Improved Bio Inspired Energy Efficient Clustering Algorithm to Enhance QoS of WSNs

Chavan, S. D.; Kulkarni, A. V.

doi:10.1007/s11277-019-06658-7

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…The Bit Error Rate (BER) is the number of bits that go wrong in a transmission, divided by the total number of bytes transferred during the study period. For example: if there are 1000 errors in 100000 transfers, then the BER would be 𝐵𝐸𝑅 𝑡𝑛𝑏𝑒/𝑡𝑛𝑏𝑟 ------ (9) When discussing the performance of any sensor network, PDR is defined as the ratio of how many packets were successfully received by all cluster heads to the number of packets that were generated. These two parameters are related to how well a sensor network can transfer information from one place to another.…”

Section: Protocol Parameters and Qos Metricsmentioning

confidence: 99%

“…This technique is further applied to recommend a single communication channel among all available channels based on the most appropriate characteristics selected for each channel. In [9] proposed the Energy Efficient Clustering Protocol for WSNs. The IBeeCup method, an extension of the Bee Cup method that finds the shortest path, was used to implement the new clustering-based route.…”

Section: Introductionmentioning

confidence: 99%

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A cross-layer analytical model for optimization of performance in wireless sensor networks

Kumar¹,

G²

2023

INDJCSE

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Objectives:To develop an analytical model to improve Qos in WSN. Method: Cross-layer protocol architecture is developed to improve energy consumption, delay, and packet delivery in a wireless sensor network. The Routing protocol to Cross layer Communication (RPTC) protocol was used for routing the packets through cross-layer interactions which helped to improve the QoS analytical results for common collector points (CCP) node selection, routing policies, and energy optimization. In the earlier study, a random routing policy was in place which did not take into account any proper mechanisms. Here, we define a policy for routing that takes into consideration Extended TDMA energy conservation techniques by choosing the shortest path with extended coverage. This will eliminate collisions, idle listening times, and entering inactive states until their allocated time slots. The extended TDMA helps reduce packet delays and guarantees reliable communication. The extension allows nodes with the largest one-hop neighbor to select multiple timeslots for themselves, which are scheduled in the MAC layer. We compared our analytical model based on delay, power consumption, and energy consumed in relation to simulation time. Findings: We found that the cross-layer analytical model achieves 20% less delay, power, and 30% less energy consumption when compared to existing cross-layer protocols such as the Hybrid Energy-Efficient Distributed Protocol (HEED), Co-ordinate Test Optimized Protocol (CTP), and Tree Cluster-Based Data Gathering Algorithm (TCBDGA). Novelty: WSNs need to be optimized across several protocol levels in order to achieve good interactions. QoS parameters are explored for this purpose and a cross-layer protocol called RPTC is analyzed using design parameters and resource levels based on the Poisson variate model (PVM). This helps improve network lifetime.

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Section: Protocol Parameters and Qos Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A cross-layer analytical model for optimization of performance in wireless sensor networks

Kumar¹,

G²

2023

INDJCSE

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“…The role of the ABC was to extend the performance of the WSN and achieve better exploration and exploitation during CH selection [102]. For the improved ABC, the goal was to obtain optimal CHs for WSNs [103] and find the shortest routing path [104]. The fractional Grasshopper Optimization Algorithm (Fractional-GOA) was utilized to reduce energy usage by implementing sleep/wake scheduling in the nodes [105].…”

Section: Hybrid Meta-heuristicsmentioning

confidence: 99%

Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols

et al. 2021

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Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.

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Cache-Aware Query-Broadcast to Improve QoS of Routing Protocols in MANETs

Ahmad¹,

Sethi

Ahmed

2020

Wireless Pers Commun

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Improved Bio Inspired Energy Efficient Clustering Algorithm to Enhance QoS of WSNs

Cited by 6 publications

References 14 publications

A cross-layer analytical model for optimization of performance in wireless sensor networks

A cross-layer analytical model for optimization of performance in wireless sensor networks

Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols

Cache-Aware Query-Broadcast to Improve QoS of Routing Protocols in MANETs

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