Smart Charging Navigation for VANET Based Electric Vehicles

Aravindhan, K.; Sangeetha, S.; Periyakaruppan, K.; E, MANOJ; Sivani, R; Ajithkumar, S.

doi:10.1109/icaccs51430.2021.9441842

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…The so-called parameter Pe strongly influences cluster formation; the probability that a packet will stop being forwarded is this parameter. To put it another way, a packet that is stored in the buffer of a vehicle can, with a probability of 1 Pe, be sent to a cooperative immediate next hop; Otherwise, forwarding will most likely come to a stop Pe if there is no other cooperating vehicle in transmission range.It was established in [5] that 𝑃 𝑒 = 𝑒 −𝜌𝑅 where 𝜌 = 𝜆 𝐸|𝑉| represents vehicular density. However, due to the fact that [5] did not take into account existence of uncooperative vehicles, formulation of Pe needs some tweaking.…”

Section: Multihop Path Based Edge Computing Based Navigation Analysismentioning

confidence: 99%

“…To put it another way, a packet that is stored in the buffer of a vehicle can, with a probability of 1 Pe, be sent to a cooperative immediate next hop; Otherwise, forwarding will most likely come to a stop Pe if there is no other cooperating vehicle in transmission range.It was established in [5] that 𝑃 𝑒 = 𝑒 −𝜌𝑅 where 𝜌 = 𝜆 𝐸|𝑉| represents vehicular density. However, due to the fact that [5] did not take into account existence of uncooperative vehicles, formulation of Pe needs some tweaking. Since cooperative vehicles are the only types of vehicles that contribute to data forwarding and arrive at a rate of (1 − P)λarrivals per unit of time, it follows that in our situation,𝑃 𝑒 = 𝑒 −(1−𝑃)𝜌𝑅 Moreover, it was…”

Section: Multihop Path Based Edge Computing Based Navigation Analysismentioning

confidence: 99%

“…Inter-vehicle attacks can damage entire network more severely than intra-vehicle attacks. Intrusion detection systems (IDS) have been widely used to detect abnormal network behaviors and mitigate VANET inter-vehicle attacks [5]. There are primarily three distinct architectures used in current IDS solutions: centralized, distributed, and hybrid IDS.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning algorithms and mechanisms in navigation for vehicular crowd managementsystems in real-time for smart transportation

Kaswan

Balu

Ojha

et al. 2023

Preprint

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Accurate predictions of vehicle mobility and density are necessary for a wide range of mobile applications, including VANETs, crowdsourcing, participatory sensing, network provisioning, and shared transportation. The difficulty of forecasting is exacerbated by the scarcity and scale of vehicular mobility data. Crowd management and navigation analysis of vehicular networks that make use of deep learning techniques are the focus of this study. Multihop path based edge computing is used to analyze vehicular network navigation, and a markov spatio reinforcement neural network is used to manage vehicular crowds. The number of vehicles in the network and its navigation analysis are the basis for the experimental analysis. Throughput, data transmission rate, latency, network traffic analysis, and scalability are the parameters analyzed.proposed technique attained data transmission rate of 94%, latency of 62%, throughput of 61%, network traffic analysis of 59%, scalability of 63%.

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Section: Multihop Path Based Edge Computing Based Navigation Analysismentioning

confidence: 99%

Section: Multihop Path Based Edge Computing Based Navigation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning algorithms and mechanisms in navigation for vehicular crowd managementsystems in real-time for smart transportation

Kaswan

Balu

Ojha

et al. 2023

Preprint

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“…It is expanding quickly and connecting billions of gadgets that we use every day. According to a Gartner event analysis, by 2020, there will be almost 25 billion connected things [ 1 ]. These interconnected devices improve daily tasks and create clever solutions.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Multilayered Neural Network-Based Intrusion Detection System in the Internet of Things Network

Sangeetha

Mani

Maheshwari

et al. 2022

Computational Intelligence and Neuroscience

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A large array of objects is networked together under the sophisticated concept known as the Internet of Things (IoT). These connected devices collect crucial information that could have a big impact on society, business, and the entire planet. In hostile settings like the internet, the IoT is particularly susceptible to multiple threats. Standard high-end security solutions are insufficient for safeguarding an IoT system due to the low processing power and storage capacity of IoT devices. This emphasizes the demand for scalable, distributed, and long-lasting smart security solutions. Deep learning excels at handling heterogeneous data of varying sizes. In this study, the transport layer of IoT networks is secured using a multilayered security approach based on deep learning. The created architecture uses the intrusion detection datasets from CIC-IDS-2018, BoT-IoT, and ToN-IoT to evaluate the suggested multi-layered approach. Finally, the new design outperformed the existing methods and obtained an accuracy of 98% based on the examined criteria.

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