Computing today requires a lot of processing power, an immersive environment, and excellent precision. In this paper, we propose a hybrid multiprocessor architecture with quantum integration for data-intensive applications like a metaverse. Regular non-computationally intensive applications are handled by the central processing unit (CPU) and video rendering, graphically intensive programs by the graphics processing unit (GPU), and enormous, parallel, and computationally intensive applications by the quantum processing unit (QPU). To achieve higher permanence computing, we provide combined CPU, GPU, and QPU architecture in this work. This paper provides a roadmap for truly high-performance computing for massive real-time systems. In addition, we present an onsite and cloud-based architecture for scalability. Also, we present multimodality security protection systems for internal and external communication based on the split computing paradigm.

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.

The Internet of Vehicles (IoV) communication platform provides seamless information exchange facilities in a dynamic mobile city environment. Heterogeneous communication is a common medium for information exchange through autonomous resources distributed and accessed using infrastructure units. Cyber-security is a primary concern in accessing autonomous information from the distributed resources due to anonymity and different types of targeted adversaries. This article proposes token-based authorization and authentication (TAA) for securing IoV communications. The proposed method relies on blockchain technology and random forest learning for authorization and key management for authentication, respectively. In this process, frequent change in tokens and key update features are restricted in a view to maximize the seamlessness in information exchange. Authentication is preceded by knowledge of the data classification without errors to prevent additional overhead. Blockchain-based authorization helps to update specific fields of the tokens to retain the communication ratio by reducing vehicle-to-vehicle losses. The performance of the proposed method is assessed using appropriate simulations for these metrics by varying vehicle density, error rate, and classification sets.

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.

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.

Smart or electronic healthcare is undergoing rapid change from the traditional specialist and hospital-centered style to a disseminated patient-centered using Internet of Things (IoT). Presently, 4G and other advanced communication standards are utilized in healthcare for intelligent healthcare services and applications. Traffic handling is an essential feature for the flexible interoperability of the internet of things (IoT) with other heterogeneous communication networks. Efficient traffic handling controls latency and communication failures due to random access and collision in cellular network overlay IoT. It is challenging for existing communication technology to achieve the necessities of time-sensitive and very dynamic healthcare applications of the future. In this manuscript, adaptive eNB selection with traffic scheduling (AeS-TS) is proposed to improve the efficiency of IoT-long term evolution (LTE) networks. AeS-Tsworks in two phases: adaptive eNB selection and gateway traffic scheduling. In eNB selection, traffic-aware radio infrastructure selection with the offloading feature is presented. eNB selection is preceded by using a preference function to improve the acceptance rate of incoming IoT traffic and minimize transmission loss. In the traffic scheduling phase, sequential and level-based slot transmission is adapted to improve traffic forwarding quality. The slots are selected by analyzing the error in time function using the recurrent learning process.

Cloud storage is a popular method of storing and sharing data. Numerous cloud service providers offer storage as a service, while others focus on sharing as a service. Many service providers focus their services on collaboration and integration with multiple tools to help with inline commenting and workflow. In recent days, working from home and new normal has attracted secure sharing services for strict access control. Data is new gold there for having the correct type of access control that satisfies the organization’s existing policies is a crucial feature. Also, the demand for extensive data sharing is increasing day by day. December 2020’s cyber-attack on SolarWinds & FireEye is the primary security service provider in the U.S. and worldwide. Organizations are more concerned about their data at rest or in motion. To meet current industry needs, we have demonstrated a secure file sharing framework with a three-prong security strategy by employing next-generation cryptography coupled with proxy re-encryption and splitting data at bit level on multiple heterogeneous clouds. This paper aims to present the idea of split-protocol as a protection technique for security, integrity, and availability of data stored on the public cloud. We have used three encryption techniques for this experiment, namely AES 128-, AES, 256, and RLP, and compared their overheads and timings with different file sizes and types. Implementation and results indicate the feasibility of the proposed architecture without client involvement.

Industry 4.0 is a promising evolution in the field of smart farming by improving the productivity and reducing human intervention to modernize agriculture. This smart paradigm incorporates different levels of the automation from cropping to production yield through sophisticated techniques. Different intelligent computing techniques and communication technologies are augmented with the industry paradigm for improving the efficiency of agriculture systems. This letter introduces information scheduling and optimization framework (ISOF) for optimizing the communication and information layer process in industry 4.0 architecture. Information scheduling and classification of agriculture information are optimized through this framework for reducing process latency and stagnancy. The control flexibility of a smart farm is determined using the latency and stagnancy at the end of yields. The classification part segregates information based on processing and completion time to reduce backlogs through offloading process. The advantage of this framework is that it inherits the advantages of Internet of Things (IoT) and edge computing (EC) technologies with interoperable feature to aid information processing, information classification, offloading, and periodic updates. The performance of the proposed framework is tested in a corn farm and some common metrics, such as delayed information, processing time, audit data, and information distribution, are analyzed for proving the reliability of the framework.

Internet of Things (IoT) has gained significant importance due to its flexibility in integrating communication technologies and smart devices for the ease of service provisioning. IoT services rely on a heterogeneous cloud network for serving user demands ubiquitously. The service data management is a complex task in this heterogeneous environment due to random access and service compositions. In this article, a machine learning aided information management scheme is proposed for handling data to ensure uninterrupted user request service. The neural learning process gains control over service attributes and data response to abruptly assign resources to the incoming requests in the data plane. The learning process operates in the data plane, where requests and responses for service are instantaneous. This facilitates the smoothing of the learning process to decide upon the possible resources and more precise service delivery without duplication. The proposed data management scheme ensures less replication and minimum service response time irrespective of the request and device density.

Searchable Symmetric Encryption (SSE) is a technique to securely outsource the data, which is encrypted using symmetric key primitives, while maintaining search functionality. While several solutions have been proposed to realize SSE over various data structures, the efficient solution using inverted index is due to Curtmola et.al. Hwang et.al. introduced a SSE scheme based on bitmaps in order to reduce the index size. In this paper, we consider Searchable Symmetric Encryption (SSE) in the presence of a Semi-Honest-But-Curious Cloud Service Provider (SHBC-CSP). We have defined a new security notion for SSE in presence of SHBC-CSP, contrived two new SSE schemes and proved their security formally in the proposed security notion. Dynamic Verifiable Encrypted Keyword Search (DVSSE), is the first SSE scheme to the best of our knowledge, which is both dynamic and verifiable. We have implemented our schemes, compared their performance and complexity with existing schemes.

Split-protocol computing paradigm uses web services on geographically distributed Web servers. A network of large Split-servers that form the cloud to handles computing and storage task that would otherwise create a heavy CPU utilization for the traditional individual server. The split-protocol concept was devised for load balancing and faster data transmission. Connection and data communication phases are clearly divided between servers without involving the client or the central controlling mechanism, like a dispatcher or a load balancer. In an earlier paper, we demonstrated that the application Split-protocol produces higher performance compared to traditional clusters. Based on the need, different types of split configurations are implemented for throughput, response time, and connection time. The observed throughput improvement was within the range 6.5% - 25% over non-split systems. This paper first time examines the empirical results of Split-system to understand its analytical behavior and compares with traditional non-split systems. Also, this paper prepares the groundwork for the scientific understanding of Split-systems. The split-system technique with given sets of constraints can produce better throughput than conventional equivalent server systems. The performance improvement resulted because of the Split-system operates as a fine-grained pipeline of the parallel system.

Computer science, information systems, information technology and other related programs have been evolving over the years to prepare students for the ever changing work force or to become research scientists. These program structures and curriculum gets updated rapidly even before a student had a chance to complete a four year cycle. When a student graduates, there may be a daunting challenge to find a right fit for a right job in todayâ??s global market. This paper proposes a revolutionary curriculum paradigm that is based on sound engineering principles and need for applied education. The curriculum proposed here is based on student needs and industry outlook. It reduces educational cost for students, administrative cost for teaching institutions and training cost for industry. It also provides a first cut of curriculum that integrates a variety of disciplines under the information technology umbrella. The curriculum taxonomies are shown to illustrate the proposed concept. An initial road map and time schedules are shown to demonstrate the feasibility of this concept. The roles of students, faculty and industry supervisors are discussed. The approach proposed here will have a broader positive impact in information technology when adopted. Further research is needed to fully exploit the proposed concept

Protocol splitting has been used to enable protocols to be split at a server level without client involvement. We describe a novel split protocol client/server architecture that completely separates connections and data transfers within a typical session. In this approach, a client becomes aware of its multiple server sources and communicates with them using their IP addresses. Specifically, a client makes a single TCP connection to a connection server and subsequently communicates with one or more data servers to obtain its data and close the connection. We also conduct experiments and measure performance to demonstrate the feasibility of this architecture. Our results indicate that scalable server cluster configurations can be built using this approach. The proposed architecture simplifies server implementations, avoids traditional load balancing techniques, and isolates clients from data servers. It also results in a scalable and distributable approach to client/server computing that provides an alternative to the current paradigm.

HTTP request splitting is a new concept where the TCP connection and data transfer phases are dynamically split between servers without using a central dispatcher or load balancer. Splitting is completely transparent to the client and provides security due to the inaccessibility and invisibility of the data servers. We study the performance of mini Web server clusters with request splitting. With partial delegation in which some requests are split, throughput is better, and response times are only marginally less than for an equivalent non-split system. For example with partial delegation, for a four-node cluster with a single connection server and three data servers serving 64 KB files, and for a three-node cluster with two connection servers and a single data server serving 4 KB files, the respective throughput improvements over non-split systems are 10% and 22%, with only a marginal increase in response time. In practice, the throughput improvement percentages will be higher and response time gaps will be lower since we ignore the overhead of a dispatcher or load balancer in non-split systems. Although these experiments used bare PC Web servers without an operating system/kernel for ease of implementation, splitting and clustering may also be implemented on conventional systems.