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Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. We present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet. [ABSTRACT FROM AUTHOR]

The dual-scrambling scheme that combines position transformation and bit-plane transformation is a popular image encryption scheme. However, such schemes need more key information, and the encryption and decryption processes are complicated. In addition, the existing quantum image dual-scrambling schemes mainly deal with square images. In this paper, we propose a hybrid scrambling encryption scheme for multi-mode quantum image representation (MQIR) images based on random permutation, in which the H × W quantum image is represented in MQIR. A random number generator factor s uniquely associates one of the random permutations of integers from 1 to a positive integer, so as to hybrid scramble both the pixel position and the binarized position of each pixel value. Meanwhile, the quantum circuits and some examples of scrambling are given. Furthermore, various analyses of the performance of this scheme were conducted, including effectiveness, key space, and computational complexity. By modifying the random generation factor to construct multiple binary grayscale images, the simulated results on the IBM Quantum Cloud platform demonstrate that the proposed quantum image encryption scheme is effective. In comparison to existing quantum image dual scrambling schemes, it is both simple and effective, offering a large key space, lower computational complexity, and applicability to non-square quantum images. [ABSTRACT FROM AUTHOR]

This research examines how a firm's cloud storage implementation affects different types of security breaches in both the short- and long-term. Building on the attention-based view, we find that cloud storage implementation positively relates to a firm's external breaches and accidental internal breaches in the short-term. However, the positive relationship between cloud storage implementation and external breaches diminishes over time and becomes insignificant long-term. Our results demonstrate a long-term security advantage of cloud storage in reducing accidental internal breaches. We did not find a significant association between cloud storage and malicious internal breaches. Findings highlight the need for firms to direct limited resources to different security risks in the short- and long-term of cloud storage implementation over time. This research contributes to our understanding of cloud storage's security implications and explicitly theorizes the role of attention in firm IT security management. We contribute to the attention-based view by contextualizing the theory to IT security. We highlight temporal dynamics through distinct attentional mechanisms, including selective attention, attentional flexibility, and attentional vigilance. [ABSTRACT FROM AUTHOR]

Cloud computing is widely applied in modern industrial areas due to its technological advancement, cost reduction, and applicability. Packets (tasks) belonging to different applications (agents) compete to share the common cloud resource through a series of edge nodes (processors) in pursuit of fast transmission. This paper abstracts the cloud computing system as a multi-agent flow-shop scheduling (MAFS) problem. The objective is to minimise the total completion time of several agents with the restriction that the maximum lateness cannot exceed a given bound. Given the complexity of the considered problem, a branch and bound algorithm combined with several pruning rules and lower bounds is proposed to obtain optimal solutions. Furthermore, the considered problem is generalised to a bi-scenario version, and a bi-population cooperative co-evolutionary (BCCE) algorithm is proposed to solve it. A reinforcement learning-based method is presented to generate the initial population. Several problem-specific intensification strategies are constructed to explore promising solutions. Comprehensive experiments verified the effectiveness of the proposed algorithms. The industrial data from the China Earthquake Network Centre further confirmed the superiority of the BCCE algorithm. Overall, the MAFS model and the proposed algorithms effectively enhance the user experience and reasonably guarantee revenue. [ABSTRACT FROM AUTHOR]

Organisations are having difficulty in determining and integrating Industry 4.0 technologies (I4.0T) to improve social dimensions of sustainable performance. We address this issue by showing how to identify the impact degree and path of I4.0T using the United Nations sustainable development goals (SDGs). First, we identify the SDGs associated with I4.0 and establish an evaluation framework. Second, we introduce an evaluation method that integrates hesitant fuzzy sets, rough sets, decision-making, trial evaluation laboratory (DEMATEL) and interpretative structural modelling methods to identify the impact degree and path of I4.0T on SDGs. Third, we confirm the effectiveness of our framework and method using secondary case data from World Economic Forum reports. The case results show that I4.0T have varying direct and indirect influences on SDGs. Policy makers should consider that 'Big Data and Analytics' and 'Cloud Computing' are central I4.0T that link other technologies and SDGs. The prioritisation of I4.0T can lead to contributions to addressing 'Responsible consumption and production' and indirectly improve SDGs. The findings set an initial stage to help sequence investment in various technologies depending on SDGs. We identify sequential paths that can aid in planning for and investing in adopting I4.0T to better achieve SDGs. [ABSTRACT FROM AUTHOR]

Along with a number of other computing technologies, cloud computing services are increasingly being promoted as a way of enabling openness, reproducibility, and the acceleration of scientific work. While there have been a variety of studies of the cloud in terms of computing performance, there has been little empirical attention to the changes going on around cloud computing at the level of work and practice. Through a qualitative, ethnographic study, we follow a cosmology research group’s transition from a shared high performance computing cluster to a cloud computing service, and examine the cloud service as a coordinative artifact being integrated into a larger ecology of existing practices and artifacts. We find that the transition involves both change and continuity in the group’s coordinative work and maintenance work, and point out some of the effects this adoption has on the group’s larger set of practices. Finally, we discuss practical implications this has for the broader adoption of cloud computing in university-based scientific work.

While interests in Industry 4.0 technologies such as cloud computing and the Internet of Things (IoT) are growing, an ongoing challenge is to properly evaluate the performance of the providers of such technologies. Methods have been developed and applied to assess the efficiency of cloud service providers (CSPs) for Industry 4.0. However, most existing methods suffer from such shortcomings as subjective weights and computing complexity, rendering it difficult to succinctly differentiate the performance of CSPs in a competitive market. Besides, the literature has not taken into account many different types of data such as ratios, integers, and undesirable factors that can affect the performance of CSPs. Most importantly, most existing studies do not consider the sustainability of CSPs in evaluating their performance. To address the above issues, we present a comprehensive analytical method based on data envelopment analysis (DEA) to gauge the sustainability of CSPs for Industry 4.0. Validating the usefulness of our method using a real-life dataset, we provide not only a viable means with sound academic underpinning but also significant managerial insights for practitioners to assess the sustainability of CSPs for Industry 4.0. [ABSTRACT FROM AUTHOR]

Summary: Disaggregated memory is a promising approach that addresses the limitations of traditional memory architectures by enabling memory to be decoupled from compute nodes and shared across a data center. Cloud platforms have deployed such systems to improve overall system memory utilization, but performance can vary across workloads. High‐performance computing (HPC) is crucial in scientific and engineering applications, where HPC machines also face the issue of underutilized memory. As a result, improving system memory utilization while understanding workload performance is essential for HPC operators. Therefore, learning the potential of a disaggregated memory system before deployment is a critical step. This paper proposes a methodology for exploring the design space of a disaggregated memory system. It incorporates key metrics that affect performance on disaggregated memory systems: memory capacity, local and remote memory access ratio, injection bandwidth, and bisection bandwidth, providing an intuitive approach to guide machine configurations based on technology trends and workload characteristics. We apply our methodology to analyze thirteen diverse workloads, including AI training, data analysis, genomics, protein, fusion, atomic nuclei, and traditional HPC bookends. Our methodology demonstrates the ability to comprehend the potential and pitfalls of a disaggregated memory system and provides motivation for machine configurations. Our results show that eleven of our thirteen applications can leverage injection bandwidth disaggregated memory without affecting performance, while one pays a rack bisection bandwidth penalty and two pay the system‐wide bisection bandwidth penalty. In addition, we also show that intra‐rack memory disaggregation would meet the application's memory requirement and provide enough remote memory bandwidth. [ABSTRACT FROM AUTHOR]

Background: During the last thirty years the world has benefited from the increasing adoption to cloud-based storage of personal health records. The concept of a Global Patient co-Owned Cloud (GPOC) of personal health records is presented in the GPOC Series, which contains a systematic review and meta-analysis, a global survey among 100% of the UN member states and a technical sandbox environment. GPOC contains patient co-ownership of personal health records. The global survey showed a consensus for the realisation of a GPOC. Methods: Here, we present a Delphi styled GPOC Summit. It also contains a final vote, that validates the results of the previous GPOC Survey. Hence, the Summit contains both multiple rounds of discussion and a voting. Results: At the GPOC Summit a unanimous consensus for patient co-ownership of the personal health records was expressed. The majority of participants underlined that GPOC may benefit global health, transfers of the personal health records globally and the dissemination of evidence-based medicine across the globe. The plausible advantages for patients, both refugees and travellers and for developing economies were underlined. GPOC, as a large substrate for global medical artificial intelligence research, was highlighted. There was unanimous positive interest for GPOC. The GPOC Summit agreed on patient co-ownership of personal health records. This collective agreement underscores the potential of GPOC to enhance global health outcomes, facilitate seamless exchange of health data, and advance medical research worldwide. Conclusions: The summit emphasised the potential benefits of GPOC for diverse populations and economies, highlighting its potential role in improving healthcare access and infrastructure. Moving forward, coordination with relevant initiatives could enhance interoperability and standardisation in healthcare data management. In conclusion, the GPOC Summit represents one step towards realising a patient-centric, globally accessible healthcare ecosystem, with possible implications for the development and global adoption of AI-driven healthcare solutions. [ABSTRACT FROM AUTHOR]

In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objective task scheduling is carried out by the incoming user utilizing the proposed hybrid fractional flamingo beetle optimization (FFBO) which is formed by integrating dung beetle optimization (DBO), flamingo search algorithm (FSA) and fractional calculus (FC). Here, the fitness function depends on reliability, cost, predicted energy, and makespan, the predicted energy is forecasted by a deep residual network (DRN). Thereafter, task scheduling is accomplished based on DL using the proposed deep feedforward neural network fused long short-term memory (DFNN-LSTM), which is the combination of DFNN and LSTM. Moreover, when scheduling the workflow, the task parameters and the virtual machine’s (VM) live parameters are taken into consideration. Task parameters are earliest finish time (EFT), earliest start time (EST), task length, task priority, and actual task running time, whereas VM parameters include memory utilization, bandwidth utilization, capacity, and central processing unit (CPU). The proposed model DFNN-LSTM+FFBO has achieved superior makespan, energy, and resource utilization of 0.188, 0.950J, and 0.238, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection. [ABSTRACT FROM AUTHOR]

Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are limited while capturing complicated temporal patterns from the load sequences. Besides, the frequently adopted global weighting strategy (e.g., the self-attention mechanism) in temporal modeling schemes has quadratic computational complexity, hindering the immediate response of cloud servers in complex real-time scenarios. To address the above limitations, we propose a Wavelet decomposition-enhanced External Transformer (WETformer) to provide accurate yet efficient load prediction for cloud servers. Specifically, we first incorporate discrete wavelet transform to progressively extract long-term trends, highlighting the intrinsic attributes of temporal sequences. Then, we propose a lightweight multi-head External Attention (EA) mechanism to simultaneously consider the inter-element relationships within load sequences and the correlations across different sequences. Such an external component has linear computational complexity, mitigating the encoding redundancy prevalent and enhancing prediction efficiency. Extensive experiments conducted on Alibaba Cloud's cluster tracking dataset demonstrate that WETformer achieves superior prediction accuracy and the shortest inference time compared to several state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

Cloud-based digital twins use real-time data from various data sources to simulate the behavior and performance of their physical counterparts, enabling monitoring and analysis. However, one restraining factor in the use of cloud computing for digital twins is its users' concerns about the security of their data. This data may be located anywhere in the cloud, with very limited control of the user to ensure its security. Cloud-based digital twins provide opportunities for researchers to collaborate yet security of such digital twins requires measures specific to cloud computing. To overcome this shortcoming, we need to devise a mechanism that not only ensures essential security safeguards but also computes a Trustworthiness value for Cloud Service Providers (CSP). This would give confidence to cloud users and enable them to choose the right CSP for their data-related interaction. This research proposes a solution, whereby the Trustworthiness of CSPs is calculated based on their Compliance with data security controls, User Feedback, and Auditor Rating. Two additional factors, Accuracy of Compliance Measurement and Control Significance Factor have been built in, to cater for other nonstandard conditions. Our implementation of Data Security Compliance Monitor and Data Trust as a Service, along with three CSPs, each with ten different settings, has supported our proposition through the devised formula. Experimental outcomes show changes in the trustworthiness value with changes in compliance level, user feedback and auditor rating. CSPs with better compliance have better trustworthiness values. However, if the Accuracy of Compliance Measurement and Control Significance Factor are low the trustworthiness is also proportionately less. This creates a balance and realism in our calculations. This model is unique and will help in creating users' trust in cloud-based digital twins. [ABSTRACT FROM AUTHOR]

Cloud computing has evolved into an indispensable tool for facilitating scientific research due to its ability to efficiently distribute and process workloads in a virtual environment. Scientific tasks that involve complicated task dependencies and user-defined constraints related to quality of service (QoS) and time constraints require the efficient use of cloud resources. Planning these scientific workflow tasks represents an NP-complete problem, prompting researchers to explore various solutions, including conventional planners and evolutionary optimization algorithms. In this study, we present a novel, multistage algorithm specifically designed to schedule scientific workflows in cloud computing contexts. This approach addresses the challenges of efficiently mapping complex workflows onto distributed cloud resources while considering factors like resource heterogeneity, dynamic workloads, and stringent performance requirements. The algorithm uses the whale optimization algorithm (WOA) with a two-phase approach to shorten execution time, minimize financial costs, and effectively maintain load balancing. [ABSTRACT FROM AUTHOR]

The "cloud lab," an automated laboratory that allows researchers to program and conduct physical experiments remotely, represents a paradigm shift in scientific practice. This shift from wet‐lab research as a primarily manual enterprise to one more akin to programming bears incredible promise by democratizing a completely new level of automation and its advantages to the scientific community. Moreover, they provide a foundation on which automated science driven by artificial intelligence (A.I.) can be built upon and thereby resolve limitations in scope and accessibility that current systems face. With a focus on DNA nanotechnology, the authors have had the opportunity to explore and apply the cloud lab to active research. This perspective delves into the future potential of cloud labs in accelerating scientific research and broadening access to automation. The challenges associated with the technology in its current state are further explored, including difficulties in experimental troubleshooting, the limited applicability of its parallelization in an academic setting, as well as the potential reduction in experimental flexibility associated with the approach. [ABSTRACT FROM AUTHOR]

Cloud computing is a popular, flexible, scalable, and cost-effective technology in the modern world that provides on-demand services dynamically. The dynamic execution of user requests and resource-sharing facilities require proper task scheduling among the available virtual machines, which is a significant issue and plays a crucial role in developing an optimal cloud computing environment. Round Robin is a prevalent scheduling algorithm for fair distribution of resources with a balanced contribution in minimized response time and turnaround time. This paper introduced a new enhanced round-robin approach for task scheduling in cloud computing systems. The proposed algorithm generates and keeps updating a dynamic quantum time for process execution, considering the available number of process in the system and their burst length. Since our method dynamically runs processes, it is appropriate for a real-time environment like cloud computing. The notable part of this approach is the capability of scheduling tasks with asymmetric distribution of burst time, avoiding the convoy effect. The experimental result indicates that the proposed algorithm has outperformed the existing improved round-robin task scheduling approaches in terms of minimized average waiting time, average turnaround time, and number of context switches. Comparing the method against five other enhanced round robin approaches, it reduced average waiting times by 15.77% and context switching by 20.68% on average. After executing the experiment and comparative study, it can be concluded that the proposed enhanced round-robin scheduling algorithm is optimal, acceptable, and relatively better suited for cloud computing environments. [ABSTRACT FROM AUTHOR]

Fog's basic distributed nature and ability to process data in transit—that is, to make decisions in real time—make it a good fit for scenarios involving several distributed devices that need to communicate, provide real‐time data analysis, and carry out storage functions. The majority of fog computing applications are driven by the user's demands and/or their desire for functioning services, either neglecting or giving security considerations second attention. Fog computing security issues have not received enough attention. Fog computing could be exploitable due to the security difficulties associated with cloud computing. Due to its flexibility to function near the end user and independence from a centralized design, fog computing provides the dependability required by time‐sensitive smart healthcare systems. There is a need for enhanced security and privacy solutions for fog computing, where trust is essential, due to the importance of healthcare data. This research aims to develop a context‐based adaptive trust solution for the smart healthcare environment utilizing Bayesian approaches and similarity measures against bad mouthing and ballot stuffing, while context‐dependent trust solutions for fogs remain an unexplored area of study. The proposed trust model has been simulated in Contiki‐Cooja to evaluate our findings. In contrast to static weighting, adaptive weights are provided to direct and indirect trust using entropy values that ensure the least degree of trust bias, and context similarity calculations eliminate recommender nodes with malicious intent by leveraging server, colleague, and service similarities. The proposed model protects smart healthcare systems from attacks using similarity metrics, incorporates context, and also uses adaptive weighting for trust calculation. By eliminating trust bias and also detecting attacks, this solution enhances the trust calculation by 10% as compared to the previous solution. This paradigm is efficient due to its small trust computation overhead and linear complexity O(n). [ABSTRACT FROM AUTHOR]

In the present times, the concept of cloud computing has played a significant role at the global level. With this approach, users can able to customize their services as per their needs. By having the connection of internet users can get able to serve various kinds of services like on-demand access, storage space, software building platforms, data recovery, etc., and pay only for that service that they have consumed. Enormous challenges in the cloud domain such as fault tolerance, energy efficiency, scheduling, resource provisioning, load balancing, etc. This paper is focused on load balancing domain. This can be defined as a redistribution of the workload among various available virtual machines in such an identical manner that would lead to a balanced state. This paper presents the evaluative and inclusive review of numerous load balancing (LB) methods. Quality of services(QoS) is vital role that contain various parameters to evaluate the load balancing methods in respect of makespan, speedup, cost, throughput, etc. This paper is highlighted numerous of load balancing methods with their brief explaination, platform used, different simulator and tools used by these methods and based on QoS parameters. [ABSTRACT FROM AUTHOR]

Cloud technology is a utility where different hardware and software resources are accessed on pay-per-user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization. In virtualization, a physical server changes into the virtual machine (VM) and acts as a physical server. Due to the large number of users sometimes the task sent by the user to cloud causes the VM to be under loaded or overloaded. This system state happens due to poor task allocation process in VM and causes the system failure or user tasks delayed. For the improvement of task allocation, several load-balancing techniques are introduced in a cloud but stills the system failure occurs. Therefore we hybrid modified artificial bee colony for improvement in task allocation of VM and minimizing time consummation in cloud datacenter like makespan, total processing time, response time of algorithms, response time of datacenter and degree of imbalance. The consequences of the proposed task-scheduling algorithm are matched with existing heuristic-based scheduling procedures. The experimental consequences demonstrate that our approach is efficient when it is compared with the existing algorithms and reduce 1.7% in time consumption for cloud datacenter. [ABSTRACT FROM AUTHOR]

Summary: This article introduces a new library that leverages and extends the MPI (message passing interface) standard, capable of aggregating servers and clusters located in multiple domains, as well as cloud resources. The execution platform is created and presented to the MPI application transparently, without the need of recompiling any code. The solution also provides functions for provisioning, adding, and removing nodes at runtime, bringing elasticity to the application. Through benchmarks, its performance was compared with the native execution of the application using the native MPI library. A prototype of an elastic application was also developed, with positive results within the expected range. [ABSTRACT FROM AUTHOR]

Summary: Fog computing, an emerging technology, extends Cloud computing services to the network's edge in the proximity of the application request. This extension yields improvement in Bandwidth (BW) utilization, faster responses to Real‐Time (RT) and Internet of Things (IoT) requests, and the provision of the heterogeneous resource services. While extensive work has been conducted on resource allocation for RT and Non‐Real‐Time (NRT) requests separately in Fog as well as Cloud computing, there is limited focus on resource provisioning for mixed RT and NRT requests in the Fog‐integrated Cloud (FiC) environment. Moreover, the majority of the existing provisioning methods primarily consider parameters from the system's perspective, overlooking crucial user aspects such as deadline and request size. To address the gap, this work introduces a resource provisioning method named "Parameter Matching of Realtime and Non‐Realtime Applications (PMRNA)," which considers user parameters and resource information, gathered by the broker. The performance evaluation of the proposed model is done in CloudSim, using various combinations of RT and NRT requests along with diverse Fog and Cloud resource configurations. Evaluation metric includes average Execution Time (ET), average Waiting Time (WT), average Turn Around Time (TAT), and resource utilization. The experimental results demonstrate a significant reduction in both average WT and average TAT for the diverse pool of RT and NRT requests in the FiC compared to the Cloud‐only environment. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions. A significant portion of the scientific literature on AI for radiology lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Here, the authors offer a blueprint for transparent AI pipelines on cloud platforms, focusing on lung cancer prediction and biomarker discovery. [ABSTRACT FROM AUTHOR]

Instantaneous data processing has the potential to enhance scalability, lessen power usage, and permit and improve data presentation in Consumer Internet of Things (CIoT) devices. In simple terms, cloud-based solutions cannot handle many IoT applications. According to Industrialized IoT (IIoT) technologies, an automated resource allocation system can improve service delivery and minimize healthcare costs. To maximize resource usage and response time for end users, there needs to be an effective method to efficiently distribute workload between Fog Layer and Cloud Connection and enhance cloud network capital allocation. Data analytics of complex and vital healthcare data requires timely responses, making it complicated. This paper proposes a design based on the Lanner Swarm Optimization (LSO) algorithm, which was developed to overcome inefficient heuristic strategies where data is transported to the cloud layer based on traffic type. The LSO algorithm is used to improve resource allocation and workload distribution in cloud-assisted CIoT applications for smart healthcare systems, improving scalability, power consumption, and data processing. The objective function determines if diverse virtual machines (VMs) vary accomplishment time the most, considering this study's updating and pruning restrictions. The experimentation analysis demonstrated that the proposed load balancing and work scheduling method outperforms evolutionary and heuristics algorithms. In experimentation, the research model attains a makespan of 10 s, response time of 5.5 s, resource utilization with a rate of 0.9, execution time of 13 s, latency of 10 ms, throughput of 0.78 s, and delivery rate of 0.74%. At resource scheduling, the LSO model had the best payload routing, latency, packet delivery ratio, and network lifetime. [ABSTRACT FROM AUTHOR]

The growing elderly population in smart home environments necessitates increased remote medical support and frequent doctor visits. To address this need, wearable sensor technology plays a crucial role in designing effective healthcare systems for the elderly, facilitating human–machine interaction. However, wearable technology has not been implemented accurately in monitoring various vital healthcare parameters of elders because of inaccurate monitoring. In addition, healthcare providers encounter issues regarding the acceptability of healthcare parameter monitoring and secure data communication within the context of elderly care in smart home environments. Therefore, this research is dedicated to investigating the accuracy of wearable sensors in monitoring healthcare parameters and ensuring secure data transmission. An architectural framework is introduced, outlining the critical components of a comprehensive system, including Sensing, Data storage, and Data communication (SDD) for the monitoring process. These vital components highlight the system's functionality and introduce elements for monitoring and tracking various healthcare parameters through wearable sensors. The collected data is subsequently communicated to healthcare providers to enhance the well-being of elderly individuals. The SDD taxonomy guides the implementation of wearable sensor technology through environmental and body sensors. The proposed system demonstrates the accuracy enhancement of healthcare parameter monitoring and tracking through smart sensors. This study evaluates state-of-the-art articles on monitoring and tracking healthcare parameters through wearable sensors. In conclusion, this study underscores the importance of delineating the SSD taxonomy by classifying the system's major components, contributing to the analysis and resolution of existing challenges. It emphasizes the efficiency of remote monitoring techniques in enhancing healthcare services for the elderly in smart home environments. [ABSTRACT FROM AUTHOR]