Multi-constrained Multicast Routing Research Articles

Comparative Study of Routing Protocols for IoT Networks

Abstract: The Internet of things (IoT) is widely used for communication between portable and intelligent heterogeneous devices, like laptops, smartphones, computers, etc. IoT networks are popular in the modern era because they allow data to be exchanged anywhere when connected to the internet. IoT networks have several challenges, including those related to routing, connectivity, privacy, security, and other issues. The major challenge is routing in terms of choosing the best route for sharing data in IoT networks; IoT routing algorithms use more time and energy. In this paper, we review relevent literature and patents and also various routing approaches are categorized into groups, like multicast, clustering, emergency application, traffic, location, tree, and residual energy based approach, and they are compared based on several parameters, like energy consumption, network lifetime, path length, packet delivery ratio, and network latency. In terms of performance metrics, like energy consumption, network lifetime, reliability, efficiency, and packet delivery ratio, the comparison shows that Routing Protocol for Low Power and Lossy Networks (RPL), Efficient Tree-based Self-organizing Protocol (ETSP), Collection Tree Protocol (CTP), and Fast Multi-constrained Multicast Routing Algorithms (FAMOUS) are the best protocols. The best approach is a tree-based one since it solves the larger problem in the hierarchy with the least amount of time complexity.

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.

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 memetic optimization algorithm for multi-constrained multicast routing in ad hoc networks.

A mobile ad hoc network is a conventional self-configuring network where the routing optimization problem—subject to various Quality-of-Service (QoS) constraints—represents a major challenge. Unlike previously proposed solutions, in this paper, we propose a memetic algorithm (MA) employing an adaptive mutation parameter, to solve the multicast routing problem with higher search ability and computational efficiency. The proposed algorithm utilizes an updated scheme, based on statistical analysis, to estimate the best values for all MA parameters and enhance MA performance. The numerical results show that the proposed MA improved the delay and jitter of the network, while reducing computational complexity as compared to existing algorithms.

Constructing multicast routing tree for inter-cloud data transmission: an approximation algorithmic perspective

Networking plays a crucial role in cloud computing especially in an inter-cloud environment, where data communications among data centers located at different geographical sites form the foundation of inter-cloud federation. Data transmissions required for inter-cloud federation in the complex inter-cloud networking system are often point-to-multi points, which calls for a more effective and efficient multicast routing algorithm in complex networking systems. In this paper, we investigate the multicast routing problem in the inter-cloud context with K constraints where K U+2265 2. Unlike most of existing algorithms that are too complex to be applied in practical scenarios, a novel and fast algorithm for establishing multicast routing tree for interclouds is proposed. The proposed algorithm leverages an entropybased process to aggregate all weights into a comprehensive metric, and then uses it to search a multicast tree U+0028 MT U+0029 on the basis of the shortest path tree U+0028 SPT U+0029.We conduct complexity analysis and extensive simulations for the proposed algorithm from the approximation perspective. Both analytical and experimental results demonstrate that the algorithm is more efficient than a representative multi-constrained multicast routing algorithm in terms of both speed and accuracy, and thus we believe that the proposed algorithm is applicable to the inter-cloud environment.

Multicast Routing for Multimedia Communications in the Internet of Things

Multicast routing that meets multiple quality of service constraints is important for supporting multimedia communications in the Internet of Things (IoT). Existing multicast routing technologies for IoT mainly focus on ad hoc sensor networking scenarios; thus, are not responsive and robust enough for supporting multimedia applications in an IoT environment. In order to tackle the challenging problem of multicast routing for multimedia communications in IoT, in this paper, we propose two algorithms for the establishing multicast routing tree for multimedia data transmissions. The proposed algorithms leverage an entropy-based process to aggregate all weights into a comprehensive metric, and then uses it to search a multicast tree on the basis of the spanning tree and shortest path tree algorithms. We conduct theoretical analysis and extensive simulations for evaluating the proposed algorithms. Both analytical and experimental results demonstrate that one of the proposed algorithms is more efficient than a representative multiconstrained multicast routing algorithm in terms of both speed and accuracy; thus, is able to support multimedia communications in an IoT environment. We believe that our results are able to provide in-depth insight into the multicast routing algorithm design for multimedia communications in IoT.

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%.

Exact Algorithm to Solve the Minimum Cost Multi-Constrained Multicast Routing Problem

The optimal solution of the multi-constrained QoS multicast routing problem is a tree-like hierarchical structure in the topology graph. This multicast route contains a feasible path from the source node to each of the destinations with respect to a set of QoS constraints while minimizing a cost function. Often, it is a tree. In other cases, the hierarchies can return several times to nodes and links of the topology graph. Similarly to Steiner problem, finding such a structure is an NP-hard problem. The usual tree and topology enumeration algorithms applied for the Steiner problem cannot be used to solve the addressed problem. In this paper, we propose an exact algorithm based on the Branch and Bound principle and improved by the Lookahead technique. We show relevant properties of the optimum hierarchy permitting efficient pruning of the search space. To our knowledge, our paper is the first to propose an exact algorithm for this non-trivial multi-constrained optimal multicast route computation. Simulations illustrate the efficiency of the proposed pruning operations. The analysis of the execution time shows that in simple topologies and with tight QoS constraints the exact algorithm requires relatively little execution time. With loose constraints the computation time cannot be tolerated even for off-line route computation. In these cases, the solution is close to a Steiner tree and heuristics can be applied. These results can serve as basis for the design of efficient, polynomial-time routing algorithms.

Rendezvous point based approach to the multi-constrained multicast routing problem

The Quality of Service (QoS) routing requires a special approach to graph algorithms modeling. One of the mathematical concepts that reflects this class of problems is the multi-constrained minimum Steiner tree problem (MCMST). In this article, the RDP (named after the concept of the RenDezvouz Point), a novel algorithm for solving the MCMST problem is presented.

The cost optimal solution of the multi-constrained multicast routing problem

In this paper, we define the cost optimal solution of the multi-constrained multicast routing problem. This problem consists in finding a multicast structure that spans a source node and a set of destinations with respect to a set of constraints, while minimizing a cost function. This optimization is particularly interesting for multicast network communications that require Quality of Service (QoS) guarantees. Finding such a structure that satisfies the set of constraints is an NP-hard problem. To solve the addressed routing problem, most of the proposed algorithms focus on multicast trees. In some cases, the optimal spanning structure (i.e. the optimal multicast route) is neither a tree nor a set of trees nor a set of optimal QoS paths. The main result of our study is the exact identification of this optimal solution. We demonstrate that the optimal connected partial spanning structure that solves the multi-constrained multicast routing problem always corresponds to a hierarchy, a recently proposed generalization of the tree concept. We define the directed partial minimum spanning hierarchies as optimal solutions for the multi-constrained multicast routing problem and analyze their relevant properties. To our knowledge, our paper is the first study that exactly describes the cost optimal solution of this NP-hard problem.

Tree-based differential evolution algorithm for QoS multicast routing

Differential evolution (DE) algorithm has attracted more and more attention due to its fast optimization performance and good stability. When DE algorithm is applied into multi-constrained multicast routing optimization problem, a common solution to such problem is to merge the paths into a tree after finding paths from the source node to each destination node. This method maybe obtains the better result, but it can consume a lot of computational time. To solve the problem, a tree-based DE algorithm is introduced in this paper. The central operations of the algorithm are realized with tree structure. This method saves the time of finding paths and integrating them to construct a multicast tree. The experiments show that the proposed algorithm can achieve higher success rate than several common algorithms with much smaller running time for different networks.

QoS multicast aggregation under multiple additive constraints

IP Multicast has been proposed in order to manage group communications over the Internet in a bandwidth efficient manner. Although such a proposition has been well studied, there are still some inherent problems for its widespread deployment. In this paper, we propose a new algorithm coined mQMA that deals with the two main problems of traditional IP multicast, i.e., multicast forwarding state scalability and multi-constrained QoS routing. The algorithm mQMA is a QoS multicast aggregation algorithm which handles multiple additive QoS constraints. It builds few trees and maintains few forwarding states for the groups thanks to the technique of multicast tree aggregation, which allows several groups to share the same delivery tree. Moreover, the algorithm mQMA builds trees satisfying multiple additive QoS constraints. We show via extensive simulations that mQMA reduces dramatically the number of trees to be maintained and reduces the utilization of the network resources, yet it leverages the same overall QoS performances as Mamcra which is the main known multi-constrained multicast routing algorithm.

A multi-constrained multicast QoS routing algorithm

Opposed to the situation that the constrained minimum Steiner tree (CMST) problem has been attracting much attention in quality of service (QoS) routing area, little work has been done on multicast routing subject to multiple additive constraints even though this is required by many emerging applications. In this paper, we propose a heuristic HMCMC to find feasible solutions to this problem. HMCMC has a provably low time complexity, and its basic idea is to construct a multicast tree step-by-step, which is done essentially based on the latest research results on multi-constrained unicast routing. Computer simulations demonstrate that HMCMC can achieve a high success ratio of finding feasible solutions.

Two algorithms for multi‐constrained optimal multicast routing

AbstractMultimedia applications, such as video‐conferencing and video‐on‐demand, often require quality of service (QoS) guarantees from the network, typically in the form of minimum bandwidth, maximum delay, jitter and packet loss constraints, among others. The problem of multicast routing subject to various forms of QoS constraints has been studied extensively. However, most previous efforts have focused on special situations where a single or a pair of constraints is considered. In general, routing under multiple constraints, even in the unicast case is an NP‐complete problem. We present in this paper two practical and efficient algorithms, called multi‐constrained QoS dependent multicast routing (M_QDMR) and (multicasting routing with multi‐constrained optimal path selection (M_MCOP)), for QoS‐based multicast routing under multiple constraints with cost optimization. We provide proof in the paper that our algorithms are correct. Furthermore, through extensive simulations, we illustrate the effectiveness and efficiency of our proposals and demonstrate their significant performance improvement in creating multicast trees with lower cost and higher success probability. Copyright © 2003 John Wiley & Sons, Ltd.