Fuzzy Clustering with Multi-Constraint QoS Service Routing in Wireless Sensor Networks

Agarkhed, Jayashree; Kadrolli, Vijayalaxmi; Patil, Siddarama R.

doi:10.26636/jtit.2019.127818

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Since only a complete end-to-end path is a solution in the QoS routing calculation of OBS networks, it is an important problem to generate feasible solutions from some VOLUME 10, 2022 solutions (including infeasible solutions) by iterative optimization [12], [13]. Our idea is to first use the Dijkstra algorithm to generate a set of initial complete paths according to the changing weight combination, and generate new paths through the appropriate crossover and mutation operation in the genetic algorithm.…”

Section: E Algorithm Idea and Modelmentioning

confidence: 99%

QoS Routing Algorithm for OBS Networks Based on a Multi-Objective Genetic Algorithm

Cui

Srivastava

2022

IEEE Access

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To optimize the QoS of optical burst switching networks, a QoS routing optimization algorithm based on a multi-objective genetic algorithm is proposed. A Bayesian network model is used to locate the fault of optical burst switching network and obtain the fault location of the transmission link of optical burst switching network; In this position, the routing optimization algorithm based on a multi-objective genetic algorithm transforms the multi constrained network quality of service routing optimization problem into a constrained multi-objective routing optimization problem. Under multiple constraints, the best path of optical burst switching network service is obtained to realize the optical burst switching network quality of service routing optimization. The results show that after applying the proposed algorithm, the average delay of video, text and picture transmission in an optical burst switching network is less than 400ms. The proposed algorithm can improve the packet delivery rate of information transmission in an optical burst switching network, reduce the transmission delay, blocking probability and use cost of an optical burst switching network, and optimize the service quality of an optical burst switching network.INDEX TERMS Bayesian network model, network failure, transmission link, routing optimization, multiple constraints, optimal path. I. INTRODUCTIONThe 21st century is an information age with networks as the core. People's demand for information is increasing day by day. Some image information related to people's vision, such as videophone, digital image, HDTV and other broadband business markets is rapidly expanding [1]. All kinds of new business, such as distance education, telemedicine, home shopping, home office are booming, which must rely on a complete network. The network has become the pulse of the information society, and it is changing all aspects of human social life [2], [3]. At present, the optical fibre communication network has become the foundation of the high-speed Internet backbone network, and the development and maturity of wavelength division multiplexing (WDM) provide massive bandwidth, which makes all-optical switching possible. To meet the explosive growth of multimedia services represented by the Internet, a variety of new optical communication technologies have been proposed and studied, and have achieved rapid development.The associate editor coordinating the review of this manuscript and approving it for publication was Zhaojun Li .

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Section: E Algorithm Idea and Modelmentioning

confidence: 99%

QoS Routing Algorithm for OBS Networks Based on a Multi-Objective Genetic Algorithm

Cui

Srivastava

2022

IEEE Access

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“…Furthermore, Agarkhed et al 30 proposed a fuzzy clustering method and a multi-constraint QoS service routing protocol in wireless sensor networks. The QoS aware routing protocol creates routes with various paths for WSNs, aiming at provision of a differentiated service path based on genuine prerequisites planned for in-between nodes communication.…”

Section: Related Workmentioning

confidence: 99%

“…Then, based on "different QoS constraints and optimum shortest paths, multiple paths between nodes through the modified QoS k-nearest neighbourhood are established." 30 The nodes in the paths can die owing to the absence of lingering energy, bandwidth, packet loss, or delay. To this end, a neighborhood node opts for the purpose of communication.…”

Section: Related Workmentioning

confidence: 99%

Sink selection and clustering using fuzzy‐based controller for wireless sensor networks

Shahidinejad

Barshandeh

2020

Int J Communication

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Summary Applying multiple sink nodes in a large‐scale wireless sensor networks (WSN) can increase the scalability and lifetime of the network. The current sink selection mechanisms assume an unlimited amount of buffer and bandwidth for the sink nodes. This can be problematic in real‐world applications, especially when many cluster heads select a specific sink node and send their data to the sink at the same time. In this situation, the sink node may not have enough buffer to receive and process data; consequently, some packets are dropped. To mitigate these occasions, a fuzzy‐based controller with reduced rules is proposed for sink selection by considering the capacity of the sink nodes. The capacity of the sink nodes is estimated using the long short‐term memory (LSTM) technique. Then another fuzzy‐based controller with reduced rules is designed to select the cluster head. The fuzzy rules are reduced by employing R‐implications method. Reducing the number of fuzzy rules decreases the complexity of the fuzzy controllers. The results show the efficiency of the proposed sink selection and clustering techniques in terms of consumed energy, remaining energy, first node dead (FND), half nodes dead (HND), last node dead (LND), packet loss, and delay.

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Fuzzy Clustering with Multi-Constraint QoS Service Routing in Wireless Sensor Networks

Cited by 2 publications

References 19 publications

QoS Routing Algorithm for OBS Networks Based on a Multi-Objective Genetic Algorithm

QoS Routing Algorithm for OBS Networks Based on a Multi-Objective Genetic Algorithm

Sink selection and clustering using fuzzy‐based controller for wireless sensor networks

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