A novel cloud-assisted framework for consumer internet of things based on lanner swarm optimization algorithm in smart healthcare systems

• Published in: Multimedia tools and applications Vol. 83; no. 26; pp. 68155 - 68179
• Main Authors: Arulkumar, V., Aruna, M., Prakash, D., Amanullah, M., Somasundaram, K., Thavasimuthu, Rajendran
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2024; Springer Nature B.V
• Subjects: Algorithms; Cloud computing; Computer Communication Networks; Computer Science; Cost allocation; Cost analysis; Data analysis; Data processing; Data Structures and Information Theory; Delivery scheduling; Design optimization; End users; Evolutionary algorithms; Experimentation; Health care; Industrial applications; Internet of Things; Multimedia Information Systems; Network latency; Power consumption; Power management; Resource allocation; Resource scheduling; Resource utilization; Response time; Special Purpose and Application-Based Systems; Virtual environments; Workload; Workloads; Cloud computing; Resource allocation and swarm intelligence; Load balancing; Multimedia IoT
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18846-0

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.