An Energy-Aware Hybrid Approach for Wireless Sensor Networks Using Re-clustering-Based Multi-hop Routing

Rezaeipanah, Amin; Amiri, Parvin; Nazari, Hamed; Mojarad, Musa; Parvïn, Hamïd

doi:10.1007/s11277-021-08614-w

Cited by 40 publications

(24 citation statements)

References 30 publications

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“…The adoptive clustering technique-based node distribution generates the cluster distributing better that balances the energy usage of network more efficiently. Rezaeipanah et al [11] presented a clustering-based multihop routing method. During the presented approach, an integration of two techniques, such as Open-Source Development Model Algorithm (ODMA) and K-means algorithm, are applied for Genetic Algorithm, and clustering is employed for multihop routing.…”

Section: Related Workmentioning

confidence: 99%

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

Manikandan¹,

Narasimhan²

2023

Intelligent Automation &Amp; Soft Computing

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Wireless sensor networks (WSNs) encompass a massive set of sensor nodes, which are self-configurable, inexpensive, and compact. The sensor nodes undergo random deployment in the target area and transmit data to base station using inbuilt transceiver. For reducing energy consumption and lengthen lifetime of WSN, multihop routing protocols can be designed. This study develops an improved rat swarm optimization based energy aware multi-hop routing (IRSO-EAMHR) protocol for WSN. An important intention of the IRSO-EAMHR method is for determining optimal routes to base station (BS) in the clustered WSN. Primarily, a weighted clustering process is performed to group the nodes into clusters and select cluster heads (CHs). Next, the IRSO-EAMHR approach derives a fitness function containing three input parameters (residual energy, distance, and node degree) for routing process. The IRSO technique was designed by the integration of Levy movement concepts into the traditional RSO algorithm. The experimental result analysis of the IRSO-EAMHR technique is carried out and the outcomes are examined in various aspects. The simulation outcomes demonstrate the promising performance of the IRSO-EAMHR technique over the recent state of art approaches.

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Section: Related Workmentioning

confidence: 99%

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

Manikandan¹,

Narasimhan²

2023

Intelligent Automation &Amp; Soft Computing

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“…The first step involves the production of primary clusters by several basic methods, where the methods have the maximum variety. In this regard, in the second step, the final clusters are performed by combining the results of the primary clusters, where a consensus function is used for this 4 …”

Section: Clustering Ensemble Conceptsmentioning

confidence: 99%

“…Data mining uses sciences such as artificial intelligence, machine learning, statistics, operational research and database management to build models and answer questions 3 . In general, data mining means extracting or exploring knowledge from large amounts of data and has been introduced as the most important step in the process of knowledge discovery 4 . There are several algorithms for discovering knowledge in data mining, each of which is used for a specific purpose.…”

Section: Introductionmentioning

confidence: 99%

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

Liu

Osmani

2022

Trans Emerging Tel Tech

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Ensemble clustering is an efficient unsupervised learning technique that has attracted a lot of attention. The purpose of this technique is to aggregate the results of several basic clustering algorithms in order to create a better clustering. This is not only possible, but has been developed with many techniques in recent years. However, there are still challenges such as similarity measure, agreement function and how to implement clustering algorithms. To address these challenges, this article proposes an ensemble clustering algorithm based on the consistency cluster consensus approach and the MapReduce model. In addition, the proposed algorithm is equipped with a new membership similarity measure. The consistency cluster consensus is defined as a new agreement function for the consensus of the results of the basic clustering methods. Besides, the proposed similarity measure consists of two factors: one is cluster similarity and the another is membership similarity. The process of the proposed ensemble clustering method is summarized in four steps. In the first step, partitions are generated by applying a number of individual clustering algorithms to the data. The second step is to convert the primary clusters from the partitions to a binary representation. The third step is to identify the candidate primary clusters for the consensus, where a subset with highest similarity between the clusters is emphasized. In the fourth step, the consistency cluster consensus is performed through a new agreement function. In addition, we use the MapReduce model to implement basic clustering algorithms to reduce computational complexity. Our method is evaluated on several real-world datasets and their performance was compared with other clustering ensemble methods, such as TWCE and RDPC-DSS. Experiments show that our method is more efficient and accurate and is 7% to 15% better than the best method compared. INTRODUCTIONData mining is the process of discovering regular and hidden patterns and processes in big data and distributed using a wide range of algorithms based on mathematical and statistical sciences. 1,2 These algorithms are usually applied to numeric and nontextual values and textual algorithms are used for textual data. Data mining uses sciences such as artificial intelligence, machine learning, statistics, operational research and database management to build models

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“…The critical issue in MPLS networks is to (re)-configure Label-Switched Paths (LSPs), particularly in cases of dynamic network conditions. Hence, the SDN controller with the global and online knowledge of the whole network condition would be able to rapidly respond to the network changes [ 28 ]. SDN controller decides on a path, being able to consider various metrics such as utilized delay and bandwidth, and dictates every single forwarding element in the network according to the current network condition to traverse a flow of packets from a given pair of source-destination.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy delay-bandwidth guaranteed routing algorithm for video conferencing services over SDN networks

Gong

Rezaeipanah²

2023

Multimed Tools Appl

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Video conferencing is one of the advanced technologies for users that allows online communication despite long distances. High quality communication and ongoing support for the principles of video conferencing service that can be achieved through Software-Defined Networking (SDN). SDN is a new architecture for computer networks that separates the control plane from the data plane to improve network resources and reduce operating costs. All routing decisions and control mechanisms are made by a device called a controller. Traffic engineering can be well implemented in SDN because the entire network topology is known to the controller. Considering SDN features, user requests can be dynamically routed according to current network status and Quality of Service (QoS) requirements. In general, the purpose of SDN routing algorithms is to maximize the acceptance rate of user requests by considering QoS requirements. In this literature, most routing studies to provide satisfactory video conferencing services have focused solely on bandwidth. Nevertheless, some studies have considered both delay and bandwidth constraints. In this paper, a Fuzzy Delay-Bandwidth Guaranteed Routing (FDBGR) algorithm is proposed that considers both delay and bandwidth constraints in routing. The proposed fuzzy system is based on rules that can postpone requests with high resource demands. Also, the purpose of the FDBGR is to distribute the network workload evenly for all requests, where this is done by maintaining the capacity to accept future requests. The combination of conventional routing algorithms and SDN provides remarkable improvements in mobility, scalability and the overall performance of the networks. Simulations are performed on different scenarios to evaluate the performance of the FDBGR compared to state-of-the-art methods. Besides, FDBGR has been compared with a number of most related previous works such as H-MCOP, MH-MCOP, QoMRA, QROUTE and REDO based on criteria such as number of accepted requests, average path length, energy consumption, load balancing, and average delay. The simulation results clearly prove the superiority of the proposed algorithm with an average delay of 48 ms in different topologies for video conferencing applications.

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An Energy-Aware Hybrid Approach for Wireless Sensor Networks Using Re-clustering-Based Multi-hop Routing

Cited by 40 publications

References 30 publications

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

A fuzzy delay-bandwidth guaranteed routing algorithm for video conferencing services over SDN networks

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