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There can be many inherent issues in the process of managing cloud infrastructure and the platform of the cloud. The platform of the cloud manages cloud software and legality issues in making contracts. The platform also handles the process of managing cloud software services and legal contract-based segmentation. In this paper, we tackle these issues directly with some feasible solutions. For these constraints, the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place. ASA is made up of the AODE and SELECT Applicable Only to Parallel Server. The AODE classifier is used to separate the data from smart city data based on the hybrid data obfuscation technique. The data from the hybrid data obfuscation technique manages 50% of the raw data, and 50% of hospital data is masked using the proposed transmission. The analysis of energy consumption before the cryptosystem shows the total packet delivered by about 71.66% compared with existing algorithms. The analysis of energy consumption after cryptosystem assumption shows 47.34% consumption, compared to existing state-of-the-art algorithms. The average energy consumption before data obfuscation decreased by 2.47%, and the average energy consumption after data obfuscation was reduced by 9.90%. The analysis of the makespan time before data obfuscation decreased by 33.71%. Compared to existing state-of-the-art algorithms, the study of makespan time after data obfuscation decreased by 1.3%. These impressive results show the strength of our methodology.

The Internet of Health Things (IoHT) is a medical concept that describes uniquely identifiable devices connected to the Internet that can communicate with each other. As one of the most important components of smart health monitoring and improvement systems, the IoHT presents numerous challenges, among which cybersecurity is a priority. As a well-received security solution to achieve fine-grained access control, ciphertext-policy weighted attribute-based encryption (CP-WABE) has the potential to ensure data security in the IoHT. However, many issues remain, such as inflexibility, poor computational capability, and insufficient storage efficiency in attributes comparison. To address these issues, we propose a novel access policy expression method using 0-1 coding technology. Based on this method, a flexible and efficient CP-WABE is constructed for the IoHT. Our scheme supports not only weighted attributes but also any form of comparison of weighted attributes. Furthermore, we use offline/online encryption and outsourced decryption technology to ensure that the scheme can run on an inefficient IoT terminal. Both theoretical and experimental analyses show that our scheme is more efficient and feasible than other schemes. Moreover, security analysis indicates that our scheme achieves security against a chosen-plaintext attack.

Summary: The rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications. [ABSTRACT FROM AUTHOR]

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%. [ABSTRACT FROM AUTHOR]

Cloud computing provides a service-oriented computing model to cloud users on a metered basis. Most of the cloud data centers are running on fossil fuels. It elevates the carbon emissions to the environment. Green cloud computing is the fusion of greenness in cloud computing to address the issues related to energy consumption and environmental sustainability. Virtual machine (VM) consolidation and live migration can provide standard solutions to energy consumption. The selection of VM for migration is a vital task. It should be performed effectively to trade-off between energy consumption and service level agreement violation (SLAV). The research activity in this article focuses on a new VM selection policy that chooses VM with high energy consumption and small size. Real-world workload traces were used to evaluate the performance of the proposed MECpVmS VM selection policy. Using CloudSim simulation, the MECpVmS VM selection policy has been implemented and assessed with existing VM selection policies. The results show an overall improvement in energy efficiency, energy consumption, SLAV. [ABSTRACT FROM AUTHOR]

Big Data applications require extensive resources and environments to store, process and analyze this colossal collection of data in a distributed manner. Containerization with cloud computing provides a pertinent remedy to accommodate big data requirements, however requires a precise and appropriate load-balancing mechanism. The load on servers increases exponentially with increased resource usage thus making load balancing an essential requirement. Moreover, the adjustment of containers accurately and rapidly according to load as per services is one of the crucial aspects in big data applications. This study provides a review relating to containerized environments like Docker for big data applications with load balancing. A novel scheduling mechanism of containers for big data applications established on Docker Swarm and Microservice architecture is proposed. The concept of Docker Swarm is utilized to effectively handle big data applications' workload and service discovery. Results shows that increasing workloads with respect to big data applications can be effectively managed by utilizing microservices in containerized environments and load balancing is efficiently achieved using Docker Swarm. The implementation is done using a case study deployed on a single server and then scaled to four instances. Applications developed using containerized microservices reduces average deployment time and continuous integration. [ABSTRACT FROM AUTHOR]

With the potential of implementing computing-intensive applications, edge computing is combined with digital twinning (DT)-empowered Internet of vehicles (IoV) to enhance intelligent transportation capabilities. By updating digital twins of vehicles and offloading services to edge computing devices (ECDs), the insufficiency in vehicles’ computational resources can be complemented. However, owing to the computational intensity of DT-empowered IoV, ECD would overload under excessive service requests, which deteriorates the quality of service (QoS). To address this problem, in this article, a multiuser offloading system is analyzed, where the QoS is reflected through the response time of services. Then, a service offloading (SOL) method with deep reinforcement learning, is proposed for DT-empowered IoV in edge computing. To obtain optimized offloading decisions, SOL leverages deep Q-network (DQN), which combines the value function approximation of deep learning and reinforcement learning. Eventually, experiments with comparative methods indicate that SOL is effective and adaptable in diverse environments. [ABSTRACT FROM AUTHOR]

Workflow scheduling is a significant challenge due to the large scale of workflows and heterogeneity of cloud resources. The vast size of the cloud makes execution times higher, leading to high computational and communication costs. Workflow scheduling is an N P -hard problem, thus, creating meta-heuristic algorithms is one of the best options for finding optimal solutions. This paper models workflow scheduling as a multi-objective optimization problem that considers execution time and communication cost. Optimization efforts are accomplished by proposing a Fitness-Dependent Optimizer (FDO) inspired by bee reproductive behavior. However, it has many drawbacks, including being a single-objective problem. To improve this, we present a Genetic Algorithm-based multi-objective FDO, eliminating many of the previous algorithm's issues. The proposed algorithm takes advantage of both the Genetic Algorithm and FDO. Moreover, it does not show signs of sticking to a local optimal solution. The proposed algorithm is compared with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), GA-PSO, and FDO, where it shows its effectiveness by performing better on both parameters. • Propose an intelligent Genetic Algorithm (GA) operators-based method for FDO to solve the task scheduling problem in workflow scheduling using standard datasets. • Making FDO work on multiple objectives so that it performs better than the previously proposed algorithm. • Propose a multi-objective approach based on FDO to decrease execution time and improve computation and communication costs. • Present graphs for time and cost, where the graphs obtained show the pareto optimality. [ABSTRACT FROM AUTHOR]

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The combination of the Internet of Things (IoT) and Cloud computing are both popular applications that are based on the Internet. However, the traditional networking structure can no longer support the transmission of the huge number of packets required by cloud computing and IoT. Therefore, a new-generation architecture, software-defined networking (SDN), came into being. The new-generation SDN can control routing through software, making flexible and convenient management a key feature of SDN. However, attacks and threats are prevalent in today's networking environment. When an SDN controller encounters a hacker attack or virus infection, it will not function properly. Hence, we need to design a fault-tolerant mechanism for the above environment. In this paper, a fault-tolerant consensus protocol is proposed to improve the fault tolerance of SDNs with multiple controllers. [ABSTRACT FROM AUTHOR]

Industry 5.0 is an emerging phase in the industrial field where humans and the latest technologies work together to bring revolutionary changes to work processes. It is the future of the upcoming generation for smart city development. Operations and designing of smart systems for Industrial Internet of Things (IIoT) devices include various types of decision-making at different levels of domains. Highly complex systems have a huge number of variables and decision-making requirements which also needs real-time data gathering from IIoT devices. Fog computing performs a key role by locating IIoT devices close to its proximity and reducing the processing time in comparison to cloud computing for smart systems. It makes smart systems work in a secure, flexible and time-sensitive manner. The presence of sensors makes data-intensive analysis challenging in these systems but fog computing handles it all. Along with other challenges, security is also one of the concerns that can affect the overall system if compromised. The main objective of this article is to introduce hierarchical fog computing-based architecture for smart energy-supplying systems as a part of Industry 5.0. This architecture will be able to support a large number of infrastructural components and provide services efficiently. In this article, validation of the amount of data transmitted by IIoT devices to the cloud per second is done for three different cases: 1. The proposed four-layer fog computing architecture, 2. Fog architecture with only layer three layers with the rest of the computing on the cloud, 3. A traditional system with cloud computing. The proposed approach is validated over state of art approaches in terms of accuracy. Moreover, the security of the architecture is provided using the Attribute-based encryption (ABE) scheme, which ensures data confidentiality with minimum overhead. • Introduce a four-layer fog computing architecture for a smart energy supply system. • The architecture is capable of ensuring safety in case of harmful events. • Fog computing is used to reduce the burden on the cloud resulting in reduced delays. • Attribute-based encryption is used to enhance security as and access to data. • The approach is validated over state of art approaches in terms of accuracy. [ABSTRACT FROM AUTHOR]

Cloud Computing services can be accessed anytime, anywhere via the Internet. The overwhelming growth of cloud data centers over the past decade has increased their costs as energy demands have risen. As a result, higher carbon dioxide emissions and other greenhouse gasses are putting a strain on our ecosystem. The main objective of this study is to reduce the power consumption in cloud computing with no or negligible trade-offs in quality of service. This paper presents a new algorithm called the energy efficiency heuristic using virtual machine consolidation to minimize the high energy consumption in the cloud. By setting two thresholds, hosts are classified into three main classes. The designed model reallocates virtual machines from one physical host to another to minimize energy consumption. The results of the proposed algorithm have been obtained in terms of virtual machine migrations, performance degradation caused by migration, service level agreement violations, and execution time, showing a significant improvement over state-of-the-art techniques. • A novel scheduling mechanism EEHVMC to reduce energy consumption is designed. • The designed EEHVMC can also reduce VR migration, and service level agreement violations. • The designed model is based on the real-time cloud computing. [ABSTRACT FROM AUTHOR]

Concrete privacy endeavours to give Confidentiality, Integrity, and Availability (CIA) measures to secure traffic streams in sensitive healthcare applications are a necessity. When talking about the access of sensitive healthcare data or the confidentiality of highly esteemed data, the first thing that comes to mind is that it should be secured. Sensitive healthcare data needs to be protected to restrict illegal access, exposure, and/or manipulation. As there is no such protection by which we can make our systems fully secure, the most acceptable methodology is to perform layered modelling of safety measures. This paper intends to provide a mathematical description of the Modular Encryption Standard (MES) and the augmentation of condition-centric risk monitoring of confidential information to provide a layered model for securing healthcare data confidentiality. Decision-making regarding the risk monitoring strategy of MES is augmented using a machine learning approach based on a Fuzzy Inference System amalgamated with Neural Networks. Result analysis shows that MES has less than a 0.005 error-rate and a 97% precision-rate, which elucidates its desideratum towards increasing security risks. [Display omitted] • Propose an approach for securing healthcare data confidentiality. • Provide mathematical modelling for the entire working of Modular Encryption Standard. • Enhance the decision-making strategy of Modular Encryption Standard using fuzzy logic. [ABSTRACT FROM AUTHOR]