine crowdsourced data collection to the required areas (indoors and semi-indoors) and prevent excess data collection outdoors, thereby reducing power drainage. Accordingly, the negative impact of data collection on the user’s device can be mitigated, participation can be encouraged, and crowdsourcing systems can be widely adopted. Full article

Drones have attracted extensive attention for their environmental, civil, and military applications. Because of their low cost and flexibility in deployment, drones with communication capabilities are expected to play key important roles in Fifth Generation (5G), Sixth Generation (6G) mobile networks, and beyond. 6G and 5G are intended to be a full-coverage network capable of providing ubiquitous connections for space, air, ground, and underwater applications. Drones can provide airborne communication in a variety of cases, including as Aerial Base Stations (ABSs) for ground users, relays to link isolated nodes, and mobile users in wireless networks. However, variables such as the drone’s free-space propagation behavior at high altitudes and its exposure to antenna sidelobes can contribute to radio environment alterations. These differences may render existing mobility models and techniques as inefficient for connected drone applications. Therefore, drone connections may experience significant issues due to limited power, packet loss, high network congestion, and/or high movement speeds. More issues, such as frequent handovers, may emerge due to erroneous transmissions from limited coverage areas in drone networks. Therefore, the deployments of drones in future mobile networks, including 5G and 6G networks, will face a critical technical issue related to mobility and handover processes due to the main differences in drones’ characterizations. Therefore, drone networks require more efficient mobility and handover techniques to continuously maintain stable and reliable connection. More advanced mobility techniques and system reconfiguration are essential, in addition to an alternative framework to handle data transmission. This paper reviews numerous studies on handover management for connected drones in mobile communication networks. The work contributes to providing a more focused review of drone networks, mobility management for drones, and related works in the literature. The main challenges facing the implementation of connected drones are highlighted, especially those related to mobility management, in more detail. The analysis and discussion of this study indicates that, by adopting intelligent handover schemes that utilizing machine learning, deep learning, and automatic robust processes, the handover problems and related issues can be reduced significantly as compared to traditional techniques. Full article

With the huge proliferation of mobile Internet of Things (IoT) devices such as connected vehicles, drones, and healthcare wearables, IoT networks are promising mobile connectivity capacity far beyond the conventional computing platforms. The success of this service provisioning is highly dependent on the flexibility offered by such enabling technologies to support IoT mobility using different devices and protocol stacks. Many of the connected mobile IoT devices are autonomous, and resource constrained, which poses additional challenges for mobile IoT communication. Therefore, given the unique mobility requirements of IoT devices and applications, many challenges are still to be addressed. This paper presents a global mobility management solution for IoT networks that can handle both micro and macro mobility scenarios. The solution exploits a path-based forwarding fabric together with mechanisms from Information-Centric Networking. The solution is equally suitable for legacy session-based mobile devices and emerging information-based IoT devices such as mobile sensors. Simulation evaluations have shown minimum overhead in terms of packet delivery and signalling costs to support macro mobility handover across different IoT domains. Full article

With the low latency, high transmission rate, and high reliability provided by the fifth-generation mobile communication network (5G), many applications requiring ultra-low latency and high reliability (uRLLC) have become a hot research topic. Among these issues, the most important is the Internet of Vehicles (IoV). To maintain the safety of vehicle drivers and road conditions, the IoV can transmit through sensors or infrastructure to maintain communication quality and transmission. However, because 5G uses millimeter waves for transmission, a large number of base stations (BS) or lightweight infrastructure will be built in 5G, which will make the overall environment more complex than 4G. The lightweight infrastructure also has to be considered together. For these reasons, in 5G, there are two mechanisms for handover, horizontal, and vertical handover; hence, it must be discussed how to handle handover to obtain the best performance for the whole network. In this paper, to address handover selection, we consider delay time, energy efficiency, load balancing, and energy consumption and formulate it as a multi-objective optimization (MOO) problem. At the same time, we propose the handover of the mobile management mechanism based on location prediction combined with heuristic algorithms. The results show that our proposed mechanism is better than the distance-based one for energy efficiency, load, and latency. It optimizes by more than about 20% at most. Full article

In the near future, electric powered vehicles will represent a major part of the road traffic. Accordingly, there will be a natural increase of accidents involving electric vehicles. There are not many cases of such accidents yet and therefore the experience and correct handling are still partially open points for the involved parties, such as the rescue services for example. The aim of this work is to provide a complete overview of the accident handling sequence in Germany, starting with the damaged vehicle on site and moving on to the risks and challenges for the stakeholders, such as transport and recycling companies. Arising from the developed overview, a handling recommendation for yet undiscussed points is given. Especially, different extinguishing and deactivation methods are compared and discussed. Due to a lack of a common live-feed from battery data on site, other criteria have to be taken into account to assess the state of the battery. The wrecked vehicle—including the high voltage system—needs to be in a definite safe state at the handover to a towing service. Depending on the case, different options for securing the vehicle will be considered in this work. Full article