Red Deer Algorithm based nano-sensor node clustering for IoNT

Güleç, Ömer; Sahin, Emre

doi:10.1016/j.jnca.2023.103591

Cited by 16 publications

(2 citation statements)

References 14 publications

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“…The motivation for focusing on this problem is that recently IoT based applications came into existence where multiple works takes place concurrently, thus we need a multi‐attribute decision‐making system for enhanced performance. In Reference 56 author has proposed modern meta‐heuristics algorithms based on distributed Nano‐sensors for clustering to provide efficient communication between Nano‐sensors by selecting cluster heads in IoNT (INTERNET OF NANO THINGS). In Reference 57 author has proposed an energy‐efficient routing protocol to improve the rationality of cluster formation and cluster head selection.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

Srivastava,

Mishra

2023

Concurrency and Computation

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SummarySensors plays an important role in day‐to‐day life as they sense and transfer data on the cloud and servers. These sensors have limited battery power due to which their optimized use is preferred with efficient energy consumption. As sensors were deployed almost everywhere under‐earth, underwater, electronic devices, and mobiles thus their enhanced performance is really important. In WSN, clustering is considered to be an essential approach that gives various advantages such as efficient energy, stability period, network lifetime, less delay, and scalability but has an issue of hot‐spot or energy‐hole problems. For this Un‐Equal Clustering is proposed where the size of clusters varies directly to the distance of the B.S. (Base Station). The main objective of Un‐Equal clustering is energy consumption, hot‐spot problem, and load balance among the cluster heads. In this paper, we have proposed an improved fuzzy‐based multi‐attributes un‐equal clustering to overcome the problem of hot‐spot problem. Earlier, researchers considered two attributes for selecting cluster heads (CHs), but later some of the MADM approaches were used that considered multiple attributes for optimal cluster head selection. In this paper, we have applied the fuzzy‐based TOPSIS method for optimized Un‐Equal clustering where the optimal node deployments with load balance among sensor nodes having optimal coverage and connectivity is considered. The results of the proposed method validate that it is one of the effective ways for selecting optimal cluster heads using fuzzy‐based multi‐attributes. Fuzzy‐based TOPSIS for cluster heads selection method shows 37% enhanced network performance with other compared existing algorithms. The performance of the proposed method is evaluated in terms of network stability, packet delivery, energy consumption, and network lifetime and F‐MAUC (Fuzzy based multi‐attributes un‐equal clustering) outperforms all other compared algorithms.

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Section: Related Workmentioning

confidence: 99%

Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

Srivastava,

Mishra

2023

Concurrency and Computation

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“…Xu et al (2021) [23] developed a multi-hop routing protocol for intra-body WNSNs which estimates link states. Gulec and Sahin (2023) [24] proposed a Red Deer Algorithm based nano-sensor node clustering method for WNSNs. Garcia-Sanchez et al (2023) [25] proposed a multi-hop routing scheme using reinforcement learning in body WNSNs.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Supported Nano-Router Localization in WNSNs

Güleç

2023

Sakarya University Journal of Science

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Sensing data from the environment is a basic process for the nano-sensors on the network. This sensitive data need to be transmitted to the base station for data processing. In Wireless Nano-Sensor Networks (WNSNs), nano-routers undertake the task of gathering data from the nano-sensors and transmitting it to the nano-gateways. When the number of nano-routers is not enough on the network, the data need to be transmitted by multi-hop routing. Therefore, there should be more nano-routers placed on the network for efficient direct data transmission to avoid multi-hop routing problems such as high energy consumption and network traffic. In this paper, a machine learning-supported nano-router localization algorithm for WNSNs is proposed. The algorithm aims to predict the number of required nano-routers depending on the network size for the maximum node coverage in order to ensure direct data transmission by estimating the best virtual coordinates of these nano-routers. According to the results, the proposed algorithm successfully places required nano-routers to the best virtual coordinates on the network which increases the node coverage by up to 98.03% on average and provides high accuracy for efficient direct data transmission.

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