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The human brain is the central organ of the human system. Many people in the world cannot move on their own and can't control things on their own. A person whose brain is active can control things using the neuro-controlled robot car. It is interesting to all types of people to measure their concentration and piece level of mind with the neuro sky...
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One notable method for recording brainwaves to identify neurological problems is electroencephalography (hereafter EEG). A trained neuro physician can learn more about how the brain functions through the use of EEGs. However conventionally, EEGs are only used to examine neurological problems (Eg. Seizures). But abnormal links to neurological circui...
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... For large WSNs, regional routing is an alternative because it only refers to surrounding and nearby facts to make forwarding decisions. This method is commonly used in the [7,43] multi-hop WSN and ad-hoc wireless networks [6,12,44]. The proposed routing technique is focused on a physical MAC protocol without any loss of data packets. ...
... In every wireless network, sensors lose energy fundamentally in packets transmitting and receiving, and listening idle while inactive (sleeping) mode [41,42]. They do not find energy usage in sleep mode because no packets are to be sent to BS [6,42,44]. The radio model [7], which shows energy usage for transmission (E xt ) and going to receiving (E xr ), shows l bits of packets in the Eq. ...
Wireless Sensor Network (WSN) fulfills the requirements to solve real-life applications due to their unattended nature. But, the main constraint faced by researchers is the energy available with sensor nodes. The network should have a longer lifetime to offer better data aggregation. So there is a need to prolong the life of sensor nodes and thus WSN. It is necessary to design energy-efficient operational schemes. One of the most suitable approaches to enhance energy efficiency is the clustering scheme, which enhances the performance parameters of WSN. Thus Adaptive Ranking based Energy-efficient Opportunistic routing (AREOR) is an opportunistic scheme that creates dynamic clusters and offers a longer lifetime to the network due to its heuristic. This scheme is modified based on the cooperation of nodes termed Cooperation Federated-AREOR (CF-AREOR), further enhancing the network's performance parameters. This protocol is implemented using a network simulator, and its performance is analyzed based on the lifetime of the network based on factors like the number of alive nodes with respect to time, the first node dies time (FND), the last node dies time (LND), network lifetime, and even remaining energy in nodes. This protocol shows improved FND time, alive nodes, and longer network lifetime than existing well-known LEACH, AODV, ACO, IACR, and EMCBR. A performance increase in FND is 52% as compared to LEACH, and LND time is up to 84% increase. It shows that most of the network nodes are available for data forwarding & which increases the lifetime of the network significantly.
... For large WSNs, regional routing is an alternative because it only refers to surrounding and nearby facts to make forwarding decisions. This method is commonly used in the [7,43] multi-hop WSN and ad-hoc wireless networks [6,12,44]. The proposed routing technique is focused on a physical MAC protocol without any loss of data packets. ...
... In every wireless network, sensors lose energy fundamentally in packets transmitting and receiving, and listening idle while inactive (sleeping) mode [41,42]. They do not find energy usage in sleep mode because no packets are to be sent to BS [6,42,44]. The radio model [7], which shows energy usage for transmission (E xt ) and going to receiving (E xr ), shows l bits of packets in the Eq. ...
Wireless Sensor Network (WSN) fulfills the requirements to solve real-life applications due to their unattended nature. But, the main constraint faced by researchers is the energy available with sensor nodes. The network should have a longer lifetime to offer better data aggregation. So there is a need to prolong the life of sensor nodes and thus WSN. It is necessary to design energy-efficient operational schemes. One of the most suitable approaches to enhance energy efficiency is the clustering scheme, which enhances the performance parameters of WSN. Thus Adaptive Ranking based Energy-efficient Opportunistic routing (AREOR) is an opportunistic scheme that creates dynamic clusters and offers a longer lifetime to the network due to its heuristic. This scheme is modified based on the cooperation of nodes termed Cooperation Federated-AREOR (CF-AREOR), further enhancing the network’s performance parameters. This protocol is implemented using a network simulator, and its performance is analyzed based on the lifetime of the network based on factors like the number of alive nodes with respect to time, the first node dies time (FND), the last node dies time (LND), network lifetime, and even remaining energy in nodes. This protocol shows improved FND time, alive nodes, and longer network lifetime than existing well-known LEACH, AODV, ACO, IACR, and EMCBR. A performance increase in FND is 52% as compared to LEACH, and LND time is up to 84% increase. It shows that most of the network nodes are available for data forwarding & which increases the lifetime of the network significantly.
... Since there are several patterns and formats, the preprocessing methods are essential to recognize those patterns and formats during the network traffic analysis [13]. • DM: DM is employed for knowledge discovery and it plays a vital role in analyzing network traffic [14]. Two important procedures for DM algorithms are, -Clustering algorithm: Clustering is the process of categorizing the given dataset into clusters or groups for their characteristics. ...
The process of examining the data flow over the internet to identify abnormalities in wireless network performance is known as network traffic analysis. When analyzing network traffic data, traffic classification becomes an important task. The traffic data classification is used to determine whether data in network traffic is in real-time or not. This analysis controls network traffic data in a network and allows for efficient network performance improvement. Real-time and non-real-time data are effectively classified from the given input data set using data mining clustering and classification algorithms. The proposed work focuses on the performance of traffic data classification with high clustering accuracy and low Classification Time (CT). This research work is carried out to fill the gap in the existing network traffic classification algorithms. However, the traffic data classification remained unaddressed for performing the network traffic analysis effectively. Then, we proposed an Enhanced Self-Learning-based Clustering Scheme (ESLCS) using an enhanced unsupervised algorithm and adaptive seeding approach to improve the classification accuracy while performing the real-time traffic data distribution in wireless networks. Test-bed results demonstrate that the proposed model enhances the clustering accuracy and True Positive Rate (TPR) effectively as well as reduces the CT time and Communication Overhead (CO) substantially to compare with the peer-existing routing techniques.
... In the end, the processed information is transferred to the output. The neural network is robust and has fault-tolerance property [25]. It can easily handle fuzzy, noisy, interrupted, and imprecise information. ...
... The hierarchy of networks from the wireless networks to the mobile networks, thus to MANETs and VANET, has been presented. The driver-vehicle model, traffic-flow model communication and application models have been covered [24][25][26]. ...
... Table 6 illustrates the epochs and times required by various standards. To assess the calculation of computational overhead of the proposed and existing VANET methods [24][25][26][27][28][29][30], Figure 15 depicts the computational overhead of signing and validating one message using one of the currently available techniques. As shown in Figure 15, the proposed approach had a reduced computing cost between the signature creation and single signature verification compared with the other existing VANET techniques. ...
Vehicular ad hoc networks (VANETs) allow communication between stationary or moving vehicles with the assistance of wireless technology. Among various existing issues in smart VANETs, secure communication is the key challenge in VANETs with a 5G network. Smart vehicles must communicate with a broad range of advanced road systems including traffic control and smart payment systems. Many security mechanisms are used in VANETs to ensure safe transmission; one such mechanism is cryptographic digital signatures based on public key infrastructure (PKI). In this mechanism, secret private keys are used for digital signatures to validate the identity of the message along with the sender. However, the validation of the digital signatures in fast-moving vehicles is extremely difficult. Based on an improved perceptron model of an artificial neural network (ANN), this paper proposes an efficient technique for digital signature verification. Still, manual signatures are extensively used for authentication across the world. However, manual signatures are still not employed for security in automotive and mobile networks. The process of converting manual signatures to pseudo-digital-signatures was simulated using the improved Elman backpropagation (I-EBP) model. A digital signature was employed during network connection to authenticate the legitimacy of the sender’s communications. Because it contained information about the vehicle on the road, there was scope for improvement in protecting the data from attackers. Compared to existing schemes, the proposed technique achieved significant gains in computational overhead, aggregate verification delay, and aggregate signature size.
... • When the data in an environment are steady or changing gradually [37], spatial correlation exists between the nodes data, whereas temporal correlation exists in a single node [38,39]. • The wireless communication system is widely employed with the growing IoT applications. ...
Energy is a critical factor to be considered in electrical and electronic systems. With the advent of technology, numerous techniques have been developed in communication systems to make the systems reliable, durable, and economic. In modern communication systems, the major requirements of an efficient radio model are to improve the delay, and throughput, reduce the energy consumption, and extend the network lifetime. So, there is a need to design a radio model to improve the quality of service (QoS) parameters. From the limitations identified in the wireless communication networks, the authors proposed an Enhanced Energy-Efficient Fuzzy-based Cognitive Radio scheme for Internet of things (IoT) networks. The proposed protocol is compared with the conventional method, Cognitive Radio-based Heterogeneous Wireless Sensor Area Network. The test-bed results show that the EEFCR protocol has achieved a significant gain on sum goodput versus a number of secondary radio users, average probability of bit error, computational time vs. sensor nodes, delay vs. sensing time. The computational time of the EEFCR protocol is shown to be 5% to 7% and 15% to 21% faster while comparing to CoRHAN and conventional methods. The EEFCR sensing time is reduced up to 80%. The average computational time for 500 nodes is reduced up to 40%. Also, 53% increment is achieved in spectrum utilization. The average bit error is reduced up to 5%.
... If it is a wired medium the attacker must use the wired medium to implement the attacks. Normally it is difficult to access a medium like wireless medium [38]. In a wireless medium, an attacker may simply copy or eavesdrop on the information. ...
A subclass of a Mobile Adhoc Network (MANET) is a Vehicular Adhoc Network (VANET). It consists of self-configuring moving vehicles which are called nodes. It is an inevitable ingredient of intelligent transportation systems. The vehicular network has some permanent devices called roadside units and moving devices called On Board Units (OBU). Every vehicle traveling on the network must possess the OBU. Safety and non-safety tidings are broadcasted in vehicular networks. Even vehicular network is derived from MANET and its characters are discriminated against the MANET. The various unique characteristics of VANETs are high mobility, changes in network topology, size of the network, long distance between the vehicles, frequently changing vehicle density, and limited time. Because of these special features, the traditional security and routing mechanisms are not suitable for VANETs. Also, the safety messages are modified or discarded by the attacker or any other node and it may lead to the loss of privacy, integrity, confidentiality, and authentication of the data. So to enhance the security of VANETs, it is very much essential to invent a secure communication protocol, to protect the infrastructure of the network and the confidentiality of the data. The simulation results reveal that the secure communication using certificate revocation approach, energy-efficient enhanced secure routing protocol, traffic-aware secure routing for VANETs using Hybrid Enhanced Glowworm Swarm Optimization (HEGSO), and trust model for secure communication in VANETs is ensuring the security in VANETs. When compared to peer existing routing protocols, the proposed scheme significantly reduces the packet failure ratio, response time, and throughput. When compared to ARIOR and I-AREOR, the proposed HEGSO technique diminished delays by 20% and 34%, respectively.
Industrial Internet of Things: An Introduction explores the convergence of IoT and machine learning technologies in transforming industries and advancing economic growth. This comprehensive guide examines foundational principles, innovative applications, and real-world case studies that showcase the power of IoT-enabled intelligent systems in enhancing efficiency, sustainability, and adaptability. The book is structured into five parts. The first part introduces industrial IoT concepts, including algorithms, deep learning prediction models, and smart production techniques. The second section addresses machine learning and collaborative technologies, focusing on artificial neural networks, and AI`s role in healthcare and industrial IoT. Subsequent chapters explore real-world applications, such as IoT adoption in healthcare during COVID-19 and intelligent transportation systems. The final sections address advanced IIoT progressions and the role of IoT in energy production using byproducts. Key Features: - Foundational concepts and algorithms for industrial IoT. - Integration of machine learning in IoT systems. - Case studies on healthcare, transportation, and sustainability. - Insights into energy production using IoT.
Vehicular Adhoc Networks (VANETs) is an emerging field that employs a wireless local area network (WLAN) characterized by an ad-hoc topology. Vehicular Ad Hoc Networks (VANETs) comprise diverse entities that are integrated to establish effective communication among themselves and with other associated services. Vehicular Ad Hoc Networks (VANETs) commonly encounter a range of obstacles, such as routing complexities and excessive control overhead. Nevertheless, the majority of these attempts were unsuccessful in delivering an integrated approach to address the challenges related to both routing and minimizing control overheads. The present study introduces an Improved Deep Reinforcement Learning (IDRL) approach for routing, with the aim of reducing the augmented control overhead. The IDRL routing technique that has been proposed aims to optimize the routing path while simultaneously reducing the convergence time in the context of dynamic vehicle density. The IDRL effectively monitors, analyzes, and predicts routing behavior by leveraging transmission capacity and vehicle data. As a result, the reduction of transmission delay is achieved by utilizing adjacent vehicles for the transportation of packets through Vehicle-to-Infrastructure (V2I) communication. The simulation outcomes were executed to assess the resilience and scalability of the model in delivering efficient routing and mitigating the amplified overheads concurrently. The method under consideration demonstrates a high level of efficacy in transmitting messages that are safeguarded through the utilization of vehicle-to-infrastructure (V2I) communication. The simulation results indicate that the IDRL routing approach, as proposed, presents a decrease in latency, an increase in packet delivery ratio, and an improvement in data reliability in comparison to other routing techniques currently available.
