Routing In Wireless Mesh Networks Research Articles

A hybrid framework for routing and channel assignment in WMN’s

Wireless mesh network (WMNs) can replace the physical existence of wired network; nevertheless, to solve the problem of traffic and congestion, they must carefully arrange radio resource assignment. In this paper, we focus on the major issues while transferring the data over the network and optimization of resources. WMN mainly suffers from congestion problem when numbers of channel transfer data at a same time which indirectly leads to overlapping channels and sometime data cannot be mitigated to destination. To overcome this problem and provide optimal solution we proposed hybrid channel assignment technique which offers four phase solution technique, in this technique firstly by mitigating the traffic than assigning the optimal path based on greedy and BFS. The purpose of this congestion control hybrid ant based greedy-BFS-load balancing (CCHAGBL) technique is to diminish the number of used slots, which is directly related to the overall resource assignment. We also provide optimal solution using Ant colony optimization technique with hybrid algorithm to achieve the network efficiency and resource utilization with randomly generated traffic. With the help of hybrid ant based greedy-BFS-load balancing (GBL) channel assignment algorithm throughput and packet delivery ratio is upgraded and delay is reduced to minimum.

Energy Efficient Routing in Wireless Mesh Networks using Multi-Objective Dwarf Mongoose Optimization Algorithm

Wireless Mesh Networks (WMNs) are part of wireless technologies that are known for their flexibility and extended coverage. Wireless applications have reached their peak in applications related to various fields such as healthcare, image processing, and so on. However, delay and energy efficiency are considered the two aspects that diminish the performance of WMNs. To overcome the aforementioned issues, this research introduces an effective routing method using Multi-Objective Dwarf Mongoose Optimization Algorithm (MO-DMOA). The MO-DMOA performs routing by considering the multiple paths using an enriched population resource. The nomadic behaviour of MO-DMOA helps in detecting the optimal routing path with minimized over-exploitation. The proposed MO-DMOA is evaluated with different routing schemes such as Load Balance and Interference Avoid-Partially Overlapped Channels Assignment (LBIA-POCA) framework, and Multi-Objective Dyna Q-based Routing (MODQR). The outcomes obtained through the experimental analysis show that the proposed approach acquires a better throughput of 13.5×105kbps for 22 flows, whereas the existing LBIA-POCA achieves a throughput 60× 103 kbps

Energy Efficient Routing in Wireless Mesh Networks using Multi-Objective Dwarf Mongoose Optimization Algorithm

Wireless Mesh Networks (WMNs) are part of wireless technologies that are known for their flexibility and extended coverage. Wireless applications have reached their peak in applications related to various fields such as healthcare, image processing, and so on. However, delay and energy efficiency are considered the two aspects that diminish the performance of WMNs. To overcome the aforementioned issues, this research introduces an effective routing method using Multi-Objective Dwarf Mongoose Optimization Algorithm (MO-DMOA). The MO-DMOA performs routing by considering the multiple paths using an enriched population resource. The nomadic behaviour of MO-DMOA helps in detecting the optimal routing path with minimized over-exploitation. The proposed MO-DMOA is evaluated with different routing schemes such as Load Balance and Interference Avoid-Partially Overlapped Channels Assignment (LBIA-POCA) framework, and Multi-Objective Dyna Q-based Routing (MODQR). The outcomes obtained through the experimental analysis show that the proposed approach acquires a better throughput of 13.5×105kbps for 22 flows, whereas the existing LBIA-POCA achieves a throughput 60× 103 kbps

Fuzzy-based optimization of AODV routing for efficient route in wireless mesh networks

The performance of any communication system heavily relies on the efficient routing of interventions. This article addresses the significant issue of routing protocol selection for optimal path determination in networks. Particularly, when wireless communication occurs among mobile nodes with limited resources, such as batteries, the routing problem becomes even more challenging. This article proposes the Fuzzy Control Energy Efficient (FCEE) routing protocol to overcome these challenges. The FCEE protocol combines the Ad-Hoc On-Demand Distance Vector (AODV) protocol with fuzzy logic techniques to enhance network lifetime and performance. The proposed approach introduces a memory-based channel integrated with fuzzy logic methodologies, which effectively restricts the forwarding of unnecessary broadcast packets based on the energy availability of the operating node. Through extensive simulations, demonstrate the promising capabilities of FCEE as a routing protocol for wireless mesh networks. To further assess the effectiveness of the FCEE protocol, the proposed article compares it with two existing routing protocols: AODV and Intelligent Routing AODV (IRAODV). The simulation results shows that the FCEE routing protocol significantly enhances the reliability of the conventional AODV, providing improved link connectivity and longer route lifetimes. Additionally, our proposed protocol enhances the quality of service (QoS) for mesh routing, with an average throughput of 351.374 (Kbps) compared to 90 (Kbps) for IRAODV and 39 (Kbps) for AODV. Moreover, FCEE exhibits superior energy efficiency with an average energy consumption of 14, while IRAODV and AODV consume 40 and 90 joules, respectively. In conclusion, the FCEE routing protocol demonstrates its potential to address the challenges of efficient routing in wireless mesh networks. By leveraging fuzzy logic and integrating it with AODV, FCEE enhances network lifetime, performance, and energy efficiency, making it a promising solution for future wireless communication systems.

PDSCM: Packet Delivery Assured Secure Channel Selection for Multicast Routing in Wireless Mesh Networks

PDSCM: Packet Delivery Assured Secure Channel Selection for Multicast Routing in Wireless Mesh Networks

A discounted‐UCB1‐tuned plus Q‐learning based anti‐jamming routing algorithm for wireless mesh networks

AbstractTo achieve autonomous anti‐jamming routing for wireless mesh networks (WMNs) under the threat of unknown removable jammers, a Q‐learning anti‐jamming routing algorithm using the improved upper‐confidence‐bound (UCB) algorithm is proposed in this paper. Specifically, the WMNs anti‐jamming routing path finding problem is first modelled as a Markov decision process. Then, to address the slow‐convergence anti‐jamming strategy induced by the drawback that the preset parameters uniquely determine the length of the traditional Q‐learning exploration process, a discounted‐UCB‐tuned algorithm (Dis‐UCB1‐tuned) is adopted as the decision algorithm to replace the ε‐greedy or softmax algorithm in traditional Q‐learning. Finally, the simulation results demonstrate the superiority of the proposed anti‐jamming scheme over the UCB‐based Q learning(UCBQ) algorithm, Dyna‐Q algorithm, and the traditional Q‐learning algorithm. To elaborate, the proposed scheme can fast converge to a superior anti‐jamming routing without any prior knowledge of jamming environment, and its performance is also independent with the preset parameter.

A Comparative Study of Single-Constraint Routing in Wireless Mesh Networks Using Different Dynamic Programming Algorithms

Finding the shortest route in wireless mesh networks is an important aspect. Many techniques are used to solve this problem like dynamic programming, evolutionary algorithms, weighted-sum techniques, and others. In this paper, we use dynamic programming techniques to find the shortest path in wireless mesh networks due to their generality, reduction of complexity and facilitation of numerical computation, simplicity in incorporating constraints, and their onformity to the stochastic nature of some problems. The routing problem is a multi-objective optimization problem with some constraints such as path capacity and end-to-end delay. Single-constraint routing problems and solutions using Dijkstra, Bellman-Ford, and Floyd-Warshall algorithms are proposed in this work with a discussion on the difference between them. These algorithms find the shortest route through finding the optimal rate between two nodes in the wireless networks but with bounded end-to-end delay. The Dijkstra-based algorithm is especially favorable in terms of processing time. We also present a comparison between our proposed single-constraint Dijkstra-based routing algorithm and the mesh routing algorithm (MRA) existing in the literature to clarify the merits of the former.

Energy-Efficient Multi-Rate Opportunistic Routing in Wireless Mesh Networks

Energy-Efficient Multi-Rate Opportunistic Routing in Wireless Mesh Networks

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

A Guaranteed Approximation Algorithm for QoS Anypath Routing in WMNs

Anypath routing is a hot research topic for QoS guarantee in wireless mesh networks (WMNs). According to time-varying characteristics of WMNs and the idea of anypath routing, a system network modeling method is proposed to address the multiple constrained optimization anypath problem. It focuses on the application of WMNs; under various QoS constraints, it satisfies a specific constraint and approaches other QoS constraints from an approximate perspective. A heuristic multi-constrained anypath algorithm with a time complexity as Dijkstra is proposed for the problem, and the algorithm is proved to be a K-1 approximation algorithm. The feasibility of the algorithm is verified, then its computational efficiency and performance are evaluated through simulation experiments, respectively. According to the application characteristics of wireless networks, the algorithm is suitable for WMNs and has good compatibility with existing routing protocols.

Neighbor Coverage and Bandwidth Aware Multiple Disjoint Path Discovery in Wireless Mesh Networks

With the ease of appending new nodes without re-installing the whole network, the Internet of Things (IoT) builds several smart applications on Wireless Mesh Network (WMN). One of the important aspects of integrating WMN and smart IoT applications is to provide an energy-efficient and reliable routing protocol. Seeking the communication route that delivers the high-quality stream quickly over WMN is an important issue, but the maximum utilization of a single high-quality path leads to poor throughput and large communication delay, including route discovery and data forwarding delay. The broadcasting mechanism creates redundant transmissions of control packets into the network and reinitializes the blind route discovery process due to link disconnections leading to network resource constraints and high delay during the route discovery process. Moreover, the congestion in the communication route incurs data transmission latency. This paper proposes the Multiple Disjoint Path Determination (MDPD) mechanism based on-demand routing in WMN to formulate the path discovery and data transmission latency. Reducing the neighbor list into the uncommon neighbor set reduces the unnecessary latency in route discovery, and deriving high capacity multiple disjoint communication routes reduce the communication delay in the proposed work. The proposed work employs the queue dynamics in queuing delay, which mainly provides adaptability to the dynamics in network capacity and efficient diversity paths to the gateway node to infer the available bandwidth and optimize the network traffic. To fully utilize the advantage of heterogeneous routers, it disables the flooding of control packets across the stable mesh routers, excluding the initial route discovery process, because it enables the available route storage system in each mesh router. Hence, the proposed work efficiently supports wireless broadband internet access with reduced delay and control overhead. The simulation results demonstrate the fast detection of the multiple disjoint routes and data traffic optimization over the discovered disjoint routes in the proposed MDPD mechanism over WMN.

Congestion Managed Multicast Routing in Wireless Mesh Network

To provide broad band connectivity to the mobile users and to build a self-structured network, where it is not possible to have wired network, “Wireless Mesh Networks” are the most vital suitable technology. Routing in Wireless Mesh Networks is a multi-objective nonlinear optimization problem with some constraints. We explore multicast routing for least-cost, delay-sensitive and congestion-sensitive in optimizing the routing in Wireless mesh networks (WMNs). In this work different parameters are associated like edge cost, edge delay and edge congestion. The aim is to create a tree traversing which the set of target nodes are spanned, so as to make the cost and congestion to be minimum with a bounded delay over the path between every pair of source and destination. Since searching optimal routing satisfying multi constraints concurrently is an NP complete problem, we have presented a competent estimated algorithm certified with experimental results, which shows that the performance of presented algorithm is nearly optimum.

An Efficient Interference Aware Partially Overlapping Channel Assignment and Routing in Wireless Mesh Networks

In recent years, multi-channel multi-radio wireless mesh networks are considered a reliable and cost effective way for internet access in wide area. A major research challenge in this network is, selecting a least interference channel from the available channels, efficiently assigning a radio to the selected channel, and routing packets through the least interference path. Many algorithms and methods have been developed for channel assignment to maximize the network throughput using orthogonal channels. Recent research and test-bed experiments have proved that POC (Partially Overlapped Channels) based channel assignment allows significantly more flexibility in wireless spectrum sharing. In this paper, first we represent the channel assignment as a graph edge coloring problem using POC. The signal-to-noise plus interference ratio is measured to avoid interference from neighbouring transmissions, when a channel is assigned to the link. Second we propose a new routing metric called signal-to-noise plus interference ratio (SINR) value which measures interference in each link and routing algorithm works based on the interference information. The simulation results show that the channel assignment and interference aware routing algorithm, proposed in this paper, improves the network throughput and performance.

Optimization of reinforcement routing for wireless mesh network using machine learning and high‐performance computing

AbstractIn the modern world, wireless mesh networks (WMN) are widely used in Internet of Things (IoT) applications. It is a dynamic, infrastructure‐less multihop network with mobility features. In WMN, to design an effective and strong routing protocol is a challenge. Conventional protocols for routing using a remote vector or link‐state routing are not ideal for a wireless network. The shortest routing path is used with all current traditional routing protocols to pick a route with a minimum number of hops. The decisions must be done in real‐time in high‐traffic networks for the route selection and for the packets to be diversified into other routes. The routing protocols should adapt to the traffic conditions, that is, the network load changes and the route must be selected accordingly. The route may not be the shortest in number, but the best in terms of the actual time a packet requires to reach the target. The key objective of this research work is to design such an adaptive routing algorithm, while minimizing the delivery time and preventing the network from congestion. The rationale for this research work is to optimize adaptive routing protocol based on reinforcement learning, which efficiently delivers packets in the WMN. The experimental evaluation demonstrates that the obtained results significantly improve the system performance compared to the benchmark routing protocols. The Quality of Service (QoS) parameters such as packet delivery ratio (PDR), delay, and throughput metrics are used to assess the performance of the proposed technique using the NS‐3 simulator.

Reinforcement learning for QoS-guaranteed intelligent routing in Wireless Mesh Networks with heavy traffic load

Wireless Mesh Networks is increasingly being applied widely with explosive traffic demand. This leads to a great challenge for traditional routing protocols in ensuring Quality of Service. We propose a QoS-guaranteed intelligent routing algorithm in this paper for WMN with heavy traffic load using reinforcement learning to improve its performance. We build a reward function for the Q-Learning algorithm to choose a route so that the packet delivery ratio is the highest. Concurrently, the learning rate coefficient is flexibly changed to determine constraints of the end-to-end delay. Our performance evaluations show that the proposed algorithm has significantly improved performance compared with other well-known routing algorithms.

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost, and transmission time-reductions. The routing algorithm plays a vital role in transferring the data between the nodes. The network’s performance is significantly impacted by the route opted by the algorithm. The router takes the decision to send the packet to the next router as per the policy of that algorithm. So even though that decision does not favor the right path selection, the router tends to follow its policy. This can be avoided by having intelligent routers that can make routing decisions on the fly.This paper presents the QL-Feed Forward routing algorithm(QFFR), a new generation of routing algorithms that combines reinforcement learning based on the Q-learning algorithm with a Feed Forward neural network. This algorithm(QFFR) can learn from the network environment and make routing decisions based on the algorithm’s learnings. The AI agent’s ability to select the fastest path, which enhances the efficiency of the routing operation, is demonstrated by the working of the suggested QFFR algorithm. This paper also evaluates the performance of traditional algorithms, namely, Ad-hoc On-Demand Distance-Vector, Optimized-Link-State-routing, Destination-Sequenced Distance-Vector and Distance Source routing. The evaluation parameters include throughput, packet delivery ratio, and delay. The parameters are the outcomes of the time the information takes to reach from source to destination. This analysis highlights the improvement in the routing decision ability of a router. As per analysis, Ad hoc On-Demand Distance Vector Algorithm outperforms with throughput 723.13 Kbps, delay 343.73 ns. Q-learning agent identifies the route and reaches the destination in average of 3.7s in non-grid architecture. The Q-learning agent takes 0.49sec with a grid size ten by ten and 0.53sec in three by four grid size. The suggested QFFR takes 7.62s score-over time with stable, consistent performance.

Optimized Reinforcement Learning Based Multipath Transfer Protocol in Wireless Mesh Network

Multiple radios working on different channels are used in Wireless Mesh Networks (WMNs) to improve network performance and reduce Energy Consumption (EC). Effective routing in Backbone WMNs is where each cross-section switch is well-organized with multiple Radio Interfaces (RI), and a subset of hubs is occupied as a gateway to the Internet. Most routing methods decrease the forward overheads by evolving one dimension, <i><i>e.g</i>.</i>, hop count and traffic proportion. With that idea, while considering these dimensions together, the complexity of the routing issue increases drastically. Consequently, an effective EC routing method considers a few performances simultaneously, and the requirement of MRC around the gateways is also considered. In this paper, the proposed Reinforcement Learning (RL) method based routing selection on MPR communication directs the network traffic in WMNs. Here the radio routing path selects the channel depending on the optimized node where optimization is agreed by Particle Swarm Optimization (PSO) technique. This aims to reduce the EC by switching states and utilizing efficient routing with the reduction in traffic demand. Experimental results showed better performance of throughput and EC compared with existing work.

Connected Dominating Set Enabled On-Demand Routing (CDS-OR) for Wireless Mesh Networks

In this letter, a connected dominating set based on-demand routing (CDS-OR) mechanism is proposed for wireless ad hoc networks. We argue that there exists a trade-off between CDS size and the amount of information exchanged for CDS formation in a fully distributed ad hoc network. Smaller CDS size is beneficial and essential in minimizing the routing cost, but the amount of information exchanged (via hello or topology control messages) for CDS formation adds significant traffic overhead to the network. The proposed CDS-OR protocol assures that the hello message sizes never exceed a small constant, and an increase in network size has no impact on the hello message sizes. At the same time, the proposed protocol yields a constant approximation to the minimum CDS (MCDS), while assuring network-wide reachability of broadcast messages. Extensive simulations against benchmark routing protocols reveal noticeable superiority of the proposed mechanism in terms of packet delivery ratio and routing cost.

Routing in Wireless Mesh Network: A New Metaheuristic Based Routing Approach

Routing is a challenging issue of WMNs due to the dynamic nature of the network. In WMNs, a node can leave or join the network at any time. So, there is a need for an efficient routing algorithm in WMNs that should quickly discover the path. The development of different networking environments has a significant effect on WMNs routing. This paper proposes a new Butterfly Optimization algorithm (BOA) based routing approach for Wireless Mesh Networks. The proposed BOA routing approach was implemented using MATLAB, and its performance was compared with Ad Hoc On-Demand Distance Vector(AODV), Ant Colony Optimization(ACO), BAT optimization algorithm, Dynamic Source Routing(DSR), and Biogeography-based optimization(BBO)based routing approaches on 500, 1000, 1500, and 2000 dynamic node scenarios. From the results, We observe that the proposed Butterfly based routing approach outperforms the existing five routing approaches.