Abstract
Cloud computing plays a significant role in healthcare services (HCS) within smart cities due to its the ability to retrieve patients’ data, collect big data of patients by sensors, diagnosis of diseases and other medicinal fields in less time and less of cost. However, the task scheduling problem to process the medical requests represents a big challenge in smart cities. The task scheduling performs a significant role for the enhancement of the performance through reducing the execution time of requests (tasks) from stakeholders and utilization of medical resources to help stakeholders for saving time and cost in smart cities. In addition, it helps the stakeholders to reduce their waiting time, turnaround time of medical requests on cloud environment, minimize waste of CPU utilization of VMs, and maximize utilization of resources. For that, this paper proposes an intelligent model for HCS in a cloud environment using two different intelligent optimization algorithms, which are Particle Swarm Optimization (PSO), and Parallel Particle Swarm Optimization (PPSO). In addition, a set of experiments are conducted to provide a competitive study between those two algorithms regarding the execution time, the data processing speed, and the system efficiency. The results showed that PPSO algorithm outperforms on PSO algorithm. In addition, this paper proposes a new PPSO dependent algorithm using CloudSim package to solve task scheduling problem to support healthcare providers in smart cities to reduce execution time of medical requests and maximize utilization of medical resources.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Singh A, Hemalatha M (2013) Cluster based Bee Algorithm for virtual machine placement in cloud data centre. JATIT 57(3):1–10
Chen L, Zhang J, Cai L, Meng T (2015) MTAD: a multitarget heuristic algorithm for virtual machine placement. Int J Distrib Sens Netw 2014:1–14
Camati R, Calsavara A, Lima L (2014) Solving the virtual machine placement problem as a multiple multidimensional Knapsack problem. IARIA, IEEE, pp 253–260
Suseela B, Jeyakrishnan V (2014) A multi-objective hybrid Aco-Pso optimization algorithm for virtual machine placement in cloud computing. IJRET 3(4):474–476
Zhao J, Hu L, Ding Y, Xu G, Hu M (2014) A heuristic placement selection of live virtual machine migration for energy-saving in cloud computing environment. PloS ONE 9(9):1–13
Boulos MN, Al-Shorbaji NM (2014) On the internet of things, smart cities and the WHO healthy cities. Int J Health Geograph 13:2–6
Alhussein M (2017) Monitoring Parkinson’s disease in smart cities”, special section on advances of multisensory services and technologies for healthcare in smart cities. IEEE, 5:19835–19841
Bhunia SS, Dhar SK, Mukherjee N (2014) iHealth: a fuzzy approach for provisioning intelligent health-care system in smart city. e-Health Pervasive Wirel Appl Serv IEEE 14:187–193
Islam M, Razzaque A, Hassan MM, Nagy W, Song B (2017) Mobile cloud-based big healthcare data processing in smart cities. IEEE 0(0):1–12
Sajjad M, Khan S, Jan Z, Muhammad K, Moon H, Kwak JT, Rho S, Baik SW, Mehmood I (2016) Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities. IEEE 0:1–15
Mishra R, Jaiswal A (2012) Bees life algorithm for job scheduling in cloud computing. ICCIT 3:186–191
Bhatt K, Bundele M (2013) CloudSim estimation of a simple particle swarm algorithm. IJARCSSE 3(8):1279–1287
Gomathi B, Krishnasamy K (2013) Task scheduling algorithm based on hybrid particle swarm optimization in cloud computing environment. JATIT 55(1):33–38
Mohana SJ, Saroja M, Venkatachalam M (2014) Comparative analysis of swarm intelligence optimization techniques for cloud scheduling. IJISET 1(10):15–19
Beegom AS, Rajasree MS (2014) A particle swarm optimization based pareto optimal task scheduling in cloud computing. ICSI 2:79–86
Kaur G, Sharma S Er. (2014) Optimized utilization of resources using improved particle swarm optimization based task scheduling algorithms in cloud computing. IJETAE 4(6):110–115
El-Sisi AB, Tawfeek MA, Keshk AE, Torkey FA (2014) Intelligent method for cloud task scheduling based on particle swarm optimization algorithm. ACIT, 39–44
Bilgaiyan S, Sagnika S, Das M (2014) An analysis of task scheduling in cloud computing using evolutionary and swarm-based algorithms. IJCA 89(2):11–18
Tawfeek M, El-Sisi A, Keshk A, Torkey F (2015) Cloud task scheduling based on ant colony optimization. IAJIT 12(2):129–137
Salama AS (2015) A swarm intelligence based model for mobile cloud computing. IJITCS 2:28–34
Awad AI, El-Hefnawy NA, Abdel_kader HM (2015) Enhanced particle swarm optimization for task scheduling in cloud computing environments. ICCMIT 65:920–929
Al-Olimat HS, Alam M, Green R, Lee JK (2015) Cloudlet scheduling with particle swarm optimization. ICCSNT, IEEE 31:991–995
Priyadarsini RJ, Arockiam L Dr (2015) An improved particle swarm optimization algorithm for meta task scheduling in cloud environment. IJCST 3(4):108–112
Vidhya M, Sadhasivam N (2015) Parallel particle swarm optimization for reducing data redundancy in heterogeneous cloud storage. IJTET 3(1):73–78
Alkhashaiand HM, Omara FA (2016) BF-PSO-TS: hybrid heuristic algorithms for optimizing task scheduling on cloud computing environment. IJACSA 7(6):207–212
Abdelaziz A, Elhoseny M, Salama AS, Riad AM, Hassanien A (2017) Intelligent algorithms for optimal selection of virtual machine in cloud environment, towards enhance healthcare services. In: Proceedings of the international conference on advanced intelligent systems and informatics, vol 639. Springer, pp 23–37
Elhoseny M, Salama AS, Abdelaziz A, Riad A (2017) Intelligent systems based on cloud computing for healthcare services: a survey. Int J Comput Intell Stud Indersci 6(2/3):157–188
Abdelaziz A, Elhoseny M, Salama AS, Riad AM (2018) A machine learning model for improving healthcare services on cloud computing environment. Measurement 119:117–128
Elhoseny M, Abdelaziz A, Salama AS, Riad AM, Muhammad K, Sangaiah AK (2018) A hybrid model of Internet of Things and cloud computing to manage big data in health services applications. Future generation computer systems
Tharwat A, Elhoseny M, Hassanien AE, Gabel T, Arun Kumar N (2018) Intelligent Beziér curve-based path planning model using chaotic particle swarm optimization algorithm. Cluster Comput, 1–22. https://doi.org/10.1007/s10586-018-2360-3
Tharwat A, Mahdi H, Elhoseny M, Hassanien AE (2018) Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm, expert systems with applications. Available online 12 Apr 2018, https://doi.org/10.1016/j.eswa.2018.04.017
Hosseinabadi AAR, Vahidi J, Saemi B, Sangaiah AK, Elhoseny M (2018) Extended genetic algorithm for solving open-shop scheduling problem. Soft Comput. https://doi.org/10.1007/s00500-018-3177-y
El Aziz MA, Hemdan AM, Ewees AA, Elhoseny M, Shehab A, Hassanien AE, Xiong S (2017) Prediction of Biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization. In: 2017 IEEE PES PowerAfrica conference, June 27–30, Accra-Ghana, IEEE, 2017, pp 115–120. https://doi.org/10.1109/powerafrica.2017.7991209
Ewees AA, El Aziz MA, Elhoseny M (2017) Social-spider optimization algorithm for improving ANFIS to predict biochar yield. In: 8th international conference on computing, communication and networking technologies (8ICCCNT), 3–5 July, Delhi-India, IEEE
Elhoseny M, Tharwat A, Yuan X, Hassanien AE (2018) Optimizing K-coverage of mobile WSNs. Expert Syst Appl 92:142–153. https://doi.org/10.1016/j.eswa.2017.09.008
Sarvaghad-Moghaddam M, Orouji AA, Ramezani Z, Elhoseny M, Farouk A, Arun Kumar N (2018) Modelling the spice parameters of SOI MOSFET using a combinational algorithm. Cluster Comput. https://doi.org/10.1007/s10586-018-2289-6
Rizk-Allah RM, Hassanien AE, Elhoseny M (2018) A multi-objective transportation model under neutrosophic environment. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2018.02.024
Batle J, Naseri M, Ghoranneviss M, Farouk A, Alkhambashi M, Elhoseny M (2017) Shareability of correlations in multiqubit states: optimization of nonlocal monogamy inequalities. Phys Rev A 95(3):032123. https://doi.org/10.1103/PhysRevA.95.032123
Elhoseny M, Nabil A, Hassanien AE, Oliva D (2018) Hybrid rough neural network model for signature recognition. In: Hassanien A, Oliva D (eds) Advances in soft computing and machine learning in image processing. Studies in computational intelligence, vol 730. Springer, Cham. https://doi.org/10.1007/978-3-319-63754-9_14
Elhoseny M, Tharwat A, Farouk A, Hassanien AE (2017) K-coverage model based on genetic algorithm to extend WSN lifetime. IEEE Sens Lett 1(4):1–4. IEEE. https://doi.org/10.1109/lsens.2017.2724846
Yuan X, Elhoseny M, El-Minir HK, Riad AM (2017) A genetic algorithm-based, dynamic clustering method towards improved WSN longevity. J Netw Syst Manage 25(1):21–46. https://doi.org/10.1007/s10922-016-9379-7
Elhoseny M, Nabil A, Hassanien AE, Oliva D (2018) Hybrid rough neural network model for signature recognition. In: Hassanien A, Oliva D (eds) Advances in soft computing and machine
Elhoseny M, Tharwat A, Farouk A, Hassanien AE (2017) K-coverage model based on genetic algorithm to extend WSN lifetime. IEEE Sens Lett 1(4):1–4. https://doi.org/10.1109/lsens.2017.2724846
Elhoseny M, Shehab A, Yuan X (2017) Optimizing robot path in dynamic environments using genetic algorithm and Bezier Curve. J Intell Fuzzy Syst 33(4):2305–2316. IOS-Press https://doi.org/10.3233/jifs-17348
Elhoseny M, Tharwat A, Hassanien AE (2017) Bezier Curve based path planning in a dynamic field using modified genetic algorithm. J Comput Sci. https://doi.org/10.1016/j.jocs.2017.08.004
Metawaa N, Kabir Hassana M, Elhoseny M (2017) Genetic algorithm based model for optimizing bank lending decisions. Expert Syst Appl 80:75–82. https://doi.org/10.1016/j.eswa.2017.03.021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Abdelaziz, A., Salama, A.S., Riad, A.M. (2019). A Swarm Intelligence Model for Enhancing Health Care Services in Smart Cities Applications. In: Hassanien, A., Elhoseny, M., Ahmed, S., Singh, A. (eds) Security in Smart Cities: Models, Applications, and Challenges. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-01560-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-01560-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01559-6
Online ISBN: 978-3-030-01560-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)