Quality Of Service Aware Algorithm Research Articles

Hybrid energy-efficient and QoS-aware algorithm for intelligent transportation system in IoT

The Internet of Things (IoT) consists of large amount of energy consuming devices which are pre-figured to progress the effective competence of several industrial applications. It is very much essential to bring down the energy use of every device deployed in the IoT network without compromising the Quality of Service (QoS) for intelligent transportation system. To achieve this objective, a multiobjective optimisation problem to accomplish the aim of estimating the outage performance of clustering process and the network lifetime is devised. Subsequently, a Hybrid Energy Efficient and QoS Aware (HEEQA) algorithm that is a combination of Quantum Particle Swarm Optimisation (QPSO) along with improved Non-dominated Sorting Genetic Algorithm (NSGA) to achieve energy balance among the devices is proposed. NSGA is applied to solve the problem of multiobjective optimisation and the QPSO algorithm is used to find the optima cooperative nodes and cluster head in the clusters.

Hybrid energy-efficient and QoS-aware algorithm for intelligent transportation system in IoT

The Internet of Things (IoT) consists of large amount of energy consuming devices which are pre-figured to progress the effective competence of several industrial applications. It is very much essential to bring down the energy use of every device deployed in the IoT network without compromising the Quality of Service (QoS) for intelligent transportation system. To achieve this objective, a multiobjective optimisation problem to accomplish the aim of estimating the outage performance of clustering process and the network lifetime is devised. Subsequently, a Hybrid Energy Efficient and QoS Aware (HEEQA) algorithm that is a combination of Quantum Particle Swarm Optimisation (QPSO) along with improved Non-dominated Sorting Genetic Algorithm (NSGA) to achieve energy balance among the devices is proposed. NSGA is applied to solve the problem of multiobjective optimisation and the QPSO algorithm is used to find the optima cooperative nodes and cluster head in the clusters.

Finding a Pareto Optimal Solution for a Multi-Objective Problem of Managing Radio Resources in LTE-A Systems: A QoS Aware Algorithm

Due to ever increasing users demands, fulfilling the needs of growing users is challenging with constrained resources for Long Term Evolution- Advanced (LTE-A). In this research paper we have proposed an innovative approach for Radio Resource Management (RRM) that makes use of the evolutionary multi-objective optimization techniques for Quality of Service (QoS) facilitation and embeds it with the modern techniques for RRM. We have proposed a novel algorithm that selects an optimal solution out of a set of a representative Pareto front in accordance with the users QoS requirements. It is completely novel as it uses the multi-objective optimization algorithms for achieving desired QoS in LTE-A system. The efficiency and performance of the proposed work has been further improved by autonomously assigning and managing resources among various users and applications. The results have been discussed that showcase efficient system performance and better user experience.