Load-aware multicast routing in multi-radio wireless mesh networks using FCA-CMAC neural network

Ramezani, Mahlagha; Jahanshahi, Mohsen

doi:10.1007/s00607-017-0579-0

Cited by 5 publications

(5 citation statements)

References 28 publications

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“…Ramezani, et al [15] in 2018, have introduced a neural network model of a Fuzzy Credit Assigned Cerebellum Model Architecture Controller (FCA-CMAC) for construct a multicast routing tree in the WMN networks this method can be used to reduce the multicast routing problem and channel assignment problem in the network efficiently. This algorithm produced an efficient multicast tree with the minimum interference.…”

Section: Related Work: a Brief Reviewmentioning

confidence: 99%

“…But it takes more time to consume energy in the network [13]. The interference aware of channel assignment strategy was described in [14] and the load-aware of channel assignment in multichannel multi-radio WMNs multicast routing approaches are illustrated in [15]. In [16], a Quality of Service Channel Assignment and multicast Routing (Q-CAR) algorithm is proposed to implement the performance of an efficient channel assignment in the wireless mesh network.…”

Section: Introductionmentioning

confidence: 99%

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Q-Learning and MADMM Optimization Algorithm Based Interference Aware Channel Assignment Strategy for Load Balancing in WMNS

Rathan¹,

Roslin²

2021

IJIES

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Wireless Mesh Networks (WMNs) have been considered one of the main technologies for configuring wireless machines since they appeared. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts on the network and allow access to the internet through the gateway routers. These wireless routers are normally equipped with the multiple radios in the wireless mesh network that operate on multiple channels with the multiple interference, which is caused to reduce the network performance and end-to-end delay. In this paper, we proposed an efficient optimization algorithm to solve the channel assignment problem which cause due to the multichannel multiradios in WMN’s. The main objective of our paper is to minimize the channel interference among networked devices. So, initially we construct a multicast tree with minimum interference by using Q-Learning algorithm, which is helps to minimize the end-to-end delay of packet delivery. From the constructed multicast tree, we intend to develop a channel assignment strategy with the minimum interference by using Modified version of Alternative Direction Method of Multipliers (MADMM) optimization algorithm, which is helps to increase the network throughput and packet delivery ratio. The proposed strategy was implemented by using NS-2 (Network Simulator-2) and the experimental result show that the performance of the proposed method is very high compared to the other method and the performance was calculated by using the feature metrics such as average throughput, packet delivery ratio, end-toend delay and total cost, which is compared with the other existing channel assignment strategies such as Learning Automata and Genetic Algorithm (LA-GA), GA-based approach, link-channel selection and rate-allocation (LCR) and learning automata based multicast routing (LAMR) channel assignment methods.

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Section: Related Work: a Brief Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Q-Learning and MADMM Optimization Algorithm Based Interference Aware Channel Assignment Strategy for Load Balancing in WMNS

Rathan¹,

Roslin²

2021

IJIES

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“…Some researches adopted machine learning to manage the paths intelligently. References [15] proposed a load-aware multicast routing algorithm based on neural networks. References [16,17] presented a preliminary traffic control system facilitated by deep learning-based routing.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Routing Control for MANET Based on Reinforcement Learning

Dong¹,

Ou²,

Min³

2018

MATEC Web Conf.

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With the rapid development and wide use of MANET, the quality of service for various businesses is much higher than before. Aiming at the adaptive routing control with multiple parameters for universal scenes, we propose an intelligent routing control algorithm for MANET based on reinforcement learning, which can constantly optimize the node selection strategy through the interaction with the environment and converge to the optimal transmission paths gradually. There is no need to update the network state frequently, which can save the cost of routing maintenance while improving the transmission performance. Simulation results show that, compared with other algorithms, the proposed approach can choose appropriate paths under constraint conditions, and can obtain better optimization objective.

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“…Addressing these drawbacks, some researchers investigated multi‐radio multi‐channel (MR‐MC) WSNs to realize the full‐duplex communication and significantly improve network capacity . Nevertheless, the existing research mainly focuses on resource‐rich ad hoc and mesh networks, and the protocol is too complicated to be implemented in low‐cost WSNs . Few researches were aimed at the MR‐MC WSN with limited resources .…”

Section: Introductionmentioning

confidence: 99%

“…28,29 Nevertheless, the existing research mainly focuses on resource-rich ad hoc and mesh networks, and the protocol is too complicated to be implemented in low-cost WSNs. [30][31][32][33][34][35][36][37][38] Few researches were aimed at the MR-MC WSN with limited resources. 39,40 But their main goal is to improve the end-to-end data communication efficiency, which is not suitable for the scenario of intensive data collection.…”

mentioning

confidence: 99%

Optimal data collection of multi-radio multi-channel multi-power wireless sensor networks for structural monitoring applications: A simulation study

Chen

et al. 2019

Struct Control Health Monit

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Summary Structural health monitoring (SHM) is a kind of data‐intensive applications for wireless sensors network (WSN), which usually requires a high network capacity. However, the bandwidth of traditional single‐radio single‐channel (SR‐SC) WSN is quite limited. In order to meet the requirement of structural monitoring, we investigate the multi‐radio multi‐channel multi‐power (MR‐MC‐MP) communication to improve the data collection performance of SHM‐oriented WSNs in terms of network capacity and power consumption. First, the data collection problem in MR‐MC‐MP WSNs is modeled as an optimization problem under the constraint of available time slots, radios, channels, and power levels. And then, combining the fast convergence of the particle swarm optimization (PSO) algorithm and high exploration performance of flower pollination optimization (FPA) algorithm, we propose a novel binary hybrid meta‐heuristic algorithm named BFPA‐PSO to solve the problem. In order to verify the advantage of the proposed BFPA‐PSO, some other meta‐heuristic algorithms are tested for the problem as well. Finally, several simulation experiments are carried out to test and compare the performance of different algorithms. Experiment results demonstrate that the proposed BFPA‐PSO algorithm has superior performance in terms of network capacity and energy consumption.

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Load-aware multicast routing in multi-radio wireless mesh networks using FCA-CMAC neural network

Cited by 5 publications

References 28 publications

Q-Learning and MADMM Optimization Algorithm Based Interference Aware Channel Assignment Strategy for Load Balancing in WMNS

Q-Learning and MADMM Optimization Algorithm Based Interference Aware Channel Assignment Strategy for Load Balancing in WMNS

Intelligent Routing Control for MANET Based on Reinforcement Learning

Optimal data collection of multi-radio multi-channel multi-power wireless sensor networks for structural monitoring applications: A simulation study

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