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As high‐performance computing (HPC) becomes a tool used in many different workflows, quality of service (QoS) becomes increasingly important. In many cases, this includes the reliable execution of an HPC job and the generation of the results by a certain deadline. The resource and job management system (RJMS) or simply RMS is responsible for receiving the job requests and executing the jobs with a deadline‐oriented policy to support the workflows. In this article, we evaluate how well static resource management policies cope with deadline‐constrained HPC jobs and explore two variations of a dynamic policy in this context. As the Hilbert curve‐based approach used by the SLURM workload manager represents the state‐of‐the‐art in production environments, it was selected as one of the static allocation strategies. The Manhattan median approach as a second allocation strategy was introduced as a research work that aims to minimize the communication overhead of the parallel programs by providing compact partitions more than the Hilbert curve approach. In contrast to the static partitions provided by the Hilbert curve approach and the Manhattan median approach, the leak approach focuses on supporting dynamic runtime behavior of the jobs and assigning nodes of the HPC system on demand at runtime. Since the contiguous leak version also relies on a compact set of nodes, the noncontiguous leak can provide additional nodes at a greater distance from the nodes already used by the job. Our preliminary results clearly show that a dynamic policy is needed to meet the requirements of a modern deadline‐oriented RMS scenario. [ABSTRACT FROM AUTHOR]

With the rapid growth and development of Internet of Things (IoT) and smart mobile devices, the volume of data generated at the network edge has increased significantly. Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational limitations of these devices by bringing cloud capabilities closer to end users. However, MEC still faces two critical challenges: edge server placement (ESP) and load distribution. The first problem involves determining the optimal locations for a set of heterogeneous computational servers within a given network of base stations (BSs) and their associated workload. The objective is to strategically position these servers to ensure efficient computational resource allocation and to enhance the quality of service (QoS) for end users. The second problem is the distribution of computational tasks/requests, which itself comprises two sub-problems: (a) identifying the most suitable server for the requests arriving at each BS, and (b) scheduling these requests on the selected servers. This paper addresses these challenges through a comprehensive approach. We model these problems using a mixed-integer nonlinear programming (MINLP) framework. For the ESP problem, the goal is to achieve load balancing among servers. For the load distribution problem, the objectives are to minimize response time and ensure that user request deadlines are met. To address these problems, we propose an ant colony optimization (ACO) algorithm to efficiently solve the ESP problem, ensuring balanced load among servers. For the load distribution problem, we introduce two heuristic algorithms: one for selecting the most suitable server to distribute incoming workloads from devices, and another for scheduling requests on each server based on their urgency and importance. We validate our proposed approach through extensive experiments using real-world data from Shanghai Telecom base stations. The experimental results demonstrate that our approach significantly outperforms state-of-the-art methods in terms of load standard deviation, average request response time, and the percentage of deadlines successfully met. Specifically, our method improves load balancing by 57.6%, reduces the average response time by 50.7%, and increases deadline compliance by 35.2%. These findings underscore the efficacy of our proposed methods in enhancing MEC system performance, thereby delivering superior QoS and user experience in IoT and mobile computing applications. [ABSTRACT FROM AUTHOR]

Summary: The ability of mobile ad hoc networks (MANET) to be used as communication tools in a variety of industries, including healthcare, the military, smart traffic, and smart cities, has drawn special consideration. Traditional Manet's multicast routing methods seem to be inappropriate to massive with Adaptive systems because the problem is NP‐complete, resulting in an enchanting QoS restrictions. In order to conquer that the paper proficiently introduces the Conglomerate Crumb Horde Formicary Meta‐Heuristic (CCHFMO) with Asymmetrical Meander Diffie‐Hellman (AMDH) to tackle the major obstacles are multicast routing problems and lack of data protection. Initially, the fusion of crumb horde optimization (CHO) and formicary optimization (FO) is exploited to strengthen QoS limitations and reduce QoS data loss. However, the massive and dynamic nature of the network with the combination of more QoS restrictions, deficient security has become extremely difficult. Therefore, the research work establishes the asymmetrical meander Diffie‐Hellman (AMDH) to significantly improve performance and concealment while ensuring channel security during data transfer. Finally, the results demonstrated that by employing the novel optimization approaches, the MANET can increase data protection while still achieving high transmission rates and sophistication of communication. As a consequence, it adequately explicates the article to improve QoS performances. [ABSTRACT FROM AUTHOR]

Performance and cost-effectiveness are sustained by efficient management of resources in cloud computing. Current autoscaling approaches, when trying to balance between the consumption of resources and QoS requirements, usually fall short and end up being inefficient and leading to service disruptions. The existing literature has primarily focuses on static metrics and/or proactive scaling approaches which do not align with dynamically changing tasks, jobs or service calls. The key concept of our approach is the use of statistical analysis to select the most relevant metrics for the specific application being scaled. We demonstrated that different applications require different metrics to accurately estimate the necessary resources, highlighting that what is critical for an application may not be for the other. The proper metrics selection for control mechanism which regulates the requried recources of application are described in this study. Introduced selection mechanism enables us to improve previously designed autoscaler by allowing them to react more quickly to sudden load changes, use fewer resources, and maintain more stable service QoS due to the more accurate machine learning models. We compared our method with previous approaches through a carefully designed series of experiments, and the results showed that this approach brings significant improvements, such as reducing QoS violations by up to 80% and reducing VM usage by 3% to 50%. Testing and measurements were conducted on the Hungarian Research Network (HUN-REN) Cloud, which supports the operation of over 300 scientific projects. [ABSTRACT FROM AUTHOR]

Bandwidth estimation in network probing techniques plays a crucial role in modern networks, particularly in ensuring superior Quality of Service (QoS). This is largely influenced by advancements in IEEE 802.11 standards, which aim to enhance service performance. Existing methodologies are designed to assess the available resources on a given channel, providing valuable support for bandwidth-constrained applications. However, these methods encounter limitations due to potential non-linearities within networks. The current approach leverages min-plus algebra from network calculus, which, although effective, falls short in certain dynamic scenarios. In this research, we propose a novel methodology for bandwidth estimation by integrating other state-of-the-art QoS protocols while maintaining the foundational principles of the min-plus algebra technique. This approach aims to develop a more versatile and adaptable system, better suited for complex network environments. [ABSTRACT FROM AUTHOR]

The satellite Internet can cover up to 70% of the surface of our planet Earth to provide network services for nearly 3 billion people. As such, it is promising to become the building block of future 6G networks. The satellite Internet is capable of providing uniform communication capacity to every part of the Earth's surface, due to its uniform and symmetrical constellation structure, while the uneven distribution of ground populations leads to globally uneven traffic delivery requests, incurring a mismatch between the capacity and traffic transmission demands. As such, traditional single-criteria (e.g., shortest delay) routing algorithms can lead to severe network congestion and cannot provision delay-deterministic data delivery. To overcome this bottleneck, we propose a multi-criteria routing and scheduling scheme to redirect time-tolerant data, thus preventing congestion for time-sensitive data, based on the spatiotemporal distribution of data traffic. First, we construct a traffic spatiotemporal distribution model, to indicate the network load status. Next, we model the satellite network multi-criteria routing problem as an integer linear programming one, which is NP-hard and challenging to solve within polynomial time. A novel link weight design based on both the link delay and load is introduced, transforming the mathematical programming problem into a routing optimization problem. The proposed correlation scheduling algorithm fully utilizes idle network link resources, significantly improving network resource utilization and eliminating resource competition between non-time-sensitive and time-sensitive services. Simulation results show that compared with traditional algorithms, the proposed method can increase the throughput of time-sensitive data by up to 20.8% and reduce the packet loss rate of time-sensitive services by up to 76.8%. [ABSTRACT FROM AUTHOR]

In 5G networks, the deployment of network slices enabled by Software-Defined Networking (SDN) is becoming a critical component for delivering tailored services to meet diverse application needs. However, this introduces challenges in network management, particularly in efficiently allocating resources to ensure that each network slice meets its specific Quality of Service (QoS) and availability requirements. Simultaneously, it must optimize overall network performance and network operator’s profit, which is linked to the Quality of Experience (QoE) of the end-users. Existing works offer either an availability-based solution or a QoE-aware solution to this problem, but not both. This paper addresses the end-to-end network slice resource allocation problem by simultaneously considering QoS and availability requirements in slice placement, while employing a QoE-aware strategy for resource allocation. We propose a framework that optimizes the network operator profit i.e. the highest QoE with the least resource usage, and can be flexibly configured to model realistic scenarios. Arbitrary network slice requirements can be defined using slice-specific QoS/QoE mapping, resource requirements, end-to-end latency and availability. For solving the formulated problem a Mixed Integer Nonlinear Programming (MINLP) formulation and efficient heuristic methods are proposed. Our solution accounts for the non-linear QoS/QoE relationship, utilizes redundantly placed Service Function Chains (SFCs) to increase availability, and supports the sharing of Virtual Network Functions (VNFs) among SFCs to optimize resource usage. Through extensive simulations on realistic network topologies and slice requests, we demonstrate the framework’s effectiveness in offering flexible and efficient network slice placement and resource allocation, utilizing a baseline heuristic from related studies. The results indicate that while the exact method delivers an optimal solution, heuristic approaches are suitable for time-sensitive tasks, such as dynamic slice configuration.

In the densification of Device-to-Device (D2D)-enabled Social Internet of Things (SIoT) networks, improper allocation of resources can lead to high interference, increased signaling overhead, latency, and disruption of Channel State Information (CSI). In this paper, we formulate the problem of sum throughput maximization as a Mixed Integer Non-Linear Programming (MINLP) problem. The problem is solved in two stages: a tripartite graph-based resource allocation stage and a time-scale optimization stage. The proposed approach prioritizes maintaining Quality of Service (QoS) and resource allocation to minimize power consumption while maximizing sum throughput. Simulated results demonstrate the superiority of the proposed algorithm over standard benchmark schemes. Validation of the proposed algorithm using performance parameters such as sum throughput shows improvements ranging from 17% to 93%. Additionally, the average time to deliver resources to CSI users is minimized by 60.83% through optimal power usage. This approach ensures QoS requirements are met, reduces system signaling overhead, and significantly increases D2D sum throughput compared to the state-of-the-art schemes. The proposed methodology may be well-suited to address the challenges SIoT applications, such as home automation and higher education systems.

Cloud security is critical for safeguarding data and services in cloud environments. Traditional deep learning methods focus primarily on improving attack detection accuracy but often neglect Quality of Service (QoS) parameters. These parameters, such as latency, bandwidth, and response time, are essential for the overall performance of cloud services. This research addresses the problem by proposing two novel methods: a lightweight EfficientNet deep learning model for accurate attack detection with minimal QoS impact and Adaptive Sparse Bayesian Optimization (ASBO) to improve hyperparameter tuning efficiency. ASBO reduces computational complexity by using sparse surrogate models, adaptive sampling, and early stopping mechanisms, ensuring the optimization process is efficient and suitable for real-time applications. The research objectives include enhancing attack prediction accuracy and QoS maintenance in cloud security. The study evaluates the proposed methods on the CICIDS2017, CICIDS2018, and UNSW-NB15 datasets, covering various attack types such as DDoS, Brute Force, SQL Injection, Botnet, Port Scanning, and Infiltration. The results demonstrate significant improvements over existing methods, achieving 5–7% higher accuracy in attack detection. The proposed EfficientNet + ASBO method also ensures better QoS, reduces latency, increases bandwidth efficiency, and improves response times compared to other models. [ABSTRACT FROM AUTHOR]

Abstract This work discusses a novel approach to a simulation-based cell selection that does not depend on static counts of cells or simplified models used in the cell selection process. The proposed method of cell selection uses advanced simulation to analyse the actual conditions and dynamically select the optimal cell to better suit the real conditions of the network and preference of the client. This research makes important theoretical and practical contributions that increase any simulation’s performance and reliability aspects. The simulations conducted in the study demonstrate several major advantages. The method results in substantially lower call drops and higher throughputs of data. The outcome of the study is an adaptive and inherently interactive method that can be used in a plurality of networking circumstances, from 4 to 5G networks. The work more than adequately addresses the issue of cell selection and connecting, and improves the reliability and efficiency of any network that uses this method. This product can be easily implemented and can be used by network developers to ameliorate their networks’ performance. The work’s main theoretical contribution is the fact that optimal methods are made that undoubtedly make a simulation a more efficient phenomenon and that the components would interact more effectively together.

Abstract This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol’s performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network’s robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol’s exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol’s prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications.

Bursty traffic patterns require precise classification, modeling, and comprehension to ensure adequate resource allocation, improved network security, and Quality of Service (QoS) assurance. This study introduces a methodology integrating three critical correlation metrics with scheduler algorithms, demonstrating adaptability and improved network performance. Our approach highlights the handling of irregular patterns, contributing to the development of systems that can quickly adapt to changes, significantly enhancing network performance in the context of scheduler algorithms, resource allocation, correlation metrics, and bursty traffic.

ABSTRACT The wireless intelligent computing paradigm has significantly provided services to various sectors in today's technology‐driven landscape. Despite its popularity, wireless intelligent computing faces challenges in addressing time‐sensitive tasks due to the physical distance between servers from users. Edge computing has been introduced for the internet of things (IoT) as an effective complement to enhance the wireless intelligent computing capacity for handling latency‐critical tasks. However, the limited resources of IoT and edge nodes can lead to suboptimal task management. In response to these challenges, we propose a lightweight approach that leverages a hybrid technique combining the whale optimization algorithm (WOA) with adaptive inertia weight and a genetic algorithm component. This method aims to enhance the efficiency of task offloading in a cloud‐edge computing environment. Experimental results demonstrate that the proposed strategy not only addresses the limitations of traditional methods but also achieves significant improvements, a 34% increase in makespan minimization, an 11% reduction in task rejection ratio, a 17% decrease in execution cost, and a 15% improvement in energy utilization compared to WOAs. The simulation results highlight the effectiveness of the proposed hybrid algorithm in enhancing quality of service (QoS) metrics for latency‐sensitive IoT applications. [ABSTRACT FROM AUTHOR]

The network design's flexibility and adaptability significantly impact the quality of service (QoS) in 5G and 6G communications. The network's service quality is simultaneously expanded because of the potential uses for the 5G vehicle network (QoS). To this aim, one of the strictest requirements for the vehicle environment has evolved into a safety–critical real-time system. Although several mathematical and computational techniques have historically been employed to improve the distribution of resources, the nonconvexity of optimization problems poses difficulties. This article informs readers how 5G vehicular network resources can be allocated to strengthen network communication using optimization and cutting-edge deep learning techniques. This paper proposes UAV path planning based on QoS constraints for minimizing the transmission power among UAVs and BS. It uses the Nonlinear Optimization method for optimizing the path and uses Multi-Agent Deep Reinforcement Learning to improve connection and enhance latency. The proposed method improves the quality of services for 5G and 6G networks and efficiently optimizes resource management. [ABSTRACT FROM AUTHOR]

Several technologies are implemented for serving industrial information and controlling the process in which the increased complexity and allied fault failed to achieve a reliable transmission to activate the timely network. These data must be transferred from one node to another node without any fault or delay in their nodes. To resolve this problem, an algorithm named SAOA is proposed to communicate the messages from various services without any delay in their nodes. IoT with mobile sink node is simulated in the network based on the MoO4RPL objective and the phases involved in this strategy are topology, route discovery, communication, and route maintenance phase. The topology network is generated for network topology and the rank is computed based on the energy, trust, delay, fault tolerance, and link quality in the multi-objective route discovery phase. In the communication phase, the optimal route is selected using proposed SAOA with fitness function factors. At last, the route maintenance phase is performed, and the proposed SAOA has achieved a minimum distance of 53.807 m, maximum energy of 0.415 J, maximum link quality of 2.163, and maximum trust of 0.701, respectively. In the future, another algorithm is implemented in IoT applications with minimum modifications to evaluate the various metric values. [ABSTRACT FROM AUTHOR]

According to the critical challenges for accessing streaming multimedia over wireless technologies such as lower ranges of data throughput and unacceptable rates for delay and packet losses, it was necessary to implement a system that makes some processes for these challenges, with considerations of coverage and capacity limitations that definitely will have a direct effect on the overall system quality. In this paper, we discuss the effect of applying radio resource management (RRM) technique and smart antenna modes through the fifth-generation (5G) radio link on the voice streaming packets flow in a network where many scenarios were simulated by OPNET while maintaining the quality of the network, regardless of the data load inside it. Also, we have a proposed algorithm to utilize RRM and smart antenna modes over 5G networks. On the other hand, we make a detailed eleven scenarios divided into two phases to discuss the effect of these parameters on quality as (i) RRM Coverage and system capacity, and (ii) smart antenna modes in terms of coverage and capacity. The results of simulation prove that adding RRM and smart antenna modes to the proposed networks over 5G radio link verifies a considerable evolution in the network on data flow of streaming voice packets, also including that end to end delay, packet delay variations, and throughput realize the overall requirements for quality of service (QoS) to access multimedia streaming services through a wide range of 5G bandwidth. The proposed system support monotonically response of delay all over the time of simulation for all QoS metrics. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Networks (VANETs) play a crucial role in the development of Intelligent Transport Systems (ITS), leveraging Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) communication models. These networks, however, face challenges such as node administration, security, and efficient routing, particularly due to the unpredictable nature of vehicle mobility. Traditional routing protocols like Adhoc On-Demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequence Distance Vector (DSDV) are often inadequate for VANETs. Addressing these challenges, we propose FLQL-VANET, a hybrid mechanism that integrates fuzzy logic and Q-learning for enhanced routing. The clustering mechanism in FLQL-VANET selects cluster heads based on parameters such as connectivity, vehicle direction, distance, velocity, path loss, signal quality, and time to leave. Q-learning optimizes the routing path through a reward mechanism, ensuring efficient data transmission. The comparative analysis demonstrates that FLQL-VANET outperforms existing methods such as RMRPTS, ICA-RBF, and KMRP. Our approach achieves an average packet delivery rate of 96.7%, an end-to-end delay of 0.61 seconds, and an average throughput of 868 kbps. These results indicate significant improvements in routing efficiency and reliability, highlighting the potential of FLQL-VANET to enhance VANET performance in real-world scenarios. [ABSTRACT FROM AUTHOR]

This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol's performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network's robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol's exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol's prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications. [ABSTRACT FROM AUTHOR]

For secure earth observation, we need an optimised routing protocol that reduces security breaches by improving the coordination of the base with the observatory using a wireless sensor network, thereby improving the earth observation systems for secure communication. One of the technologies that are used for networking is known as a wireless sensor network. In this type of network, sensor nodes are linked together using wireless communication methods. A random and haphazard dispersal of nodes is carried out during the deployment procedure inside the network region. This distribution is carried out throughout the network. The consequence of this is that the distances that separate the nodes do not stay the same. However, the nodes have a severely limited battery life and a restricted range of perception, despite the fact that they have a great sensing capacity. All nodes inside their respective sensing regions may sense the wireless data. A unique routing protocol is essential to increase throughput and Quality of Service (QoS), which will help resolve these problems and improve WSN performance. The Dijkstra method was most frequently used by researchers to discover the shortest route. However, it is not more flexible in terms of WSN routing. The Floyd-Warshall (FW) technique is also employed to choose the shortest path. However, in the current WSN, varied sensors are dispersed throughout several sites and convey data to various coordinators, who are likewise dispersed across countless sites. As a result, the FW technique must be modified to discover the shortest path among any two nodes. In this study, the Improved Floyd-warshall (IFW) algorithm is proposed. Data structures like adjacency lists and sparse vectors are used by the IFW method to describe the WSN graph. When sparse WSNs need less memory, this helps make that possible. The Floyd-Warshall method, which figures out distances between nodes in a network based on their actual lengths, can get even better by adding location information. Using the LEACH method to split the network into smaller groups makes the network more manageable. The LEACH method is then used directly in each cluster to improve both scaling and processing speed. This is done so that the goals can be met. The computer can also handle situations where there is not a legal way to connect two nodes, and it can handle information going both ways between nodes. [ABSTRACT FROM AUTHOR]

This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML's potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment. [ABSTRACT FROM AUTHOR]

This work discusses a novel approach to a simulation-based cell selection that does not depend on static counts of cells or simplified models used in the cell selection process. The proposed method of cell selection uses advanced simulation to analyse the actual conditions and dynamically select the optimal cell to better suit the real conditions of the network and preference of the client. This research makes important theoretical and practical contributions that increase any simulation's performance and reliability aspects. The simulations conducted in the study demonstrate several major advantages. The method results in substantially lower call drops and higher throughputs of data. The outcome of the study is an adaptive and inherently interactive method that can be used in a plurality of networking circumstances, from 4 to 5G networks. The work more than adequately addresses the issue of cell selection and connecting, and improves the reliability and efficiency of any network that uses this method. This product can be easily implemented and can be used by network developers to ameliorate their networks' performance. The work's main theoretical contribution is the fact that optimal methods are made that undoubtedly make a simulation a more efficient phenomenon and that the components would interact more effectively together. Article Highlights: Dynamic cell selection improves performance, reducing call drops and enhancing throughput. Simulation framework adapts to real-time conditions, offering flexibility for 4G and 5G networks. Scalable method provides valuable insights for optimizing cellular network efficiency and QoS. [ABSTRACT FROM AUTHOR]

As a revolution in the field of transportation, the demand for communication of vehicles is increasing. Therefore, how to improve the success rate of vehicle spectrum access has become a major problem to be solved. The case of a single vehicle accessing a channel was only considered in the previous research on dynamic spectrum access in cognitive vehicular networks (CVNs), and the spectrum resources could not be fully utilized. In order to fully utilize spectrum resources, a model for spectrum sharing among multiple secondary vehicles (SVs) and a primary vehicle (PV) is proposed. This model includes scenarios where multiple SVs share spectrum to maximize the average quality of service (QoS) for vehicles. And the condition is considered that the total interference generated by vehicles accessing the same channel is less than the interference threshold. In this paper, a deep Q-network method with a modified reward function (IDQN) algorithm is proposed to maximize the average QoS of PVs and SVs and improve spectrum utilization. The algorithm is designed with different reward functions according to the QoS of PVs and SVs under different situations. Finally, the proposed algorithm is compared with the deep Q-network (DQN) and Q-learning algorithms under the Python simulation platform. The average access success rate of SVs in the IDQN algorithm proposed can reach 98 % , which is improved by 18 % compared with the Q-learning algorithm. And the convergence speed is 62.5 % faster than the DQN algorithm. At the same time, the average QoS of PVs and the average QoS of SVs in the IDQN algorithm can reach 2.4, which is improved by 50 % and 33 % compared with the DQN algorithm, and improved by 60 % and 140 % compared with the Q-learning algorithm. [ABSTRACT FROM AUTHOR]

Addressing latency concerns and ensuring high-quality video services in Heterogeneous Mobile Adhoc Networks (H-MANETs) are paramount challenges. This paper presents a pioneering solution: the Pre-emptive Priority Queueing based Multipath Routing algorithm (PPQM). Our approach prioritizes video traffic within OpenFlow switches, directing it across multiple paths in H-MANETs. Integrating the PPQ module within Cluster Heads operating in the software-defined networking (SDN) architecture is central to our design. We rigorously evaluate delay for each path by employing an M/M/1 queueing policy based on a Poisson arrival process and an exponential service time distribution. Utilizing Burke's theorem, our calculation spans the entire route from the cluster head to a sink node. By meticulously assessing the delay characteristics of individual paths, our model facilitates the selection of the most optimal path to minimize overall delay and enhance network performance. Our proposed model amalgamates clustering, FIFO with M/M/1 queueing, and SDN techniques. In a comprehensive evaluation against existing technologies, the implementation of PPQM demonstrates superior performance in crucial Quality of Service (QoS) metrics, including end-to-end delay, queue size, waiting time, throughput, and response time. Furthermore, our research achieves a significant 4.2% improvement in QoS metrics compared to contemporary approaches, highlighting the effectiveness of the PPQM algorithm in enhancing network performance. This research contributes a robust solution for advancing QoS in H-MANETs, demonstrating the efficacy of the PPQM algorithm compared to contemporary approaches. [ABSTRACT FROM AUTHOR]

Summary Due to the rise in information rate prerequisite and the heterogeneity level, the modification in network traffic in the upcoming wireless communication (WC) encompasses innovative challenges in the case of energy efficiency (EE) and spectrum management. To tackle this issue, several existing techniques have been imposed but none of the frameworks provided effective solutions to compatible with recent WC applications. This framework introduces an innovative deep learning (DL)–based distributed cognitive radio network (DCRN). The proposed scheme emphasizes single base station (BS) management, where resource effectiveness is obtained by solving active resource allocation (RA) problems using a bipartite matching (BM) technique. A DL scheme is emphasized to predict the traffic load (TL) for effective EE using a residual inception‐enriched recurrent convolutional neural network (R‐InceptionRCNN). The proposed method is implemented in Python, and the performance metrics including uplink (UL) achievable capacity per secondary user (SU), UL achievable capacity per SU, cost of energy consumption, EE, and mean energy saving (MES) are scrutinized and compared with conventional techniques. The proposed scheme achieved the overall costs, EE, MES, and UL capacity of 14.33 C/J, 149.99 J/MB, 13.49%, and 22.33 Mbps, respectively, on performing RA and TL prediction in the CRN platform. [ABSTRACT FROM AUTHOR]

A Vehicular Ad Hoc Network (VANET) is an emergent wireless technology that enables high-speed communication. It became attractive to automobile manufacturers because of the secure information transmission without fatal accidents. It has several unique features, such as data dissemination, frequently disconnected networks during transmission, dynamic network density, and dynamic topology, that differentiate VANETs. Data dissemination is vital because it ensures the safety and performance of the vehicle. Conversely, VANET has limitations in providing effective communication between the vehicles due to delays and frequent disruptions. Thus, data dissemination needs an effective routing and scheduling process to avoid collisions of vehicles. Based on this fact, we proposed a novel Authenticate Relay Selection and Scheduling Framework (ARSSF) for secure and quality data transmission in VANET. ARSSF is a novel VANET communication method that prioritizes trustworthy relay nodes and dynamic scheduling to increase network performance and safety as data dissemination needs rise. It used the SNR, utility, and relay selection schemes for choosing the relay nodes that can cover the capacity of the network, thus minimizing the data dissemination delay. Additionally, an authentication method was utilized during the relay transmission phase to ensure both data security and authorization. The proposed framework performance was assessed by NS2 simulation. The results demonstrate that ARSSF is beneficial. ARSSF's minimum delay of 47 ms, throughput of 98.17%, and security measures of 98.38% show its value in VANETs. It enhances VANET safety, efficiency, and data distribution and could serve as a high-end vehicular network research platform. [ABSTRACT FROM AUTHOR]

Bursty traffic patterns require precise classification, modeling, and comprehension to ensure adequate resource allocation, improved network security, and Quality of Service (QoS) assurance. This study introduces a methodology integrating three critical correlation metrics with scheduler algorithms, demonstrating adaptability and improved network performance. Our approach highlights the handling of irregular patterns, contributing to the development of systems that can quickly adapt to changes, significantly enhancing network performance in the context of scheduler algorithms, resource allocation, correlation metrics, and bursty traffic. [ABSTRACT FROM AUTHOR]

The paper presents a new routing scheme using the information on the locations of nodes to create a routing region that controls the region of packet routing to achieve route optimization. The proposed scheme aimed to reduce the occurrence of packet detours or other routing overheads caused by the undirected packet transmission. The strength of this approach is that it can improve the lifetime of nodes in the network while decreasing the time taken for a packet to arrive at its destination or base station (BS). The proposed scheme used a self-adaptive algorithm that dynamically adjusted the routing region based on the BS’s calculation of the network layer parameters to achieve energy efficiency while satisfying data quality. The routing region limits the area of routing and restricts data flooding in the entire network, which potentially will waste resources and cause data redundancy. The simulation showed that the proposed scheme outperformed, the original fitness scheme and SPEED, according to energy consumption, transmission delay, throughput, and reliability (packet delivery ratio) under different congestion levels. The proposed scheme offered double the throughput and shortened packet delay by 20%. Furthermore, it had a longer lifetime, exceeding other schemes by approximately twofold when the traffic was not too congested. However, the gap decreases when the network becomes worse.

Wireless Multimedia Sensor Networks (WMSNs) are networks consisting of sensors that have limitations in terms of memory, computational power, bandwidth and battery life. Multimedia transmission using Wireless Sensor Network (WSN) is a difficult task because certain Quality of Service (QoS) guarantees are required. These guarantees include a large quantity of bandwidth, rigorous latency requirements, improved packet delivery and lower loss ratio. The main area of research would be to investigate the process of greedy techniques that could be modified to guarantee QoS provisioning for multimedia traffic in WSNs. This could include optimization of routing decisions, dynamic allocation of resources and effective congestion management. This study introduces a framework called Epsilon Greedy Strategy based Routing Protocol (EGS-RP) for multimedia content transmission over WSN. The framework focuses on energy efficiency and QoS by using reinforcement learning to optimize rewards. These incentives are determined by a number of variables, including node residual energy, communication energy and the effectiveness of sensor type-dependent data collection. Experimental analysis was conducted to evaluate the effectiveness of the proposed routing strategy and compare it with the performance of standard energy-aware routing algorithms. The proposed EGS-RP achieves a throughput of 217 kbps, a bandwidth of 985 bps, a packet delivery ratio of 94.45% and an energy consumption of 32%.

The Internet of Things (IoT) concept is being extensively embraced in several industries, such as Intelligent Transportation Systems (ITS), to improve effectiveness and sustainability by utilizing advanced transportation resource data. Researching routing protocols is essential for maximizing the efficiency of dynamic Vehicular Ad Hoc Networks (VANETs) in ITS.VANETS utilize wireless technology, including both cellular and non-cellular, to facilitate the transmission of information. The former offers minimal latency but is expensive to operate, but the latter is preferable for lower-data-rate applications decreases costs, and is more energy-efficient. Developing countries are currently enhancing their 5G assistance, but the financial viability of Low Earth Orbit (LEO) satellite technology remains low. Underdeveloped nations require cost-effective, comprehensive wireless communication solutions in both urban and rural regions. One of the prominent choices for the Low Power Wide Area Network (LPWAN) in the 5G ecosystem is the Long Range Wide Area Network (LoRaWAN). This paper presents an enhanced routing protocol that is secure against attacks on the network and offers improved performance metrics. The protocol is designed for information routing in the proposed Vehicular Communication System (VCS). The VCS is tested to verify its efficacy in detecting blind spots. A proof-of-concept prototype for VCS is developed to integrate LoRaWAN technology with an enhanced routing protocol. This integration aims to enhance signal strength, improving communication reliability, network coverage, and security. Furthermore, the system is engineered to optimize vehicle energy efficiency, rendering it well-suited for communication in underdeveloped urban and rural regions. [ABSTRACT FROM AUTHOR]

In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in 5th-generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs), such as user experience, quality of service (QoS), or other important aspects of the network. Advanced wireless technologies have been integrated into 5G network designs at multiple network layers to address this difficulty. The integration of emerging technology trends, such as machine learning (ML), which is a subset of artificial intelligence (AI), and AI's rapid improvements have made the integration of these trends into 5G networks a significant topic of research. The primary objective of this survey is to analyze AI's integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience. [ABSTRACT FROM AUTHOR]

Cloud computing, a cornerstone of modern technological infrastructure, offers unparalleled flexibility and on-demand services, encompassing storage, deployment platforms, and seamless access to web services. However, the persistent challenge of Load Balancing (LB) in cloud environments poses significant impacts on application performance, Quality of Service (QoS), and adherence to Service Level Agreements (SLAs). The equitable distribution of workloads among servers remains a complex task for cloud providers, necessitating effective LB techniques to optimize resource utilization and ensure high user satisfaction. This paper conducts a comprehensive review of diverse LB techniques, spanning static, dynamic, and nature-inspired approaches within the cloud environment. The primary focus is on addressing Data Center Response Time and enhancing overall system performance. An analytical assessment of these techniques reveals insights into their strengths and limitations, while also identifying crucial research gaps for future exploration. [ABSTRACT FROM AUTHOR]

Ensuring optimal load balancing is imperative for maintaining reliability and upholding quality of service as specified in service-level agreements (SLAs) for cloud computing providers. This research addresses the most common shortcomings of existing state-of-the-art methods, which often lack responsiveness and struggle to adapt to exponentially increasing demand, especially in the era of the internet of things (IoT). The proposed hybrid approach surpasses current literature approaches in performance metrics such as makespan, response time, number of cloudlet migrations, and SLA violations. It operates on two levels, initially employing a k-means clustering algorithm to group servers within each datacenter based on similar utilization rates. Subsequently, a round-robin method allocates task groups sequentially to non-overloaded clusters, and within each cluster, a genetic algorithm optimally assigns tasks to servers. This multilayered approach facilitates hot-deployment and scalability in operational cloud environments while promoting strong interoperability and decoupling of core mechanisms missions. Simulation experiments conducted on CloudSim Plus validate the superiority of our method, positioning it as a robust solution for enhancing load balancing and tasks scheduling in cloud environments, especially in the face of rapidly increasing IoT-related demands. [ABSTRACT FROM AUTHOR]

In the last decade, the bit rates achieved on fiber broadband networks have increased multiple times, and every new generation of passive optical network (PON) technologies must unlock more of fiber's potential and must be backward compatible to ensure easy and iterative upgrade of operator's networks. In this paper, we present two novel dynamic wavelength (in the case of multichannel system) and bandwidth allocation models for realization of fully QoS (Quality of Service)-aware single-channel, i.e., multichannel Ethernet passive optical network (EPON). The both presented models include a novel approach for inter-/intra-ONU (Optical Network Unit) scheduling mechanism as well as advanced support for multimedia and business-related services. Moreover, the single-channel system could be upgraded to multichannel system on per-need basis, i.e., only nodes with higher traffic demands could be upgraded. The aim of this approach is twofold. On the one side, we hope to avoid the implementation of additional, complex algorithms usually related to QoS support in the access segment and, on the other side, to develop a solution in which new and old EPON technologies are able to coexist. This approach would eliminate the need for complex migrations, and complicated and time-consuming procedures, as well as possible service outages in the access network. We present detailed simulation experiments with the aim of conducting a study of the performances of both presented approaches and validating both of them in terms of the future application, further service development, and implementation in the next-generation broadband networks (NGNs). [ABSTRACT FROM AUTHOR]

This article introduces a novel approach to enhancing mobile network operator (MNO) performance ranking in Nigeria through the development and implementation of a Quality of Service Key Performance Indicator (QoSKPI) Index. As mobile technologies evolve and reliance on mobile services increases, the need for robust and comprehensible network performance metrics becomes critical. This study addresses gaps in current methodologies, which often assess Quality of Service (QoS) parameters independently, by aggregating multiple QoS Key Performance Indicators (KPIs) into a single comprehensive metric. By applying the Analytical Hierarchy Process (AHP), the research develops a QoSKPI Index that combines various network performance indicators into a unified ranking system. This index is validated against consumer complaints and empirical data to ensure its relevance and accuracy in reflecting the actual network performance experienced by users. Significant findings illustrate the utility of the QoSKPI Index in benchmarking MNOs' QoS performance, providing a clear empirical basis for ranking that can influence user choice and regulatory strategies aimed at enhancing network quality across Nigeria. [ABSTRACT FROM AUTHOR]