IEDA-HGEO: Improved Energy Efficient with Clustering-Based Data Aggregation and Transmission Protocol for Underwater Wireless Sensor Networks

Joshi, Shubham; Anithaashri, T.P; Rastogi, Ravi; Choudhary, Gaurav; Dragoni, Nicola

doi:10.3390/en16010353

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…To securely transmit healthcare data, active sensors are selected by the Archimedes algorithm and the shortest path is selected by an attribute-centered binary scheme [29]. Similarly, clustering and data aggregation is performed for underwater WSNs [30,31]. The first layer contains medical sensors to capture the patient's condition and data is transferred to a fog server.…”

Section: Clustering-based Data Aggregation Schemesmentioning

confidence: 99%

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT

Khan,

Cao,

Tang

et al. 2024

Future Internet

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The rapid development of the Internet of Things (IoT) has opened the way for transformative advances in numerous fields, including healthcare. IoT-based healthcare systems provide unprecedented opportunities to gather patients’ real-time data and make appropriate decisions at the right time. Yet, the deployed sensors generate normal readings most of the time, which are transmitted to Cluster Heads (CHs). Handling these voluminous duplicated data is quite challenging. The existing techniques have high energy consumption, storage costs, and communication costs. To overcome these problems, in this paper, an innovative Energy-Efficient Fuzzy Data Aggregation System (EE-FDAS) has been presented. In it, at the first level, it is checked that sensors either generate normal or critical readings. In the first case, readings are converted to Boolean digit 0. This reduced data size takes only 1 digit which considerably reduces energy consumption. In the second scenario, sensors generating irregular readings are transmitted in their original 16 or 32-bit form. Then, data are aggregated and transmitted to respective CHs. Afterwards, these data are further transmitted to Fog servers, from where doctors have access. Lastly, for later usage, data are stored in the cloud server. For checking the proficiency of the proposed EE-FDAS scheme, extensive simulations are performed using NS-2.35. The results showed that EE-FDAS has performed well in terms of aggregation factor, energy consumption, packet drop rate, communication, and storage cost.

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Section: Clustering-based Data Aggregation Schemesmentioning

confidence: 99%

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT

Khan,

Cao,

Tang

et al. 2024

Future Internet

View full text Add to dashboard Cite

show abstract

“…The sensor node receives energy from the battery to conduct works such as data sensing, aggregation, data receipt and transfer. 34,38,39 The nature of all the nodes is presumed to be homogeneous. BS receives the information.…”

Section: Network Model and Assumptionsmentioning

confidence: 99%

“…A non‐rechargeable battery is included with each sensor node. The sensor node receives energy from the battery to conduct works such as data sensing, aggregation, data receipt and transfer 34,38,39 . The nature of all the nodes is presumed to be homogeneous.…”

Section: Preliminariesmentioning

confidence: 99%

Load balancing clustering and routing for IoT‐enabled wireless sensor networks

Singh

Anand

2023

Int J Network Mgmt

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Internet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy‐efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c‐means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO‐based clustering (GWO‐C) and Cat Swarm Optimization based Energy‐Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria.

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Hybrid Red Deer and Improved Fireworks Optimization Algorithm–based Clustering Protocol for improving network longevity with energy stability in WSNs

Anupkant,

Yugandhar

2024

Int J Communication

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SummaryClustering of nodes in wireless sensor networks (WSNs) plays a dominant role in gathering environmental data from the specific area of monitoring over which they are deployed for achieving a reactive decision‐making process. The design and development of an energy‐efficient clustering strategy with a potential cluster head (CH) selection process is a herculean task. This development of the CH selection scheme is referred as a non‐deterministic polynomial (NP) hard problem as it needs to optimize different parameters that influence the selection of potential sensor nodes as CH. It needs to concentrate on the process of enhancing network lifespan with energy efficiency by selecting optimal routing path during data dissemination activity. In this paper, a Hybrid Red Deer and Fireworks Optimization Algorithm (HIRDIFOA)–based energy efficient clustering technique is proposed for extending network lifespan with maximized stability in the network energy. This proposed HRDFOA integrated the exploration capability of Improved Red Deer Optimization (IRDOA) with the maximized exploitation tendency of the Modified Firework Optimization Algorithm (MFWOA) during the CH selection process. It facilitated the CH selection by evaluating the fitness functions that integrate the factors of residual energy (RE), distance between sensor and CH, distance between CH and sink, and radius of communication. It significantly adopted MWFOA for achieving sink node mobility such that data can be reliably routed from CH to sink. The outcomes of HRDFOA confirm better throughput of 19.21% with reduced energy consumption of 17.42% and reduced end‐to‐end delay of 18.52% in contrast to the competitive CH selection schemes used for investigation.

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IEDA-HGEO: Improved Energy Efficient with Clustering-Based Data Aggregation and Transmission Protocol for Underwater Wireless Sensor Networks

Cited by 10 publications

References 27 publications

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT

Load balancing clustering and routing for IoT‐enabled wireless sensor networks

Hybrid Red Deer and Improved Fireworks Optimization Algorithm–based Clustering Protocol for improving network longevity with energy stability in WSNs

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