Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Justus, J. Jean; Thirunavukkarasan, M.; Dhayalini, K.; Visalaxi, G.; Khelifi, Adel; Elhoseny, Mohamed

doi:10.32604/cmc.2022.019122

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…The results of EEHRP outcomes the Simple Hybrid Routing Protocol (SHRP), and DyMORA algorithms. A novel Type II Fuzzy Logic-based Cluster Head (CH) selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN has been developed in [24]. The model includes a two-phase process (clustering and data aggregation).…”

Section: Related Workmentioning

confidence: 99%

Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

Faris¹,

Mahmood²,

Alomari³

et al. 2022

Computers, Materials &Amp; Continua

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Wireless sensor networks (WSNs) are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data, and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol. Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node. All routing protocols are large consumers of energy, as they represent the main source of energy cost through data exchange operation. Clusterbased hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs. The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy (LEACH), which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation. This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process, thus increasing the network lifespan. This paper proposes the LEACH-CHIO, a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer (CHIO) algorithm. CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process. LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios, which consist of a variable number of nodes ranging from 20 to 100. To evaluate the algorithm performances, three evaluation indicators have been examined, namely, power consumption, number of live nodes, and number of incoming packets. The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.

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Section: Related Workmentioning

confidence: 99%

Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

Faris¹,

Mahmood²,

Alomari³

et al. 2022

Computers, Materials &Amp; Continua

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“…Assuring that any transport system can provide a high degree of resilience with every network Blue Brain node alive and reachable for communication is the final worry [10]. The approach has to decrease the number of CHs because Blue Brain nodes have limited processing and battery life [11][12][13]. It is necessary to consider each of these issues while developing a new routing method.…”

Section: Introductionmentioning

confidence: 99%

Energy capable protocol for heterogeneous blue brain network

DENNİSON,

DASEBENEZER,

DENNİSON

2024

Turkish Journal of Engineering

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The Blue Brain has a wide range of applications, which raises a number of challenging issues. Electronics may continuously monitor their surroundings depending on the real data that their Blue Brain nodes are acquiring by employing situational intelligence based on the Blue Brain environment. The Blue Brain does more than only monitor user behavior when utilizing this technology. Blue Brain is linked to a critical prerequisite for energy-efficient communication methodologies. Through the Blue Brain network, it utilizes the heterogeneity and variety of the interconnected components. Blue Brain nodes that are outsourced and have limited energy resources must utilize less energy. IoT nodes with differing energy levels are frequently dispersed across different geographic regions. The main goal of this work is to provide an energy-efficient Blue Brain framework capable of managing cluster head (CH) selection and Blue Brain node clustering. The appropriate CHs are selected, and an energetic cutoff concept is developed to guarantee that energy is shared equally among the CHs and participating Blue Brain nodes. The proposed concept envisions three different kinds of Blue Brain nodes for a Blue Brain infrastructure: expert, intermediary, and normal Blue Brain nodes. Level 1 Blue Brain nodes are regarded as normal nodes; level 2 nodes are regarded as intermediate Blue Brain nodes; and level 3 nodes are regarded as expert Blue Brain nodes. Level 1 Blue Brain nodes use the least amount of energy. The outcomes of the simulation demonstrate that the recommended strategy outperforms other existing methods.

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“…The data are then obtained by the CHs from “contributing nodes,” which are nodes that are linked to one another. The nodes that send their readings to CH are typically located near the CH [ 4 ]. To be used in the clustering approach, the data collected by each sensor must be sent to the sink via the cluster head.…”

Section: Introductionmentioning

confidence: 99%

“…e nodes that send their readings to CH are typically located near the CH [4]. To be used in the clustering approach, the data collected by each sensor must be sent to the sink via the cluster head.…”

Section: Introductionmentioning

confidence: 99%

Remote Monitoring of COVID-19 Patients Using Multisensor Body Area Network Innovative System

Al_Barazanchi

Hashim

Alkahtani

et al. 2022

Computational Intelligence and Neuroscience

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As of late 2019, the COVID19 pandemic has been causing huge concern around the world. Such a pandemic posed serious threats to public safety, the well-being of healthcare workers, and the overall health of the population. If automation can be implemented in healthcare systems, patients could be better cared for and health industries could be less burdened. To combat such challenges, e-health requires apps and intelligent systems. Using WBAN sensors and networks, a doctor or medical professional can advise patients on the best course of action. Patients' fitness could be assessed using WBAN sensors without interfering with their daily activities. When designing a monitoring system, system performance reliability for competent healthcare is critical. Existing research has failed to create a large device capable of handling a large network or to improve WBAN topologies for fast transmitting and receiving patient data. As a result, in this research, we create a multisensor WBAN (MSWBAN) intelligent system for transmitting and receiving critical patient data. To gather information from all cluster nodes and send it to multisensor WBAN, a novel additive distance-threshold routing protocol (ADTRP) is proposed. In small networks where data are managed by the transmitting node and the best data route is determined, this protocol has less redundancy. An edge-cutting-based routing optimization (ES-EC-R ES-EC-RO) is used to find the best route. The Trouped blowfish MD5 (TB-MD5) algorithm is used to encrypt and decrypt data, and the encrypted data are stored in a cloud database for security. The performance metrics of our proposed model are compared to current techniques for the best results. End-to-end latency is 63 ms, packet delivery is 95%, security is 95.7%, and throughput is 9120 bps, according to the results. The purpose of this article is to encourage engineers and front-line workers to develop digital health systems for tracking and controlling virus outbreaks.

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Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Cited by 5 publications

References 24 publications

Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

Energy capable protocol for heterogeneous blue brain network

Remote Monitoring of COVID-19 Patients Using Multisensor Body Area Network Innovative System

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