R2SCDT: robust and reliable secure clustering and data transmission in vehicular ad hoc network using weight evaluation

Hosmani, Sridevi; Mathapati, Basavaraj

doi:10.1007/s12652-021-03414-3

Cited by 8 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 35 ], authors S.Hosmani and B.Mathapati focus shifts to Robust and Reliable Secure Clustering and Data Transmission (R2 SCDT), which is based on trust values, providing secure and reliable communication. The authors A. Ali et al in [ 36 ] introduce the Harris Hawks Optimization (HHO) algorithm to address network fragmentation, scalability, overhead, and packet routing issues, to improve the performance effectively.…”

Section: Literature Surveymentioning

confidence: 99%

“…rand i w1 is a random number between [0, 1], Z i is a random number between [-1, 1] that changes at each iteration, the value gives the vultures hunger state based on the condition given in Eq (34). itr i denotes the current iteration number, mitr i denote the total number of iterations, k is a parameter with a fixed number set which indicates the optimization operation based on the condition given in Eq (35). h t is a random number between [− 2, 2].…”

Section: Second Phase: Computing Vultures Starvation Ratementioning

confidence: 99%

See 1 more Smart Citation

Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system

Yerrathi,

Pakala

2024

PLoS ONE

View full text Add to dashboard Cite

The Internet of Vehicles (IoV) is one of the developing paradigms that integrates the automotive industry with the Internet of Things (IoT). The evolution of traditional Vehicular Ad-hoc Networks (VANETs), which are a layered framework for Intelligent Transportation Systems (ITS), is employed to provide Quality of Service (QoS) to end users in hazardous situations. VANETs can set up ad-hoc networks and share information among themselves using Peer-to-Peer (P2P) communication. Dynamic properties in VANETs such as dynamic topology, node mobility, sparse vehicle distribution, and bandwidth constraints can have an impact on scalability, routing, and security. This can result in frequent link failures, instability, reliability, and QOS concerns, as well as the inherent complexity of NP-hard problems. Researchers have proposed several techniques to achieve stability; the most prominent one is clustering, which relies on mobility metrics. However, existing clustering techniques generate overwhelming clusters, resulting in greater resource consumption, communication overhead, and hop count, which may lead to increased latency. Therefore, the primary objective is to achieve stability by increasing cluster lifetime, which is accomplished by generating optimal clusters. A nature-inspired meta-heuristic algorithm titled African Vulture Optimization Based Clustering Algorithm (AVOCA) is implemented to achieve it. The proposed algorithm can achieve load optimization with efficient resource utilization by mitigating hidden node challenges and ensuring communication proficiency. By maintaining an equilibrium state between the exploration and exploitation phases, AVOCA avoids local optima. The paper explores a taxonomy of the techniques used in Cluster Head (CH) selection, coordination, and maintenance to achieve stability with lower communication costs. We evaluated the effectiveness of AVOCA using various network grid sizes, transmission ranges, and network nodes. The results show that AVOCA generates 40% less clusters when compared to the Clustering Algorithm Based on Moth-Flame Optimization for VANETs (CAMONET). AVOCA generates 45% less clusters when compared to Self-Adaptive Multi-Kernel Clustering for Urban VANETs (SAMNET), AVOCA generates 43% less clusters when compared to Intelligent Whale Optimization Algorithm (i-WOA) and AVOCA generates 38% less clusters when compared to Harris Hawks Optimization (HHO). The results show that AVOCA outperforms state-of-the-art algorithms in generating optimal clusters.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Second Phase: Computing Vultures Starvation Ratementioning

confidence: 99%

Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system

Yerrathi,

Pakala

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Hosmani and Mathapati [22] developed robust and reliable secure clustering and data transmission (RRSCDT) to protect the network against malicious vehicles. In RRSCDT, the selection of secure and optimal cluster head (CH) was done based on the weight values.…”

Section: Introductionmentioning

confidence: 99%

Trust based multi objective honey badger algorithm to secure routing in vehicular ad-hoc networks

Mutalik¹,

Patil²

2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">A vehicular ad-hoc network (VANET) is a set of intelligent vehicles that interact without any fixed infrastructure. Data transmission between each transmitter/receiver pair is accomplished using routing protocols. However, communication over the VANET is vulnerable to malicious attacks, because of the unavailability of fixed infrastructure and wireless communication. In this paper, the trust based multi objective honey badger algorithm (TMOHBA) is proposed to achieve secure routing over the VANET. The TMOHBA is optimized by incorporating different cost functions, namely, trust, end to end delay (EED), routing overhead, energy, and distance. The developed secure route discovery using the TMOHBA is used to improve the robustness against the malicious attacks, for increasing the data delivery. Moreover, the shortest path discovery is used to minimize the delay while improving the security of VANET. The TMOHBA method is evaluated using the packet delivery ratio (PDR), throughput and EED. Existing researches such as hybrid enhanced glowworm swarm optimization (HEGSO) and ad-hoc on-demand distance vector based secure protocol (AODV-SP) are used to evaluate the TMOHBA method. The PDR of the TMOHBA method for 10 malicious attacks is 90.6446% which is higher when compared to the HEGSO and AODV-SP.</span>

show abstract

A novel model for optimal selection of relay bus with maximum link reliability in VANET using hybrid fuzzy niching grey wolf optimization

Vinnarasi,

Karuppiah,

Rose

et al. 2024

Wireless Netw

View full text Add to dashboard Cite

R2SCDT: robust and reliable secure clustering and data transmission in vehicular ad hoc network using weight evaluation

Cited by 8 publications

References 36 publications

Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system

Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system

Trust based multi objective honey badger algorithm to secure routing in vehicular ad-hoc networks

A novel model for optimal selection of relay bus with maximum link reliability in VANET using hybrid fuzzy niching grey wolf optimization

Contact Info

Product

Resources

About