A Novel Optimization based Energy Efficient and Secured Routing Scheme using SRFIS-CWOSRR for Wireless Sensor Networks

Rao, Sumangala; Reddy, Karthikeyan; Chand, S. Ravi

doi:10.37391/ijeer.100338

Cited by 5 publications

(3 citation statements)

References 31 publications

(28 reference statements)

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“…The introduction of IoT in farming has completely changed how farmers oversee and track their crops. In this study, a unique method for path and CH selection optimization utilizing enhanced crow swarm optimization and updated fuzzy logic (ECSO) [15]. To determine which CH is best, fuzzy logic is utilized to analyze pertinent factors such energy, distance, overhead, trust, and node degree.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient cluster head using modified fuzzy logic with WOA and path selection using Enhanced CSO in IoT-enabled smart agriculture systems

Rai

Rajasekaran

2023

Preprint

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The advent of Internet of Things (IoT) in agriculture has revolutionized the way farmers monitor and manage their crops. IoT-enabled sensors can provide real-time data on various environmental parameters such as temperature, humidity, soil moisture, and crop growth, which can be used to make informed decisions and optimize crop yield. However, the vast amount of data generated by these sensors poses a significant challenge in terms of data processing and communication. To address this challenge, clustering is often used to group the sensors into clusters and elect a Cluster Head (CH) to communicate with the gateway node. The selection of an appropriate CH and the optimal path for data transmission are critical factors that affect the performance of the IoT system. In this paper, we propose a novel approach to optimize the CH selection and path selection using modified Fuzzy Logic, Whale optimization algorithm (WOA) and Enhanced Crow Swarm Optimization (ECSO). Fuzzy Logic is used to evaluate the relevant parameters such as energy, distance, overhead, trust, and node degree to select the most suitable CH. ECSO is then employed to find the optimal path for data transmission based on the selected CH. We evaluate the proposed approach using simulation experiments in a smart agriculture scenario. The results show that our approach outperforms existing approaches in terms of throughput, packet delivery ratio, delay, and energy efficiency. Our proposed approach can significantly improve the performance of IoT-enabled smart agriculture systems, leading to better crop yield and higher profitability for farmers. The results of our simulation experiments demonstrate the superiority of our approach over existing one’s throughput, Packet Delivery Ratio (PDR), delay, energy consumption efficiency is found in the result section.

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Section: Introductionmentioning

confidence: 99%

Energy efficient cluster head using modified fuzzy logic with WOA and path selection using Enhanced CSO in IoT-enabled smart agriculture systems

Rai

Rajasekaran

2023

Preprint

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“…One of the essential technologies in recent years is WSNs [1,2]. The "carry-send" form of data transmission is a capability of WSNs.…”

mentioning

confidence: 99%

A Novel Congestion Control Scheme using Firefly Algorithm Optimized Fuzzy-PID Controller in Wireless Sensor Network

Tweh

Ataro

Nyakoe

2023

IJEER

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Wireless Sensor Networks (WSNs) consist of several sensor nodes, each of which may collect, receive and transmit data. In recent years, WSNs have emerged as essential technologies due to their ubiquity in applications such as the military, smartphones, disaster management, healthcare monitoring, and other surveillance systems. The inability to send data from the sensor node promptly and the impossibility of new data reaching the node's queue indicate of network congestion. The packet will be either discarded or delayed, which will cause more data loss, longer transmission delays, reduced network throughput, and lower network quality of service. To address this problem, this paper proposes an efficient and novel Firefly Algorithm-optimized Fuzzy-PID (FA-Fuzzy-PID) controller for congestion control in Wireless Sensor Networks (WSNs). The proposed control technique used a fuzzy control algorithm to overcome the standard PID controller's slow optimization parameter, low calculation accuracy, and limited adaptability.

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“…Additionally, the proposed system is implemented with the combination of MLP with a feed-forward ANN model and back propagation algorithm for accurately mapping the training and testing datasets. Data routing within WSNs will be challenging owing to their dynamic architecture [24,25].…”

mentioning

confidence: 99%

An Intrusion Detection Using Machine Learning Algorithm Multi-Layer Perceptron (MlP): A Classification Enhancement in Wireless Sensor Network (WSN)

Reddy¹,

Kadiyala²,

Potluri³

et al. 2022

IJRITCC

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During several decades, there has been a meteoric rise in the development and use of cutting-edge technology. The Wireless Sensor Network (WSN) is a groundbreaking innovation that relies on a vast network of individual sensor nodes. The sensor nodes in the network are responsible for collecting data and uploading it to the cloud. When networks with little resources are deployed harshly and without regulation, security risks occur. Since the rate at which new information is being generated is increasing at an exponential rate, WSN communication has become the most challenging and complex aspect of the field. Therefore, WSNs are insecure because of this. With so much riding on WSN applications, accuracy in replies is paramount. Technology that can swiftly and continually analyse internet data streams is essential for spotting breaches and assaults. Without categorization, it is hard to simultaneously reduce processing time while maintaining a high level of detection accuracy. This paper proposed using a Multi-Layer Perceptron (MLP) to enhance the classification accuracy of a system. The proposed method utilises a feed-forward ANN model to generate a mapping for the training and testing datasets using backpropagation. Experiments are performed to determine how well the proposed MLP works. Then, the results are compared to those obtained by using the Hoeffding adaptive tree method and the Restricted Boltzmann Machine-based Clustered-Introduction Detection System. The proposed MLP achieves 98% accuracy, which is higher than the 96.33&percnt; achieved by the RBMC-IDS and the 97% accuracy achieved by the Hoeffding adaptive tree.

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A Novel Optimization based Energy Efficient and Secured Routing Scheme using SRFIS-CWOSRR for Wireless Sensor Networks

Cited by 5 publications

References 31 publications

Energy efficient cluster head using modified fuzzy logic with WOA and path selection using Enhanced CSO in IoT-enabled smart agriculture systems

Energy efficient cluster head using modified fuzzy logic with WOA and path selection using Enhanced CSO in IoT-enabled smart agriculture systems

A Novel Congestion Control Scheme using Firefly Algorithm Optimized Fuzzy-PID Controller in Wireless Sensor Network

An Intrusion Detection Using Machine Learning Algorithm Multi-Layer Perceptron (MlP): A Classification Enhancement in Wireless Sensor Network (WSN)

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