Skip to main content

Advertisement

SpringerLink
Log in

An Energy Efficient e-Healthcare Framework Supported by Novel EO-μGA (Extremal Optimization Tuned Micro-Genetic Algorithm)

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

The edge/fog computing has the potential to gear up the healthcare industry by providing better and faster health services to the patients. In healthcare systems where every second is crucial, the edge computing can be helpful to reduce the time between data capture and analytics in a powerful manner. In edge computing, the network edge devices are configured in such a manner that they can handle critical analysis and make necessary decisions instead of sending the captured health data directly to the cloud. However, lifetime of the edge network is a critical factor and thus an energy efficient network architecture has to be designed to achieve the above mentioned goal. In this regard, this research presents a new extremal optimization tuned micro genetic algorithm (EO-μGA) based clustering technique for the sake of efficient routing and prolonging network lifetime by saving the battery power of network edge devices. Moreover, a novel fitness function with a set of relevant criteria of edge devices such as energy factor, average intra-cluster distance, average distance to cluster leader over data analytics center, average sleeping time, and computational load has been considered for the selection of the cluster leader which will be responsible for managing intra-cluster and inter-cluster data communication. The simulation results show that the proposed EO-μGA based clustering model offers a higher network lifetime and a least amount of transmission energy consumption per node as compared to various state of the art optimization algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Aazam, M., Khan, I., Alsaffar, A. A., & Huh, E. N. (2014). Cloud of Things: Integrating Internet of Things and Cloud Computing and the Issues Involved. In Proc. of 11th international Bhurban conference on applied sciences and technology (Vol. 414–19, p. 2014). IBCAST.

  • Aazam, M., Huh, E. N., St-Hilaire, M., Lung, C. H., & Lambadaris, I. (2015). Cloud of things: Integration of IoT with cloud computing. Robots and Sensor Clouds., 36, 77–94.

    Article  Google Scholar 

  • Abidoye, A. P., & Obagbuwa, I. C. (2017). Models for integrating wireless sensor networks into the internet of things. IET Wireless Sensor Systems., 7(3), 65–72.

    Article  Google Scholar 

  • Akgül, Ö. U., & Canberk, B. (2016). Self-organized things (SoT): An energy efficient next generation network management. Computer Communications, 74, 52–62.

    Article  Google Scholar 

  • Bagula, A., Abidoye, A. P., & Zodi, G. A. L. (2015). Service-aware clustering: An energy-efficient model for the internet-of-things. Sensors., 16(1), 9.

    Article  Google Scholar 

  • Barger, M., Brown, T., & Alwan, D. (2005). Health status monitoring through analysis of behavioral patterns. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 35(1), 22–27.

    Article  Google Scholar 

  • Bhatia, M., & Sood, S. K. (2017). A comprehensive health assessment framework to facilitate IoT-assisted smart workouts: A predictive healthcare perspective. Computers in Industry., 92, 50–66.

    Article  Google Scholar 

  • Boettcher, S., & Percus, A. G. (2003). Extremal optimization: An evolutionary local-search algorithm. Operations Research/ Computer Science Interfaces Series., 21, 61–77.

    Google Scholar 

  • Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and internet of things: A survey. Future Generation Computer Systems., 56, 684–700.

    Article  Google Scholar 

  • Catarinucci, L., De Danilo, D., Luca, M., Luca, P., Luigi, P., Maria, S. L., & Luciano, T. (2015). An IoT-aware architecture for smart healthcare systems. IEEE Internet of Things Journal., 2(6), 515–526.

    Article  Google Scholar 

  • Chakravarty, S., Mittra, R., & Williams, N. R. (2002). Application of a microgenetic algorithm (MGA) to the design of broadband microwave absorbers using multiple frequency selective surface screens buried in dielectrics. IEEE Transactions on Antennas and Propagation, 50(3), 284–296.

    Article  Google Scholar 

  • Chang, J. Y. (2015). A Distributed Cluster Computing Energy-Efficient Routing Scheme for Internet of Things Systems. Wireless Personal Communications, 82(2), 757–776.

    Article  Google Scholar 

  • Chen, X., Ma, M., & Liu, A. (2017). Dynamic power management and adaptive packet size selection for IoT in e-healthcare. Computers and Electrical Engineering, 65, 1–19.

    Google Scholar 

  • Díaz, M., Martín, C., & Rubio, B. (2016). State-of-the-art, challenges, and open issues in the integration of internet of things and cloud computing. Journal of Network and Computer Applications., 67, 99–117.

    Article  Google Scholar 

  • Gelogo, Y. E., Hwang, H. J., & Kim, H. (2015). Internet of things ( IoT ) framework for u-healthcare system. International Journal of Smart Home., 9(11), 323–330.

    Article  Google Scholar 

  • Hao, L., Gang, X., Gui, Y. D., & Yu, B. S. (2014). Human behavior based particle swarm optimization, The Scientific World Journal. Hindawi Publishing Corporation, 2014, 1–10.

    Google Scholar 

  • Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73.

    Article  Google Scholar 

  • Hossain, M. S. (2017). Cloud-supported cyber-physical localization framework for patients monitoring. IEEE Systems Journal., 11(1), 118–127.

    Article  Google Scholar 

  • Hossain, M. S., & Muhammad, G. (2016). Cloud-assisted industrial internet of things (IIoT) - enabled framework for health monitoring. Computer Networks, 101, 192–202.

    Article  Google Scholar 

  • Hussain, A., Wenbi, R., Lopes, A., Nadher, M., & Mudhish, M. (2015). The journal of systems and software health and emergency-care platform for the elderly and disabled people in the Smart City. The Journal of Systems & Software, 110, 253–263.

    Article  Google Scholar 

  • Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An information framework for creating a smart city through internet of things. IEEE Internet of Things Journal, 1(2), 112–121.

    Article  Google Scholar 

  • Kaur, N., & Sood, S. K. (2017). An energy-efficient architecture for the internet of things (IoT). IEEE Systems Journal, 11(2), 796–805.

    Article  Google Scholar 

  • Kennedy, J. and Eberhart, R. C., Particle Swarm Optimization. Proceedings of the 1995 IEEE International Conference on Neural Networks, IEEE Service Center, Piscataway, NJ, 1995.

  • Krishnakumar, K. (1990). Micro-genetic algorithms for stationary and non-stationary function optimization. Intelligent control and adaptive systems, International Society for Optics and Photonics, 1196, 289–297.

    Article  Google Scholar 

  • Laskar, N. M., Guha, K., Chatterjee, I., Chanda, S., Baishnab, K. L., & Paul, P. K. (2019). HWPSO: A new hybrid whale-particle swarm optimization algorithm and its application in electronic design optimization problems. Applied Intelligence, 49(1), 265–291.

    Article  Google Scholar 

  • Liang, J. M., Chen, J. J., Cheng, H. H., & Tseng, Y. C. (2013). An energy-efficient sleep scheduling with QoS consideration in 3GPP LTE-advanced networks for internet of things. IEEE Journal on Emerging and Selected Topics in Circuits and Systems., 3(1), 13–22.

    Article  Google Scholar 

  • Majumdar, A., Debnath, T., Sood, S. K., & Baishnab, K. L. (2018). Kyasanur Forest disease classification framework using novel Extremal optimization tuned neural network in fog computing environment. Journal of Medical Systems, 42(10), 187.

    Article  Google Scholar 

  • Majumdar, A., Laskar, N. M., Biswas, A., Sood, S. K., & Baishnab, K. L. (2019). Energy efficient e-healthcare framework using HWPSO-based clustering approach. Journal of Intelligent & Fuzzy Systems, 36(5), 3957–3969.

    Article  Google Scholar 

  • Mirjalili, S. (2016). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073.

    Article  Google Scholar 

  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm, Advances in Engineering Software. Elsevier, 95, 51–67.

    Google Scholar 

  • Orsino, A., Araniti, G., Militano, L., Alonso-Zarate, J., Molinaro, A., & Iera, A. (2016). Energy efficient IoT data collection in smart cities exploiting D2D communications. Sensors, 16(6), 836.

    Article  Google Scholar 

  • Praveen Kumar Reddy, M., & Rajasekhara Babu, M. (2017). Energy Efficient Cluster Head Selection for Internet of Things. New Review of Information Networking, 22(1), 54–70.

    Article  Google Scholar 

  • Rahmani, A. M., Nguyen, T., Negash, B., & Anzanpour, A. (2018). Exploiting smart e-health gateways at the edge of healthcare internet-of-things: A fog computing approach. Future Generation Computer Systems, 78, 641–658.

    Article  Google Scholar 

  • Rani, S., Talwar, R., Malhotra, J., Ahmed, S. H., Sarkar, M., & Song, H. (2015). A novel scheme for an energy efficient internet of things based on wireless sensor networks. Sensors., 15(11), 28603–28626.

    Article  Google Scholar 

  • Roy, R. K. (2001). Design of Experiments Using the Taguchi approach: 16 steps to product and process improvement. John Wiley and Sons, Inc.: Press.

    Google Scholar 

  • Roy, R. K. Report# 1. Multiple Criteria of Evaluations for Designed Experiments. Available: http://nutek-us.com/wp-tec.html .

  • Saaty, T.L., The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making. Multiple criteria decision analysis: state of the art surveys, Springer, New York, NY. 345–405, 2005.

  • Santos, D. F. S., Almeida, H. O., & Perkusich, A. (2015). A personal connected health system for the internet of things based on the constrained application protocol. Computers and Electrical Engineering., 44, 122–136.

    Article  Google Scholar 

  • Singh, A. K., Patowari, P. K., & Deshpande, N. V. (2016). Experimental analysis of reverse micro-EDM for machining microtool. Materials and Manufacturing Processes., 31(4), 530–540.

    Article  Google Scholar 

  • Song, L., Chai, K. K., Chen, Y., Schormans, J., Loo, J., & Vinel, A. (2017). QoS-aware energy-efficient cooperative scheme for cluster-based IoT systems. IEEE Systems Journal., 11(3), 1447–1455.

    Article  Google Scholar 

  • Tang, J., Zhou, Z., Niu, J., & Wang, Q. (2014). An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the internet of things. Journal of Network and Computer Applications, 40, 1–11.

    Article  Google Scholar 

  • Verma, P., & Sood, S. K. (2018). Cloud-centric IoT based disease diagnosis healthcare framework. Journal of Parallel and Distributed Computing., 116, 27–38.

    Article  Google Scholar 

  • Wangikar, S. S., Patowari, P. K., & Misra, R. D. (2017). Effect of process parameters and optimization for photochemical machining of brass and german silver. Materials and Manufacturing Processes., 32(15), 1747–1755.

    Article  Google Scholar 

  • Whitmore, A., Agarwal, A., & Da Xu, L. (2015). The internet of things—A survey of topics and trends. Information Systems Frontiers, 17(2), 261–274.

    Article  Google Scholar 

  • Zhou, Z., Tang, J., Zhang, L. J., Ning, K., & Wang, Q. (2014). EGF-tree: An energy-efficient index tree for facilitating multi-region query aggregation in the internet of things. Personal and ubiquitous computing, 18(4), 951–966.

    Article  Google Scholar 

Download references

Acknowledgments

This publication is an outcome of the R&D work undertaken project under the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Abhishek Majumdar, Arpita Biswas & Krishna Lal Baishnab

  2. Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Tapas Debnath

  3. Department of Computer Science and Engineering, Gurunanak Dev University, Regional Campus, Gurdaspur, Punjab, India

    Sandeep K. Sood

Authors
  1. Abhishek Majumdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tapas Debnath
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arpita Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sandeep K. Sood
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Krishna Lal Baishnab
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhishek Majumdar.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Majumdar, A., Debnath, T., Biswas, A. et al. An Energy Efficient e-Healthcare Framework Supported by Novel EO-μGA (Extremal Optimization Tuned Micro-Genetic Algorithm). Inf Syst Front 23, 1039–1056 (2021). https://doi.org/10.1007/s10796-020-10016-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-020-10016-5

Keywords

Search

Navigation