Hybrid energy efficient network using firefly algorithm, PR-PEGASIS and ADC-ANN in WSN

Ali, Shokat; Kumar, Rakesh

doi:10.1016/j.sintl.2021.100154

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…Link Quality (LQ R ) is one of the typical restrictions in the best possible routing and shows the quality of data packets that the destination has received. Equation (7) illustrates the way to calculate it in WSN. The ratio of the number of received to the sent packets is termed LQ R .…”

Section: ) Link Quality (Lq R )mentioning

confidence: 99%

“…(89 : ) = ; < ; = (7) where P r denotes the number of packets received and P t denotes the number of packets transmitted.…”

Section: ) Link Quality (Lq R )mentioning

confidence: 99%

“…To address energy-based challenges, hierarchical clustering techniques, which have advantages related to sustainability and effectiveness, are being considered as solutions for WSNs [6]. Utilizing data collection and clustering approaches can effectively regulate node energy in WSNs [7][8]. A well-known cluster-based routing technique called LEACH involves sensor nodes (non-CH) to collect data through their sensing area and send it to the CH [9].…”

Section: Introductionmentioning

confidence: 99%

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Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction

Vikhyath,

Prasad

2023

Eng. Technol. Appl. Sci. Res.

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The significant advances in Wireless Sensor Networks (WSNs) facilitate many latest applications, such as intelligent battlefield, home automation, traffic control, and more. WSNs comprise small autonomously organized sensor nodes that are powered by batteries. The processes of collecting information and data storage, processing, and transmission deplete the energy of these small devices. Energy efficiency is still a major issue to address in WSN routing. Clustering is the best method that has been developed to reduce node energy consumption. However, current clustering methods are unable to effectively distribute the energy requirements of the nodes without considering energy characteristics, number of nodes, and flexibility. This study proposed a new cluster-based routing model for WSNs and emphasized the need for an improved clustering process with new optimization techniques. In particular, the improved DeepMaxout model was adopted to predict the energy of the nodes. Cluster Head (CH) selection is performed considering the nodes' energy as a prime factor. After choosing the CH, the CIOO algorithm incorporates new link quality and trust evaluations while determining the routing process. Finally, a comparison of energy utilization factors was performed between the suggested and traditional approaches.

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Section: ) Link Quality (Lq R )mentioning

confidence: 99%

“…(89 : ) = ; < ; = (7) where P r denotes the number of packets received and P t denotes the number of packets transmitted.…”

Section: ) Link Quality (Lq R )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction

Vikhyath,

Prasad

2023

Eng. Technol. Appl. Sci. Res.

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“…Wireless sensor networks (WSNs) unavoidably face issues like network lifetime, energy, and security. Out of these, network lifetime and energy are the most critical and highly sensitive problems in a WSN [3][4][5]. As the energy depletion of each node can interrupt data transmission from certain regions, causing the network to become dysfunctional.…”

Section: Introductionmentioning

confidence: 99%

ANN Based Clusterhead Selection Process in WSN

GANGAL,

SESLI,

HACIOGLU

2024

Preprint

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Wireless sensor networks often face a significant challenge due to their limited energy capacity. To address this challenge, numerous studies are currently underway to explore innovative ways to optimize energy usage. This study proposes an energy-efficient cluster head selection method that is supported by artificial neural networks for cluster-based sensor networks, thereby enhancing their energy efficiency. The neural network is placed in each sensor node. The nodes decide to be the cluster head according to the threshold they set using the ANN, without the need for centralized control. According to the simulation studies conducted, the ANN trained with 5-5-5 neurons survived for 3497 rounds and successfully transmitted 96757 packets to the sink. In contrast, the LEACH algorithm could only survive for 1773 rounds and transmitted 61766 packets to the sink under the same conditions. The study confidently concluded that there was a significant and noteworthy improvement in both the network lifetime and the number of packets sent to the base station. On the other hand, it was also observed that the proposed method showed a notable improvement when compared with similar recent studies conducted with the same parameters by a factor ranging from 3.63 to 6.26.

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“…It likewise ensures proficient resource exploitation and the board of restricted network assets, saving energy and monitoring the solidness of the organization [6]. Clustering models can be derived in the WSN to assuring effective resource utilization, reduced transmission overhead, minimum energy consumption, and low interference [7,8]. It involves the process of portioning the network into several groups of clusters.…”

Section: Introductionmentioning

confidence: 99%

Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

Surendran¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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Recently, wireless sensor networks (WSNs) find their applicability in several real-time applications such as disaster management, military, surveillance, healthcare, etc. The utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and governments. Real-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor nodes. Therefore, the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the network. In this aspect, this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering (LOWGO-EAC) scheme for WSN-assisted real-time disaster management. The major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster regions. To achieve this, the LOWGO-EAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning (LOBL) concept with the traditional WGO algorithm to improve the convergence rate. In addition, the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy (RE), distance to the base station (BS) (DBS), and node degree (ND). The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management scenarios. The experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

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Hybrid energy efficient network using firefly algorithm, PR-PEGASIS and ADC-ANN in WSN

Cited by 10 publications

References 13 publications

Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction

Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction

ANN Based Clusterhead Selection Process in WSN

Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

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