Lifetime and Energy Efficiency Improvement Techniques for Hierarchical Networks

Suhas, A. R.; M, Manoj Priyatham

doi:10.35940/ijeat.a1013.1291s619

Cited by 8 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section discusses the simulation and network tool with different evaluation criteria. For the urban environment, the urban mobility simulation with the Network Simulator Version 2 (NS2) simulator evaluates the proposed research (Suhas and Manoj Priyatham, 2019). The experiments are conducted using the NS2 simulator and evaluated using different performance metrics (computational cost, accuracy, precision, recall, communication overhead, etc., and simple attack and opinion tampering attacks).…”

Section: Resultsmentioning

confidence: 99%

“…The BDN-based RFO algorithm is used to detect the obstacles that are present in both the LoS and nLoS conditions to reduce the accident rate. The input parameters to determine the distance between the obstacles in both LoS and nLoS conditions are radio signal strength, PDR, distance, velocity and bandwidth (Suhas and Manoj Priyatham, 2019). The LoS blocked by both the movable and fixed objects is determined in this phase.…”

Section: Measurement Module Based On Plausibilitymentioning

confidence: 99%

See 1 more Smart Citation

An optimized deep learning-based trust mechanism In VANET for selfish node detection

Jyothi¹,

Patil²

2021

IJPCC

View full text Add to dashboard Cite

Purpose This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection. Design/methodology/approach The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance. Findings A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead. Practical implications The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks. Originality/value This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Measurement Module Based On Plausibilitymentioning

confidence: 99%