Vehicular Intelligence System: Time-Based Vehicle Next Location Prediction in Software-Defined Internet of Vehicles (SDN-IOV) for the Smart Cities

Rani, Preeti; Hussain, Naziya; Khan, Rais Abdul Hamid; Sharma, Yogesh; Shukla, Piyush Kumar

doi:10.1007/978-3-030-82800-4_2

Cited by 32 publications

(11 citation statements)

References 40 publications

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“…(8) e standard deviation of cluster size: to calculate the SN distribution between clusters, the standard deviation (SD) of cluster size (σ SD ) is used. is standard deviation (SD) is defined by equation (8), where m indicates the number of optimal clusters, M i indicates the number of optimal cluster i members, and M indicates the average number of optimal cluster i members: As shown in Figure 3, the benchmark performance of EMEECP-IoT is compared to that of other existing approaches that create dead SNs per round. A total of 200 SNs have been considered for simulation.…”

Section: Results Simulation and Analysismentioning

confidence: 99%

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EMEECP-IOT: Enhanced Multitier Energy-Efficient Clustering Protocol Integrated with Internet of Things-Based Secure Heterogeneous Wireless Sensor Network (HWSN)

Wang

Zhang

2022

Security and Communication Networks

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In recent years, Internet of Things (IoT)-based constrained wireless sensor networks (WSN) have been observed with radical developments. The aligned phenomenon such as maximizing resource utilization and the delivery of efficient services is the need of the hour to suffice for the purpose of seamless connectivity. Typically, IoT devices have limited battery life and are deployed in remote areas. As a result, limited battery life limits the use of networks in some situations. To develop energy-efficient techniques for the IoT requires innovative ideas where the current techniques of using WSNs cannot directly apply to the IoT due to protocol issues, SLAs (service level agreements), scalability issues, and complexity levels. To overcome these issues, this article proposes an Enhanced Multitier Energy-Efficient Clustering Protocol integrated with the Internet of Things (EMEECP-IOT) based on the heterogeneous wireless sensor networks (HWSN) technique. The proposed approach has been compared with the current and standard protocols with respect to the parameters such as network stability, packets received, average throughput, standard deviation (SD), and residual energy. The simulation results show significantly better performance of the proposed EMEECP-IoT-based HWSN technique than the existing technique. Extensive performance factors show that the proposed EMEECP-IOT extends the network lifetime by 35% and decreases the network’s energy consumption by 21%. This proposed approach can be applied to save energy and enhance network lifetime for WSN and IoT-oriented applications.

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Section: Results Simulation and Analysismentioning

confidence: 99%

“…Different sensor nodes (SNs) with diverse networking knowledge capabilities are interconnected to provide the users with information and different high-quality services efficiently and at any time and place [5]. e WSN is different from the VANET [6][7][8] network due to its mobility [9].…”

Section: Introductionmentioning

confidence: 99%

EMEECP-IOT: Enhanced Multitier Energy-Efficient Clustering Protocol Integrated with Internet of Things-Based Secure Heterogeneous Wireless Sensor Network (HWSN)

Wang

Zhang

2022

Security and Communication Networks

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“…Rani et al proposed a unique time-frequency spectrum estimation approach for multichannel data [ 25 ]. Furthermore, it is applicable to the epileptic type of electroencephalography (EEG).…”

Section: Literature Reviewmentioning

confidence: 99%

An IoMT‐Based Approach for Real‐Time Monitoring Using Wearable Neuro‐Sensors

Bhatt

Sharma

2023

Journal of Healthcare Engineering

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The Internet of Things (IoT) has demonstrated over the past few decades to be a powerful tool for connecting various medical equipment with in-built sensors and healthcare professionals to deliver superior health services that also reach remote areas. In addition to reducing healthcare costs, increasing access to clinical services, and enhancing operational effectiveness in the healthcare industry, it has also enhanced patient health safety. Recent research has focused on using EEG to assist and comprehend brain changes in rehabilitation facilities. These technologies can spot fluctuations in EEG constraints during treatment, which could result in more effective therapy and better functional outcomes. As a result, we have tried to use an IoT-based system for real-time monitoring of the constraints. Another unknown patient who is suffering from acute ischemic stroke may experience stroke-in-evolution or an early worsening of neurological symptoms, which is frequently associated with poor clinical outcomes. Because of this, managing an acute stroke requires early detection of these indications. The present investigation work will act as a standard reference for academic researchers, medical professionals, and everyone else involved in the use of IoMT. This study aims to anticipate strokes sooner and prevent their consequences by early intervention using an Internet of Things (IoT)-based system. Also, this study proposes usage of wearable equipment that can monitor and analyze brain signals for improved treatment and the prevention of stroke-related complications.

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“…As a result, they are particularly well suited for the analysis of no stationary signals whose spectral features change with time. The SLEX functions are orthogonal and localised in both time and frequency at the same time because they are generated by applying a projection operator rather than a window or taper to the input signals [ 29 ].…”

Section: Background Analysismentioning

confidence: 99%

Wearable Sensors with Internet of Things (IoT) and Vocabulary-Based Acoustic Signal Processing for Monitoring Children’s Health

Nagwanshi

Noonia

Tiwari³

et al. 2022

Computational Intelligence and Neuroscience

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The brain is the most complex organ in the human body, and it is also the most complex organ in the whole biological system, making it the most complex organ on the planet. According to the findings of current studies, modern study that properly characterises the EEG data signal provides a clear classification accuracy of human activities which is distinct from previous research. Various brain wave patterns related to common activities such as sleeping, reading, and watching a movie may be found in the Electroencephalography (EEG) data that has been collected. As a consequence of these activities, we accumulate numerous sorts of emotion signals in our brains, including the Delta, Theta, and Alpha bands. These bands will provide different types of emotion signals in our brain as a result of these activities. As a consequence of the nonstationary nature of EEG recordings, time-frequency-domain techniques, on the other hand, are more likely to provide good findings. The ability to identify different neural rhythm scales using time-frequency representation has also been shown to be a legitimate EEG marker; this ability has also been demonstrated to be a powerful tool for investigating small-scale neural brain oscillations. This paper presents the first time that a frequency analysis of EEG dynamics has been undertaken. An augmenting decomposition consisting of the “Versatile Inspiring Wavelet Transform” and the “Adaptive Wavelet Transform” is used in conjunction with the EEG rhythms that were gathered to provide adequate temporal and spectral resolutions. Children’s wearable sensors are being used to collect data from a number of sources, including the Internet. The signal is conveyed over the Internet of Things (IoT). Specifically, the suggested approach is assessed on two EEG datasets, one of which was obtained in a noisy (i.e., nonshielded) environment and the other was recorded in a shielded environment. The results illustrate the resilience of the proposed training strategy. Therefore, our method contributes to the identification of specific brain activity in children who are taking part in the research as a result of their participation. On the basis of several parameters such as filtering response, accuracy, precision, recall, and F-measure, the MATLAB simulation software was used to evaluate the performance of the proposed system.

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Vehicular Intelligence System: Time-Based Vehicle Next Location Prediction in Software-Defined Internet of Vehicles (SDN-IOV) for the Smart Cities

Cited by 32 publications

References 40 publications

EMEECP-IOT: Enhanced Multitier Energy-Efficient Clustering Protocol Integrated with Internet of Things-Based Secure Heterogeneous Wireless Sensor Network (HWSN)

EMEECP-IOT: Enhanced Multitier Energy-Efficient Clustering Protocol Integrated with Internet of Things-Based Secure Heterogeneous Wireless Sensor Network (HWSN)

An IoMT‐Based Approach for Real‐Time Monitoring Using Wearable Neuro‐Sensors

Wearable Sensors with Internet of Things (IoT) and Vocabulary-Based Acoustic Signal Processing for Monitoring Children’s Health

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