Multicast Tree Research Articles

Hard‐state Protocol Independent Multicast—Source‐Specific Multicast (HPIM‐SSM)

AbstractSource‐specific multicast is a key technology for multicast services such as IPTV broadcasting, which relies on IGMPv3/MLDv2 for source‐group membership signalling and multicast routing protocols such as PIM‐SSM for building and maintaining receiver‐initiated source‐based distribution trees across the network. The authors propose the Hard‐state Protocol Independent Multicast—Source‐Specific Multicast (HPIM‐SSM), a novel multicast routing protocol that keeps the design principles of PIM‐SSM but overcomes its limitations, such as slow convergence and the possibility of creating suboptimal trees. The state machines of HPIM‐SSM were designed to react promptly to all network events susceptible to reconfiguring the multicast trees, avoiding the need for soft‐state maintenance through the periodic transmission of control messages. Moreover, the authors eliminated the need for designated routers, which led to suboptimal trees, and introduced a control‐driven assert protocol that operates per source, allowing for considerable memory savings. Finally, the protocol enables the coexistence of multiple unicast routing protocols. HPIM‐SSM was implemented in Python, and its correctness was extensively validated through model‐checking techniques. Furthermore, a comparison between HPIM‐SSM and PIM‐SSM was conducted, encompassing both theoretical analysis and experimental evaluation of convergence times. The results demonstrate clearly that HPIM‐SSM outperforms PIM‐SSM, exhibiting significantly faster convergence times and completely avoiding suboptimal trees.

Data sharing mode of dispatching automation system based on distributed machine learning

AbstractThe difficulties of sending massive amounts of data between several data centres are examined in this work, with particular attention paid to how poorly current scheduling algorithms handle point‐to‐multipoint transfers and transmission time limits. In this research, a new method called multicast source‐based tree (MSBT) is proposed for effectively handling point‐to‐multipoint transmissions in a certain amount of time. By allowing receivers to simultaneously receive data from several source points, MSBT introduces the idea of ‘source selection’ for the creation of multicast tree structure‐based algorithms. Large data blocks are distributed as efficiently as possible using this method, which also guarantees effective transmission from a single‐source point to several recipient locations. Furthermore covered in the article is how PV producers and sellers' capacity allocation is affected by the discount rate. These results offer insightful information on how decisions are made in related sectors. The development of new energy big data platforms underscores their significance; leaders in the industry, like United Power, Vision Energy and Goldwind, serve as prime examples.

Multi-core shared tree based MP2MP RWA algorithms in large scale and multi-domain optical networks

The increasing demands of bandwidth-intensive parallel computing and collaborative applications, efficient service provisioning to support multipoint to multipoint (MP2MP) communications have attracted increasing attention. However, with the development of larger-scale and multi-domain optical networks, MP2MP RWA are introduced optimal domain sequence selection and core nodes belong to which domains problems which cannot be tackled by the conventional algorithms proposed aim at a signal domain. In this paper, we proposed a multi-core node shared multicast tree heuristic algorithm (MCSMT) which calculation could be parallelized, delay and minimal cost constrained for multi-domain optical networks. It could realize the accurate calculation of minimum number of cores and the hosted domains of the cores. The source and destination nodes were added in different shared trees respectively with delay constrained algorithms according to specific QoS selection strategy. At the same time, according to more complicated MP2MP algorithms we propose a PCE-cloud based control architecture for optical networks by applying the cloud computing technology (e.g. virtualization and parallel computing) to reform the control plane for improving system reliability, intelligence and maximizing resource utilization. The performances of the proposed heuristic with regard to the number of multi-core nodes, wavelength channel occupied and MP2MP setup latency are compared. In addition, the performance about path computing latency for PCE-cloud control architecture is compared with conversional control architecture.

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

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.

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

This article studies time-sensitive multicast in wireless sensor networks (WSNs) with link uncertainty, where information from the 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 the 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 a 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.

DLSMR: Deep Learning-Based Secure Multicast Routing Protocol against Wormhole Attack in Flying Ad Hoc Networks with Cell-Free Massive Multiple-Input Multiple-Output.

The network area is extended from ground to air. In order to efficiently manage various kinds of nodes, new network paradigms are needed such as cell-free massive multiple-input multiple-output (CF-mMIMO). Additionally, security is also considered as one of the important quality-of-services (QoS) parameters in future networks. Thus, in this paper, we propose a novel deep learning-based secure multicast routing protocol (DLSMR) in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO). We consider the problem of wormhole attacks in the multicast routing process. To tackle this problem, we propose the DLSMR protocol, which utilizes a deep learning (DL) approach to predict the secure and unsecured route based on node ID, distance, destination sequence, hop count, and energy to avoid wormhole attacks. This work also addresses key concerns in FANETs such as security, scalability, and stability. The main contributions of this paper are as follows: (1) We propose a deep learning-based secure multicast routing protocol (DLSMR) to establish a high-stability multicast tree and improve security performance against wormhole attacks. In more detail, the DLSMR protocol predicts whether the route is secure based on network information such as node ID, distance, destination sequence, hop count, and remaining energy or not. (2) To improve the node connectivity and manage multicast members, we propose a top-down particle swarm optimization-based clustering (TD-PSO) protocol to maximize the cost function considering node degree, cosine similarity, cosine distance, and cluster head energy to guarantee convergence to the global optima. Thus, the TD-PSO protocol provides more strong connectivity. (3) Performance evaluations verify the proposed routing protocol establishes a secure route by avoiding wormhole attacks as well as by providing strong connectivity. The TD-PSO clustering supports connectivity to enhance network performance. In addition, we exploit the impact of the mobility model on the network metrics such as packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses.

Maximizing the Capacity of Edge Networks with Multicasting

Edge networks employ local computing and caching resources to process data, thus alleviating the bandwidth pressure on backbone networks and improve users’ quality of experience. System capacity is one of the key metrics to evaluate the performance of edge networks. However, maximizing system capacity in edge scenarios faces challenges due to the dynamical user sessions and the changing content popularity. This study reports on the influence of multicast communication on the capacity of edge caching networks. When large amounts of content are requested simultaneously or over a short period, one-to-one unicast transmission will consume copious network resources due to repetitious transmission. To solve this problem, this study used the one-to-many multicast scheme to realize cooperative transmission between edge servers. First, multiple copies of the content are distributed to multiple small base stations (SBSs) based on the content’s popularity and the SBSs’ cache sizes. Second, a multicast tree is constructed by using, as the root node, the SBS that stores the content. Third, the content is transmitted along the path of the multicast tree to each end-user. Finally, a simulation platform is constructed to analyze the performance of the two transmission schemes. The results of simulating on edge caching networks show that multicast communication responds well to users’ requests even when the requested content requires sudden transmission, is highly popular or is requested often within short time. This system’s capacity has been significantly improved compared to the classical methods.

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

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.

Distributed Multicast Traffic Engineering for Multi-Domain Software-Defined Networks

Previous research on SDN multicast traffic engineering mainly focused on intra-domain optimization. However, the main traffic on the Internet is inter-domain, and the selection of border nodes and sharing of network information between domains are usually distributed but ignored in previous works. In this article, we explore multi-domain online distributed multicast traffic engineering (MODMTE). To effectively solve MODMTE, we first prove that MODMTE is inapproximable within <inline-formula><tex-math notation="LaTeX">$|D_{\max }|$</tex-math></inline-formula> , which indicates that it is impossible to find any algorithm with a ratio better than <inline-formula><tex-math notation="LaTeX">$|D_{\max }|$</tex-math></inline-formula> for MODMTE, and <inline-formula><tex-math notation="LaTeX">$|D_{\max }|$</tex-math></inline-formula> is the maximum number of destinations for a multicast tree. Then, we design a <inline-formula><tex-math notation="LaTeX">$|D_{\max }|$</tex-math></inline-formula> -competitive distributed algorithm with the ideas of Domain Tree, Dual Candidate Forest Construction, and Forest Rerouting to achieve the tightest performance bound for MODMTE. Experiments on a real SDN with YouTube traffic manifest that the proposed distributed algorithm can reduce more than 30% of the total cost of bandwidth consumption and rule updates for multicast tree rerouting compared with the state-of-the-art algorithms.

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

AbstractThe 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.

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.

Tree-based Multicast Routing and Channel Assignment for Enhanced Throughout in Emerging Cognitive Radio Networks

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.

Generation of Low-Delay and High-Stability Multicast Tree

Generation of Low-Delay and High-Stability Multicast Tree

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.

Genetic algorithm enabled virtual multicast tree embedding in Software-Defined Networks

The recent network virtualization technology enables the multi-tenancy, where various virtual network requests can share the same physical network by decoupling network services from the underlying hardware architecture. The process of virtual node and link mapping onto a shared Substrate Network (SN) by satisfying the requested network resources (i.e., bandwidth, computing capacity, etc.) is referred to as Virtual Network Embedding (VNE), which is known as an NP-Hard problem. The problem of VNE aims to exhibit one-to-one (unicast) communication. However, the motivation of this research is to explore how to efficiently map virtual networks with one-to-many (multicast) communications, which are in the form of Virtual Multicast Trees (VMTs), onto an SN. This problem differs from the traditional VNE problem and has not been well-studied by the research community. To this end, we propose a novel algorithm, Modified Genetic Algorithm for Virtual Multicast Tree Embedding (MGA-VMTE), to embed VMTs onto a shared SN in this research. The proposed MGA-VMTE algorithm focuses on minimizing the network resource consumption (i.e., bandwidth) under end-to-end delay constraint in the SN while satisfying the computing request of virtual nodes. Our extensive simulations demonstrate that the MGA-VMTE algorithm outperforms the dynamic impact factor and traditional greedy-based virtual multicast tree embedding approaches regarding bandwidth consumption, acceptance ratio, and resource depletion ratio metrics on NSFNET, USNET, Random-60 node, and Random-120 node network topologies.

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.

Topology Inference With Multivariate Cumulants: The Möbius Inference Algorithm

Many tasks regarding the monitoring, management, and design of communication networks rely on knowledge of the routing topology. However, the standard approach to topology mapping—namely, active probing with traceroutes—relies on cooperation from increasingly non-cooperative routers, leading to missing information. Network tomography, which uses end-to-end measurements of additive link metrics (like delays or log packet loss rates) across monitor paths, is a possible remedy. Network tomography does not require that routers cooperate with traceroute probes, and it has already been used to infer the structure of multicast trees. This paper goes a step further. We provide a tomographic method to infer the underlying routing topology of an arbitrary set of monitor paths using the joint distribution of end-to-end measurements, without making any assumptions on routing behavior. Our approach, called the Möbius Inference Algorithm (MIA), uses cumulants of this distribution to quantify high-order interactions among monitor paths, and it applies Möbius inversion to “disentangle” these interactions. In addition to MIA, we provide a more practical variant called Sparse Möbius Inference, which uses various sparsity heuristics to reduce the number and order of cumulants required to be estimated. We show the viability of our approach using synthetic case studies based on real-world ISP topologies.

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.

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.

Design and implementation of event-based multicast AODV routing protocol for ubiquitous network

We designed and implemented the ECA (Event Condition Action) based Multicast Ad hoc On-demand Distance Vector (ECA-MAODV) routing protocol for the ubiquitous network. The ECA-MADOV protocol plays a predominant role in selecting an efficient route for the creation of an efficient multicast tree and selecting a unique group leader in the multicast group to share and exchange information among group members in an efficient way using a Computational Intelligence (CI) approach. The vague set is a CI that is an enhanced fragment of a fuzzy set. Each element in the Fuzzy set is mapped to [0,1], reflecting its membership grade. We estimated ECA-MAODV’s time and spatial complexity. Besides, we assessed ECA-MAODV performance and compared it with conventional routing protocols such as MAODV and AODV. Finally, simulation results indicate that the ECA scheme proposed is both effective and powerful.