QoS-aware Routing Research Articles

Optimization model for mass casualty management system using qos-aware routing protocol and casualty triage prediction

Abstract Purpose Unexpected events, whether man-made or natural, cause significant distress and disorder. The number and magnitude of disasters and catastrophes have been rising globally in recent decades, according to historical data. Continued real-time monitoring of mass casualty along with the arrangement of needed medical resources to handle the mass casualty is required to reduce the mortality and morbidity rates. Methods Electronic tag-based casualty monitoring and machine learning-based Decision Support Systems are emerging as a more effective and proactive solution that provides continuous remote monitoring of patients. A novel framework based on Body-to-Body Network, Prediction model, and Genetic Algorithm-based medical resource optimization is proposed for the continuous monitoring of the mass casualty and medical resource allocation at the incident. The aim of this work is to give priority to the handling of critical casualties. Firstly, a Quality of Service and load-sensitive routing protocol for transmitting mass casualties' physiological parameters across a wireless network is proposed, with the critical casualty being emphasized. Secondly, the clinical seriousness degree of the mass casualty is predicted using Backpropagation Artificial Neural Network. Finally, an optimization model using a Genetic algorithm and queuing theory is proposed to find the required optimal number of medical resources to handle critical and non-critical casualties separately. Also, the proposed optimization model considers the predicted clinical level transition rates of the mass casualty. Results The performance and accuracy of the proposed framework are evaluated using the MIMIC-II dataset. The outcome demonstrates that the framework emphasizes critical casualty management. Furthermore, the framework allocates an adequate number of servers by incorporating the proposed routing protocol in comparison to the AODV protocol. Conclusion The inclusion of a prediction model in the framework aids in allocating an adequate number of servers by considering the predicted clinical deteriorating transition rates of casualties at a mass casualty incident. In terms of the estimated length of the casualty at the incident, the results suggest that incorporating a medical resource optimization model outperforms the non-optimal option.

A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

Reinforcement learning-based SDN routing scheme empowered by causality detection and GNN.

In recent years, with the rapid development of network applications and the increasing demand for high-quality network service, quality-of-service (QoS) routing has emerged as a critical network technology. The application of machine learning techniques, particularly reinforcement learning and graph neural network, has garnered significant attention in addressing this problem. However, existing reinforcement learning methods lack research on the causal impact of agent actions on the interactive environment, and graph neural network fail to effectively represent link features, which are pivotal for routing optimization. Therefore, this study quantifies the causal influence between the intelligent agent and the interactive environment based on causal inference techniques, aiming to guide the intelligent agent in improving the efficiency of exploring the action space. Simultaneously, graph neural network is employed to embed node and link features, and a reward function is designed that comprehensively considers network performance metrics and causality relevance. A centralized reinforcement learning method is proposed to effectively achieve QoS-aware routing in Software-Defined Networking (SDN). Finally, experiments are conducted in a network simulation environment, and metrics such as packet loss, delay, and throughput all outperform the baseline.

Analyze the Impact of Mobility and Pause Time on ACO based Manet Routing Protocols - Review

Abstract: Recently, many researchers have studied its performance within Mobile Ad-hoc Networks (MANETs). Every ad hoc network protocol focuses on a different set of measurements and traits. The majority of the currently utilized mobility models are employed to provide realistic movement patterns for MANET scenarios. A crucial element in the creation of MANET protocols is simulation. The future topology of a network can be predicted, which enables Quality of service- aware routing to choose a dependable link for data transmission. Ant algorithms, also known as swarm intelligence, are a wider field of study that deals with algorithmic approaches that are motivated by the behaviour of ant colonies and other insects. Ant Colony Optimization (ACO) is a subset of this larger topic. Based on the pheromone value deposited over the link, we identified two categories for the link quality in the proposed approach. We have demonstrated how the network's performance is enhanced by the suggested technique. The nodes are moving in the specified terrain dimensions through the Enhanced Manhattan Mobility Model (EMMM). The five QoS performance metrics are analyzed over three MANET routing protocols. The comparison is made over two important factors-mobility and pause time with the proposed ACO technique of reliable links. In the first experiment of different speeds, DSR is 25.5% better than AODV and 5.3% better than DSDVin metric packet dropping, 1.15% better than AODV and 1.01% better than DSDV in Packet Delivery Ratio, 1.5% better than AODV and 1.04% better than DSDV in packet overhead, and 62.7% better than AODV and 73.1% better than DSDV in average end-to-end delay. In the second experiment of pause times, DSR is 25.05% better than AODV and 4.5% better than DSDV in packet dropping, 1.18% better than AODV and 1.01% better than DSDV in packet delivery ratio, 1.01% better than DSDV in throughput, 0 to 0.002% in packet overhead, and 0.02% to 0.005% shorter delay.

QQAR: A Q-learning-based QoS-aware routing for IoMT-enabled wireless body area networks for smart healthcare

Recently, the Internet of Medical Things (IoMT) has gained broad acclaim in the academic and industrial sectors due to its use in integrating medical devices and healthcare systems. Wireless body area networks (WBANs) are a promising technology in smart healthcare for real-time patient monitoring and health data analysis by medical professionals. Efficient data transmission is essential in ensuring reliable and timely delivery of healthcare facilities. Healthcare services require reliable, delay-sensitive, energy- and congestion-aware communication, which makes it challenging to design a routing protocol. To address quality of services (QoS) in IoMT-enabled WBANs for healthcare, we propose Q-learning-based quality of services (QoS) aware routing (QQAR). Firstly, we used two-hop-based link reliability estimation to ensure link reliability from sources to sink nodes. Routing decisions using two-hop neighbor information guarantees successful packet delivery. Secondly, we differentiated the packets based on traffic priority, which assures the delivery of critical packets even in a congested network. Particularly, considering two-hop node velocity with energy balancing enhanced the guarantee of timely packet delivery before deadlines. In addition, the multi-sink approach enhanced network reliability. Finally, we utilized Q-learning to select suitable neighbor nodes for packet forwarding in a decentralized fashion. A multi-sink load balance and congestion control mechanism adjusts the routing decisions in the QQAR to avoid congestion. Our simulation and comparison results showed that QQAR outperforms the existing routing protocols across various performance metrics under distinct network conditions.

Komodo Mlipir Algorithm-based optimal route determination mechanism for improving Quality of Service in Vehicular ad hoc network

In Vehicular ad hoc network (VANETs), Quality of Service (QoS)- aware protocols helps in handling the necessitated demand of delay sensitive applications for facilitating intelligent transportation. The fundamental challenge of VANETs lies in the process of establishing vehicle to infrastructure and vehicle-to-vehicle communication that are prone to link failure. Bio-inspired algorithms are identified to provide reliable solutions for securing the links of VANETs. In this paper, Komodo Mlipir Algorithm-based Optimal Route Determination Mechanism (KMAORDM) is proposed for achieving best optimal route that guarantees enhanced QoS. This QoS aware routing protocol utilizes the exceptional potentiality of Komodo Mlipir Algorithm attributed in terms of exploration and exploitation for the objective of evaluating and memorizing the impactful factors that helps in exchanging the necessitated messages to its neighbours. It adopts Mlipir as a best mode of modelling the behaviours of vehicular nodes during the process of data routing in VANETs. It uses a cache strategy which discovered reliable traversed routing paths such that the pre-cached route is used for data transmission. This cache strategy paves the way for preventing the route identification and maintenance process with minimized routing overhead. It also identifies the fittest vehicular node from its one hop distance as the successive forwarder in the absence of pre-cached route for addressing link failure during reliable packet transmission. The simulation results of this proposed KMAORDM approach with different vehicular nodes confirmed minimized communication overhead of 19.32 %, reduced network latency of 18.64 %, maximized throughput of 18.54 % better than benchmarked approaches.

Design an Improved Trust-based Quality of Service AwareRouting in Cognitive Mobile Ad-Hoc Network

Mobile ad hoc networks (MANETs) are wireless networks that can be configured at will. It has no infrastructure and centralized control, so it is only suitable for provisional communications. In a dynamically topological and resource-constrained network, ensuring QoS and security is challenging. MANETs are dynamic networks, so navigating them can be challenging and more susceptible to attacks. MANET requires significant memory, speed, and transmission bandwidth for conventional security measures like cryptographic techniques. Consequently, these methods are unsuitable for identifying malicious behaviour or self-centered nodes. Nodes that are malicious, selfish, or malfunctioning can be identified based on the trust method, which calculates how much trust exists between them. A trust-based QOS-aware routing protocol is proposed in this paper to calculate trust in MANET (I-TQAR). The tree important performance metrics are considered for result validation such as delay, throughput and packet delivery ratio (PDR). I-TQAR offers significantly improved performance in all areas compared to the existing TQR and TQOR protocols.

SSFSCE: Design of a Sleep Scheduling based Fan Shaped Clustering Model to enhance working Energy Efficiency of WSN

To enhance energy level in WSN is a research requirement, which assists in improving their lifetime over a series of communications. To achieve this target, a various variety of clustering & sleep scheduling models are discussed by researchers. Most of these models deploy static clustering & sleep scheduling operations, which limits their applicability & scalability levels. Moreover, dynamic clustering & scheduling models are highly complex, which reduces temporal QoS performance under real-time use cases. In order to reduce the probability of these issues, this text discusses design of the proposed Sleep Scheduling based Fan Shaped Clustering Model to enhance working Energy Efficiency of WSN. The proposed model initially deploys a Grey Wolf Optimization (GWO) Method for dynamic sleep scheduling via temporal performance analysis. The GWO Method models a fitness function that combines temporal usage levels, temporal Quality of Service (QoS), and temporal energy levels. Based on this modelling process, nodes were categorized into wake & sleep nodes, which were further clustered via destination-aware Fan Shaped Clustering (FSC), that assisted in improving QoS performance under multiple scenarios. The FSC Model was combined with a QoS-aware routing model, that assisted in selection of routing paths that can achieve low delay, high throughput, and high packet delivery with higher energy efficiency levels. Efficiency of the model was tested on node & network conditions, and its QoS performance was checked in terms of communication delay, consumption of energy, level of throughput, and Packet Delivery Ratio (PDR) levels. On the basis of these comparative evaluations, it is observed that the new proposed model is able to enhance end-to-end delay by 8.5%, reduce level of energy by 15.5%, while increasing throughput by 8.3%, and PDR by 1.5%, thus making it useful for a different real-time conditions.

DFQIoV: Design of a Dynamic Fan-Shaped-Clustering Model for QoS-aware Routing in IoV Networks

Internet of Vehicles (IoV) is a steadily growing field of research that deals with highly ad-hoc wireless networks. These networks require design of high-speed & high-efficiency routing models, that can be applied to dynamically changing network scenarios. Existing models that perform this task are highly complex and require larger delays for estimation of dynamic routes. While, models that have faster performance, do not consider comprehensive parameters, which limits their applicability when used for large-scale network scenarios. To overcome these limitations, this text proposes design of a novel dynamic fan-shaped clustering model for QoS-aware routing in IoV networks. The model initially collects network information sets including node positions, & energy levels, and combines them with their temporal packet delivery & throughput performance levels. These aggregated information sets are processed via a hybrid bioinspired fan shaped clustering model, that aims at optimization of routing performance via deployment of dynamic clustering process. The model performs destination-aware routing process which assists in reducing communication redundances. This is done via a combination of Elephant Herding Optimization (EHO) with Particle Swarm Optimization (PSO), which integrates continuous learning for router level operations. The integrated model is able to reduce communication delays by 5.9%, while improving energy efficiency by 8.3%, throughput by 4.5%, and packet delivery performance by 1.4% under different network scenarios. Due to which the proposed model is capable of deployment for a wide variety of dynamic network scenarios.

Quality of service aware routing in software defined video streaming: a survey

Quality of service aware routing in software defined video streaming: a survey

Energy-efficient routing protocol on public roads using real-time traffic information

Wireless technology has been significantly expanding to perform automotive services related to enhancing road networks and increasing traffic safety. Internet of Vehicles (IoV), as a part of wireless sensor technology, has succeeded in making considerable progress in promoting and protecting human lives from the damage caused by traffic accidents. The road statistical reports demonstrated that utilizing data dissemination techniques in IoV to share timely information about the vehicle’s position with drivers plays a key in avoiding accidents occurrence. This type of technique enables data propagation in a collaboratively and efficient manner to boost real-time performance. However, several limitations have arisen in network bandwidth usage and power consumption, as well as communication reliability with increasing packet loss probability. The main objective of this study is to present a new vehicular architecture that supports the integration of Software-Defined networks and fog computing for enhancing VANET performance. An energy-efficient and QoS-aware routing technique allowing for control of excessive dissemination of data traffic across the networks is also proposed. The simulation results show that the proposed routing protocol introduced an accurately controlling methodology in the transmission rate of data packets and communication reliability of VANET. There is a 50–60% enhancement of the whole power consumption and network throughput, depending on the implementation of the proposed geographical routing methodology.

SMNBMQR: optimisation of sleep schedules in multimedia networks via bioinspired modelling for QoS-aware routing operations

SMNBMQR: optimisation of sleep schedules in multimedia networks via bioinspired modelling for QoS-aware routing operations

Enhanced QoS-Aware Routing Protocol for Delay Sensitive Data in Wireless Body Area Networks

Enhanced QoS-Aware Routing Protocol for Delay Sensitive Data in Wireless Body Area Networks

SMNBMQR: Optimization of Sleep Schedules in Multimedia Networks via Bioinspired Modelling for QoS-aware Routing operations

SMNBMQR: Optimization of Sleep Schedules in Multimedia Networks via Bioinspired Modelling for QoS-aware Routing operations

AQROM: A quality of service aware routing optimization mechanism based on asynchronous advantage actor-critic in software-defined networks

In Software-Defined Networks (SDNs), determining how to efficiently achieve Quality of Service (QoS)-aware routing is challenging but critical for significantly improving the performance of a network, where the metrics of QoS can be defined as, for example, average latency, packet loss ratio, and throughput. The SDN controller can use network statistics and a Deep Reinforcement Learning (DRL) method to resolve this challenge. In this paper, we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism (AQROM) to determine routing strategies that balance the traffic loads in the network. AQROM can improve the QoS of the network and reduce the training time via dynamic routing strategy updates; that is, the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic pattern. AQROM can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states, and actions with respect to the operations in the SDN. Extensive simulations were conducted using OMNeT++ and the results demonstrated that AQROM 1) achieved much faster and stable convergence than the Deep Deterministic Policy Gradient (DDPG) and Advantage Actor-Critic (A2C), 2) incurred a lower packet loss ratio and latency than Open Shortest Path First (OSPF), DDPG, and A2C, and 3) resulted in higher and more stable throughput than OSPF, DDPG, and A2C.

QoS aware routing protocol using robust spatial gabriel graph based clustering scheme for ad hoc network

SummaryMANET (mobile ad hoc network) comprises of a set of wireless mobile node connected in a self‐healing and self‐configured network devoid of any fixed infrastructure. The cognitive radio (CR) in the MANET system has been developed for addressing the spectrum congestion issue. However, the existing techniques failed to solves the complex convex problem of channel assignment. To address this issue, the proposed protocol is designed. In the proposed scheme, both primary user (PU) and secondary user (SU) are clustered initially based on the graph theory process. After that, in each cluster, formation of robust spatial Gabriel graph (RS‐GG) takes place at which the neighboring nodes are predicted by estimating the weighted end‐to‐end delay. Once the multi path decision‐making condition is satisfied, the route path is established, and the communication takes place based on QoS constraint. This can lead to the enhancement of PDR, network connectivity maintenance and network lifetime. The performance analysis of the proposed methods is carried out for PDR, control overheads, energy consumption, and End‐to‐End delay and the analysis is compared with existing protocols to prove the effectiveness of proposed design. The simulation outcomes illustrate that the suggested strategy performs well and improves the data transmission.

FybrrLink: Efficient QoS-Aware Routing in SDN Enabled Future Satellite Networks

Providing high-speed Internet services using satellite networks have attracted researchers from both academia and industry mainly due to the characteristics of Low Earth Orbit (LEO) satellite networks such as global coverage, scalability, and lower transmission delays. Implementation of Software-Defined Networking (SDN) in Non-Terrestrial Networks (NTNs) can help to achieve the goals set for 5G and beyond networks. Since satellite networks have a specific architecture, some of the legacy protocols no longer remain useful. Therefore, to satisfy the diverse Quality of Service (QoS) requirements for a variety of applications, we propose a novel and centralized QoS-aware routing algorithm, called fybrrLink in which the global view of the network in SDN is utilized. We implement a modified Bresenham’s algorithm and Dijkstra’s algorithm to find the optimal path in a significantly reduced computation time. Also, taking advantage of the deterministic satellite constellation, we propose a flow rule transfer algorithm and a topology monitoring algorithm. Further, fybrrLink is evaluated with multiple NS3 simulations, and results confirm that our approach outperforms other state-of-the-art algorithms.

OQR-SC: An Optimal QoS Aware Routing Technique for Smart Cities Using IoT Enabled Wireless Sensor Networks

Internet of things (IoT) makes a machines optimization in everyday which processing the data by very intelligently and make communication more effectively and efficiently. In smart city environments, the data collection and communication play an important role in defining quality. Since the research period, it has been recommending a new data acquisition and data communication software framework for IoT smart applications. For further improvements, we recommend an optimal QoS aware routing technique for smart cities using IoT enabled wireless sensor networks (OQR-SC). In data gathering phase, the proposed system introduce chaotic bird swarm optimization (CBSO) algorithm for IoT sensor cluster formation; the improved differential search (IDS) algorithm used to estimate the faith degree of each sensor node, the highest trust node act as cluster head (CH). In data transferring phase, first illustrates lightweight signcryption technique for data encryption between two IoT sensors. Then, we use optimal decision making (ODM) algorithm to compute the optimal path between source–destination in IoT platform. Finally, the proposed OQR-SC technique is implemented using network simulation (NS2) tool and analyzes the performance of proposed technique with existing state-of-art techniques. The result summarizes that average energy consumption of proposed OQR-SC technique is 12.39% lower than the existing techniques; the average network lifetime of proposed OQR-SC technique is 15.96% higher than the existing techniques; the average delay of proposed OQR-SC technique is 21.08% lower than the existing techniques; and the average throughput of proposed OQR-SC technique is 17.89% higher than the existing techniques.

QoS-Aware routing, space, and spectrum assignment in space division multiplexing networks

Nowadays, Space Division Multiplexing (SDM) is one of the solutions proposed to increase the capacity and optimal allocation of resources in Elastic Optical Networks (EON). The issue of allocating resources in these networks involves the optimal allocation of route, space and spectrum for the establishment of traffic requests. The quality of service (QoS) provided for establishing traffic requests must vary according to their importance and priority, so that high quality requests require guaranteed bandwidth and higher levels of service than low quality requests. Hence, reducing the blocking probability of high-quality data traffic is one of the most important goals in networks. In this paper, three new QoS-aware algorithms are proposed for route, space and spectrum assignment of dynamic traffic in SDM-EONs that reduce the blocking probability of high-quality requests as follows: Reservation without Common Core (RwoC), Reservation with One Common Core (RwC), and Reservation and Recoring with One Common Core (RRwC). The results of utilizing these algorithms and also adding distance adaptive modulation format show that the blocking rate of high-quality requests is decreased and the general spectrum utilization is increased specially in RRwC algorithm. Of course, this decrease causes a slight increase in the blocking rate of low-quality requests. Another advantage of using the RRwC algorithm is that using the slots with the same index in the recoring method reduces costs because in this case there is no need to change the frequency of transceivers. Since, this algorithm is more cost-effective. In the first proposed algorithm, dedicated cores are designed to create high quality traffic. In the second algorithm, in addition to dedicated cores, a common core is also used between high and low-quality traffic. Finally, the recoring method has been used for high quality traffic to further reduce their blocking rates.

PQROM: To optimize software defined network QoS-aware routing with proximal policy optimization

Currently, Quality-of-Service (QoS)-aware routing is one of the crucial challenges in Software Defined Network (SDN). The QoS performances, e.g. latency, packet loss ratio and throughput, must be optimized to improve the performance of network. Traditional static routing algorithms based on Open Shortest Path First (OSPF) could not adapt to traffic fluctuation, which may cause severe network congestion and service degradation. Central intelligence of SDN controller and recent breakthroughs of Deep Reinforcement Learning (DRL) pose a promising solution to tackle this challenge. Thus, we propose an on-policy DRL mechanism, namely the PPO-based (Proximal Policy Optimization) QoS-aware Routing Optimization Mechanism (PQROM), to achieve a general and re-customizable routing optimization. PQROM can dynamically update the routing calculation by adjusting the reward function according to different optimization objectives, and it is independent of any specific network pattern. Additionally, as a black-box one-step optimization, PQROM is qualified for both continuous and discrete action space with high-dimensional input and output. The OMNeT ++ simulation experiment results show that PQROM not only has good convergence, but also has better stability compared with OSPF, less training time and simpler hyper-parameters adjustment than Deep Deterministic Policy Gradient (DDPG) and less hardware consumption than Asynchronous Advantage Actor-Critic (A3C).