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Sensors (Basel, Switzerland)2023; 23(11); 5349; doi: 10.3390/s23115349

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain.

Abstract: The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.
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Publication Date: 2023-06-05 PubMed ID: 37300076PubMed Central: PMC10256013DOI: 10.3390/s23115349Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The principal concept of this research paper is a novel, energy-aware artificial intelligence-based load balancing model designed for the rapidly growing cloud-enabled Internet of Things in health care. Through the use of the Chaotic Horse Ride Optimization Algorithm and big data analytics, the model aims to optimize energy resources and effectively manage large volumes of data.

Internet of Things and Need for Load Balancing

  • The Internet of Things (IoT) has seen rapid growth thanks to advancements in information and communication technologies. Its evolution into the Internet of Everything (IoE) allows unprecedented levels of connectivity and data generation.
  • The implementation of this technology, particularly in the health care field where large amounts of data are generated, faces challenges including limited energy resources and processing power.
  • This creates a need for energy-efficient, intelligent load balancing models to handle the data traffic and ensure efficient system operation.

Proposed Solution – Chaotic Horse Ride Optimization Algorithm

  • In this paper, the authors propose a solution using an energy-aware artificial intelligence-based load balancing model. The model employs the Chaotic Horse Ride Optimization Algorithm (CHROA) along with big data analytics.
  • CHROA enhances the optimization capacity of the original Horse Ride Optimization Algorithm (HROA), using the principles of chaos theory to contribute to higher levels of dynamism and randomness.
  • Through the application of artificial intelligence techniques, the proposed model can balance the load and optimize available energy resources in a cloud-enabled IoT environment.

Comparison with Existing Models

  • This model is evaluated using various metrics and compared with existing models including the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA).
  • Experimental results revealed that the proposed model outperforms the existing models. For example, while the ABC, GSA, and WD-FA techniques had average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps respectively, the CHROA model delivered an average throughput of 70.122 Kbps.

Impact and Future Applications

  • This research indicates the potential of the proposed CHROA-based model as it offers an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments.
  • Its effectiveness in addressing the critical challenges associated with cloud-enabled IoT in healthcare implies its potential for broader application in IoT/IoE solutions, providing efficiency and sustainability.

Cite This Article

APA
Aqeel I, Khormi IM, Khan SB, Shuaib M, Almusharraf A, Alam S, Alkhaldi NA. (2023). Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain. Sensors (Basel), 23(11), 5349. https://doi.org/10.3390/s23115349

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 23
Issue: 11
PII: 5349

Researcher Affiliations

Aqeel, Ibrahim
  • College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.
Khormi, Ibrahim Mohsen
  • College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.
Khan, Surbhi Bhatia
  • Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon.
  • Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK.
Shuaib, Mohammed
  • College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.
Almusharraf, Ahlam
  • Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Alam, Shadab
  • College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.
Alkhaldi, Nora A
  • Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Hasa 31982, Saudi Arabia.

MeSH Terms

  • Animals
  • Horses
  • Artificial Intelligence
  • Algorithms
  • Intelligence
  • Awareness
  • Internet

Conflict of Interest Statement

The authors declare no conflict of interest.

References

This article includes 53 references
  1. Alam S, Siddiqui ST, Ahmad A, Ahmad R, Shuaib M. Internet of Things (IoT) Enabling Technologies, Requirements, and Security Challenges. Lecture Notes in Networks and Systems Volume 94; pp. 119–126; Springer; Berlin/Heidelberg, Germany: 2020.
  2. Di Martino B., Li K.-C., Yang L.T., Esposito A. Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives. Springer; Berlin/Heidelberg, Germany: 2018.
  3. Singh A, Joshi K, Alam S, Bharany S, Shuaib M, Ahmad S. Internet of Things-Based Integrated Remote Electronic Health Surveillance and Alert System: A Review. Proceedings of the 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET); Bhopal, India. 23–24 December 2022; pp. 1–6.
  4. Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Commun. Surv. Tutor. 2015;17:2347–2376.
    doi: 10.1109/COMST.2015.2444095google scholar: lookup
  5. Neelakandan S, Berlin MA, Tripathi S, Devi VB, Bhardwaj I, Arulkumar N. IoT-Based Traffic Prediction and Traffic Signal Control System for Smart City. Soft Comput. 2021;25:12241–12248.
    doi: 10.1007/s00500-021-05896-xgoogle scholar: lookup
  6. Han Z, Yang Y, Wang W, Zhou L, Gadekallu TR, Alazab M, Gope P, Su C. RSSI Map-Based Trajectory Design for UGV Against Malicious Radio Source: A Reinforcement Learning Approach. IEEE Trans. Intell. Transp. Syst. 2023;24:4641–4650.
    doi: 10.1109/TITS.2022.3208245google scholar: lookup
  7. Kamalraj R, Neelakandan S, Kumar MR, Rao VCS, Anand R, Singh H. Interpretable Filter Based Convolutional Neural Network (IF-CNN) for Glucose Prediction and Classification Using PD-SS Algorithm. Measurement 2021;183:109804.
    doi: 10.1016/j.measurement.2021.109804google scholar: lookup
  8. Swarna Priya RM, Bhattacharya S, Maddikunta PKR, Somayaji SRK, Lakshmanna K, Kaluri R, Hussien A, Gadekallu TR. Load Balancing of Energy Cloud Using Wind Driven and Firefly Algorithms in Internet of Everything. J. Parallel Distrib. Comput. 2020;142:16–26.
  9. Kavitha T, Mathai PP, Karthikeyan C, Ashok M, Kohar R, Avanija J, Neelakandan S. Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images.. Interdiscip Sci 2022 Mar;14(1):113-129.
    doi: 10.1007/s12539-021-00467-ypubmed: 34338956google scholar: lookup
  10. Neelakandan S, Arun A, Bhukya RR, Hardas BM, Kumar TC, Ashok M. An Automated Word Embedding with Parameter Tuned Model for Web Crawling. Intell. Autom. Soft Comput. 2022;32:1617–1632.
    doi: 10.32604/iasc.2022.022209google scholar: lookup
  11. Rani S, Babbar H, Srivastava G, Gadekallu TR, Dhiman G. Security Framework for Internet-of-Things-Based Software-Defined Networks Using Blockchain. IEEE Internet Things J. 2023;10:6074–6081.
    doi: 10.1109/JIOT.2022.3223576google scholar: lookup
  12. Gupta P, Varshney A, Khan MR, Ahmed R, Shuaib M, Alam S. Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Study of Balancing Techniques. Procedia Comput. Sci. 2023;218:2575–2584.
    doi: 10.1016/j.procs.2023.01.231google scholar: lookup
  13. Shuaib M, Bhatia S, Alam S, Masih RK, Alqahtani N, Basheer S, Alam MS. An Optimized, Dynamic, and Efficient Load-Balancing Framework for Resource Management in the Internet of Things (IoT) Environment. Electronics 2023;12:1104.
    doi: 10.3390/electronics12051104google scholar: lookup
  14. Alam S, Bhatia S, Shuaib M, Khubrani MM, Alfayez F, Malibari AA, Ahmad S. An Overview of Blockchain and IoT Integration for Secure and Reliable Health Records Monitoring. Sustainability 2023;15:5660.
    doi: 10.3390/sᔇ5660google scholar: lookup
  15. Alam S. Security Concerns in Smart Agriculture and Blockchain-Based Solution; Proceedings of the 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON); Raigarh, India. 8–10 February 2023; pp. 1–6.
  16. Alam S, Shuaib M, Ahmad S, Jayakody DNK, Muthanna A, Bharany S, Elgendy IA. Blockchain-Based Solutions Supporting Reliable Healthcare for Fog Computing and Internet of Medical Things (IoMT) Integration. Sustainability 2022;14:15312.
    doi: 10.3390/sᐢ15312google scholar: lookup
  17. Cao J, Xu L, Abdallah R, Shi W. EdgeOS_H: A Home Operating System for Internet of Everything. Proceedings of the 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS); Atlanta, GA, USA. 5–8 June 2017; pp. 1756–1764.
  18. Naranjo PGV, Pooranian Z, Shojafar M, Conti M, Buyya R. FOCAN: A Fog-Supported Smart City Network Architecture for Management of Applications in the Internet of Everything Environments. J. Parallel Distrib. Comput. 2019;132:274–283.
    doi: 10.1016/j.jpdc.2018.07.003google scholar: lookup
  19. Singh P, Nayyar A, Kaur A, Ghosh U. Blockchain and Fog Based Architecture for Internet of Everything in Smart Cities. Future Internet 2020;12:61.
    doi: 10.3390/fi12040061google scholar: lookup
  20. Neelakandan S., Divyabharathi S., Rahini S., Vijayalakshmi G. Large Scale Optimization to Minimize Network Traffic Using MapReduce in Big Data Applications; Proceedings of the 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC); Melmaruvathur, India. 20–21 April 2016; pp. 193–199.
  21. Miao Y, Liu X, Choo K-KR, Deng RH, Wu H, Li H. Fair and Dynamic Data Sharing Framework in Cloud-Assisted Internet of Everything. IEEE Internet Things J. 2019;6:7201–7212.
    doi: 10.1109/JIOT.2019.2915123google scholar: lookup
  22. Xiao H, Zhang Z, Zhou Z. GWS-A Collaborative Load-Balancing Algorithm for Internet-of-Things.. Sensors (Basel) 2018 Jul 31;18(8).
    doi: 10.3390/s18082479pmc: PMC6111855pubmed: 30065224google scholar: lookup
  23. Ramalingam C, Mohan P. An Efficient Applications Cloud Interoperability Framework Using I-Anfis. Symmetry 2021;13:268.
    doi: 10.3390/sym13020268google scholar: lookup
  24. Garzia F., Papi L. An Internet of Everything Based Integrated Security System for Smart Archaeological Areas; Proceedings of the 2016 IEEE International Carnahan Conference on Security Technology (ICCST); Orlando, FL, USA. 24–27 October 2016; pp. 1–8.
  25. Babou CSM, Fall D, Kashihara S, Taenaka Y, Bhuyan MH, Niang I, Kadobayashi Y. Hierarchical Load Balancing and Clustering Technique for Home Edge Computing. IEEE Access 2020;8:127593–127607.
    doi: 10.1109/ACCESS.2020.3007944google scholar: lookup
  26. Lv Z, Yu Z, Xie S, Alamri A. Deep Learning-Based Smart Predictive Evaluation for Interactive Multimedia-Enabled Smart Healthcare. ACM Trans. Multimed. Comput. Commun. Appl. 2022:18.
    doi: 10.1145/3468506google scholar: lookup
  27. Sangaiah AK, Javadpour A, Ja’fari F, Pinto P, Ahmadi H, Zhang W. CL-MLSP: The Design of a Detection Mechanism for Sinkhole Attacks in Smart Cities. Microprocess. Microsyst. 2022;90:104504.
    doi: 10.1016/j.micpro.2022.104504google scholar: lookup
  28. Dong C, Xu X, Liu A, Liang X. Load Balancing Routing Algorithm Based on Extended Link States in LEO Constellation Network. China Commun. 2022;19:247–260.
    doi: 10.23919/JCC.2022.02.020google scholar: lookup
  29. Jeyaraj R, Balasubramaniam A, Ajay Kumara MA, Guizani N, Paul A. Resource Management in Cloud and Cloud-Influenced Technologies for Internet of Things Applications. ACM Comput. Surv. 2023;55:1–37.
    doi: 10.1145/3571729google scholar: lookup
  30. Tarahomi M, Izadi M, Ghobaei-Arani M. An Efficient Power-Aware VM Allocation Mechanism in Cloud Data Centers: A Micro Genetic-Based Approach. Cluster Comput. 2021;24:919–934.
    doi: 10.1007/s10586-020-03152-9google scholar: lookup
  31. Saba T, Rehman A, Haseeb K, Alam T, Jeon G. Cloud-edge load balancing distributed protocol for IoE services using swarm intelligence.. Cluster Comput 2023 Jan 4;:1-11.
    doi: 10.1007/s10586-022-03916-5pmc: PMC9812543pubmed: 36624887google scholar: lookup
  32. Ghobaei-Arani M, Shahidinejad A. A Cost-Efficient IoT Service Placement Approach Using Whale Optimization Algorithm in Fog Computing Environment. Expert Syst. Appl. 2022;200:117012.
    doi: 10.1016/j.eswa.2022.117012google scholar: lookup
  33. Farahbakhsh F, Shahidinejad A, Ghobaei-Arani M. Multiuser Context-aware Computation Offloading in Mobile Edge Computing Based on Bayesian Learning Automata. Trans. Emerg. Telecommun. Technol. 2021;32:e4127.
    doi: 10.1002/ett.4127google scholar: lookup
  34. Quy VK, Hau NV, Anh DV, Ngoc LA. Smart healthcare IoT applications based on fog computing: architecture, applications and challenges.. Complex Intell Systems 2022;8(5):3805-3815.
    doi: 10.1007/s40747-021-00582-9pmc: PMC8595960pubmed: 34804767google scholar: lookup
  35. Khanh QV, Nguyen V-H, Minh QN, Van AD, Le Anh N, Chehri A. An Efficient Edge Computing Management Mechanism for Sustainable Smart Cities. Sustain. Comput. Inform. Syst. 2023;38:100867.
    doi: 10.1016/j.suscom.2023.100867google scholar: lookup
  36. Emmanuel AA, Awokola JA, Alam S, Bharany S, Agboola P, Shuaib M, Ahmed R. A Hybrid Framework of Blockchain and IoT Technology in the Pharmaceutical Industry: A Comprehensive Study. Mob. Inf. Syst. 2023;2023:3265310.
    doi: 10.1155/2023/3265310google scholar: lookup
  37. Liu Y, Sun Q, Sharma A, Sharma A, Dhiman G. Line Monitoring and Identification Based on Roadmap towards Edge Computing. Wirel. Pers. Commun. 2021;127:441–464.
    doi: 10.1007/s11277-021-08272-ygoogle scholar: lookup
  38. Hakak S, Gadekallu TR, Maddikunta PKR, Ramu SP, M P, De Alwis C, Liyanage M. Autonomous Vehicles in 5G and beyond: A Survey. Veh. Commun. 2023;39:100551.
    doi: 10.1016/j.vehcom.2022.100551google scholar: lookup
  39. Amr ME, Al-Awamry AA, Elmenyawi MA, Eldien AST. Design and Implementation of a Low-Cost IoT Node for Data Processing, Case Study: Smart Agriculture. J. Commun. 2022;17:99–109.
    doi: 10.12720/jcm.17.2.99-109google scholar: lookup
  40. Zaman U, Imran, Mehmood F, Iqbal N, Kim J, Ibrahim M. Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications. Electronics 2022;11:1893.
    doi: 10.3390/electronics11121893google scholar: lookup
  41. Khriji S, Benbelgacem Y, Chéour R, El Houssaini D, Kanoun O. Design and Implementation of a Cloud-Based Event-Driven Architecture for Real-Time Data Processing in Wireless Sensor Networks. J. Supercomput. 2022;78:3374–3401.
    doi: 10.1007/s11227-021-03955-6google scholar: lookup
  42. Al Sohan M.F.A., Nahar A. A Low-Power Wireless Sensor Network for a Smart Irrigation System Powered by Solar Energy; Proceedings of the 2nd International Conference on Computing Advancements; Dhaka, Bangladesh. 10–12 March 2022; New York, NY, USA: Association for Computing Machinery; pp. 537–543.
  43. Mousavi SM, Khademzadeh A, Rahmani AM. The Role of Low-Power Wide-Area Network Technologies in Internet of Things: A Systematic and Comprehensive Review. Int. J. Commun. Syst. 2022;35:e5036.
    doi: 10.1002/dac.5036google scholar: lookup
  44. Bouguera T, Diouris JF, Chaillout JJ, Jaouadi R, Andrieux G. Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN.. Sensors (Basel) 2018 Jun 30;18(7).
    doi: 10.3390/s18072104pmc: PMC6068831pubmed: 29966354google scholar: lookup
  45. Song Y, Xin R, Chen P, Zhang R, Chen J, Zhao Z. Identifying Performance Anomalies in Fluctuating Cloud Environments: A Robust Correlative-GNN-Based Explainable Approach. Futur. Gener. Comput. Syst. 2023;145:77–86.
    doi: 10.1016/j.future.2023.03.020google scholar: lookup
  46. Amro D. Dynamic Energy-Efficient Routing Protocol for Wireless Sensor Networks. Palest. J. Tech. Appl. Sci. 2019;6:23–36.
  47. Bomgni AB, Ali HM, Shuaib M, Mtopi Chebu Y. Multihop Uplink Communication Approach Based on Layer Clustering in LoRa Networks for Emerging IoT Applications. Mob. Inf. Syst. 2023;2023:5828671.
    doi: 10.1155/2023/5828671google scholar: lookup
  48. Reshma G, Al-Atroshi C, Nassa VK, Geetha BT, Sunitha G, Galety MG, Neelakandan S. Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images. Intell. Autom. Soft Comput. 2022;31:621–634.
    doi: 10.32604/iasc.2022.019117google scholar: lookup
  49. Kirola M., Memoria M., Shuaib M., Joshi K., Alam S., Alshanketi F. A Referenced Framework on New Challenges and Cutting-Edge Research Trends for Big-Data Processing Using Machine Learning Approaches; Proceedings of the 2023 International Conference on Smart Computing and Application (ICSCA); Hail, Saudi Arabia. 5–6 February 2023; pp. 1–5.
  50. Alam S, Mohammad OKJ, Alfurhood BS, Saxena KK, Anand M, Mahaveerakannan R, Savitha V. Effective Sound Detection System in Commercial Car Vehicles Using Msp430 Launchpad Development. Multimed. Tools Appl. .
    doi: 10.1007/s11042-023-15373-2google scholar: lookup
  51. Shu B, Chen H, Sun M. Dynamic Load Balancing and Channel Strategy for Apache Flume Collecting Real-Time Data Stream. Proceedings of the 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC); Guangzhou, China. 12–15 December 2017; pp. 542–549.
  52. Neelakandan S., Anand J.G. Trust Based Optimal Routing in MANET’s; Proceedings of the 2011 International Conference on Emerging Trends in Electrical and Computer Technology; Nagercoil, India. 23–24 March 2011; pp. 1150–1156.
  53. Asha P, Natrayan L, Geetha BT, Beulah JR, Sumathy R, Varalakshmi G, Neelakandan S. IoT enabled environmental toxicology for air pollution monitoring using AI techniques.. Environ Res 2022 Apr 1;205:112574.
    doi: 10.1016/j.envres.2021.112574pubmed: 34919959google scholar: lookup

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This article has been cited 6 times.
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    doi: 10.1371/journal.pone.0329765pubmed: 40924788google scholar: lookup
  2. Samah T, Samar M. Investigating the Key Trends in Applying Artificial Intelligence to Health Technologies: A Scoping Review. PLoS One 2025;20(5):e0322197.
    doi: 10.1371/journal.pone.0322197pubmed: 40372995google scholar: lookup
  3. Khan A, Zubair S, Shuaib M, Sheneamer A, Alam S, Assiri B. Development of a robust parallel and multi-composite machine learning model for improved diagnosis of Alzheimer's disease: correlation with dementia-associated drug usage and AT(N) protein biomarkers. Front Neurosci 2024;18:1391465.
    doi: 10.3389/fnins.2024.1391465pubmed: 39308946google scholar: lookup
  4. Kumar V, Ali A, Mittal P, Aqeel I, Shuaib M, Alam S, Aalsalem MY. E2SVM: Electricity-Efficient SLA-aware Virtual Machine Consolidation approach in cloud data centers. PLoS One 2024;19(6):e0303313.
    doi: 10.1371/journal.pone.0303313pubmed: 38857300google scholar: lookup
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  6. Reddy CKK, Kaza VS, Anisha PR, Khubrani MM, Shuaib M, Alam S, Ahmad S. Optimising barrier placement for intrusion detection and prevention in WSNs. PLoS One 2024;19(2):e0299334.
    doi: 10.1371/journal.pone.0299334pubmed: 38422084google scholar: lookup
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