QoS Multicast Routing Research Articles

Reliability assessment and optimization of computer networks based on neural networks

Abstract Amidst the swift evolution of computer technologies, the prevalence of computer networks has become pivotal across diverse sectors, increasingly reliant on their robust functionality. This study rigorously evaluates and enhances the reliability of computer networks by leveraging an index system and an evaluation model devised through neural networks. To strengthen network dependability, this research employs a Hopfield neural network to address multi-constraint Quality of Service (QoS) multicast routing challenges, thereby elevating network reliability. Simulation experiments demonstrate that the Hopfield neural network effectively mitigates network latency and exhibits superior convergence performance compared to conventional QoS multicast routing methods. Further, this paper applies the neural network-based evaluation model to analyze the reliability of the air traffic network within the H-area after integrating the optimized computer network framework. It is observed that the most significant contributor to traffic flow loss is the network's degree value. An analysis of traffic flow density, employing actual sector flow data, reveals that high traffic volumes typically precipitate congestion. Nonetheless, the traffic flow density value consistently exceeds 100, suggesting that the enhanced computer network model holds practical applicability in real-world scenarios.

WITHDRAWN: A QoS Multicast Protocol Optimization for MANET in Wireless Communications

The principle objective of QoS multicast routing is to build a multicast communication in form of shared tree, gathering multiple QoS constraints and Mobile Ad Hoc Networks (MANETs) are an up-and-coming kind of wireless communication networks, in which mobile nodes link on an ad hoc basis. MANETs can change their areas without anyone else too having capacity to design all alone, encouraging friend level communications among mobile nodes independent of fixed infrastructure. Multicast routing problem with numerous QoS limitations, which may cope the deferral, postpone jitter, transmission capacity, and parcel misfortune measurements. additionally, depicts an association model for investigating directing issue, in this paper we presenting IP-EMRP convention which centers around upgrade of demonstration of MANET with assistance of an energy effective steering convention for usage of Quality of service (QoS) and it requires data transfer capacity reservation by assessing the presentation of the organization regarding throughput, time delay and steering overheads for various number of hubs.

Optimal relay and channel selection schemes for multiconstrained QoS multicast routing in cognitive radio ad hoc networks

SummaryDuring multicasting in cognitive radio ad hoc network (CRAHN), apart from energy consumption, interchannel interference, path delay, and channel switching delay should be minimized. The channel selection should be done, based on spectrum opportunities and channel quality. Hence, the multicast routing problem becomes a multiconstrained QoS routing problem. This paper proposes optimal relay and channel selection schemes (ORCS) for multiconstrained QoS multicast routing in CRAHNs. For multicast tree construction, distributed minimum spanning tree (DMST) is used. For meeting the delay, energy, and interference constraints, relay nodes are chosen with minimum path delay, less energy consumption, and minimum interference. For effective channel assignment, channels with good channel quality, maximum spectrum accessibility, and sufficient common slots are selected. By simulation results, it has been shown that the proposed ORCS minimize the energy consumption and end‐to‐end delay.

Adaptive QoS Multicast Routing with Mobility Prediction in MANETs

Adaptive QoS Multicast Routing with Mobility Prediction in MANETs

Enhanced QoS Multicast Routing Protocol in MANETs using Trust and Load Balance

Mobile ad-hoc network (MANET) is a wireless networks without fixed infrastructure, where each node can interact with its one hop distance neighbors only. Since the nodes are continuously moving from one location to another location, the network topology is not constant. Provisioning of Quality of Service (QoS) in MANETs is a challenging task because of its unstable nature. In multicast routing protocol, a sender node is sending the data to the group of members (multiple destination nodes), where the intermediate nodes resources are consumed at higher rate. In this paper, utilization of node resources (QoS parameters) is minimized by considering node attitude (trust) and load balancing. Here node energy and link bandwidth are considered as the QoS parameters. The proposed method "Enhanced QoS Multicast Routing Protocol in MANETs Using Trust and Load Balance-QMTL" identifies the intermediate nodes with higher trust values and constructs the multicast tree with optimized load balance. The performance of proposed method is analyzed theoretically (using asymptotic notations) and practically (using simulation tool-ns2)..

The Multi-Constrained Multicast Routing Improved by Hybrid Bacteria Foraging-Particle Swarm Optimization

To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. In this paper, PSO has been embedded with BFO to improve the convergence speed and avoid premature convergence that will be used for solving QoS multicast routing problem. The algorithm proposed here generates a set of delay compelled links to every destination present in the multicast group. Then the Bacteria Foraging Algorithm (BFA) selects the paths to all the destinations sensibly from the set of least delay paths to construct a multicast tree. The robustness of the algorithm being proposed had been established through the simulation. The efficiency and effectiveness of the algorithm being proposed was validated through the comparison study with other existing meta-heuristic algorithms. It shows that our proposed algorithm IBF-PSO outperforms its competitive algorithms.

A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem

Quality of service multicast routing is an important research topic in networks. Research has sought to obtain a multicast routing tree at the lowest cost that satisfies bandwidth, delay and delay jitter constraints. Due to its non-deterministic polynomial complete problem, many meta-heuristic algorithms have been adopted to solve this kind of problem. The paper presents a new hybrid algorithm, namely ACO&CM, to solve the problem. The primary innovative point is to combine the solution generation process of ant colony optimization (ACO) algorithm with the Cloud model (CM). Moreover, within the framework structure of the ACO, we embed the cloud model in the ACO algorithm to enhance the performance of the ACO algorithm by adjusting the pheromone trail on the edges. Although a high pheromone trail intensity on some edges may trap into local optimum, the pheromone updating strategy based on the CM is used to search for high-quality areas. In order to avoid the possibility of loop formation, we devise a memory detection search (MDS) strategy, and integrate it into the path construction process. Finally, computational results demonstrate that the hybrid algorithm has advantages of an efficient and excellent performance for the solution quality.

A hybrid quantum evolutionary algorithm with cuckoo search algorithm for QoS multicast routing problem

The QoS multicast routing problem is one of the most interesting combinatorial optimisation problems for transmission in the areas of networks and distributed systems. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve this problem. This paper proposes two-hybrid approaches based on the hybridisation of cuckoo search (CS) algorithm with quantum evolutionary algorithm (QEA) to solve the quality of service (QoS) multicast routing problem. The main idea of the first approach, named CSQEA1, is the integration of the evolutionary equation of CS in the evolutionary operator of QEA, the rotation angle of quantum gates is adjusted by the evolution equation of CS; while the main idea of the second approach, named CSQEA2, is the replacement of the evolutionary equation of QEA by the evolutionary equation of CS, the rotation angle is used to encode the new Q-bit. The aim of these approaches is to avoid the premature convergence, ensure the diversity of the solutions and improve the effectiveness and the robustness of QEA. The simulation results show the successfulness and the efficiency of our proposed algorithms compared with other existing algorithms in the literature.

A hybrid quantum evolutionary algorithm with cuckoo search algorithm for QoS multicast routing problem

A hybrid quantum evolutionary algorithm with cuckoo search algorithm for QoS multicast routing problem

The Multi-Constrained Multicast Routing Improved by Hybrid Bacteria Foraging-Particle Swarm Optimization

To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. In this paper, PSO has been embedded with BFO to improve the convergence speed and avoid premature convergence that will be used for solving QoS multicast routing problem. The algorithm proposed here generates a set of delay compelled links to every destination present in the multicast group. Then the Bacteria Foraging Algorithm (BFA) selects the paths to all the destinations sensibly from the set of least delay paths to construct a multicast tree. The robustness of the algorithm being proposed had been established through the simulation. The efficiency and effectiveness of the algorithm being proposed was validated through the comparison study with other existing meta-heuristic algorithms. It shows that our proposed algorithm IBF-PSO outperforms its competitive algorithms.

Fuzzy Logic Aware QoS Multicasting in MANETs with Load Balance

Mobile ad-hoc network (MANET) is a decentralized and infrastructure less network where a nodes can communicate with other nodes within the access region. Due to mobility node can enter and leave a network at any moment. Due to unstable nature of MANETs, the provision of Quality of Service (QoS) to the applications is a difficult task. In this paper, fuzzy logic enabled QoS multicast routing is proposed. Here energy, bandwidth and link expiry time are considered as a QoS parameters. The existing methods lost their performance in handling multi constrained QoS protocols, since defining the dynamic priorities among the multiple QoS parameters is not a trivial task. In the proposed method “Fuzzy Logic Aware QoS Multicasting in MANETs with Load Balance-FQML”, this issue is overcome by using fuzzy logic. The Competency Factor of each intermediate node along the route is calculated by aggregating it’s QoS parameters using fuzzy inference system. In the classical multicast protocols, some of the nodes in the multicast tree are overloaded by having multiple branches towards the destination nodes. This leads to partition of the multicast tree and degrades the performance of protocols. In the proposed method, this problem is handled by limiting the number of branches at intermediate nodes. The results are taken in network simulator-ns2, where the proposed method could measure less number of path failures and improved results than existing methods.

A resource‐tuned QoS multicast routing protocol

SummaryThis paper presents a novel framework for quality‐of‐service (QoS) multicast routing with resource allocation that represents QoS parameters, jitter delay, and reliability, as functions of adjustable network resources, bandwidth, and buffer, rather than static metrics. The particular functional form of QoS parameters depends on rate‐based service disciplines used in the routers. This allows intelligent tuning of QoS parameters as functions of allocated resources during the multicast tree search process, rather than decoupling the tree search from resource allocation. The proposed framework minimizes the network resource utilization while keeping jitter delay, reliability, and bandwidth bounded. This definition makes the proposed QoS multicast routing with resource allocation problem more general than the classical minimum Steiner tree problem. As an application of our general framework, we formulate the QoS multicast routing with resource allocation problem for a network consisting of generalized processor sharing nodes as a mixed‐integer quadratic program and find the optimal multicast tree with allocated resources to satisfy the QoS constraints. We then present a polynomial‐time greedy heuristic for the QoS multicast routing with resource allocation problem and compare its performance with the optimal solution of the mixed‐integer quadratic program. The simulation results reveal that the proposed heuristic finds near‐optimal QoS multicast trees along with important insights into the interdependency of QoS parameters and resources.

A Reference-Based Multiobjective Bacteria Foraging Optimization Technique for QoS Multicast Routing

Nowadays multicast services are gaining high popularity in supporting various and continuous media applications over wired and wireless networks. But the desired quality of service for these applications makes multicast routing optimization a NP Hard problem. In this manuscript, we propose a reference-based multiple objective bacteria foraging algorithm that solves multicasting routing problem, based on user’s preference. Instead of searching for a preferred solution from the obtained Pareto front, we would rather search for a set of solutions in the desired objective space simultaneously. The algorithm starts with a single-step tree construction method to generate multicast trees as initial bacteria population. The Pareto optimal front generated will comprise of solutions, each of which best suits the user’s choice. The multiobjective bacteria foraging optimization algorithm used here is based on the reference-based method, where the user inputs a reference point in the possible region of the problem search space. Hence the Pareto optimal front generated is in the proximity of the given reference point which would help the user in making more feasible and reliable decisions. Simulations have been carried upon various benchmark as well as some random networks to study and analyze the performance of the proposed algorithm with the existing algorithms. The simulation results reveal that our proposed algorithm (RBMOBFO) yields better results in terms of solution quality and convergence speed.

A multipath QoS multicast routing protocol based on link stability and route reliability in mobile ad-hoc networks

Recently, the use of real-time multimedia applications has attracted the attention of mobile ad hoc network users. These applications support service quality. The characteristics of mobile ad hoc networks such as lack of central coordinator, mobility of hosts, dynamic changes in network topology and the limitation of access to resources have caused remarkable challenges in providing high quality services for mobile ad hoc networks. Bandwidth limitation of wireless nodes and the lack of adequate multicast trees are regarded as the main challenges for quality of service-based multicast routing. In this paper, an attempt was made to propose a more stable and more reliable in multi-path quality of service multicast routing protocol (SR-MQMR) for mobile ad-hoc networks. In the proposed method, considering the requested bandwidth, the researchers first used the signal strength of nodes to select the most stable nodes. Then, using the two parameters of route expiration time and the number of hops, we selected a route which had low delay and high stability. The results of simulations conducted in the present study indicated that the SR-MQMR protocol used less time slots than the MQMR protocol in the routing process which resulted in an increase in success ratio. Furthermore, the production of stable routes led to a significant enhancement of reliability. Since the reduction of route request packet exchange decreased overhead in the proposed method, the degree of consumed bandwidth decreased which led to an increase in network lifetime.

QoS multicast routing for wireless mesh network based on a modified binary bat algorithm

The quality of service multicast routing problem is a very important research issue for transmission in wireless mesh networks. It is known to be NP-complete problem, so many heuristic algorithms have been employed for solving the multicast routing problem. This paper proposes a modified binary bat algorithm applied to solve the QoS multicast routing problem for wireless mesh network which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate to get low-cost multicasting tree. The binary bat algorithm has been modified by introducing the inertia weight w in the velocity update equation, and then the chaotic map, uniform distribution and gaussian distribution are used for choosing the right value of w. The aim of these modifications is to improve the effectiveness and robustness of the binary bat algorithm. The simulation results reveal the successfulness, effectiveness and efficiency of the proposed algorithms compared with other algorithms such as genetic algorithm, particle swarm optimization, quantum-behaved particle swarm optimization algorithm, bacteria foraging-particle swarm optimization, bi-velocity discrete particle swarm optimization and binary bat algorithm.

Multi Criterion Multicast Routing Algorithm Comparison for Large Networks

Abstract QoS enabled multicast routing is known to be of non-polynominal complexity, which leads to the necessity of using heuristic algorithms to find sub-optimal solutions to the problems of this class. The evaluation of such algorithms requires the use of the simulation techniques as the heuristics’ results are of stochastic nature. Because of the problem complexity the simulation times increase significantly in the function of the network size, therefore the results presented in the literature are often limited to only small models. In this article the results of the evaluation of different multicast QoS routing algorithms (further referred to as the Multi-Constrained Minimum Steiner Tree Problem - MCMST) have been presented for a wide range of network sizes reaching thousands of nodes.

Ant based Pareto optimal solution for QoS aware energy efficient multicast in wireless networks

Most of the group communication technologies support real-time multimedia applications such as video conferencing and distributed gaming. These applications require quality-of-service (QoS) aware multicast routing protocol to deliver the same data stream to a predefined group of receivers. Since nodes in wireless networks are severely energy constrained due to finite battery source, hence it is of paramount importance that QoS aware multicast routing protocol be energy efficient. Transmission power control is one of the methods used to save energy. In this method, the nodes dynamically adjust the transmission power so that energy consumption in the tree is minimized. However, reduction in the transmission power increases the number of forwarding nodes in the multicast tree. This negatively impacts the QoS in terms of propagation delay, delay jitter, and packet loss etc. In wireless networks, there is a trade-off between the energy consumption and the QoS guarantees provided by the network. We unify these requirements into a multiobjective framework referred to as Energy Efficient QoS Multicast Routing (E2QoSMR). The goal is to simultaneously optimize the total power consumption and the QoS parameters in the multicast tree. We extend two algorithms based on metaphor of swarm intelligence for finding an energy efficient multicast tree satisfying the QoS guarantees. Extensive simulations have been conducted to validate the correctness and efficiency of the algorithms. The simulation result of the algorithms is compared with the nondominated sorting genetic algorithm, NSGA-III. The experimental results are consolidated by statistical analyses that demonstrate the ability of the algorithms to generate the Pareto optimal solution set.

Q-CAR: an intelligent solution for joint QoS multicast routing and channel assignment in multichannel multiradio wireless mesh networks

Multicast routing improves the efficiency of a network by effectively utilizing the available network bandwidth. In multichannel multiradio wireless mesh networks the channel allocation strategy plays a vital role along with multicast tree construction. However, the multicast routing problem in multichannel multiradio wireless mesh networks is proven to be NP-hard. With this paper, we propose a Quality of Service Channel Assignment and multicast Routing (Q-CAR) algorithm. The proposed algorithm jointly solves the channel assignment and multicast tree construction problem by intelligent computational methods. We use a slightly modified differential evolution approach for assigning channels to links. We design a genetic algorithm based multicast tree construction strategy which determines a delay, jitter bounded low cost multicast tree. Moreover, we define a multi objective fitness function for the tree construction algorithm which optimizes interference as well as tree cost. Finally, we compare the performance of Q-CAR with QoS Multicast Routing and Channel Assignment(QoS-MRCA) and intelligent Quality of service multicast routing and Channel Assignment(i-QCA) algorithm in multichannel multiradio wireless mesh network (simulated) environments. Our experimental results distinctly show the outstanding performance of the proposed algorithm.

Fuzzy cost probability-based suppressed flooding multi-constrained QoS multicast routing for MANETs

The core objective of our approach is to build a highly strong forwarding group and a stable mesh structure using fuzzy inference system. The fuzzy inference system takes multiple interrelated QoS parameters such as residual bandwidth (RB), residual energy (RE), link loss ratio (LLR), end-to-end delay (D), number of intermediate nodes (N) and link expiration time (LET) to find a strong node from a set of mobile nodes. A node can be classified as strong when the fuzzy cost probability (FCP) is high otherwise it is classified as a weak node. The proposed fuzzy cost probability-based multi-constrained QoS multicast routing (FCPMQMR) consists of two phases. The first phase performs the selection of forwarding node using fuzzy logic technique. The second phase builds a stable backbone mesh structure. In case of node failure, an alternative path from the primary route through another forwarding node is selected for communication. Simulation results demonstrate that the proposed FCPMQMR improves the packet delivery ratio by 5%-10% success ratio by 35%, and decreases the average end-to-end delay by 10%-15% and control overhead by 45%.

A quantum-inspired binary firefly algorithm for QoS multicast routing

A quantum-inspired binary firefly algorithm for QoS multicast routing