Intelligent Transportation Systems (ITSs) rely on environmental information for communication, navigation, and driving assistance. This technology-based interconnected vehicular network provides a wide range of support for heterogeneous real-time applications. However, the network relies on secure and robust information for providing driving application support, which is defaced at times due to fraudulent devices and information. In this article, Graph-Transient Security Method (GTSM) is proposed for improving the cybersecurity features of ITS. The proposed method uses a trusted graph model for identifying reliable infrastructure and neighboring units in communication. The units are verified based on the information exchanged for identifying the frauds through the mutual trust sharing paradigm. In the mutual trust sharing process, fastened classifier learning is employed for assessing the trust of the communicating vehicles and infrastructure units. Based on the output of the classifier learning, a connected trust-based transportation network is constructed. This helps to replace, transform the connected scenario depending on the cybersecurity requirement. The proposed method improves sharing rate by 11.4% and detection ratio by 7.84% and reduces down-time and latency by 11.53% and 10.7% in different sharing intervals.

Network-in-box (NIB) architectures are designed to improve communication information sharing in an ad hoc manner with limited infrastructure support. These architectures are interoperable, and hence it is capable of providing services based on sixth-generation (6G) communication technologies. Resource allocation for massive machine-type communications in the 6G platform aided for NIB architectures is challenging due to the terahertz and high throughput features. Then it comes to resource allocation, and the most difficult part is figuring out what capacity is to check the availability of work on a project is difficult to determine. In this manuscript, the attuned slicing-dependent concurrent resource allocation (AS-CRA) method is formulated for improving the service reliability of the 6G users in NIB architecture. Learning assisted slicing and concurrent resource allocation process is jointly exploited in this proposed method to improve the users’ service reliability. The output of the learning process is useful in classifying resource allocation and user mapping irrespective of the limited NIB infrastructure support. Virtualization and concurrency in resource allocation are balanced based on the user capacity and network blocking rate to achieve optimality in service responses. The performance of the proposed resource allocation method is verified using simulations, and the performance is verified using the metrics capacity 89.726%, latency 81.32%, resource utilization rate 0.963%, response ratio 92.309, and blocking rate 0.047%.

Intelligent Transportation Systems provide ubiquitous communication for the driving users through heterogeneous interconnections. The heterogeneous interconnections are required for uninterrupted resource sharing. Spontaneous resource availability due to vehicle speed and infrastructure connectivity disturb prompt service utilization. In this manuscript, a Permissible Service Selection and Allocation (PSSA) method is proposed to address spontaneous issues in vehicular communication and connection. This method considers vehicle displacement and minimum interconnection factors in accessing a cloud service. Both factors and their balancing impact are analyzed throughout the vehicle’s service requesting interval. In this process, random forest learning is induced to identify the balancing factors’ adjustments. The service access is probed through the active infrastructure based on the balancing factor. The ordering process of the learning intervals provides ease of service selection and allocation. In this process, reallocation is not preferred due to the random displacement of the vehicles. Therefore, the interval dropouts are reduced in both handoff and non-handoff communication scenarios. Further metrics such as service ratio, delay, and connectivity are used in validating the proposed method’s performance.

Intelligent Transportation security requires cooperative credentials for sharing navigation and communication data between the vehicles. However due to the dynamic environment, communication is interrupted by the adversaries, resulting in non-privacy issues. This article introduces an Agreement-induced Data Verification Model (ADVM) for securing vehicular communication against adversaries. The connected vehicles in a grid communicate with each other based on direct and indirect recommendation. This recommendation is based on mutual identity sharing between the vehicles for masked information exchange. Non-replicated and recommendation based verifications are performed using the vector classification learning. In this learning process, the credential validity and communication tolerance amid adversaries are augmented. The constraint-failing vehicles are disconnected from the communication grid, preventing its insecure impact over the communication. The proposed model’s performance is verified using false rate, success ratio, processing time, complexity, and recommendation ratio. For the different vehicles, the proposed model achieves 9.69% less false rate, 10.3% success ratio, 10.49% less processing time, 10.3% less complexity, and 12.87% high recommendation ratio.

Bin packing problem (BPP) is a classical combinatorial optimization problem widely used in a wide range of fields. The main aim of this paper is to propose a new variant of whale optimization algorithm named improved Lévy-based whale optimization algorithm (ILWOA). The proposed ILWOA adapts it to search the combinatorial search space of BPP problems. The performance of ILWOA is evaluated through two experiments on benchmarks with varying difficulty and BPP case studies. The experimental results confirm the prosperity of the proposed algorithm in proficiency to find the optimal solution and convergence speed. Further, the obtained results are discussed and analyzed according to the problem size.

This book discusses the detailed analysis of diabetic retinopathy, symptoms, causes, and screening methodologies using deep learning concepts

The sixth-generation (6G) communication technology provides a high level of interoperability through terahertz data transfer and latency-less service sharing. Due to its interoperable nature, the integration of heterogeneous networks, such as the Internet of Things (IoT) and cloud radio access networks (CRANs), is performed at ease. This integration is managed using software-defined networks (SDNs) for managing the Quality of Service (QoS) experience of the users, irrespective of the application. This manuscript proposes the service virtualization and flow management framework (SVFMF) for the reliable utilization of resources in the 6G-cloud environment. The imbalance in a service request and response due to overloaded and idle virtual resources is addressed in this framework. For this purpose, this framework endorses service virtualization and user allocation modules for mitigating the drawbacks of imbalanced service allocations. Linear decision making of the service virtualization process helps to reduce the computation and service discovery by identifying overloaded services and performing a reallocation. The purpose of user allocation is to distribute the service requests to the idle service providers to reduce the prolonged wait time of the increasing user requests. The performance of the proposed framework is verified using experimental analyses, for the metrics service discovery and computation time, service failure ratio, and flows. The reliability of SVFMF is proved by varying the density of users, virtual machines, service requests, and user allocation per virtual machine, respectively.

Intelligent Transportation System (ITS) assists communication and navigation for users and vehicles in roadside movements. It integrates information technology, computational intelligence, and distributed service platforms for providing classified assistance. The classified assistance ensures object detection, navigation, route identification, and messaging application support. This article introduces a novel Collaborative Computational Method (CCM) using Transfer Learning (TL) for condensed information analysis. In this method, application-centric computations are performed for decision-making and thwarting replicated and false information handling. The information is computed by exploiting the previous application-accuracy knowledge segregating different inputs. This selective computation relies on current and previous information knowledge collaboratively. The learning process is responsible for shift-based validation of computation accuracy using collaborative information. The proposed method’s performance is analyzed using accuracy, computation time, complexity, and information backlogs. The proposed CCM-TL improves by 8.5% and 4.91% accuracy and information sharing. It similarly reduces computation time, complexity, and backlogs by 14.97%, 6.7%, and 16.67%, respectively.

Block chain provides an innovative solution to information storage, transaction execution, security, and trust building in an open environment. The block chain is technological progress for cyber security and cryptography, with efficiency-related cases varying in smart grids, smart contracts, over the IoT, etc. The movement to exchange data on a server has massively increased with the introduction of the Internet of Things. Hence, in this research, Splitting of proxy re-encryption method (Split-PRE) has been suggested based on the IoT to improve security and privacy in a private block chain. This study proposes a block chain-based proxy re-encryption program to resolve both the trust and scalability problems and to simplify the transactions. After encryption, the system saves the Internet of Things data in a distributed cloud. The framework offers dynamic, smart contracts between the sensor and the device user without the intervention of a trustworthy third party to exchange the captured IoT data. It uses an efficient proxy re-encryption system, which provides the owner and the person existing in the smart contract to see the data. The experimental outcomes show that the proposed approach enhances the efficiency, security, privacy, and feasibility of the system when compared to other existing methods.

An asset security domain, throughout its life cycle, focuses on handling, collecting, and protecting information. This domain, as a primary step, classifies information based on its value to the organization. Depending on the classification, all follow-on actions vary. For example, unclassified data uses fewer security controls, whereas highly classified data requires stringent security controls.

Resource allocation and offloading in green Internet of Things (IoT) relies on the multi-level heterogeneous platforms. The energy expenses of the platform determine the reliability of green IoT based services and applications. This manuscript introduces a decisive energy management scheme for optimal resource allocation and offloading along with energy constraints. This scheme handles both the allocation and energy-cost in a balanced manner through deterministic task offloading. In particular, resource allocation solution for non-delay tolerant green IoT applications is focused by confining the failures of discrete tasks through neural learning. The dropout process augmented with the learning process improves the feasible conditions for resource handling and task offloading among the active IoT service providers. Through extensive simulations the performance of the proposed scheme is analyzed and energy consumption, failure rate, processing, and completion time metrics are used for a comparative study. Further, the optimal utilization and on-demand dissipation of such stored resources help to improve the sustainability of green power and communication technologies in the smart city environment.