An Enhanced Cluster Head Technique for Energy Consumption in Data Fusion

Brumancia, E.; Samuel, S. Justin

doi:10.1109/icict48043.2020.9112583

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…The DP has a restricted amount of battery, also suffers from various heavy packet loads with power consumption. The data collection performed on the individual DP can have duplicate information and this can be reduced with the help of the fusion method and also helps in the improvement of heal ratio for the GDPN (Brumancia and Samuel, 2020). The selection of SDP is done based on the combination of remaining energy, distance with respect to other SDPs along with a number of DPs present in a specific GPDN.…”

Section: Related Workmentioning

confidence: 99%

Heal nodes specification improvement using modified CHEF method for group based detection point network

Ar¹,

Priyatham²

2021

IJPCC

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Purpose The purpose of the paper is to make use of multiple parameters namely; residual energy, closeness to centre and mobility of detection point (DP) for the selection of detection point network (DPN). In the novel method proposed, the path will have less number of DPs participating in the entire DPN. Design/methodology/approach The proposed novel method will find out the special detection point (SDP) based on three criteria, namely, the amount of mobility for DP, the amount of remaining energy and the amount of distance between two DPs. This proposed method is an attempt to resolve the network lifetime problems during the communication of DPs over a period of time. It is developed for increasing the lifetime ratio, throughput, residual energy, number of alive nodes. Findings The simulation results of the novel method show the improvement over the existing methods investigated based on the lifetime ratio, throughput, remaining energy and alive nodes. Practical implications In the proposed method, the communication is done between different DPs in the network. The commutation is done using SDPs only from one cluster to another cluster. It is proposed for the implementation of energy efficient data sensing in mobile communication networks. Originality/value It is a significant mechanism for energy efficient data sensing of one DP to another DP of different clusters in the network. The total energy consumed for a period of time by the network is significantly reduced from the novel method.

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Section: Related Workmentioning

confidence: 99%

Heal nodes specification improvement using modified CHEF method for group based detection point network

Ar¹,

Priyatham²

2021

IJPCC

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“…Dense networks require complicated management solutions. Some data fusion systems can manage message losses, delays, and discards, allowing for greater flexibility in network technology and data distribution [4], [5]. Data fusion analyzes all sensory data to get more exact information.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient data fusion approach using squirrel search optimization and recurrent neural network

Varatharajan,

Ramasamy,

Marappan

et al. 2023

IJEECS

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Sensor networks have helped wireless communication systems. Over the last decade, researchers have focused on energy efficiency in wireless sensor networks. Energy-efficient routing remains unsolved. Because energyconstrained sensors have limited computing capabilities, extending their lifespan is difficult. This work offers a simple, energy-efficient data fusion technique employing zonal node information. Using the witness-based data fusion technique, the evaluated network lifetime, energy consumption, communication overhead, end-to-end delay, and data delivery ratio. Energyefficient data fusion optimizes energy utilization using squirrel search optimization and a recurrent neural network. The method allows the system to recognize a sensor with excessive energy dissipation and relocate data fusion to a more energy-efficient node. The proposed model was compared against artificial neural network-particle swarm optimization (ANN-PSO), cuckoo optimization algorithm-back propagation neural network (COABPNN), Elman neural network-whale optimization algorithm (ENN-WOA), and extreme learning machine-particle swarm optimization (ELM-PSO). The model achieved 94.50% network lifetime, 26.63% communication overhead, 93.85% data delivery ratio, 10.50 ms end-to-end delay, and 282 J energy usage.

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An Enhanced Cluster Head Technique for Energy Consumption in Data Fusion

Cited by 2 publications

References 14 publications

Heal nodes specification improvement using modified CHEF method for group based detection point network

Heal nodes specification improvement using modified CHEF method for group based detection point network

Energy efficient data fusion approach using squirrel search optimization and recurrent neural network

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