Efficient load balancing Adaptive BNBKnapsack Algorithm for Edge computing to improve performance of network

Malti Nagle; Prakash Kumar

doi:10.4108/eetsis.3924

Authors

Malti Nagle Jaypee Institute of Information Technology
Prakash Kumar Jaypee Institute of Information Technology

DOI:

https://doi.org/10.4108/eetsis.3924

Keywords:

IoT, Cloud Computing, Context aware system, Scheduling, load balancing algorithm, EEG sensor, BLE, CloudSim, iFogSim, Stress related health issues, Adaptive BNBKnapsackAlgorithm Introduction

Abstract

INTRODUCTION: In present days, Automation of everything has become essential. Internet of things (IoT) play an important role among all medical advances of IT. In this paper, feasible solutions are discussed to compare and design better healthcare systems. A thorough investigation and survey of suitable approaches were done to select IoT based systems in hospitals consisting of various high precision sensors.

OBJECTIVES: The challenge healthcare system face is to manage the real time patient’s data with high accuracy. Second challenge is at fog devices level to manage the load distribution to all sensors with limited availability of bandwidth.

METHODS: This paper summarizes the selection criterions of suitable load balancing algorithms to reduce energy consumption and computational cost of fog devices and increase the network usage that are supposed to be used in IoT based healthcare systems. According to the survey BNBKnapack algorithm has been selected as best suitable approach to analyze the overall performance of fog devices and results are also verify the same.

RESULTS: Comparative analysis of Overall performance of fog devices has been proposed with using SJF algorithm and Adaptive BNBKnapsack algorithm. It has been observed by analysing system performance, which is found as best among other load balancing algorithm Adaptive BNBKnapsack is successfully reduce the energy consumption by (99.29%), computational cost by (98.34%) and increase the network usage by (99.95%) of system

CONCLUSION: It has been observed by analysing system performance, Adaptive BNBKnapsack Load balancing is successfully able to reduce the computational cost and energy consumption also increase the network usage of the fog network. The performance of the system is found best among other load balancing algorithm.

References

D. Rahbari, M. Nickray, Low-latency and energy-efficient scheduling in fog-based IoT applications, TJEECS, pp. 1406 – 1427, (2019).

B. Baker, W. Xiang,I. Atinkson, Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities, IEEEAccess, Volume 5- 2017, pp.25621 – 25644, (2017).

M. Sain, Y. J. kang, H. J. Lee, Survey on Security in internet of the things: state of the art and challenges, ICACT2017, pp. 699 – 704, Feb (2017).

D. Rahbari, M. Nickray. Scheduling of Fog Networks with Optimized Knapsack by Symbiotic Organisms Search, preceding of the 21st conference of fruct association, (2019).

A. M. Rahmani, T. N. Gia, B. Negash, A. Anzanpour, I. Azimi, M. Jiang, and P. Liljeberg, Exploiting smart e-health gateways at the edge of healthcare internet-of-things: a fog computing approach, Future Generation Computer Systems,( 2017).

D. Rahbari, M. Nickray. Low-latency and energy-efficient scheduling in fog-based IoT applications, Turkish Journal of Electrical Engineering & Computer Sciences, (2019).

Naina Gupta, Hera Saeed, SanjanaJha, ManishaChahande and Sujata Pandey, Study and implementation of IOT vased smart healthcare System, ICEI, pp 541-546,( 2017).

A. Pantelopoulos and N. Bourbakis, A survey on wearable sensor-based systems for health monitoring and prognosis, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., vol. 40, no. 1, pp. 1–12, Jan. (2010).

L. Savu, Cloud Computing: Deployment Models, Delivery Models, Risks and Research Challenges, Computer and Management (CAMAN), International Conference on, pp. 1-4, (2011).

P., Metri, G., Sarote, Privacy Issues and Challenges in Cloud Computing, International Journal of Advanced Engineering Sciences and Technologies (IJAEST), vol. 5, pp.1-6, (2011)

Shweta, PadmawatiKhandnoe, Neelesh Kumar, A Survey of Activity Recognition Process Using Inertial sensors and Smartphone Sensors, ICCCA, pp. 607-612, (2017).

P. K. Schwab, The Fourth Industrial Revolution: What it Means, and How to Respond. Cologny, Switzerland: World Economic Forum, (2016).

Australian Institute of Health and Welfare. Australia's Health. [Online]. Available: http://www.aihw.gov.au/WorkArea/DownloadAsset.aspx-?id=60129548150, (2014).

A. Bahga and V. Madisetti, Cloud Computing: A Hands-on Approach, CreateSpace, (2013).

A. Bahga and V. Madisetti, Internet of Things: A Hands-on Approach, CreateSpace.263, (2014) .

A. Bahga and V. Madisetti, A Cloud-Based Approach for Interoperable Electronic Health Records (EHRs), IEEE J. Biomedical and Health Informatics, vol. 17, no. 5, pp. 894–906, (2013).

J. Mohammed, C.-H. Lung, A. Ocneanu, A. Thakral, C. Jones, and A. Adler, Internet of Things: Remote patient monitoring using Web services and cloud computing, in Proc. IEEE Int. Conf. Internet Things (iThings), IEEE Green Comput. Commun. (GreenCom) IEEE Cyber, Phys. Social Comput. (CPSCom), pp. 256–263, Sep. (2014).

M. Díaz, C. Martín, and B. Rubio, State-of-the-art, challenges, and open issues in the integration of Internet of Things and cloud computing, J. Netw. Comput. Appl., vol. 67, pp. 99–117, May (2016).

B. Díaz, M. Martín, and C. Rubio, λ-CoAP: An Internet of Things and cloud computing integration based on the lambda architecture and CoAP, in Proc. Int. Conf. Collaborative Comput., Netw., Appl. Work-sharing, vol. 163. Cham, Switzerland: Springer, pp. 195–206, (2015).

G. Aloi et al., Enabling IoT interoperability through opportunistic smartphone-based mobile gateways, J. Netw. Comput. Appl., vol. 81, pp. 74–84, Mar. (2017).

Rodolfo S. Antunes, Lucas A. Seewald, Vinicius F. Rodrigues, Cristiano A. Da Costa, Luiz Gonzaga Jr., And Rodrigo R. Righi, Andreas Maier, BjörnEskofier, MalteOllenschläger, And FarzadNaderi,RebeccaFahrig, Sebastian Bauer, Sigrun Klein, And GelsonCampanatti, A Survey Of Sensors In Healthcare workflow Monitoring”, ACM Computing Surveys, Vol. 51, No. 2, Article 42, April (2018).

BoyiXu, Li Da Xu, Cheng Xie, Jingyuan Hu, and Fenglin Bu, Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services, Ieee Transactions On Industrial Informatics, Vol. 10, No. 2, pp. 1578-1586, MAY (2014).

M. Ashouri, F. Lorig, P. Davidsson, R. Spalazzese, Edge computing simulators for IoT system design: An analysis of qualities and metrics, Future internet, vol – 11, pp. 12, (2019).

https://www.chapter247.com/key-benefits-of-cloud-computing-in-healthcare-768x768/

Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R. Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience; 41(1): 23-50. doi: 10.1002/spe.995, (2011).

web reference :Edge computing now a days.

JitendraSingh, Study of Response Time in Cloud Computing, IEEB, vol 5, pp 36-43, 2014.

Sagar Sharma, Keke Chen and Amit Sheth, Toward Practical Privacy-Preserving Analytics for IoT and Cloud-Based Healthcare Systems, in healthcare informatics and privacy, IEEE Internet Computing, pp 1089-7801, (2018).

KavitaJaiswalIIIT et al., An IoT-Cloud based smart healthcare monitoring system using container based virtual environment in Edge device, Proceeding of ICETIETR, (2018).

Prabal V, Sandeep K. Sood , SheetalKalra, Cloud-centric IoT based student healthcare monitoring framework, J Ambient Intell Human Compute, vol 9, pp 1293-1309, (2018).

Maulik Parekh, Dr.Saleena B., Designing a Cloud based Framework for HealthCare System and applying Clustering techniques for Region Wise Diagnosis, Elsevier, ScienceDirect, 2nd International Symposium on Big Data and Cloud Computing, Procedia Computer Science vol 50, pp 537 – 542, (2015).

Niharika Kumar, IoTArchtecture and System Design for Healthcare System, ICOSTSN pp 1118-1123, (2017).

Naina Gupta, Hera Saeed, SanjanaJha, ManishaChahande and Sujata Pandey, Study and implementation of IOT vased smart healthcare System, ICEI, pp 541-546, (2017).

A. Jindal, A. Dua, N. Kumar, A. V. Vasilakos, and J. J. P. C. Rodrigues, An efficient fuzzy rule-based big data analytics scheme for providing Healthcare-as-a-Service, presented at IEEE Int. Conf. Commun., Paris, France, May 21–25, pp. 1–6, (2017).

QueenyIp, Daniel C. Malone, Jenny Chong, Robin B. Harris, David M. Labiner, An update on the prevalence and incidence of epilepsy among older adults, Epilepsy Research, pp. 107-112, (2018).

ParthaPratim Ray, A survey of IoT cloud platforms, Future Computing and Informatics Journal, vol 1, pp. 35-46, (2017).

Praful P. Pai, Pradyut K. Sanki, Sudeep K. Sahoo, Arijit De, Sourangshu Bhattacharya, and Swapna Banerjee, Cloud Computing-Based Non-Invasive Glucose Monitoring for Diabetic Care, Ieee Transactions on Circuits And Systems–I: Regular Papers, VOL. 65, NO. 2, pp. 663-676, (2018).

Byungseok Kang, Daecheon Kim, and HyunseungChoo, Internet of Everything: A Large-Scale Autonomic IoT Gateway Byungseok, IEEE Transactions on Multi-Scale Computing Systems, Vol. 3, No. 3, Pp. 206-214, July-September (2017).

H. Khazaei, J. Misic, and V.B. Misic, Performance Analysis of Cloud Computing Centers, Proc. Seventh Int’l ICST Conf. Heterogeneous Networking for Quality, Reliability, Security and Robustness (Qshine), (2010).