Showing papers on "Distance Vector Multicast Routing Protocol published in 2023"

HPIM‐DM: A fast and reliable dense‐mode multicast routing protocol

Abstract: The Hard-state Protocol Independent Multicast—Dense Mode (HPIM-DM) multicast routing protocol is proposed. HPIM-DM is a hard-state version of PIM-DM that overcomes its poor convergence times and lack of resilience to replay attacks. Like PIM-DM, HPIM-DM is meant for dense networks and supports its operation on a unicast routing protocol and reverse path forwarding. However, routers maintain sense of the multicast trees at all times, allowing fast reconfiguration in the presence of network failures or unicast route changes. This is achieved by (i) keeping information on all upstream neighbours from which multicast data can be received, (ii) ensuring the reliable transmission and sequencing of control messages, and (iii) synchronizing the routing information immediately when a new router joins the network. The correctness of the protocol was extensively validated using model checking and logical reasoning. The protocol was fully implemented in Python, and the implementation is publicly available. Finally, we show both theoretically and experimentally that HPIM-DM has much better convergence times than PIM-DM.

DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN

Abstract: The optimal multicast tree problem in the Software-Defined Networking (SDN) multicast routing is an NP-hard combinatorial optimization problem. Although existing SDN intelligent solution methods, which are based on deep reinforcement learning, can dynamically adapt to complex network link state changes, these methods are plagued by problems such as redundant branches, large action space, and slow agent convergence. In this paper, an SDN intelligent multicast routing algorithm based on deep hierarchical reinforcement learning is proposed to circumvent the aforementioned problems. First, the multicast tree construction problem is decomposed into two sub-problems: the fork node selection problem and the construction of the optimal path from the fork node to the destination node. Second, based on the information characteristics of SDN global network perception, the multicast tree state matrix, link bandwidth matrix, link delay matrix, link packet loss rate matrix, and sub-goal matrix are designed as the state space of intrinsic and meta controllers. Then, in order to mitigate the excessive action space, our approach constructs different action spaces at the upper and lower levels. The meta-controller generates an action space using network nodes to select the fork node, and the intrinsic controller uses the adjacent edges of the current node as its action space, thus implementing four different action selection strategies in the construction of the multicast tree. To facilitate the intelligent agent in constructing the optimal multicast tree with greater speed, we developed alternative reward strategies that distinguish between single-step node actions and multi-step actions towards multiple destination nodes.

WALS: A Lightweight Stateless Label Switching Framework Based on Prime Theorem for MANETs

Abstract: This article proposes an innovative unicast/multicast unified, cross-layer, and control/user-plane splitting forwarding method, called Wireless Arithmetic Label Switching (WALS), for mobile ad-hoc networks (MANETs). The WALS is based on a stateless approach: an arithmetic source-routed label is processed by the source and intermediate nodes without table lookup, causing lower protocol overheads and forwarding latency than the conventional stateless unicast/multicast routing protocols such as the Dynamic Source Routing (DSR) and the Differential Destination Multicast (DDM) respectively. Different from conventional unicasting/multicasting designs demanding distinct routing protocol stacks, the WALS requires a unified protocol stack, avoiding the need to distinguish unicast and multicast frames, thus simplifying the design of MANET devices. Mathematical analysis indicates that the WALS is highly scalable for both the network sizes and the number of multicast groups. Simulation results reveal that WALS effectively performs network scalability and the required overhead for the unicast and multicast capabilities. Additionally, WALS achieves superior to the other three state-of-the-art competitive multicast routing methods including DVMRP, ODMRP, and MAODV on the performance of the packet processing latency and packet delivery ratio. As a result, the WALS can significantly enable the employment and management of integrated unicast and multicast services for MANETs.

SGMRP-Utilizing a Mobile Ad Hoc Network to Develop a Scalable Geographic Multicast Routing Protocol for Localization

Abstract: Group’s communications using Mobile Ad hoc Networks (MANETs) have recently drawn a lot of interest. Information is delivered to a group of receivers simultaneously through a process known as multipath. Therefore, to enable team communication activities, an effective and efficient multipath routing protocol is essential. Enhancing multicast routing has been the subject of numerous initiatives. However, they don't account for scalability. A scalable geographic multicast routing protocol was proposed in this research (SGMRP). This protocol's primary goal is to create a compact, adaptable multipath routing strategy that works regardless of the number of broadcast participants or the magnitude of the network. Using a prediction model and cross-validation techniques, the model performance evaluation tool assesses model effectiveness. The satisfaction with the service was also evaluated using a question. In addition to reducing the number of participating nodes in localization, the proposed method also gets rid of duplicate packets between clusters. The article introduces the framework's fundamental idea, details its transparent implementations, and finishes with a preliminary assessment of the framework based on typical mobile software kinds.

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

Abstract: 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 dynamic network changes 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.

A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method

Abstract: Network layer multicast is a powerful method for transmitting data from sources to multiple group members. When joining a multicast group, a group member first sends a request to a designated router (DR). Then, the DR selects a node in the existing multicast tree (known as a multicast joining node, or MJN) to establish a multicast distribution path from the MJN to itself. The MJN selection method runs on the DR and has a significant impact on the distribution of the multicast tree, that directly affects the load distribution in the network. However, the current MJN selection method cannot effectively detect the load status of the downlink multicast path in the case of asymmetric routing, leading to network congestion and limiting the number of multicast groups that the network can accommodate (multicast capacity). To solve this problem, we propose an MJN selection method based on the reverse shortest path tree (RSPT). RSPT can effectively detect the load status of downlink multicast paths in case of routing asymmetry. Based on the detection results of RSPT, DR can select the MJN with the lowest path load to join the multicast tree. Our experimental results indicate that compared to existing multicast methods, our method has a lower cost and delay, and can effectively balance the network load in the case of asymmetric routing, increasing multicast capacity by more than two times.

An Analysis of Multicasting Optimisation Mechanisms for Intelligent Edge Computing with Low-Power and Lossy Networks

Abstract: This work studies the built-in multicast model in Contiki OS to provide the basis of a comparative evaluation for a new optimisation model using Radio Duty Cycling (RDC) mechanism. A significant amount of energy is consumed at the edge node executing various multicast routing protocols in Low-Power and Lossy Networks (LLN). The optimisation of the routing protocol and selection of an efficient multicast transmission model has the potential to reduce energy consumption in Edge Computing (EC) enabled LLN. With the precise objective of reducing energy consumption, this paper utilises a well-known RDC technique in multicast communication scenarios. To this end, a series of experiments are conducted to evaluate the performance of the existing RDC mechanisms proposed in the literature. The evaluation results are then utilised to develop an efficient RDC-based multicast transmission model. The comparative performance analysis reveals a 23.7% reduction in the RDC rate compared to the traditional model, consequently improving the energy consumption of EC-enabled LLN.

The Effect of Symmetry on the De-anonymizability of Social Networks

Abstract: This paper studies time-sensitive multicast in wireless sensor networks (WSNs) with link uncertainty, where information from source needs to be delivered to multiple receivers within an imposed delay constraint. Prior art on static WSNs minimizes the multicast delay via the construction of a multicast tree that approximates Steiner tree in length, which, however, may be invalidated by the time-varying network topology of WSNs with uncertain link states. Moreover, for multicast in WSNs with link uncertainty, the possible link failure necessitates a suitable measurement of the uncertain communication distance and calls for the performance guarantee in both delay and delivery ratio. In this work, by modeling a WSN as a random graph with each link associated with a transmission probability, we propose FlowerCast, an efficient multicast scheme, to jointly minimize the expected multicast delay and to maximize the expected delivery ratio of multicast under delay constraint. The core of FlowerCast is to quantify the uncertain communication distance by the expected transmission delay of a time-varying path, based on which a delay-optimal multicast tree is constructed in accordance with the directionality of delay. Candidate paths with high expected delivery ratio and low expected delay are then selected in a distributed manner to conditionally connect adjacent multicast members and thus transform the multicast tree into a multicast flower. Despite the NP-hardness of optimal candidate paths addition, the transformation with the highest expected delivery ratio of multicast under delay constraint can be guaranteed through a pseudo-polynomial time derandomization-based greedy approach. We further demonstrate the time and energy efficiency of FlowerCast through asymptotic analysis. To make full use of the possible overlapping links in a multicast flower, a hybrid routing strategy is presented to wisely switch between sequential routing and synchronous routing for extra enhancement of the multicast performance. Extensive experiments on various datasets verify the superiority of FlowerCast and hybrid routing over baselines and indicate their wide applicability to practical scenarios.

Performance evaluation and comparative analysis of CrowWhale-energy and trust aware multicast routing algorithm

Abstract: Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, CrowWhale-energy and trust-aware multicast routing (CrowWhale-ETR) have been devised. In this research, the routing performance of CrowWhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the Spider Monkey Optimization algorithm (SMO), Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO) algorithm, Water Wave Optimization (WWO) algorithm, Crow Search Algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing Algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of CrowWhale-ETR. In addition, the performance of CrowWhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness.

Application of fish swarm algorithm in space-based dynamic multicast routing

Abstract: By applying the fish swarm algorithm to the generation of the multicast tree under the dynamic satellite network, a method for generating the multicast tree with good adaptability and inheritance was found, and the generation efficiency of the multicast tree was improved.

Multicast Service Function Chain Orchestration in SDN/NFV-Enabled Networks: Embedding, Readjustment, and Expanding

Abstract: Multicast is an effective transmission mode to support ever-growing multimedia applications. The introduction of software defined networking (SDN) and network function virtualization (NFV) makes the multicast service operation more flexible and efficient. Nevertheless, one main challenge of SDN/NFV-enabled multicast is optimally orchestrating the service function chain (SFC) to match service and network resources. Compared with unicast, multicast SFC orchestration (MSO) is more challenging due to the features of multicast service like multicast routing and user fluidity (i.e., frequent user arrival and departure). There are still some gaps in the joint optimization of MSO and multicast routing, and very little attention is paid to user fluidity. In this paper, we study the MSO in SDN/NFV-enabled networks encompassing multicast SFC embedding (MSE), multicast SFC readjustment (MSR), and multicast SFC expanding (MSEP), three types of MSO. For each kind of MSO, we simultaneously consider several key optimization factors when jointly optimizing MSO and multicast routing. Besides, aside from MSE, we investigate two new types of MSO: MSR and MSEP, for efficient orchestration under the fluidity of users. Specifically, we define and formulate MSE, MSR, and MSEP problems and develop three novel algorithms to respectively solve them. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

Tree-based multicast routing and channel assignment for enhanced throughout in emerging cognitive radio networks

Abstract: In future multi-hop wireless networks like 5G and B5G, efficient large-scale video sharing and data dissemination are expected to rely heavily on multicast routing and Cognitive Radio (CR) technology. While multicast routing is efficient when the network always has access to the spectrum, the dynamic nature of Primary User (PU) activities, heterogeneous spectrum across the CR Network (CRN), and PU access priority make it challenging to implement efficient multicast routing protocols in CRNs. This paper proposes a hierarchical multicast routing mechanism for multi-hop CRNs that exploits the Shortest Path Tree (SPT) and Minimum Spanning Tree (MST) concepts. The proposed multicast routing mechanism consists of tree construction and channel assignment algorithms. The tree-construction algorithm models the network topology as a multicast tree rooted at the CR source and spanning all the CR nodes. Based on the constructed tree, the channel assignment algorithm employs the Probability Of Success (POS) metric to assign channels to the various layers defined by the constructed SPT or MST, ensuring that the most reliable channel is used for the multi-hop multicast transmissions. Simulation experiments are conducted to evaluate the mechanism’s effectiveness, revealing significant improvements in throughput and Packet Delivery Rate (PDR) compared to state-of-the-art protocols under different network conditions. The simulations also show that the SPT-based mechanism outperforms the MST-based mechanism in terms of throughput but has a higher tree construction complexity.

FlowerCast: Efficient Time-Sensitive Multicast in Wireless Sensor Networks with Link Uncertainty

Abstract: This paper studies time-sensitive multicast in wireless sensor networks (WSNs) with link uncertainty, where information from source needs to be delivered to multiple receivers within an imposed delay constraint. Prior art on static WSNs minimizes the multicast delay via the construction of a multicast tree that approximates Steiner tree in length, which, however, may be invalidated by the time-varying network topology of WSNs with uncertain link states. Moreover, for multicast in WSNs with link uncertainty, the possible link failure necessitates a suitable measurement of the uncertain communication distance and calls for the performance guarantee in both delay and delivery ratio. In this work, by modeling a WSN as a random graph with each link associated with a transmission probability, we propose FlowerCast, an efficient multicast scheme, to jointly minimize the expected multicast delay and to maximize the expected delivery ratio of multicast under delay constraint. The core of FlowerCast is to quantify the uncertain communication distance by the expected transmission delay of a time-varying path, based on which a delay-optimal multicast tree is constructed in accordance with the directionality of delay. Candidate paths with high expected delivery ratio and low expected delay are then selected in a distributed manner to conditionally connect adjacent multicast members and thus transform the multicast tree into a multicast flower. Despite the NP-hardness of optimal candidate paths addition, the transformation with the highest expected delivery ratio of multicast under delay constraint can be guaranteed through a pseudo-polynomial time derandomization-based greedy approach. We further demonstrate the time and energy efficiency of FlowerCast through asymptotic analysis. To make full use of the possible overlapping links in a multicast flower, a hybrid routing strategy is presented to wisely switch between sequential routing and synchronous routing for extra enhancement of the multicast performance. Extensive experiments on various datasets verify the superiority of FlowerCast and hybrid routing over baselines and indicate their wide applicability to practical scenarios.