A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

  • Muhammad Asim Shahid Sir Syed University of Engineering and Technology Karachi, Pakistan
  • Rizwan Bin Faiz Riphah International University, Islamabad
  • Muhammad Mansoor Alam Riphah International University, Islamabad, Pakistan
  • M.S. Mazliham Multimedia University (MMU) Cyberjaya, Malaysia

Abstract

Cloud computing (CC) provides fast and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is a new technology that brings CC and mobile devices together. MCC allows mobile devices access to cloud services. Describe the basic architectures for various MCC applications, as well as a brief comparison of cloud and MCC. As CC becomes more common, researchers in this field may need to conduct real-world experiments in their studies. Configuring and running these experiments in real-world cloud environments is costly. As a consequence, modeling and simulation approaches are suitable solutions that can be used to simulate cloud computing environments. Several simulation tools tailored to CC have been developed. The most powerful simulation methods in this field of study are described in this article. Among them are CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports.

References

[1] K. Bahwaireth, L. Tawalbeh, E. Benkhelifa, Y. Jararweh, and M. A. Tawalbeh, “Experimental comparison of simulation tools for efficient cloud and mobile cloud computing applications,” EURASIP J. on Info. Security, vol. 2016, no. 1, p. 15, Dec. 2016, doi: 10.1186/s13635-016-0039-y.
[2] S. K. Mishra, B. Sahoo, and P. P. Parida, “Load balancing in cloud computing: A big picture,” Journal of King Saud University - Computer and Information Sciences, vol. 32, no. 2, pp. 149–158, Feb. 2020, doi: 10.1016/j.jksuci.2018.01.003.
[3] A. Rashid and A. Chaturvedi, “Cloud Computing Characteristics and Services A Brief Review,” ijcse, vol. 7, no. 2, pp. 421–426, Feb. 2019, doi: 10.26438/ijcse/v7i2.421426.
[4] P. Prajapati and A. K. Sariya, “A Review: Methods of Load Balancing on Cloud Computing,” vol. 6, no. 1, p. 8.
[5] T. H. Noor, S. Zeadally, A. Alfazi, and Q. Z. Sheng, “Mobile cloud computing: Challenges and future research directions,” Journal of Network and Computer Applications, vol. 115, pp. 70–85, Aug. 2018, doi: 10.1016/j.jnca.2018.04.018.
[6] A. Aliyu et al., “Mobile Cloud Computing: Taxonomy and Challenges,” Journal of Computer Networks and Communications, vol. 2020, pp. 1–23, Jul. 2020, doi: 10.1155/2020/2547921.
[7] https://publication.babcock.edu.ng/asset/docs/publications/COSC/9664/5407.pdf
[8] H. Allam, A. Rajan, and J. Ahmad, “A Critical Overview of Latest Challenges and Solutions of Mobile Cloud Computing,” p. 5, 2017.
[9] S. Abolfazli, Z. Sanaei, M. H. Sanaei, M. Shojafar, and A. Gani, “MOBILE CLOUD COMPUTING: THE STATE-OF-THE-ART, CHALLENGES, AND FUTURE RESEARCH,” p. 16.
[10] B. B. Gupta, G. M. Perez, D. P. Agrawal, and D. Gupta, Eds., Handbook of Computer Networks and Cyber Security: Principles and Paradigms. Cham: Springer International Publishing, 2020.
[11] I. Bambrik, “A Survey on Cloud Computing Simulation and Modeling,” SN COMPUT. SCI., vol. 1, no. 5, p. 249, Sep. 2020, doi: 10.1007/s42979-020-00273-1.
[12] N. Mansouri, R. Ghafari, and B. M. H. Zade, “Cloud computing simulators: A comprehensive review,” Simulation Modelling Practice and Theory, vol. 104, p. 102144, Nov. 2020, doi: 10.1016/j.simpat.2020.102144.
[13] A. Sundas and S. N. Panda, “An Introduction of CloudSim Simulation tool for Modelling and Scheduling,” in 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, Mar. 2020, pp. 263–268, doi: 10.1109/ESCI48226.2020.9167549.
[14] S. Puhan, D. Panda, and B. K. Mishra, “Energy Efficiency for Cloud Computing Applications: A Survey on the Recent Trends and Future Scopes,” in 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur, India, Mar. 2020, pp. 1–6, doi: 10.1109/ICCSEA49143.2020.9132878.
[15] Department of Computer Science, Virtual University of Pakistan and M. Hassaan, “A Comparative Study between Cloud Energy Consumption Measuring Simulators,” IJEME, vol. 10, no. 2, pp. 20–27, Apr. 2020, doi: 10.5815/ijeme.2020.02.03.
[16] A. Ismail, “Energy-driven cloud simulation: existing surveys, simulation supports, impacts and challenges,” Cluster Comput, vol. 23, no. 4, pp. 3039–3055, Dec. 2020, doi: 10.1007/s10586-020-03068-4.
[17] M. C. Silva Filho, R. L. Oliveira, C. C. Monteiro, P. R. M. Inacio, and M. M. Freire, “CloudSim Plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness,” in 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, May 2017, pp. 400–406, doi: 10.23919/INM.2017.7987304.
[18] N. Naik and M. A. Mehta, “Comprehensive and Comparative Study of Cloud Simulators,” in 2018 IEEE Punecon, Pune, India, Nov. 2018, pp. 1–7, doi: 10.1109/PUNECON.2018.8745422.
[19] A. Markus and A. Kertesz, “A survey and taxonomy of simulation environments modelling fog computing,” Simulation Modelling Practice and Theory, vol. 101, p. 102042, May 2020, doi: 10.1016/j.simpat.2019.102042.
[20] “A REVIEW ON EFFECTIVE UTILIZATION OF COMPUTATIONAL RESOURCES USING CLOUDSIM,” jcr, vol. 7, no. 13, Jul. 2020, doi: 10.31838/jcr.07.13.177.
[21] J. M. Nandhini and T. Gnanasekaran, “An Assessment Survey of Cloud Simulators for Fault Identification,” in 2019 3rd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India, Feb. 2019, pp. 311–315, doi: 10.1109/ICCCT2.2019.8824915.
[22] D. G. Arseniev, L. Overmeyer, H. Kälviäinen, and B. Katalinić, Eds., Cyber-Physical Systems and Control, vol. 95. Cham: Springer International Publishing, 2020.
[23] M. I. Bala and M. A. Chishti, “Offloading in Cloud and Fog Hybrid Infrastructure Using iFogSim,” in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, Jan. 2020, pp. 421–426, doi: 10.1109/Confluence47617.2020.9057799.
[24] D. Seo et al., “Dynamic iFogSim: A Framework for Full-Stack Simulation of Dynamic Resource Management in IoT Systems,” in 2020 International Conference on Omni-layer Intelligent Systems (COINS), Barcelona, Spain, Aug. 2020, pp. 1–6, doi: 10.1109/COINS49042.2020.9191663.
[25] T. Zaidi, “Analysis of Energy Consumption on IaaS Cloud Using Simulation Tool,” SSRN Journal, 2020, doi: 10.2139/ssrn.3553711.
[26] F. Fakhfakh, H. H. Kacem, and A. H. Kacem, “Simulation tools for cloud computing: A survey and comparative study,” in 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), Wuhan, China, May 2017, pp. 221–226, doi: 10.1109/ICIS.2017.7959997.
[27] N. Motlhabane, N. Gasela, and M. Esiefarienrhe, “Comparative Analysis of Cloud Computing Simulators,” in 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, Dec. 2018, pp. 1309–1316, doi: 10.1109/CSCI46756.2018.00254.
[28] T. L. A. Beena and J. J. Lawanya, “Simulators for Cloud Computing - A Survey,” p. 7, 2018.
[29] Vishnu Institute of Technology, Bhimavaram, India and P. S. Suryateja, “A Comparative Analysis of Cloud Simulators,” IJMECS, vol. 8, no. 4, pp. 64–71, Apr. 2016, doi: 10.5815/ijmecs.2016.04.08.
[30] R. Khurana and R. K. Bawa, “Quality based cloud simulators: State-of-the-art road ahead,” in 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Dec. 2016, pp. 101–106, doi: 10.1109/PDGC.2016.7913123.
[31] J. Byrne et al., “A Review of Cloud Computing Simulation Platforms and Related Environments:,” in Proceedings of the 7th International Conference on Cloud Computing and Services Science, Porto, Portugal, 2017, pp. 679–691, doi: 10.5220/0006373006790691.
[32] A. A. Khan, M. Aleem, and A. Sajjad, “Energy-Aware Cloud Computing Simulators: A State of the Art Survey,” ijamec, vol. 6, no. 2, pp. 15–20, Jun. 2018, doi: 10.18100/ijamec.2018241182.
[33] K. M. Khalil, M. Abdel-Aziz, T. T. Nazmy, and A.-B. M. Salem, “CLOUD SIMULATORS – AN EVALUATION STUDY,” p. 23.
[34] https://www.cloudsimtutorials.online/ifogsim-project-structure-a-beginners-guide/#:~:text=iFogSim%20is%20a%20java%20programming,relevant%20network%2Drelated%20workload%20handling.
[35] Z. Pang, L. Sun, Z. Wang, E. Tian, and S. Yang, “A Survey of Cloudlet Based Mobile Computing,” in 2015 International Conference on Cloud Computing and Big Data (CCBD), Shanghai, China, Nov. 2015, pp. 268–275, doi: 10.1109/CCBD.2015.54.
[36] “Cloud Computing Tools: Inside Views and Analysis - ScienceDirect.” https://www.sciencedirect.com/science/article/pii/S187705092031557X (accessed Mar. 23, 2021).
[37] “(PDF) Comparison of Different Task Scheduling Algorithms in Cloud Computing Environment Using Cloud Reports.” https://www.researchgate.net/publication/336105107_Comparison_of_Different_Task_Scheduling_Algorithms_in_Cloud_Computing_Environment_Using_Cloud_Reports (accessed Mar. 23, 2021).
[38] “(PDF) Study of Simulation Tools in Cloud Computing Environment.” https://www.researchgate.net/publication/337185231_Study_of_Simulation_Tools_in_Cloud_Computing_Environment (accessed Mar. 23, 2021).
[39] Department of Computer Science, Christ University, Bangalore-560029, India, J. A. P. Esparcia, M. Singh, and Department of Computer Science, Christ University, Bangalore-560029, India, “Comprehensive study of multi-resource cloud simulation tools,” Int. j. adv. appl. sci., vol. 4, no. 7, pp. 29–38, Jul. 2017, doi: 10.21833/ijaas.2017.07.006.
[40] M. S. Shakir and E. A. Razzaque, “Performance Comparison of Load Balancing Algorithms using Cloud Analyst in Cloud Computing.,” p. 5.
[41] A. Vashistha and S. Sholliya, “COMPARATIVE STUDY OF OPEN SOURCE CLOUD SIMULATION TOOLS,” International Journal of Engineering Research, vol. 4, no. 2, p. 5, 2017.
[42] K. A. Kumar, “A Study on Simulation Tools in Cloud Computing,” p. 7.
[43] M. Malhotra, “A Study and Analysis on Simulators of Cloud Computing Paradigm,” vol. 4, no. 5, p. 3.
[44] Ashalatha R., J. Agarkhed, and S. Patil, “Analysis of simulation tools in cloud computing,” in 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India, Mar. 2016, pp. 748–751, doi: 10.1109/WiSPNET.2016.7566233.
[45] M. A. Kaleem and P. M. Khan, “Commonly Used Simulation Tools for Cloud Computing Research,” p. 8, 2015.
[46] A. Maarouf, A. Marzouk, and A. Haqiq, “Comparative study of simulators for cloud computing,” in 2015 International Conference on Cloud Technologies and Applications (CloudTech), Marrakech, Morocco, Jun. 2015, pp. 1–8, doi: 10.1109/CloudTech.2015.7336989.
[47] J. D. Pagare and D. N. A. Koli, “Design and simulate cloud computing environment using cloudsim,” vol. 6, p. 8, 2015.
[48] K. Gupta, R. Beri, and V. Behal, “Cloud Computing: A Survey on Cloud Simulation Tools,” Cloud Computing, vol. 2, no. 11, p. 5.
[49] D. Asir Antony Gnana Singh, R. Priyadharshini, and E. Jebamalar Leavline, “Analysis of Cloud Environment Using CloudSim,” in Artificial Intelligence and Evolutionary Computations in Engineering Systems, vol. 668, S. S. Dash, P. C. B. Naidu, R. Bayindir, and S. Das, Eds. Singapore: Springer Singapore, 2018, pp. 325–333.
[50] E. Andrade and B. Nogueira, “Performability Evaluation of a Cloud-Based Disaster Recovery Solution for IT Environments,” J Grid Computing, vol. 17, no. 3, pp. 603–621, Sep. 2019, doi: 10.1007/s10723-018-9446-2.
[51] M. A. Shahid, N. Islam, M. M. Alam, M. M. Su’ud, and S. Musa, “A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach,” IEEE Access, vol. 8, pp. 130500–130526, 2020, doi: 10.1109/ACCESS.2020.3009184.
[52] “Highlight the Features of AWS, GCP and Microsoft Azure that Have an Impact when Choosing a Cloud Service Provider,” IJRTE, vol. 8, no. 5, pp. 4124–4232, Jan. 2020, doi: 10.35940/ijrte.D8573.018520.
[53] “Routers Perspective Simulation-Based Analysis of EIGRP and OSPF Routing Protocol for an Organizational Model,” IJITEE, vol. 9, no. 4, pp. 2013–2019, Feb. 2020, doi: 10.35940/ijitee.B6509.029420.
[54] M. A. Kamal, M. M. Alam, H. Khawar, and M. S. Mazliham, “Play and Learn Case Study on Learning Abilities Through Effective Computing in Games,” in 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), Karachi, Pakistan, Dec. 2019, pp. 1–6, doi: 10.1109/MACS48846.2019.9024771.
[55] M. R. Belgaum, S. Soomro, Z. Alansari, and M. Alam, “Cloud Service Ranking Using Checkpoint-Based Load Balancing in Real-Time Scheduling of Cloud Computing,” in Progress in Advanced Computing and Intelligent Engineering, vol. 563, K. Saeed, N. Chaki, B. Pati, S. Bakshi, and D. P. Mohapatra, Eds. Singapore: Springer Singapore, 2018, pp. 667–676.
[56] M. R. Belgaum, S. Musa, M. M. Alam, and M. M. Su’ud, “A Systematic Review of Load Balancing Techniques in Software-Defined Networking,” IEEE Access, vol. 8, pp. 98612–98636, 2020, doi: 10.1109/ACCESS.2020.2995849.
[57] A. Iftikhar, S. Musa, M. Alam, M. M. Su’ud, and S. M. Ali, “A survey of soft computing applications in global software development,” in 2018 IEEE International Conference on Innovative Research and Development (ICIRD), Bangkok, May 2018, pp. 1–4, doi: 10.1109/ICIRD.2018.8376330.
[58] A. Iftikhar, M. Alam, S. Musa, and M. M. Su’ud, “Trust Development in virtual teams to implement global software development (GSD): A structured approach to overcome communication barriers,” in 2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences (ICETSS), Bangkok, Aug. 2017, pp. 1–5, doi: 10.1109/ICETSS.2017.8324169.
[59] M. Z. Asghar et al., “A Deep Neural Network Model for the Detection and Classification of Emotions from Textual Content,” Complexity, vol. 2022, pp. 1–12, Jan. 2022, doi: 10.1155/2022/8221121.
[60] M. Ishaq, A. Khan, M. M. Su’ud, M. M. Alam, J. I. Bangash, and A. Khan, “An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences,” Computational and Mathematical Methods in Medicine, vol. 2022, pp. 1–11, Jan. 2022, doi: 10.1155/2022/8691646.
[61] M. Z. Asghar et al., “Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic,” Front. Public Health, vol. 10, p. 855254, Mar. 2022, doi: 10.3389/fpubh.2022.855254.
[62] F. R. Albogamy et al., “Decision Support System for Predicting Survivability of Hepatitis Patients,” Front. Public Health, vol. 10, p. 862497, Apr. 2022, doi: 10.3389/fpubh.2022.862497.
[63] S. F. Ahmad, M. M. Alam, Mohd. K. Rahmat, M. S. Mubarik, and S. I. Hyder, “Academic and Administrative Role of Artificial Intelligence in Education,” Sustainability, vol. 14, no. 3, p. 1101, Jan. 2022, doi: 10.3390/su14031101.
[64] S. F. Ahmad, Mohd. K. Rahmat, M. S. Mubarik, M. M. Alam, and S. I. Hyder, “Artificial Intelligence and Its Role in Education,” Sustainability, vol. 13, no. 22, p. 12902, Nov. 2021, doi: 10.3390/su132212902.
[65] I. U. Khan et al., “A Review of Urdu Sentiment Analysis with Multilingual Perspective: A Case of Urdu and Roman Urdu Language,” Computers, vol. 11, no. 1, p. 3, Dec. 2021, doi: 10.3390/computers11010003.
[66] M. Nazar, M. M. Alam, E. Yafi, and M. M. Su’ud, “A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques,” IEEE Access, vol. 9, pp. 153316–153348, 2021, doi: 10.1109/ACCESS.2021.3127881.
[67] A. Iftikhar, M. Alam, R. Ahmed, S. Musa, and M. M. Su’ud, “Risk Prediction by Using Artificial Neural Network in Global Software Development,” Computational Intelligence and Neuroscience, vol. 2021, pp. 1–25, Dec. 2021, doi: 10.1155/2021/2922728.
[68] N. Khan et al., “Big Data: Survey, Technologies, Opportunities, and Challenges,” The Scientific World Journal, vol. 2014, pp. 1–18, 2014, doi: 10.1155/2014/712826.
[69] M. R. Belgaum, S. Soomro, Z. Alansari, S. Musa, M. Alam, and M. M. Su’ud, “Challenges: Bridge between cloud and IoT,” in 2017 4th IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS), Salmabad, Nov. 2017, pp. 1–5. doi: 10.1109/ICETAS.2017.8277844.
[70] “Prior investigation for flash floods and hurricanes, concise capsulization of hydrological technologies and instrumentation: A survey | IEEE Conference Publication | IEEE Xplore.” https://ieeexplore.ieee.org/document/8324170 (accessed Apr. 28, 2022).
[71] M. Alam, M. M. Suud, P. Boursier, S. Musa, and J. C. M. Yusuf, “Predicted and Corrected Location Estimation of Mobile Nodes Based on the Combination of Kalman Filter and the Bayesian Decision Theory,” in Mobile Wireless Middleware, Operating Systems, and Applications, vol. 48, Y. Cai, T. Magedanz, M. Li, J. Xia, and C. Giannelli, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 313–325. doi: 10.1007/978-3-642-17758-3_24.
[72] T. A. Khan, M. Alam, K. Kadir, and Z. Shahid, “A Novel Approach for the Investigation of Flash Floods using Soil Flux and CO2: An Implementation of MLP with Less False Alarm Rate,” p. 5, 2018.
[73] Muhammad and Shahrulniza, “Power Management for Portable Devices by using Clutter Based Information,” 2009. https://www.semanticscholar.org/paper/Power-Management-for-Portable-Devices-by-using-Muhammad-Shahrulniza/4abc4eb680398395c6d263ad0125a7e5873c7c1b (accessed Apr. 28, 2022).
[74] M. R. Belgaum, S. Musa, M. MohdSu’ud, M. Alam, S. Soomro, and Z. Alansari, “Secured Approach towards Reactive Routing Protocols Using Triple Factor in Mobile Ad Hoc Networks,” AETiC, vol. 3, no. 2, pp. 32–40, Apr. 2019, doi: 10.33166/AETiC.2019.02.004.
[75] T. Khan, K. Kadir, M. Alcm, Z. Fchiihid, and M. S. Mazliham, “Geomagnetic field measurement at earth surface: Flash flood forecasting using tesla meter,” in 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T), Kuala Lumpur, Sep. 2017, pp. 1–4. doi: 10.1109/ICE2T.2017.8215991.
[76] T. A. Khan, M. Alam, Z. Shahid, and M. S. Mazliham, “Comparative Performance Analysis of Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient for the Prediction of flash Floods,” p. 7, 2019.
[77] T. A. Khan, M. Alam, Z. Shahid, S. F. Ahmed, and Ms. Mazliham, “Artificial Intelligence based Multi-modal sensing for flash flood investigation,” in 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Bangkok, Thailand, Nov. 2018, pp. 1–6. doi: 10.1109/ICETAS.2018.8629147.
[78] M. R. Belgaum, S. Soomro, Z. Alansari, M. Alam, S. Musa, and M. M. Su’ud, “Load balancing with preemptive and non-preemptive task scheduling in cloud computing,” in 2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences (ICETSS), Bangkok, Aug. 2017, pp. 1–5. doi: 10.1109/ICETSS.2017.8324145.
[79] M. R. Belgaum, S. Soomro, Z. Alansari, and M. Alam, “Treatment of Reactive Routing Protocols Using Second Chance Based on Malicious behavior of Nodes in MANETS,” Pakistan Journal of Engineering, Technology & Science, vol. 6, no. 2, Feb. 2018, doi: 10.22555/pjets.v6i2.1961.
[80] F. Riaz, M. Alam, and A. Ali, “Filtering the big data based on volume, variety and velocity by using Kalman filter recursive approach,” in 2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences (ICETSS), Bangkok, Aug. 2017, pp. 1–6. doi: 10.1109/ICETSS.2017.8324195.
[81] J. Che Mustapha Yusuf, P. Boursier, M. Mohd Su’ud, and M. Alam, “Extensive overview of an ontology-based architecture for accessing multi-format information for disaster management,” in 2012 International Conference on Information Retrieval & Knowledge Management, Kuala Lumpur, Malaysia, Mar. 2012, pp. 294–299. doi: 10.1109/InfRKM.2012.6204994.
[82] A. Muhammad, P. Boursier, M. S. Mazliham, M. Shahrulniza, and Jawahir Che Mustapha, “Terrain/clutter based error calculation in location estimation of wireless nodes by using receive signal strength,” in 2010 2nd International Conference on Computer Technology and Development, Cairo, Egypt, Nov. 2010, pp. 95–99. doi: 10.1109/ICCTD.2010.5646073.
[83] M. Khan, M. Khan, M. Alam, and W. Ali, “Impact of Big Data over Telecom Industry,” Pakistan Journal of Engineering, Technology & Science, vol. 6, Feb. 2018, doi: 10.22555/pjets.v6i2.1958.
[84] N. Khan, A. A. B. Sajak, M. Alam, and M. S. Mazliham, “Analysis of Green IoT,” J. Phys.: Conf. Ser., vol. 1874, no. 1, p. 012012, May 2021, doi: 10.1088/1742-6596/1874/1/012012.
[85] T. Ahmed Khan, M. Junaid Tahir, M. Alam, K. A. Kadir, and Z. Shahid, “Optimized health parameters using PSO: a cost effective rfid based wearable gadget with less false alarm rate,” IJEECS, vol. 15, no. 1, p. 230, Jul. 2019, doi: 10.11591/ijeecs.v15.i1.pp230-239.
[86] T. Khan et al., “Flash Floods Prediction using Real Time data: An Implementation of ANN-PSO with less False Alarm,” in 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Auckland, New Zealand, May 2019, pp. 1–6. doi: 10.1109/I2MTC.2019.8826825.
[87] A. S. Siddiqui, M. Alam, and A. H. Memon, “Experimental Study to Assess the Performance of Combined Savonius Darrieus Vertical Axis Wind Turbine at Different Arrangements,” p. 8.
[88] A. Muhammad, M. S. Mazliham, P. Boursier, M. Shahrulniza, and J. C. M. Yusuf, “Terrain/clutter based location prediction by using multi-condition Bayesian decision theory,” in Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication - ICUIMC ’12, Kuala Lumpur, Malaysia, 2012, p. 1. doi: 10.1145/2184751.2184878.
[89] T. Khan, M. Alam, K. Kadir, Z. Shahid, S. Khan, and M. Miqdad, “Recognizing Foreign Object Debris (FOD): False Alarm Reduction Implementation,” vol. 11, no. 1, p. 6, 2018.
[90] M. A. Shahid, N. Islam, M. M. Alam, M. S. Mazliham, and S. Musa, “Towards Resilient Method: An exhaustive survey of fault tolerance methods in the cloud computing environment,” Computer Science Review, vol. 40, p. 100398, May 2021, doi: 10.1016/j.cosrev.2021.100398
Published
2022-07-28