Cloud computing has revolutionized organizational operations by providing convenient, on-demand access to resources. The emergence of the Internet of Things (IoT) has introduced a new paradigm for collaborative computing, leveraging sensors and devices that generate and process vast amounts of data, thereby resulting in challenges related to scalability and security, making the significance of conventional security methods even more pronounced. Consequently, in this paper, we propose a novel Scalable and Secure Cloud Architecture (SSCA) that integrates IoT and cryptographic techniques, aiming to develop scalable and trustworthy cloud systems, thus enabling multi-user systems and facilitating simultaneous access to cloud resources by multiple users. The design adopts a decentralized approach, utilizing multiple cloud nodes to handle user requests efficiently and incorporates Multicast and Broadcast Rekeying Algorithm (MBRA) to ensure the privacy and confidentiality of user information, utilizing a hybrid cryptosystem that combines MBRA, Post Quantum Cryptography (PQC) and blockchain technology. Leveraging IoT devices, the architecture gathers data from distributed sensing resources and ensures the security of collected information through robust MBRA-PQC encryption algorithms, while the blockchain ensures that the confidential data is stored in distributed and immutable records. The proposed approach is applied to several datasets and the effectiveness is validated through various performance metrics, including response time, throughput, scalability, security, and reliability. The results highlight the effectiveness of the proposed SSCA, showcasing a notable reduction in response time by 1.67 seconds and 0.97 seconds for 250 and 1000 devices, respectively, in comparison to the MHE-IS-CPMT. Likewise, SSCA demonstrated significant improvements in the AUC values, exhibiting enhancements of 6.30%, 6.90%, 7.60%, and 7.30% at the 25-user level, and impressive gains of 5.20%, 9.30%, 11.50%, and 15.40% at the 50-user level when compared to the MHE-IS-CPMT, EAM, SCSS, and SHCEF models, respectively.

Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.

The Internet of Things (IoT) based Smart city applications are the latest technology-driven solutions designed to collect and analyze data to enhance the quality of life for urban residents by creating more sustainable, efficient, and connected communities. Communication nodes are networked independently to monitor the circumstances, where they require being more energy-efficient and securing, to improve the performance of sustainable cities. Because, the enormous number of networked devices makes the smart cities application vulnerable to many security breaches, all of which have serious ramifications for a city and its residents’ safety, well-being, and economic development. Low-powered sensors have limitations in terms of battery life, short transmission range, and security considerations, despite the fact that the combination of edge computing and Green IoT considerably enhances network performance in terms of processing and data storage. Consequently, it is necessary to implement an advanced approach to provide energy resources with secure data transmission for sustainable cities. Therefore, this research proposes an Intelligent Buffalo-based Secure Edge-enabled Computing (IB-SEC) framework for smart cities by IoT platform, which aims to enhance communication efficiency, and reliability, with minimizing latency in terms of energy consumption and data security for data transmission. The developed IB-SEC platform utilizes a combination of the African Buffalo Optimization (ABO) algorithm and a Distributed Hash function-based security algorithm to enhance the efficiency, security, and reliability of data transmission in IoT-based smart city networks. This platform leverages the capabilities of secure edge computing and MAC protocols to achieve its goals, by implementing encryption, authentication, and access control mechanisms that ensure the wireless communication is secure, and the data is protected against unauthorized access. Overall, the IB-SEC framework provides an advanced approach to secure IoT networks and enable smart city applications. Moreover, the developed platform enables the integration of heterogeneous edge devices, sensors, and systems which is effectively managed and adapted by Median Access Control (MAC) protocols. The IB-SEC is implemented in MATLAB and validated through cutting-edge security algorithms in terms of improved security, reliability, throughput, and reduced latency and energy consumption.

The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

The computational cloud aims to move traditional computing from personal computers to cloud providers on the internet. Cloud security represents an important research area. Confidentiality, integrity, and availability are the main cloud security characteristics addressed. Cloud providers apply dynamic load balancing and reactive fault tolerance techniques to build secure cloud services to achieve high service availability. Dynamic cloud load balancing approaches distribute submitted tasks to virtual machines during tasks execution and the load of virtual machines is updated based on the system’s state. Reactive cloud fault tolerance is activated for system process failures after failure effectively happens. Reactive cloud fault tolerance handles failure after the fault has occurred. Despite the significance of dynamic load balancing and reactive fault tolerance techniques and mechanisms, few reviews focus on these approaches in a systematic, unbiased method focusing on integrating cloud dynamic load balancing and reactive fault tolerance techniques. This paper conducts a systematic literature review of the existing literature concerning reactive fault tolerance, dynamic load balancing, and their integration in their basic approaches, types, frameworks, and future directions.

Early diagnosis of diseases such as cancer, cardiovascular, diabetes, HIV, AIDS, Lyme, and tuberculosis can enable timely treatment, which enhances efficacy and helps reduce the severity and mortality while lowering overall healthcare costs. Predictive analytics is an emerging approach that comprehends trends in patient data to identify patterns in patient data, detect subtle signs of diseases, and enable early diagnosis for more effective treatment. However, traditional predictive diagnosis approaches rely on statistical models that are contingent on the data’s average pattern and are unable to capture the data’s inherent patterns, leading to ineffective and inaccurate diagnoses. In this paper, we introduce a novel Artificial Spider Monkey-based Random Forest (ASM-RF) hybrid framework that combines the predictive analytics of a Random Forest algorithm with Artificial Intelligence to evaluate patient health data, spot patterns, diagnose modest indications, and automate intelligent decisions for enhancing the healthcare system. The proposed ASM-RF hybrid framework employs a fitness function to evaluate the spider monkey’s performance at the classification layer and update accuracy and recall, resulting in more accurate patient disease diagnoses and automating timely treatment decisions for improving the overall healthcare system. Moreover, we use Identity-based Encryption (IBE), which enables the encryption of data with private and public keys coupled with users’ identities as the encryption keys, assisting in enhancing the security of the healthcare system. A dataset collected from three different IoT sensor devices, ten participants, and twelve activities is employed to simulate the proposed ASM-RF hybrid framework, which is then contrasted to cutting-edge predictive diagnostic algorithms in terms of accuracy, precision, Area under the Curve, execution time, recall, and F-measure. The proposed method exhibits superior performance when compared to conventional methods, as evidenced by its exceptional accuracy (99.52%), precision (99.12%), Area Under the Curve (AUC) (99.00%), recall (99.22%), F-measure (97.12%), and significantly reduced execution time (6s).