Multicast Routing Research Articles

Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN.

Multicast communication technology is widely applied in wireless environments with a high device density. Traditional wireless network architectures have difficulty flexibly obtaining and maintaining global network state information and cannot quickly respond to network state changes, thus affecting the throughput, delay, and other QoS requirements of existing multicasting solutions. Therefore, this paper proposes a new multicast routing method based on multiagent deep reinforcement learning (MADRL-MR) in a software-defined wireless networking (SDWN) environment. First, SDWN technology is adopted to flexibly configure the network and obtain network state information in the form of traffic matrices representing global network links information, such as link bandwidth, delay, and packet loss rate. Second, the multicast routing problem is divided into multiple subproblems, which are solved through multiagent cooperation. To enable each agent to accurately understand the current network state and the status of multicast tree construction, the state space of each agent is designed based on the traffic and multicast tree status matrices, and the set of AP nodes in the network is used as the action space. A novel single-hop action strategy is designed, along with a reward function based on the four states that may occur during tree construction: progress, invalid, loop, and termination. Finally, a decentralized training approach is combined with transfer learning to enable each agent to quickly adapt to the dynamic changes of network link information and accelerate convergence. Simulation experiments show that MADRL-MR outperforms existing algorithms in terms of throughput, delay, packet loss rate, etc., and can establish more intelligent multicast routes. Code and model are available at https://github.com/GuetYe/MADRL-MR_code.

TEAMR-AMAA: Trust and Energy Aware Multicast Routing Based On Adaptive Mexican Axolotl Algorithm in WSN

TEAMR-AMAA: Trust and Energy Aware Multicast Routing Based On Adaptive Mexican Axolotl Algorithm in WSN

Efficient Network Selection Using Multi-Depot Routing Problem for Smart Cities

Smart cities make use of a variety of smart technology to improve societies in better ways. Such intelligent technologies, on the other hand, pose significant concerns in terms of power usage and emission of carbons. The suggested study is focused on technological networks for big data-driven systems. With the support of software-defined technologies, a transportation-aided multicast routing system is suggested. By using public transportation as another communication platform in a smart city, network communication is enhanced. The primary objective is to use as little energy as possible while delivering as much data as possible. The Attribute Decision Making with Capacitated Vehicle (CV) Routing Problem (RP) and Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used in the proposed research. For the optimum network selection, a Multi-Attribute Decision Making (MADM) method is utilized. For the sake of reducing energy usage, the Capacitated Vehicle Routing Problem (CVRP) is employed. To reduce the transportation cost and risk, Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used. Moreover, a mixed-integer programming approach is used to deal with the problem. To produce Pareto optimal solutions, an intelligent algorithm based on the epsilon constraint approach and genetic algorithm is created. A scenario of Auckland Transport is being used to validate the concept of offloading the information onto the buses for energy-efficient and delay-tolerant data transfer. Therefore the experiments have demonstrated that the buses may be used effectively to carry out the data by customer requests while using 30% of less energy than the other systems.

Energy saving slot allocation-based multicast routing in cloud wireless mesh network

Many techniques of cloud computing are affected by surge load and time issues. This proposed method for virtual cloud planning utilise simple protocols in a single or multiple data centres. A new load balanced amortised multi-scheduling algorithm (LBAM) is proposed for assigning the task to cloud based on active load in the cloud environment. The proposed system calculates the multiple attribute weight for each workplace and introduces an application source that changes in application virtualisation environment. The system calculates the cloud data weight based upon the allocation of data and its consequence based on the processing efficiency of the cloud machine. This approach works effectively in most configurations, also virtual machines hold the ability to process load with high pace and efficient memory management. A detailed comparative analysis is made with the existing methods namely SLA, FPGGA and DJS algorithms. The obtained results indicated that the LBAM model is superior to compared methods under several aspects.

Energy saving slot allocation-based multicast routing in cloud wireless mesh network

Energy saving slot allocation-based multicast routing in cloud wireless mesh network

TEAMR-AMAA: trust and energy aware multicast routing based on adaptive Mexican axolotl algorithm in WSN

TEAMR-AMAA: trust and energy aware multicast routing based on adaptive Mexican axolotl algorithm in WSN

A Novel Gradient Boosted Energy Optimization Model (GBEOM) for MANET

Mobile Ad Hoc Network (MANET) is an infrastructure-less network that is comprised of a set of nodes that move randomly. In MANET, the overall performance is improved through multipath multicast routing to achieve the quality of service (quality of service). In this, different nodes are involved in the information data collection and transmission to the destination nodes in the network. The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes. The route identification and routing are established based on the data broadcast by the network nodes. In transmitting the data packet, evaluating the data delivery ratio is necessary to achieve optimal data transmission in the network. Furthermore, energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery rate. In this paper, a Gradient-Based Energy Optimization model (GBEOM) for the route in MANET is proposed to achieve an improved data delivery rate. Initially, the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing (WMC-SMLB) technique is utilized for obtaining energy efficiency and load balancing routing. The WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in MANET. Load balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth estimation. Next, the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing efficiency. The proposed GBEOM exhibits ∼4% improved performance in MANET routing.

Quality of Service Stability Based Multicast Routing Protocol for MANETs

Quality of Service Stability Based Multicast Routing Protocol for MANETs

QoS-Aware Software-Defined Multicast in LEO Satellite Networks

The emerging low earth orbit (LEO) broadband constellations are capable of distributing videos via advanced multicasting techniques to multiplex bandwidth in both satellite access and backbone networks, thereby reducing huge amounts of traffics. Previous work on LEO-based multicast routing relied on IP multicast, which fails to employ global information to achieve the optimal bandwidth saving. In this article, we leverage the novel software-defined multicasting paradigm on LEO constellations to empower advanced video distribution with optimized multicast trees, thereby significantly outperforming conventional multicasting techniques. In the presence of quality-of-service (QoS) constraints, we propose weighted rectilinear Steiner minimal trees (WRSTs), which balance bandwidth saving and QoS requirements. Our multicast tree algorithm adopts the Voronoi diagram and Delaunay triangulation to obtain suitable candidate Steiner points, which are exploited by subsequent iterative edge substitutions for WRST construction. We also design QoS-aware multicast management schemes to deal with frequent member updates and potential failures in LEO constellations. We carry out thorough experiments to demonstrate the effectiveness and efficiency of the proposed routing tree construction algorithm and multicasting protocols when compared with traditional algorithms.

An advancement in energy efficient clustering algorithm using cluster coordinator-based CH election mechanism (CCCH)

A mobile ad hoc network comprises several nodes, and each node can send messages (data) on demand without any established infrastructure. Mobile nodes dynamically enter and exit the network often, resulting in unstable network topology in MANET. As a result, maintaining a stable network becomes a challenging task. Path preservation, battery life, safety, dependability, and unexpected connection characteristics are the major issues in MANET. The network's quality of service (QoS) would be impaired as a result (QoS). The routing protocol is vital for MANETs' pathway detection and maintenance. The multicast routing technology may significantly increase the reliability of the MANET network. Clustering methods, which enable the structuring of networks into groups known as entities or clusters, are invariably employed to address such problems. This paper presents a cluster coordinator-based CH election mechanism (CCCH). The cluster coordinator (CC) is in charge of carrying out the entire operation and is the most important entity in this scenario. The cluster coordinator (CC), cluster head (CH), and cluster members (CM) are the three entities that make up the proposed model. Next to the cluster coordinator, the Cluster Head (CH) is the important serving node in each cluster. Each node in a cluster has a unique value for factors such as node mobility, degree, identity, and energy, among others. With the least amount of time and energy use, the combined proposed architecture successfully constructed reliable routing. A comparison study is done with the existing Optimal Route Selection for Mobile Ad Hoc Networks (ORSMAN) [13] and ticket ID cluster manager protocol (TID-CMGR) [14] to assess the performance of the proposed CCCH. The comparison result proves that the proposed algorithms' performance is far better than the others in all evaluation metrics, such as energy consumption, packet delivery ratio (PDR), and the number of CH changes.

UGAVs-MDVR: A Cluster-Based Multicast Routing Protocol for Unmanned Ground and Aerial Vehicles Communication in VANET

Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols.

A two-level network-on-chip architecture with multicast support

It is essential for implementing processing systems of edge computing, internet of things (IoT) and wireless multimedia sensor networks (WMSN) to use low-power parallel and distributed architectures with high speed and low power consumption. Most of the artificial intelligence and machine learning applications need to be executed on efficient distributed architectures with multicast support. In this paper, TLAM, a high-performance and low-cost NoC architecture is proposed. TLAM stands for Two-Level network-on-chip Architecture with Multicast support and includes a hybrid path/tree based multicast routing algorithm and a specific two-level mesh topology. The routing algorithm of TLAM is basically working according to the Hamiltonian paths. In the proposed architecture, the topology is partitioned and the two-level links provide an efficient infrastructure for low-cost and low-latency transmission of multicast and broadcast messages between partitions. In TLAM, in addition to multicasting routing algorithm, hardware components for handling multicasting packets are designed in order to achieve performance improvements. The goal is to improve the efficiency and performance, especially latency, while handling both unicast and multicast packets. Experimental evaluations including network-level and router-level analysis with different configurations under various traffic patterns were performed. The evaluations in terms of latency, throughput, area and power consumption, indicate that TLAM provides higher performance, especially for dense multicasting and broadcasting, in comparison with the existing architectures.

Robust load-balanced backbone-based multicast routing in mobile opportunistic networks

Robust load-balanced backbone-based multicast routing in mobile opportunistic networks

Secure multicasting in wireless sensor networks using identity based cryptography

SummarySecurity in wireless sensor networks (WSNs) is challenging, owing to resource, computing, and environmental constraints. Researchers have proposed several security systems for multicasting in WSNs, but none have offered foolproof security. To make high‐security multicasting a reality, the proposed I‐RLNMCDS‐ODMRP protocol uses state‐of‐the‐art techniques such as identity‐based cryptography, random linear network coding, the minimum connected dominating set, and the on‐demand multicast routing protocol. Also, this article analyzes computation and memory overhead in the proposed protocol in terms of time in seconds and bytes, respectively, and examines its security properties. In the experimental results, the proposed protocol does not take much time to compute encryption and decryption parameters. Further, it requires little memory to store multicast message information. In the future, the proposed protocol will be tested on healthcare applications for intrabody communication through wireless body area networks.

DRL-M4MR: An intelligent multicast routing approach based on DQN deep reinforcement learning in SDN

Traditional multicast routing methods have some problems in constructing a multicast tree. These problems include limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multiobjective optimization problem, and DRL-M4MR, an intelligent multicast routing algorithm based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in a software-defined network. First, combining the characteristics of SDN global network-aware information, the multicast tree state matrix, link bandwidth matrix, link delay matrix and link packet loss rate matrix are designed as the state space of the reinforcement learning agent to solve the problem in that the original method cannot make full use of network status information. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree in four cases. Third, single-step and final reward function forms are designed to guide the agent to make decisions to construct the optimal multicast tree. The double network architectures, dueling network architectures and prioritized experience replay are adopted to improve the learning efficiency and convergence of the agent. Finally, after the DRL-M4MR agent is trained, the SDN controller installs the multicast flow entries by reversely traversing the multicast tree to the SDN switches to implement intelligent multicast routing. The experimental results show that, compared with existing algorithms, the multicast tree constructed by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment. Code and DRL model are available at https://github.com/GuetYe/DRL-M4MR.

MT3A : A Novel Multicast Routing, Spectrum and Modulation-Level Assignment in Elastic Optical Networks

Elastic Optical Networks (EONs) were introduced to eliminate the challenges of Wavelength Division Multiplexing (WDM) networks. EONs can improve resource efficiency compared to WDM networks. The issue of resource allocation in EONs is a major challenge for the optimal use of maximum network capacity for each communication demand. On the other hand, multicasting is defined as the transfer of data from one source to multiple destinations and it is one of the most effective methods based on cost and efficiency in order to provide flow in computer networks. In this article, we present a novel Multicast Two-part Two-path Two-direction Allocation (MT3A) scheme in order to routing, spectrum and modulation-level assignment for dynamic multicast traffic in EONs. With this scheme, each sub-request related to a multicast request is divided into two parts considering the property of Sliceable Bandwidth Variable Transponders (SBVTs) and is set up in two different directions in two different light-paths. By using the MT3A scheme compared to comparative schemes, with increasing the data rate, Bandwidth Blocking Probability (BBP) is reduced, which is very important due to increasing demand for contents with high data rate.

Improving Efficiency of Large RFID Networks Using a Clustered Method: A Comparative Analysis

Radio Frequency Identification (RFID) is primarily used to resolve the problems of taking care of the majority of nodes perceived and tracking tags related to the items. Utilizing contactless radio frequency identification data can be communicated distantly using electromagnetic fields. In this paper, the comparison and analysis made between the Clustered RFID with existing protocols Ad hoc On-demand Multicast Distance Vector Secure Adjacent Position Trust Verification (AOMDV_SAPTV) and Optimal Distance-Based Clustering (ODBC) protocols based on the network attributes of accuracy, vulnerability and success rate, delay and throughput while handling the huge nodes of communication. In the RFID Network, the clustering mechanism was implemented to enhance the performance of the network when scaling nodes. Multicast routing was used to handle the large number of nodes involved in the transmission of particular network communication. While scaling up the network, existing methods may be compromised with their efficiency. However, the Clustered RFID method will give better performance without compromising efficiency. Here, Clustered RFID gives 93% performance, AOMDV_SAPTV can achieve 79%, and ODBC can reach 85% of performance. Clustered RFID gives 14% better performance than AOMDV_SAPTV and 8% better performance than ODBC for handling a huge range of nodes.

A Game-Theoretic Approach for Cost-Effective Multicast Routing in the Internet of Things

Internet of Things (IoT) devices have enabled communications in resource-limited computing environments. Sensor nodes from the multiple IoT devices collectively work for many applications, such as disaster management, border security management, smart farming, smart cities, etc. In such applications, the data from a single source node is often destined for multiple nodes. Multicast communication is preferred over unicast or broadcast communication for such applications, as multicast uses fewer resources. Efficient construction of the multicast tree leads to cost-effective multicast transmission. This article introduces a path selection game (PSGame), a game-theoretic approach that formulates the construction problem of the least-cost multicast tree as a potential game. Our proposed path selection algorithm (PSA) quickly converges to the pure Nash equilibrium (PNE), bringing the least cost multicast tree. Our findings show that the overhead incurred in terms of energy consumption and delay is minimal in the proposed algorithm compared to other mechanisms. The theoretical analysis proves that the proposed algorithm quickly converges to PNE in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n.r_{\max })$ </tex-math></inline-formula> steps. It also proves that the cost ratio between the proposed solution and the centralized optimum will be bounded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\log (n)$ </tex-math></inline-formula> . The numerical analysis substantiates the theoretical analysis.

Fitness Sorted Red Deer-Cat Swarm Optimization-based Autonomous QoS-aware Multicast Communication System in MANET

A mobile ad hoc network (MANET) is a set of autonomous mobile devices connected by wireless links in a distributed manner and without a fixed infrastructure. Mobile Ad-Hoc Networks (MANETs) is considered as an infrastructure-less and constant self-configured network of wireless devices. Here, multicast has emerged as an efficient tool for communication. Recently, distinct research works have been studied in wireless MANETs for designing multicast routing protocols. Multicasting is a procedure used for allowing the users to send the same messages simultaneously to a set of users. Though, it suffers from different problems regarding implementation in ad-hoc networks owing to its shorter battery lifetime, and lack of bandwidth dynamic nature of the mobile devices. Hence, the main aim of this paper is to propose a new QoS-aware multi-cast routing protocol in MANET using the hybrid meta-heuristic algorithm. The proposed routing protocol will focus on the development of a hybrid meta-heuristic-based “reliable neighbor nodes selection” scheme over the Multicast Ad-Hoc on Demand Distance Vector routing protocol (MAODV) protocol. The “reliable neighbor nodes selection” is accomplished by Fitness sorted Red Deer-Cat Swarm Optimization (FRD-CSO) algorithm focusing on the constraints like energy model, mobility model, and reliability pair factor. Once the optimal node selection is performed, the proposed QoS-aware protocol is validated by the route discovery mechanism and route reply process. The simulation has been performed to show the efficiency of the designed multicast routing over other multicast routing protocols that demonstrates the noteworthy enhancement in terms of diverse performance measures.

On Efficient Delay-Aware Multisource Multicasting in NFV-Enabled Softwarized Networks

To comply with security and performance policies, multicast communication requires inline service that chains an ordered sequence of virtualized network functions (VNFs) with an emerging paradigm of network function virtualization (NFV). As many-to-many multicast pattern is widely used especially in current MEC, multimedia and big data industries, in this paper we focus on multi-source NFV-enabled multicasting in software-defined networks (SDN). By jointly investigating VNF placement and multicast tree routing strategy, it is envisioned to construct service function tree with minimum network resource expenditure while yielding lower delay. We explore this problem in static and online scenarios individually. In static situation with a bunch of multicast requests to be served, a column generation based computing model is first formulated, based on which an approximation algorithm MDNM that leverages logic of Viterbi and puts emphasis on resource optimization and segmental delay satisfaction is developed. In online situation with a single incoming request, another ILP model to enhance VNF instance reuse is established. Then, SVM algorithm giving importance to source determination and VNF instance consolidation is designed to efficiently construct a service function tree with minimum resource usage and delay. Simulation results show that the gap between each of the two proposed algorithms and their respective ILP models is marginal, and both MDNM and SVM outperform the state-of-art work.