Abstract
In this letter, an optimal arrangement of the Light‐Emitting Diodes (LED) for an indoor Visible Light Communication (VLC) setup is presented by using the simulation model. The idea is to get ...the maximum angle of covering the area of the LED as an access point during user mobility in keeping the connectivity between the user and the access point. As a result, an analysis of data rate measurement is discussed using the maximum angle rotation of the LED as an access point. The authors present validation simulation results for a Line‐of‐Sight configuration. The authors demonstrated that the angle change movement can be carried out efficiently. Results suggest that the directivity at the transmitter can enlarge the area region of the transmitter and user mobility.
As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced ...that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.
The smart grid control applications necessitate real-time communication systems with time efficiency for real-time monitoring, measurement, and control. Time-efficient communication systems should ...have the ability to function in severe propagation conditions in smart grid applications. The data/packet communications need to be maintained by synchronized timing and reliability through equally considering the signal deterioration occurrences, which are propagation delay, phase errors and channel conditions. Phase synchronization plays a vital part in the digital smart grid to get precise and real-time control measurement information. IEEE C37.118 and IEC 61850 had implemented for the synchronization communication to measure as well as control the smart grid applications. Both IEEE C37.118 and IEC 61850 experienced a huge propagation and packet delays due to synchronization precision issues. Because of these delays and errors, measurement and monitoring of the smart grid application in real-time is not accurate. Therefore, it has been investigated that the time synchronization in real-time is a critical challenge in smart grid applications, and for this issue, other errors raised consequently. The existing communication systems are designed with the phasor measurement unit (PMU) along with communication protocol IEEE C37.118 and uses the GPS timestamps as the reference clock stamps. The absence of GPS increases the clock offsets, which surely can hamper the synchronization process and the full control measurement system that can be imprecise. Therefore, to reduce this clock offsets, a new algorithm is needed which may consider any alternative reference timestamps rather than GPS. The revolutionary Artificial Intelligence (AI) enables the industrial revolution to provide a significant performance to engineering solutions. Therefore, this article proposed the AI-based Synchronization scheme to mitigate smart grid timing issues. The backpropagation neural network is applied as the AI method that employs the timing estimations and error corrections for the precise performances. The novel AIFS scheme is considered the radio communication functionalities in order to connect the external timing server. The performance of the proposed AIFS scheme is evaluated using a MATLAB-based simulation approach. Simulation results show that the proposed scheme performs better than the existing system.
The Internet of Vehicles (IoV) and Vehicle‐to‐Everything (V2X) concept have emerged from IoT technology, which refers to connecting many vehicles with various applications to the internet. The 5G new ...radio is based on a cloud‐radio access network (CRAN), considered as the communication infrastructure for IoV. However, due to the significant challenges and issues, researchers have been working on IoV and V2X. One of the main challenges for V2X is resource allocation and management for a high‐speed vehicular environment. This paper discusses and provides complete detail for resource allocation and management for IoV over 5G RAN networks focusing on artificial intelligence techniques. The paper also presented reviews on integrating the multi‐layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management issues.
Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. ...Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.
E-commerce implies an electronic purchasing and marketing process online by using typical Web browsers. As e-commerce is quickly developing on the planet, particularly in recent years, many areas of ...life are affected, particularly the improvement in how individuals regulate themselves non-financially and financially in different transactions. In electronic payment or e-commerce payment, the gateway is a major component of the structure to assure that such exchanges occur without disputes, while maintaining the common security over such systems. Most Internet payment gateways in e-commerce provide monetary information to customers using trusted third parties directly to a payment gateway. Nonetheless, it is recognized that the cloud Web server is not considered a protected entity. This article aims to develop an efficient and secure electronic payment protocol for e-commerce where consumers can immediately connect with the merchant properly. Interestingly, the proposed system does not require the customer to input his/her identity in the merchant’s website even though the customer can hide his/her identity and make a temporary identity to perform the service. It has been found that our protocol has much improved security effectiveness in terms of confidentiality, integrity, non-repudiation, anonymity availability, authentication, and authorization.
Logistics Performance (LP) is one of the fundamental catalysts that serve as a podium for the integration of the world economy. This study is conducted to observe the combined effects of Liner ...Shipping Connectivity (LSC) and Global Competitiveness Index (GCI) on LP in mediating the Quality of Port and Infrastructure (PORT). We selected 28 Asian economies and 1 special administrative region (Hong Kong) counting the year of 2007–2018. Partial Least Square – Structural Equation Model (PLS–SEM) with an extension of the Importance–Performance Map Analysis (IPMA) was applied. Empirical evidence derived from the path diagram has revealed that LSC and global competitiveness in mediating the PORT have significant effects of accelerating LP, leading to higher competitiveness in terms of strategic development and yielding better connectivity. Due to limited resources, Asian decision-makers need a guidance to focus on how to improve LP. This work also helps to highlight the dimensions of LSC and global competitiveness factors to be concentrated on and the policies to be implemented in this regard.
Vehicular ad-hoc network (VANET) is the direct application of mobile ad-hoc network (MANET) in which the nodes represent vehicles moving in a city or highway scenario. The deployment of VANET relies ...on routing protocols to transmit the information between the nodes. Different routing protocols that have been designed for MANET were proposed to be applied in VANET. However, the real-time implementation is still facing challenges to fulfill the quality of service (QoS) of VANET. Therefore, this study mainly focuses on the well-known MANET proactive optimized link state routing (OLSR) protocol. The OLSR in VANET gives a moderate performance; this is due to its necessity of maintaining an updated routing table for all possible routes. The performance of OLSR is highly dependent on its parameter. Thus, finding optimal parameter configurations that best fit VANET features and improve its quality of services is essential before its deployment. The harmony search (HS) is an emerging metaheuristic optimization algorithm with features of simplicity and exploration efficiency. Therefore, this paper aims to propose an improved harmony search optimization (EHSO) algorithm that considers the configuration of the OLSR parameters by coupling two stages, a procedure for optimization carried out by the EHSO algorithm based on embedding two popular selection methods in its memory, namely, roulette wheel selection and tournament selection. The experimental analysis shows that the proposed approach has achieved the QoS requirement, compared to the existing algorithms.
The Sixth Generation network (6G) can support autonomous driving along with various vehicular applications like Vehicular Edge Computing (VEC), a distributed computing architecture for connected ...autonomous vehicles. Computational offloading and resource management of Vehicular Edge Computing can help sort out some issues, such as high communication costs, privacy protection, an excessively long training process, etc., by proposing an efficient training model of the Federated Learning for computational offloading and resource management in a vehicular environment. Two research issues are highlighted in this paper. One problem is related to the current offloading system: the smart structure and operating system. Consistent access to cloud computing services, regardless of the installed operating system or used hardware, is still challenging. Another issue is related to security and privacy. Security and privacy are two important features that should be maintained in cloud data centers and data transmission during offloading and resource management. In this survey paper, a system is going to be proposed which will give a partial solution for these issues. The proposed solution, which is found while conducting this review, offers a system that can train a model and help update the edge devices' information. The entire edge cloud system can provide updated information for edge devices and can solve the difficulties of getting some key information necessary for model-related optimization. This also can enhance the effectiveness of the frameworks of the 6G-V2X network for communication.
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