E-FEERP: Enhanced Fuzzy Based Energy Efficient Routing Protocol for Wireless Sensor Network

Narayan, Vipul; Daniel, A. K.; Chaturvedi, Pooja

doi:10.1007/s11277-023-10434-z

Cited by 26 publications

(6 citation statements)

References 35 publications

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“…Narayan et al [37] employs fuzzy logic to optimize routing decisions, considering energy efficiency. This protocol aims to extend the network's lifespan by intelligently routing data while minimizing energy consumption, improving the overall performance of wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

“…The data collected by the sensors can be related to various environmental parameters, such as temperature, humidity, light, or motion, and they are used for applications like environmental monitoring, surveillance, or industrial automation. The nodes in a WSN communicate wirelessly, forming a distributed network that enables real-time data collection and analysis for various applications [1][2] [3]. The ubiquitous deployment of WSNs in various fields, including environmental monitoring, healthcare, and industrial automation, has brought about significant challenges related to energy efficiency and network longevity.…”

Section: Introductionmentioning

confidence: 99%

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OEE-WCRD: Optimizing Energy Efficiency in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics

Lalit Kumar Tyagi,

Kumar

2024

ICST Transactions on Scalable Information Systems

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Wireless Sensor Networks (WSNs) play a pivotal role in various applications, including environmental monitoring, industrial automation, and healthcare. However, the limited energy resources of sensor nodes pose a significant challenge to the longevity and performance of WSNs. To address this challenge, this paper presents an Optimized Energy Efficient Protocol in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics (OEE-WCRD). This research paper presents a novel approach to cluster head selection in WSNs by harnessing a combination of residual energy and distance metrics. The proposed method aims to significantly enhance the energy efficiency of WSNs by prioritizing nodes with ample residual energy and proximity to their neighbors as cluster heads. Through extensive simulations and evaluations, we demonstrate the effectiveness of this approach in prolonging network lifetime, optimizing data aggregation, and ultimately advancing the energy efficiency of WSNs, making it a valuable contribution to the field of WSNs protocols.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

OEE-WCRD: Optimizing Energy Efficiency in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics

Lalit Kumar Tyagi,

Kumar

2024

ICST Transactions on Scalable Information Systems

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show abstract

“…Moreover, a research work [22] suggested that there is an immense need for efficient data transmission and collection methods in WSNs due to limited battery power in nodes. The authors propose a cluster formation technique using Particle Swarm Optimisation and a Fuzzy-based Energy Efficient Routing Protocol (E-FEERP) to transmit data optimally from cluster heads to the Base Station.…”

Section: Literature Reviewmentioning

confidence: 99%

A conditioned reflex action embedded associative context learning‐based energy efficient paradigm in smart city milieu

Hussain

Bilal

Faheem

et al. 2023

IET Wireless Sensor Systems

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An intelligent video surveillance system is crucial to enhance public safety, crime prevention, traffic, and crowd management in a smart city milieu. Situational awareness is an essential aspect of these surveillance systems and it is inferred through underlying context aware frameworks. However, these systems may not possess the ability to proactively disseminate the real‐time context among its sensor nodes. Moreover, in the specific conditions of occurrence of related or repeated events, these systems may also perform inefficiently through afresh context processing and disseminate cycles, without learning from the relevant context that has already been occurred and processed by the system. It leads to deteriorated performance, especially delay in reaction, overwhelmed processing, and energy expenditures. Therefore, to counter such issues, this research work proposes an energy efficient situational aware framework deployed in visual sensors network that is incorporated with context associative learning. System observes currently occurring context at each instance of an event. Overtime, context is refined and stored in context database. Such mechanism empowers the system to learn from previous experiences and develop relationship among the subsequent events that is embedded through this associative (adaptive) learning. Eventually, each event is processed through intelligent resource allocation, supported through mechanism of context learning that further illustrates the independent functions of reduced processing and improved (rapid) decision making resulting in evolution of energy efficient computing paradigm. Ultimately, the capability of learned reflex‐action is induced through introspectively evolved context of the system in entirety and against specific condition of recurred situation depicting minimum energy expenditure.

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“…In 2023, ref. [20] the WSN is responsible for gathering and arranging sensed data before sending it to the base station (BS). As the battery power of sensor nodes is limited, it is crucial to employ effective techniques for data collection and transmission to ensure the extended operation of the sensor network.…”

Section: Related Workmentioning

confidence: 99%

MHSEER: A Meta-Heuristic Secure and Energy-Efficient Routing Protocol for Wireless Sensor Network-Based Industrial IoT

et al. 2023

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Several industries use wireless sensor networks (WSN) for various tasks such as monitoring, data transmission, and data gathering. They find applications in the industrial internet of things (IIoT). WSNs are utilized to track and monitor changes in the environment. Since they include multiple small sensor nodes (SN), they are severely constrained, so resource management geared toward energy efficiency is crucial in this kind of network. Minimizing the power to interpret, transmit, and store data between various sensors poses important challenges. Experts have considered various ways to address these issues that unavoidably affect the network’s performance: reducing energy usage while maintaining system throughput remains the primary research issue. Another important concern relates to network security. Specifically, intrusion detection and avoidance are major concerns. In this work, we introduce the meta-heuristic-based secure and energy-efficient routing (MHSEER) protocol for WSN-IIoT. The protocol learns the forwarding decisions using the number of hops, connection integrity characteristics, and accumulated remaining energy. To make the method more secure, the protocol also employs counter-encryption mode (CEM) to encrypt the data. A meta-heuristics study designed to achieve reliable learning is used in the suggested protocol. The protocol consists of two stages. The first stage uses a heuristics method to improve the option for dependable data routing. Security based on a computationally simple and random CEM is accomplished in the second stage. The proposed MHSEER protocol has been compared to the secure trust routing protocol for low power (Sectrust-RPL), heuristic-based energy-efficient routing (HBEER), secure and energy-aware heuristic-based routing (SEHR), and secure energy-aware meta-heuristic routing (SEAMHR) in terms of packet drop ratio, throughput, network delay, energy usage, and faulty pathways. The proposed protocol increases throughput to 95.81% and decreases the packet drop ratio, packet delay, energy consumption, and faulty pathways to 5.12%, 0.10 ms, 0.0102 mJ, and 6.51%, respectively.

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E-FEERP: Enhanced Fuzzy Based Energy Efficient Routing Protocol for Wireless Sensor Network

Cited by 26 publications

References 35 publications

OEE-WCRD: Optimizing Energy Efficiency in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics

OEE-WCRD: Optimizing Energy Efficiency in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics

A conditioned reflex action embedded associative context learning‐based energy efficient paradigm in smart city milieu

MHSEER: A Meta-Heuristic Secure and Energy-Efficient Routing Protocol for Wireless Sensor Network-Based Industrial IoT

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