The evolution of 5G technology towards 5G-Advanced has introduced advanced vehicular applications with stringent Quality-of-Service (QoS) requirements. Addressing these demands necessitates intelligent resource management within the standard 3GPP network slicing framework. This paper proposes a novel resource management scheme leveraging a Deep Deterministic Policy Gradient (DDPG) algorithm implemented in the Network Slice Subnet Management Function (NSSMF). The scheme dynamically allocates resources to network slices based on real-time traffic demands while maintaining compatibility with existing infrastructure, ensuring cost-effectiveness. The proposed framework features a two-level architecture: the gNodeB optimizes slice-level resource allocation at the upper level, and vehicles reserve resources dynamically at the lower level using the 3GPP Semi-Persistent Scheduling (SPS) mechanism. Evaluation in a realistic, trace-based vehicular environment demonstrates the scheme’s superiority over traditional approaches, achieving higher Packet Delivery Ratio (PDR), improved Spectral Efficiency (SE), and adaptability under varying vehicular densities. These results underscore the potential of the proposed solution in meeting the QoS demands of critical 5G-Advanced vehicular applications.

NR-V2X Mode 2 is introduced by the third generation partnership project (3GPP) to support vehicle-to-everything (V2X) communication. In NR-V2X Mode 2, vehicles select resources for the exchange of cooperative awareness messages (CAM) in a decentralized manner based on their local observation using semi-persistent scheduling. Resources are distributed over the 2-D frequency and time domain, following the long-term evolution frame structure. Since vehicles select resources based on their local observations and due to spectrum scarcity, this may lead to contention. Hence, selecting a resource is challenging, and as each vehicle strives to select a resource, it becomes a consensus problem. To resolve resource contention, in this article, we propose a knowledge-empowered distributed multiagent deep reinforcement learning (K-MADRL) approach. Based on traffic flow information, long short-term memory (LSTM) is employed to deploy Unmanned Internet of Aerial Agents (UIAAs) to collect vehicle state information. UIAAs gather vehicle state knowledge and train the local deep reinforcement learning (DRL) model. The locally trained model at the UIAA is shared and aggregated at the gNB for the global model update. The trained policy is then sent to the vehicles over system synchronization blocks for distributed execution. Moreover, the vehicles select the resource based on the joint action, i.e., by anticipating the actions of the neighboring vehicles. Our scheme is compared with other methods, such as DRL, optimization techniques, the SPS method, and random allocation methods, used in the NR-V2X environment. The results of the simulations show that our scheme outperforms the other methods.

The Third Generation Partnership Project (3GPP) introduced cellular vehicle-to-everything (C-V2X) for vehicular communications. In the standard, C-V2X Mode 4 is defined for the distributed resource selection. Subsequently, in 3GPP Release 16, NR-V2X is introduced with Mode 1 and Mode 2 for vehicular communications. Likewise C-V2X Mode 4, NR-V2X Mode 2 is used for decentralized resource scheduling. The vehicles select the resources based on their local observations by utilizing the semi-persistent scheduling (SPS). Since, the vehicles select the resources based on the local observation, sensing nature of SPS is challenged by the hidden node problem that lead to resource conflict. To resolve the contention, 3GPP also introduced the physical sidelink feedback channel (PSFCH) to assist the distributive resource scheduling based on the receiver feedback. However, this incurred a signaling overhead. In this work, federated learning is exploited for distributive training via offline method and distributive multiagent-based resource scheduling is performed following the principles of NR-V2X Mode 2. Distributed training favors the model accuracy by accommodating the varying affect of the environment due to the high mobile dynamics. Simulation is conducted by integrating SUMO in conjunction with 3GPP NR-V2X standard. Performance results demonstrate a substantial improvement compared to other deep learning methods, where centralized training and random resource selection procedures are employed. This research marks a significant stride toward efficient and conflict-resilient resource allocation in vehicular communications.

Network slicing, a key technology of next-generation wireless networks, has undergone significant evolution from its inception as Dedicated Core Network (DCN) in 4G-LTE to its current state in 5G-Advanced. This paper provides a comprehensive analysis of network slicing enhancements across 3GPP releases 13 to 17, categorized into three phases: 5G-Basic (Release 15), early 5G-Evolution (Release 16), and advanced 5G-Evolution (Release 17). Furthermore, our study identifies persistent challenges in network slicing implementation and proposes innovative enhancements for 5G-Advanced (Release 18), including a novel machine learning-based approach to minimize service interruptions within a Registration Area (RA). This approach combines predictive insights from a Long Short-Term Memory (LSTM) model with a Dynamic Proportional Resource Allocation (DPRA) method for resource reconfiguration. Evaluation of the LSTM-DPRA scheme demonstrates significant performance improvements and reduced service interruptions compared to benchmark schemes, contributing to the development of more efficient and reliable network slicing.

Content retrieval in content-centric vehicular networks faces challenges that include high latency, especially when content is stored far from the requesting vehicle. On-path caching feature in the conventional vehicular named data Networks (VNDN) enables content storage that can reduce latency. However, due to the constantly changing dynamic ad hoc nature of the vehicular network, the availability of stored content for the requester vehicle cannot be guaranteed. In addition, without knowing which content will be requested, where it will be requested and when it will be requested, the content caching functionality of VNDN is underutilized. To address this issue, this manuscript proposes a content prefetching scheme for the Content-centric Internet of Vehicles (CIoV) by introducing the content Value of Popularity ($VoP$) matrix. Considering vehicles requesting content of similar interests, we evaluate$VoP$through three value update functions that follow the power law of the time elapsed since the last content requested. By multiple parameters of consumer vehicle similarity, an on-road proactive content retriever vehicle is selected. The simulation results showed that the proposed proactive on-path content prefetching mechanism significantly reduces the content delivery delay while increasing the success delivery ratio by 48% and extends the spread of content within the network by 53%.

Seamless connectivity between the vehicles and the infrastructures is required for the provisioning of future vehicular applications. However, the infrastructures in vehicular ad hoc networks (VANETs) such as roadside units (RSUs) are insufficient to provide ubiquitous connectivity and coverage in urban areas. Thus the services get interrupted, which results in lower network performance in VANETs. Motivated by this in this paper, we proposed a Proactive UAV Placement (PUP) technique to assist the RSUs in delivering the services in the out-of-coverage region by placing the UAVs in the appropriate positions. We first predicted the distribution of the future vehicles by utilizing the LSTM neural network. Based on the distribution of the future vehicles, the optimization problem for optimal UAV placement is formulated and solved by utilizing the Particle Swarm Optimization (PSO) algorithm. The results demonstrated that our scheme achieves better average coverage compared to two other UAV-assisted schemes.

Day by day innovation in wireless communications and micro-technology has evolved in the development of wireless sensor networks. This technology has applications such as healthcare supervision, home security, battlefield surveillance and many more. However, due to the use of small batteries with low power this technology faces the issue of power and target monitoring. There is much research done to overcome these issues with the development of different architecture and algorithms. In this paper, a scheduling machine learning algorithm called adaptive learning automata algorithm(ALAA) is used. It provides an efficient scheduling technique. Such that each sensor node in the network has been equipped with learning automata, and with this, they can select their proper state at any given time. The state of the sensor is either active or sleep. For the experiment, different parameters are used to check the consistency of the algorithm to schedule the sensor node such that it can cover all the targets with the use of less power. The results obtained from the experiments show that the proposed algorithm is an efficient way to schedule the sensor nodes to monitor all the targets with use of less power. On the whole, this paper manages to achieve its goal by contributing to the related research on wireless sensor networks with a new design of a learning automata scheduling algorithm. The ability of this proposed algorithm to use the minimum number of sensors to be in active state verified to reduce the use of power in the network. Thus, achieving the goal by enhancing the lifetime of wireless sensor networks.

Hydrological cycle is comprised of many constituents, the most important of which is precipitation. Simulation results greatly depend upon the quality of precipitation data as it is entered as the primary input for hydrological model simulations. Due to its chronological and spatial unpredictability, rainfall is considered as one of the most unreliable and irregular atmospheric parameters and therefore rain gauges serve as the main resource of measurement of rainfall. Rain-gauge network is the most commonly used source for rainfall measurement as it works by offering direct measurements of precipitation intensity and time duration at individual point sites. The collected information is further used by Meteorologists, hydrologists and weather reporters who subsequently report how much rain was received in a specific area in an individual event as well as in a particular span of time. Over the years, researchers have been trying to overcome the difficulties faced in the installation of rain gauge networks. The main reason for the errors occurring in the aerial rainfall data for a region is inadequate gauge density. Due to rapidly increasing urbanization and climate change, critical attention is being paid not only to rainfall monitoring but also to the urban waterlogging. Siting selection methods that are used conventionally do not contemplate the environmental surroundings and spatial-temporal scale to choose a site for rain gauge networks. Therefore, the objective of this study was to observe and calculate the need of additional numbers of rain gauges in an area of interest. To execute the intended purpose, the technique of "Estimation of Optimum number of Rain Gauges proposed by Das & Saikia" is used, whereas the appropriate site selection for rain gauges in study area is determined using "Multi-criteria Decision Analysis (MCDA)" using Geographic Information System (GIS). The variables which were incorporated for multi criteria assessment include; elevation, slope, rainfall data (2010), land use/cover, and exiting number of rain gauges in study area. A scale ranges from 1 to 3, "1" being the least suitable, "2" being mildly suitable and "3" being the highly suitable. A site suitability map of the study area is drawn by overlaying these layers in GIS. Results of the experiment exhibit that the suggested methods are appropriate for site selection for rain gauges in urban areas which will also play an important role in the selection of the sites for hydrological facilities, such as water gauge.