Archive

Abstract:

Unmanned Aerial Vehicles (UAVs) or flying drones are employing to retrieve data from their respective sources and help to accomplish Flying Ad hoc Networks (FANETs). These wireless networks deal with challenges and difficulties such as power consumption, packet losses, and weak links between the nodes. This is due to the high mobility of nodes, frequent network partitioning, and uncertain flying movement of the flying drones. Consequently, reduce the reliability of data delivery. Moreover, unbalanced energy consumption results in an earlier failure of flying drone and accelerate the decrease of network life. The performance of FANETs depends on the capabilities of energy consumption of each flying drone. They are expected to live for a longer period to manage the cost overhead. Energy-efficient routing is an important factor that helps in improving the lifetime of FANETs. In this research, we propose an Energy-Efficient Grey Wolf (EEGW) routing protocol for FANETs. This protocol is comprised of Grey Wolf Optimizer (GWO) inspired by the leadership hierarchy of grey wolves. It helps in minimizing the energy consumption, packet loss ratio, and aerial transmission loss incurred during transmission.

Keywords:

Energy efficient,Flying drones,Routing protocol,Grey wolf algorithm,

Refference:

I. A. Ravi, Leela Satyanarayana. V. : ‘GREY WOLF OPTIMIZATION WITH WAVELET SCHEME FOR SAR IMAGES DENOISING’. J. Mech. Cont.& Math. Sci., Vol.-14, No.-5, September-October (2019) pp 558-570. DOI : 10.26782/jmcms.2019.10.00040
II. Ahmed Sheeraz. “Nature Inspired Optimization Techniques, A review for FANETs.” Sukkur IBA Journal of Emerging Technologies 3, no. 2 (2020): 40-58.
III. Anand, M., and T. Sasikala. “Efficient energy optimization in mobile ad hoc network (MANET) using better-quality AODV protocol.” Cluster Computing 22, no. 5 (2019): 12681-12687.
IV. Arafat, Muhammad Yeasir, and Sangman Moh. “Location-aided delay tolerant routing protocol in UAV networks for post-disaster operation.” IEEE Access 6 (2018): 59891-59906.
V. Kharb, Seema, and Anita Singhrova. “A survey on network formation and scheduling algorithms for time slotted channel hopping in industrial networks.” Journal of Network and Computer Applications 126 (2019): 59-87.
VI. Krishna, Kowligi R. Unmanned Aerial Vehicle Systems in Crop Production: A Compendium. CRC Press, 2019.
VII. Lebedev, I., Ianin, A., Usina, E., & Shulyak, V. (2021). Construction of Land Base Station for UAV Maintenance Automation. In Proceedings of 15th International Conference on Electromechanics and Robotics” Zavalishin’s Readings” (pp. 499-511). Springer, Singapore.
VIII. Mahmud, Imtiaz, and You-Ze Cho. “Adaptive hello interval in FANET routing protocols for green UAVs.” IEEE Access 7 (2019): 63004-63015.
IX. Mariyappan, K., Mary Subaja Christo, and Rashmita Khilar. “Implementation of FANET energy efficient AODV routing protocols for flying ad hoc networks [FEEAODV].” Materials Today: Proceedings (2021).
X. Mekikis, Prodromos-Vasileios, and Angelos Antonopoulos. “Breaking the boundaries of aerial networks with charging stations.” In ICC 2019-2019 IEEE International Conference on Communications (ICC), pp. 1-6. IEEE, 2019.
XI. Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. “Grey wolf optimizer.” Advances in engineering software 69 (2014): 46-61.
XII. Nayyar, Anand. “Flying adhoc network (FANETs): simulation based performance comparison of routing protocols: AODV, DSDV, DSR, OLSR, AOMDV and HWMP.” In 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), pp. 1-9. IEEE, 2018.
XIII. Odonkor, Philip, Zachary Ball, and Souma Chowdhury. “Distributed operation of collaborating unmanned aerial vehicles for time-sensitive oil spill mapping.” Swarm and Evolutionary Computation 46 (2019): 52-68.
XIV. Pham, Quoc-Viet, Ming Zeng, Rukhsana Ruby, Thien Huynh-The, and Won-Joo Hwang. “UAV communications for sustainable federated learning.” IEEE Transactions on Vehicular Technology 70, no. 4 (2021): 3944-3948.
XV. Srivastava, Ashish, and Jay Prakash. “Future FANET with application and enabling techniques: Anatomization and sustainability issues.” Computer Science Review 39 (2021): 100359.
XVI. Sufian, Abu, Farhana Sultana, and Paramartha Dutta. “Data load Balancing in Mobile ad hoc network using Fuzzy logic (DBMF).” arXiv preprint arXiv:1905.11627 (2019).
XVII. Waqas Khan, Vishwesh Laxmikant Akre, Khalid Saeed, Asif Nawaz, Tariq Bashir, Adil Khan, Naveed Jan, Sheeraz Ahmed, Zia Ullah Khan. : ‘IMPACT OF BLACK HOLE ATTACK ON THE PERFORMANCE OF DYNAMIC SOURCE ROUTING AND OPTIMIZED LINK STATE ROUTING PROTOCOLS IN MANETS’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-3, March (2021) pp 13-30. DOI : 10.26782/jmcms.2021.03.00002
XVIII. Zeng, Yong, and Rui Zhang. “Energy-efficient UAV communication with trajectory optimization.” IEEE Transactions on Wireless Communications 16, no. 6, pp. 3747-3760, 2017.
XIX. Zhang, Shuhang, Hongliang Zhang, Boya Di, and Lingyang Song. “Cellular UAV-to-X communications: Design and optimization for multi-UAV networks.” IEEE Transactions on Wireless Communications 18, no. 2 (2019): 1346-1359.

IGNITION BEHAVIOR OF SUPERCRITICAL LIQUID FUEL IN COMBUSTION SYSTEM

Authors:

Moheez Ur Rahim, Mukkarum Hussain, Syed Azeem Inam, Hassan Hashim

DOI NO:

https://doi.org/10.26782/jmcms.2021.08.00003

Abstract:

In systems that involve super-critical liquid fuel combustion, the temperature of the propellants is in the sub-critical state when they are injected into the combustion chamber. However, during the process of combustion, the system experiences a shift in its state of thermodynamics from subcritical to supercritical. The present study predicts the ignition behavior for super-critical liquid fuel combustion through the techniques of computational fluid dynamics (CFD). Simulations are carried out for a single shear coaxial injector's test case of the combustion chamber. For super-critical combustion, the present research uses kerosene as a fuel and gaseous oxygen as the oxidizer. Simulations are carried out at a steady state for various values of rich flammability limit (RFL). The real gas model, Soave-Redlich-Kwong (SRK) is used for performing simulations in the present study. On the other hand, for the various values of rich flammability limit (RFL), transient simulations are carried out for ideal gas. It has been observed that the simulations performed for steady-state closely approximate the experimental data in comparison to transient simulations. It is also observed that the inherent stability issues involved in transient simulations emphasize the use of an ideal gas model for its computation.

Keywords:

CFD,Ignition transients,Kerosene oxygen combustion,Real gas,Shear coaxial injector,SRK model,

Refference:

I. A. Tarafder and S. Sarangi, “CRESP-LP – A Dynamic Simulator for Liquid-Propellant Rocket Engines,” in 36th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, number AIAA 2000-3768, 2000.

II. A. Tishin and L. Gurova, “Liquid Rocket Engine Modeling,” Proceedings of the Aircraft Engineering College, vol. Vol. 32(3), pp. 99-101, 1989.

III. D. Kim and K. Lee, “Ignition Transient of Supercritical Oxygen/Kerosene Combustion System,” 25th ICDERS, August 2 – 7, 2015 Leeds, UK.

IV. D. Kim, M. Son and J. Koo, “Ignition Transition of Coaxial Kerosene/Gaseous Oxygen Jet,” Combustion Science and Technology, 2016.

V. E. N. Belyaev, V. K. Chvanov and V. V. Chervak, “Mathematical Modelling of the Workflow of Liquid Rocket Engines,” MAI, 1999.

VI. F. D. Matteoxz, M. D. Rosa and M. Onofrix, “Start-Up Transient Simulation of a Liquid Rocket Engine,” in 47th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit 31 July – 03 August 2011, San Diego, California.

VII. G. E. Bogin, Jr. and A. M. Dean, “Modeling the Fuel Spray and Combustion Process of the Ignition Quality Tester with KIVA-3V,” in NREL/CP-540-46738 conference paper 2010, 2009.

VIII. G. Odonneau, G. Albano and J. M. Carins, “A New Versatile and Flexible Tool for Engine Transient Prediction,” in 4th International Conference on Launcher Technology, Space Launcher Liquid Propulsion, 2002.

IX. G. P. Sutton and O. Biblarz, Rocket Propulsion Element, John Wiley & Sons Inc., 7th Edition, 2001.

X. H. Karimi and R. Mohammadi, “Modeling and simulation of a two combustion chambers liquid propellant engine,” in Aircraft Engineering and Aerospace Technology, 2007, p. 390 – 397 (Vol.79).

XI. J. Keppeler, E. Boronine and F. Fassl, “Startup Simulation of Upper Stage Propulsion System of ARIANE 5,” in 4th International Conference on Launcher Technology, Space Launcher Liquid Propulsion, 2002.

XII. L. Kun and Z. Yulin, “A Study on Versatile Simulation of Liquid Propellant Rocket Engine Systems Transients,” in In AIAA/ASME/SAE/ASEE 36th Joint Propulsion Conference and Exhibit, number AIAA 2000-3771, July 2000.

XIII. M. De Rosa, J. Steelant and J. Moral, “ESPSS: European Space Propulsion System Simulation,” in Space Propulsion Conference, 2008.

XIV. Mohanad Aldhaidhawi, Muneer Naji, Abdel Nasser Ahmed. : ‘ EFFECT OF IGNITION TIMINGS ON THE SI ENGINE PERFORMANCE AND EMISSIONS FUELED WITH GASOLINE, ETHANOL AND LPG‘. J. Mech. Cont.& Math. Sci., Vol.-15, No.-6, June (2020) pp 390-401. DOI : 10.26782/jmcms.2020.06.00030

XV. R. Rhote-Vaney, V. Thomas and A. Lekeux, “Transient Modeling of Cryogenic Rocket Engines a Modular Approach,” in 4th International Conference on Launcher Technology, Space Launcher Liquid Propulsion, 2002.

XVI. S. A. Inam, M. Hussain and M. M. Baig, “Numerical Simulation of Liquid Fuel Injection in Combustion Chamber,” Arabian Journal for Science and Engineering , 2019.

XVII. Smith JJ, “High Pressure LOx/H2 Combustion & Flame Dynamics Preliminary Results,” in 40th AIAA/ASME/SAE/ASEE Jt. Propuls. Conf. Exhib.: AIAA, 2004.

XVIII. V. Kalnin and V. Sherstiannikov, Hydrodynamic Modelling of the Starting Process in Liquid-Propellant Engines (vol.8) Page number 231-242, Acta Astronautica, 1981, pp. 231-242.

XIX. Y. Cengel and M. Boles, Thermodynamics: An Engineering Approach. 5th ed. McGraw-Hill College (ISBN 0-07-288495-9)., 2005.

DECENTRALIZED ELECTRONIC VOTING USING SMART CONTRACTS (DEV-SC)

Authors:

Urooj Waheed, Muhammad Ahsan Khan, Yusra Mansoor

DOI NO:

https://doi.org/10.26782/jmcms.2021.08.00004

Abstract:

For a citizen, voting is the fundamental tool to bring change in the country for good governance, through electing suitable candidates or a party to give them the power to govern. There are many forms of elections be it in the democratic or monarch systems. Each way, the vote has the power to elect the next people’s representatives. For a long time, paper-based voting was the only system being used globally for years; after the dot-com bubble, many countries emerged with electronic voting. But the problems such as security, transparency, and integrity of elections and the voting process are still under question. The issue with paper-based voting was accessibility, voter turns around and tallies, electronic voting has its own advantages and disadvantages such as a single point of failure, trustful systems, and loopholes to forge the electronic voting systems to alter the outcomes. To solve the problems related to the electronic voting process security, integrity, and transparency, an advanced approach is required to adopt. With the advancement of technologies, 4^th industry revolution technologies give us Blockchain, Distributed Ledger and Smart Contract types of technologies which may be beneficial to solve the current problems in electronic voting systems. In this paper, we proposed a research-based case study to implement Decentralized Electronic Voting using Smart Contracts (DEV-SC) to solve security, transparency, and integrity-related problems available in the electronic voting process. This will ensure and enhance the voting process easily, trustable, and verifiable.

Keywords:

e-voting,b-voting,electronic voting,smart contracts,blockchain voting.,

Refference:

I. Abuidris, Y., Hassan, A., Hadabi, A., & Elfadul, I. (2019, December). Risks and Opportunities of Blockchain Based on E-Voting Systems. In 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing (pp. 365-368). IEEE.
II. Abuidris, Y., Kumar, R., & Wenyong, W. (2019, December). A survey of blockchain based on e-voting systems. In Proceedings of the 2019 2nd International Conference on Blockchain Technology and Applications (pp. 99-104).
III. Achieng, M., & Ruhode, E. (2013). The adoption and challenges of electronic voting technologies within the South African context. arXiv preprint arXiv:1312.2406.
IV. Al-madani, A. M., Gaikwad, A. T., Mahale, V., & Ahmed, Z. A. (2020, October). Decentralized E-voting system based on Smart Contract by using Blockchain Technology. In 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC) (pp. 176-180). IEEE.
V. Ambrus, A., Greiner, B., & Zednik, A. (2019). The effects of a ‘None of the above’ballot paper option on voting behavior and election outcomes. Economic Research Initiatives at Duke (ERID) Working Paper, (277).
VI. Arnob, M. U. M. S., Sarker, N., Haque, M. I. U., & Sarwar, M. G. (2020). Blockchain-based secured e-voting system to remove the opacity and ensure the clarity of election of developing countries. International Research Journal of Engineering and Technology (IRJET), 7.
VII. Bulut, R., Kantarcı, A., Keskin, S., & Bahtiyar, Ş. (2019, September). Blockchain-based electronic voting system for elections in turkey. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (pp. 183-188). IEEE.
VIII. Daramola, O., & Thebus, D. (2020, June). Architecture-Centric Evaluation of Blockchain-Based Smart Contract E-Voting for National Elections. In Informatics (Vol. 7, No. 2, p. 16). Multidisciplinary Digital Publishing Institute.
IX. Hjálmarsson, F. Þ., Hreiðarsson, G. K., Hamdaqa, M., & Hjálmtýsson, G. (2018, July). Blockchain-based e-voting system. In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD) (pp. 983-986). IEEE.
X. Islam, A., Kader, M., & Shin, S. Y. (2018). BSSSQS: A Blockchain Based Smart and Secured Scheme for Question Sharing in the Smart Education System. arXiv preprint arXiv:1812.03917.
XI. Liacha, A., Oudjida, A. K., Ferguene, F., Bakiri, M., & Berrandjia, M. L. (2017). Design of high-speed, low-power, and area-efficient FIR filters. IET Circuits, Devices & Systems, 12(1), 1-11.
XII. Lyu, J., Jiang, Z. L., Wang, X., Nong, Z., Au, M. H., & Fang, J. (2019, August). A secure decentralized trustless E-Voting system based on smart contract. In 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 570-577). IEEE.
XIII. Park, J., Kim, H., Kim, G., & Ryou, J. (2021). Smart Contract Data Feed Framework for Privacy-Preserving Oracle System on Blockchain. Computers, 10(1), 7.
XIV. Patil, H. V., Rathi, K. G., & Tribhuwan, M. V. (2018). A study on decentralized e-voting system using blockchain technology. Int. Res. J. Eng. Technol, 5(11), 48-53.
XV. Ravikumar, S. (2021). E-Voting System using Blockchain with Network Security. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(9), 19-22.
XVI. Ruangwises, S., & Itoh, T. (2021). Physical zero-knowledge proof for numberlink puzzle and k vertex-disjoint paths problem. New Generation Computing, 39(1), 3-17.
XVII. Sadia, K., Masuduzzaman, M., Paul, R. K., & Islam, A. (2020). Blockchain-based secure e-voting with the assistance of smart contract. In IC-BCT 2019 (pp. 161-176). Springer, Singapore.
XVIII. Sheraz Ahmed, Muhammad Arif Shah, Ghufran Ullah, Karzan Wakil. : ‘A SYSTEMATIC LITERATURE REVIEW PROTOCOL FOR BLOCKCHAIN REVOLUTIONIZING ARENAS OF SMART CITY’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-5, May (2020) pp 127-136. DOI : 10.26782/jmcms.2020.05.00011
XIX. Taş, R., & Tanrıöver, Ö. Ö. (2020). A systematic review of challenges and opportunities of blockchain for E-voting. Symmetry, 12(8), 1328.
XX. Vivek, S. K., Yashank, R. S., Prashanth, Y., Yashas, N., & Namratha, M. (2020, July). E-voting systems using blockchain: An exploratory literature survey. In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 890-895). IEEE.
XXI. Yogesh Sharma, B. Balamurugan. : ‘A SURVEY ON PRIVACY PRESERVING METHODS OF ELECTRONIC MEDICAL RECORD USING BLOCKCHAIN’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-2, February (2020) pp 32-47. DOI : 10.26782/jmcms.2020.02.00004
XXII. Zhang, S., Wang, L., & Xiong, H. (2020). Chaintegrity: blockchain-enabled large-scale e-voting system with robustness and universal verifiability. International Journal of Information Security, 19(3), 323-341.

A SURVEY ON UNDERWATER WIRELESS SENSOR NETWORKS REQUIREMENTS

Authors:

Irfan Ahmad, Ubaid Ullah, Junaid Masood, Hasan Ali Khattak, Asim Ali, Sheeraz Ahmed

DOI NO:

https://doi.org/10.26782/jmcms.2021.08.00005

Abstract:

Underwater wireless sensor networks are currently conducting substantial research in a different environment for benefit of humans. UWSNs are known to be one of the fastest-growing technical fields, due to the many advantages of their application. UWSNs consist of underwater sensors with minimal resources and use the acoustic connection as a communications medium. Current technical advancements have given rise to opportunities for underwater exploration by using sensors at all levels. However, this article provide an overview of the major requirements of UWSNs to accomplish the crucial services. This article also provides an overview of underwater wireless communications.

Keywords:

UWSNs,acoustic communication,WSN,Requirements,

Refference:

I. Atif Ishtiaq, Sheeraz Ahmed, Asif Nawaz, Mohammad Shahzad, Rehan Ali Khan, Muneeb Sadat, Farrukh Hassan, Zeeshan Najam. A COMPREHENSIVE SURVEY ON CHANNEL BONDING TECHNIQUES IN WIRELESS SENSOR NETWORKS AND FUTURISTIC COGNITIVE RADIO NETWORKS. J. Mech. Cont.& Math. Sci., Vol.-15, No.-9, September (2020) pp 80-95. DOI : 10.26782/jmcms.2020.09.00007.
II. A. Majid, I. Azam, T. Khan, Sangeen, Z. A. Khan, U. Qasim, & N. Javaid, “A Reliable and Interference-Aware Routing Protocol for Underwater Wireless Sensor Networks,” in 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), 2016.
III. A. Majid, I. Azam, A. Waheed, M. Zain-Ul-Abidin, T. Hafeez, Z. Ali Khan, U. Qasim, & N. Javid, “An Energy Efficient and Balanced Energy Consumption Cluster Based Routing Protocol for Underwater Wireless Sensor Networks,” in IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016.
IV. A. Dubey, A. Rajawat, “Impulse effect of node mobility on delay sensitive routing algorithm in underwater sensor network,” in International Conference on Internet of Things and Applications (IOTA), 2016.
V. A. Sánchez, S. Blanc, P. Yuste, A. Perles, & J. José Serrano, “An Ultra-Low Power and Flexible Acoustic Modem Design to Develop Energy- Efficient Underwater Sensor Networks,” Sensors 2012, vol. 12, no. 6, pp. 6837–6856, May 2012.
VI. A. Codarin, L.E. Wysocki, F. Ladich, & Marta Picciulin, “Effects of ambient and boat noise on hearing and communication in three fish species living in a marine protected area (Miramare, Italy),” Marine Pollution Bulletin, vol. 58, no. 12, pp. 1880–1887, Dec. 2009.
VII. G. ZAIBI, N. NASRI, A.KACHOURI & M.SAMET, “Survey of Temperature Variation Effect on Underwater Acoustic Wireless Transmission,” in 5th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, March 22-26, 2009.
VIII. G. Xiang-ping, Y. yan, H. Rong-lin, “Analyzing the Performance of Channel in Underwater Wireless Sensor Networks (UWSN),” Elsevier, vol. 15, pp. 95–99, 2011.
IX. J. Lloret, “Underwater Sensor Nodes and Networks,” Sensors 2013, vol. 13, no. 9, pp. 11782–11796, Sep. 2013.
X. J. Liu, H. Yin, F. Xing, X. Ji, & Bin Wu, “An Energy-Efficient Routing Algorithm for Underwater Wireless Optical Sensor Network,” in 10th International Conference on Communication Software and Networks (ICCSN), 2018.
XI. J. M. Jornet, M. Stojanovic, & M. Zorzi, “Focused beam routing protocol for underwater acoustic networks,” in Proceedings of the third ACM international workshop on Underwater Networks, 2008, pp. 75–82.
XII. J. Qadar, A. Khan, H. Mahmood, “DNAR: Depth and Noise Aware Routing for Underwater Wireless Sensor Networks”. In Proceedings of the Conference on Complex, Intelligent, and Software Intensive Systems, Matsue, Japan, 4–6 July 2018; Springer: Cham, Switzerland, 2018; pp. 240–251.
XIII. K. Pervaiz, A. Wahid, M. Sajid, M. Khizar, Z. Ali Khan, U. Qasim, & N. Javaid, “DEAC: Depth and Energy Aware Cooperative Routing Protocol for Underwater Wireless Sensor Networks,” in 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), 2016.
XIV. L. Lanbo, Z. Shengli, & C. Jun‐Hong, “Prospects and problems of wireless communication for underwater sensor networks,” WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, vol. 8, pp. 977–994, Jul. 2008.
XV. M. Ayaz, L.T. Jung, A. Abdullah, & I. Ahmad, “Reliable data deliveries using packet optimization in multi-hop underwater sensor networks,” Journal of King Saud University – Computer and Information Sciences, vol. 24, no. 1, pp. 41–48, Nov. 2011.
XVI. M. Faheem, G. Tuna, & V. C. Gungor, “QERP: Quality-of-Service (QoS) Aware Evolutionary Routing Protocol for Underwater Wireless Sensor Networks,” IEEE Systems Journal, vol. 12, no. 3, pp. 2066–2073, Sep. 2018.
XVII M. Sunitha, R.K. Karunavathi “Localization of Nodes in Underwater Wireless Sensor Networks,” in 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), 2020, pp. 820–823.
XVIII. M.A. Rahman, Y. Lee, I. Koo, “EECOR: An energy-efficient cooperative opportunistic routing protocol for underwater acoustic sensor networks. IEEE Access 2017, 5, 14119–14132.
IX. N. Javaid, M. Ejaz, W. Abdul, A. Alamri, A. Almogren, A.N. Iftikhar, G. Nadra, “Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs”. Sensors 2017, 3, 580.
XX. N. Javaid, S. Hussain, A. Ahmad, M. Imran, A. Khan, G. Mohsen, “Region based cooperative routing in underwater wireless sensor networks”. J. Netw. Comput. Appl. 2017, 92, 31–41.
XXI N. Saeed, A. Celik, T. Y.Al-Naffouri, & M.-Slim Alouini, “Underwater optical wireless communications, networking, and localization: A survey,” Ad Hoc Networks, vol. 94, Nov. 2019.
XXII S. Lan, X. Du, F. Liu, Z.X. Feng, “Level-based adaptive geo-routing for underwater sensor network,” Application Research of Computers, vol. 31, no. 1, pp. 236–238, 2014.
XXIII S. Basagni, C. Petrioli, R. Petroccia, & M. Stojanovic, “Choosing the packet size in multi-hop underwater networks,” in OCEANS’10 IEEE SYDNEY, 2010.
XXIV T. Islam, Y.K. Lee “A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks,” Sensors 2019, vol. 19, no. 19, p. 4256, Sep. 2019.
XXV T. H. Won, S. J. Park, “Design and Implementation of an Omni-Directional Underwater Acoustic Micro-Modem Based on a Low-Power Micro- Controller Unit”, vol. 12, pp. 2309-2323, 2012.
XXVI T. Divin Ganpathi, Dhananjay A. M., Jalendra H. E., Kavya A. P. : REVIEW ON TARGET TRACKING METHODS FOR UNDERWATER ACOUSTIC SENSORS. J. Mech. Cont.& Math. Sci., Vol.-15, No.-2, February (2020) pp 341-348. DOI : 10.26782/jmcms.2020.02.00031.
XXVII. U. Lee, Paul Wang, Y. Noh, L.F. M. Vieira, M. Gerla, & Jun-Hong Cui, “Pressure Routing for Underwater Sensor Networks,” in Proceedings IEEE INFOCOM, 2010.
XXVIII. Y. Noh, U. Lee, S. Lee, P. Wang, L.F. M. Vieira, Jun-H. Cui, M. Gerla, & K. Kim, “HydroCast: Pressure Routing for Underwater Sensor Networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 1, pp. 333–347, Jan. 2015.

NUMERICAL INVESTIGATION OF THERMOHYDRAULIC PERFORMANCE OF TRIPLE CONCENTRIC-TUBE HEAT EXCHANGER WITH LONGITUDINAL FINS

Authors:

Shafquat Hussain, Umair Ahmed Rajput, Qadir Bukhsh, Qamar Abbas Kazi, Sanaullah Mastoi

DOI NO:

https://doi.org/10.26782/jmcms.2021.08.00006

Abstract:

In this work, a triple concentric-tube heat exchanger (TCTH) with or without the application of longitudinal fins is numerically studied concerning its thermohydraulic performance. The computational fluid dynamics (CFD) program, Ansys FLUENT was used to perform the simulations to study the heat transfer enhancement using three different types of hot fluids, i.e. Crude oil, engine oil, and light diesel oil. The validated numerical model was first employed to investigate the heat transfer performance of unfinned TCTHE. Then, longitudinal fins were modeled and investigated for comparative analyses of the thermohydraulic performances of both constructions. To predict the heat exchanger performance, key parameters such as heat flux and temperature field distribution were evaluated. Results revealed that modifying the heat exchanger with longitudinal fins on the tube surface dramatically improves its heat transfer rate. Therefore, this research is designed to keep in view further exploring the potential of longitudinal fins in obtaining an improved performance from these types of heat exchanger devices. The results showed that the crude oil fluid has high heat transfer rate than the other two fluids light diesel oil and engine oil. With the application of fins on the tubes’ surfaces, a significant heat transfer exchange among the fluids streams is observed.

Keywords:

TCTHE,Longitudinal fin,Heat transfer rate,Temperature field distribution,

Refference:

I. AR Ansari, UA Rajput, M Imran, M Shariq, MS Abdel-wahab, AH Hammad, Impact of the Microwave Power on the Structural and Optical Properties of Nanocrystalline Nickel Oxide Thin Films. Brazilian Journal of Physics 51 (2021), 499-506.
II. AZuritz, C., On the design of triple concentric-tube heat exchangers. Journal of Food Process Engineering, 2007. 12: p. 113-130; BRadulescu, S., I. Negoita, and I. Onutu, Heat Transfer Coefficient Solver for a Triple Concentric-tube Heat Exchanger in Transition Regime. Revista de Chimie, 2012. 63: p. 820-824.
III. AQuadir, G.A., Experimental investigation of the performance of a triple concentric pipe heat exchanger. International Journal of Heat and Mass Transfer, 2013. 62: p. 562-566;
IV. AFLUENT, ANSYS Fluent Users Guide. Canonsburg, PA. 2013
V. B Yau, Y. and H. Poh, Study on Flow Behavior and Heat Exchange Characteristics of a Capillary Tube-Suction Line Heat Exchanger. Heat Transfer Engineering, 2018. 40(7): p. 574-587.
VI. BQuadir, G.A., I.A. Badruddin, and N.J. Salman Ahmed, Numerical investigation of the performance of a triple concentric pipe heat exchanger. International Journal of Heat and Mass Transfer, 2014. 75: p. 165-172.
VII. CSarker, D. and J.H. Jeong, Development of empirical correlations for non-adiabatic capillary tube based on mechanistic model. International Journal of Refrigeration, 2012. 35(4): p. 974-983.
VIII. Ch. Sreekanth Reddy, P. Rajendra Prasad, and D.M. Krishnudu, Experimental Analysis Of Triple Tube Heat Exchanger With TiO2 Nanofluid. International journal of scientific & technology research, 2019.
IX. DYang, C. and P.K. Bansal, Numerical investigation of capillary tube-suction line heat exchanger performance. Applied Thermal Engineering, 2005. 25(13): p. 2014-2028.
X. EAblanque, N., et al., Numerical simulation of non-adiabatic capillary tubes. Special emphasis on the near-saturation zone. International Journal of Refrigeration, 2015. 55: p. 153-167.
XI. El Maakoul, A., et al., Numerical investigation of thermohydraulic performance of air to water double-pipe heat exchanger with helical fins. Applied Thermal Engineering, 2017. 127: p. 127-139.
XII. Ghiwala, N.M. and N.V.k. Matawla, Sizing of triple concentric pipe heat exchanger. International Journal of Engineering Development and Research, 2014. 2: p. 1683-1692.
XIII. Moraveji, M.K. and R. Ardehali, CFD modeling (comparing single and two-phase approaches) on thermal performance of Al2o3/water nanofluid in mini-channel heat sink. International Communications in Heat and Mass Transfer, 2013. 44: p. 157-164.
XIV. Mohammed, H., A. Abbas, and J.M. Sheriff, Influence of geometrical parameters and forced convective heat transfer in transversely corrugated circular tubes. International Communications in Heat and Mass Transfer, 2013. 44: p. 116-126.
XV. Rohdin, P. and B. Moshfegh, Numerical predictions of indoor climate in large industrial premises. A comparison between different k–ε models supported by field measurements. Building and Environment, 2007. 42(11): p. 3872-3882.
XVI. S Mastoi, WAM Othman, U Ali, UA Rajput, G Fizza. : Numerical Solution of the Partial Differential Equation using Randomly Generated Finite Grids and Two-Dimensional Fractional-Order Legendre Function. J. Mech. Cont. & Math. Sci., Vol.-16, No.-6, June (2021) pp 39-51. DOI : 10.26782/jmcms.2021.06.00004
XVII. S. Mastoi, A.B. Mugheri, N.B. Kalhoro, and A.S. Buller, Numerical solution of Partial differential equations(PDE’s) for nonlinear Local Fractional PDE’s and Randomly generated grids. International Journal of Disaster Recovery and Business Continuity 11 (2020) 2429-2436.
XVIII. S. Mastoi, N.B. Kalhoro, A.B.M. Mugheri, U.A. Rajput, R.B. Mastoi, N. Mastoi, and W.A.M. Othman, Numerical solution of Partial differential equation using finite random grids. International Journal of Advanced Research in Engineering and Technology (IJARET) (2021).
XIX. S. Vignesh, V. Shantha Moorthy, and D.G. Nallakumarasamy, Experimental and CFD Analysis of Concentric Dimple Tube Heat Exchanger. International Journal of Emerging Technologies in Engineering Research (IJETER), 2017. 5(7).
XX. S. Mastoi, and W.A.M. Othman, A Finite difference method using randomly generated grids as non-uniform meshes to solve the partial differential equation. International Journal of Disaster Recovery and Business Continuity 11 (2020) 1766-1778.
XXI. S. Mastoi, A.B.M. Mugheri, N.B. Kalhoro, U.A. Rajput, R.B. Mastoi, N. Mastoi, and W.A.M. Othman, Finite difference algorithm on Finite random grids. International Journal of Advanced Research in Engineering and Technology (IJARET) (2021).
XXII. S. Mastoi, W.A.M. Othman, and K. Nallasamy., Numerical Solution of Second order Fractional PDE’s by using Finite difference Method over randomly generated grids. International Journal of Advance Science and Technology 29 (2020) 373-381.
XXIII. S. Mastoi, W.A.M. Othman, and K. Nallasamy., Randomly generated grids and Laplace Transform for Partial differential equation. International Journal Disaster Recovery and Business continuity 11 (2020) 1694-1702.
XXIV. U Ali, S Mastoi, WAM Othman, MMA Khater, M Sohail, Computation of traveling wave solution for nonlinear variable-order fractional model of modified equal width equation. AIMS Mathematics 6 (2021), 10055-10069
XXV. Umair Ahmed Rajput, MA Mujtaba, Yat Huang Yau, Stratum Ventilation: A Viable Air Distribution for Nightingale Wards to Reduce the Risk of Airborne Transmission. Book Abstracts 2021.

OBSERVED ISSUES IN CLOUD-BASED WEB COMMERCE ADOPTION FOR THE FINANCIAL TRANSACTIONS IN HYDERABAD

Authors:

Srinivasa Rao Gundu, Panem Charan Arur, Thimmapuram Anuradha

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00001

Abstract:

In the present day scenario, maximum financial transactions are being carried out with the help of Cloud-Based Web Trade (CBWT). These Cloud Oriented Web-Based Financial Transactions provide numerous advantages to the end-users. The Commodities are available at a much cheaper rate and numerous choices are left over to the customers and they are also reducing the shopping time. Particularly the time like Pandemic Situation would provide a better way to purchase multiple goods at their fingertips. There are many numbers of reasons are leftover behind the success and the downfall of such Cloud Oriented Web-Based Financial Transactions. Some of these include financial conditions, technical feasibility, and geographical location, etc. However, nowadays there it is facing many Ethical, Service-oriented, and financial challenges in this area. There is needed to make a SWOT Analysis since it is going to be the major financial gateway for numerous people.

Keywords:

Cloud-Based Web Trade (CBWT),SWOT Analysis,Online Banking,Hacking,Security,Business,

Refference:

I. A. A. Abdulrazaq, M. B. Abdulrazaq, I. J. Umoh and E. A. Adedokun, “Fraud Detection in Credit Card and Application of VAT Clustering Algorithm: A Review,” 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), Zaria,Nigeria, 2019, pp. 1-7, doi: 10.1109/NigeriaComputConf45974.2019.8949660.
II. A. Mitrokotsa, N. Komninos and C. Douligeris, “Intrusion Detection with Neural Networks and Watermarking Techniques for MANET,” IEEE International Conference on Pervasive Services, Istanbul, 2007, pp. 118- 127.
III. Anitha Vemulapalli, Nandula Anuradha, Geeta Mahadeo Ambildhuke. ‘RISK ASSESSMENT FOR BIG DATA IN CLOUD COMPUTING ENVIRONMENT FROM THE PERSPECTIVE OF SECURITY, PRIVACY AND TRUST’. J. Mech. Cont. & Math. Sci., Vol.-15, No.-8, August (2020) pp 391-402. DOI : 10.26782/jmcms.2020.08.00036
IV. B. Li and Y. Wang, “RZKPB: A Privacy-Preserving Block chain-Based Fair Transaction Method for Sharing Economy,” 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), New York, NY, 2018, pp. 1164-1169, doi:10.1109/TrustCom/BigDataSE.2018.00161.
V. B. Galloway and G. P. Hancke, “Introduction to industrial control networks,” IEEE Communications Surveys Tutorials, vol. 15, no. 2, pp. 860–880, Second 2013.
VI. C. Chang, J. Yang and K. Chang, “An Efficient and Flexible Mobile Payment Protocol,” 2012 Sixth International Conference on Genetic and Evolutionary Computing, Kitakushu, 2012, pp. 63-66, doi: 10.1109/ICGEC.2012.43.

VII. C. Piao, Y. Zuo and C. Zhang, “Research on Hybrid-Cloud-Based User Privacy Protection of O2O Platform,” 2016 IEEE 13th International Conference on e-Business Engineering (ICEBE), Macau, 2016, pp. 214- 219, doi: 10.1109/ICEBE.2016.044.
VIII. G. A. Lopes Ferreira et al., “Internet of Things and the Credit Card Market: How Companies Can Deal with the Exponential Increase of Transactions with Connected Devices and Can Also be Efficient to Prevent Frauds,” 2015 12th International Conference on Information Technology – New Generations, Las Vegas, NV, 2015, pp. 107-111, doi: 10.1109/ITNG.2015.23.
IX. G. Drakopoulos, E. Kafeza and H. Al Katheeri, “Proof Systems In Blockchains: A Survey,” 2019 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Piraeus, Greece, 2019, pp. 1-6.
X. H. D. Thai, C. Lee, D. Niyato and P. Wang, “A Survey of mobile cloud computing: Architecture, applications, and approaches,” Wireless Communications and Mobile Computing, vol. 13, no. 18, 2013, pp. 1587– 1611.
XI. Hu Dongxing,et al. Information Network Security Situation Aware Technology Based on Artificial Intelligence [J]. Journal of Information Communication, 2012 (6):80-81.
XII. H. Elayan, O. Amin, R. M. Shubair, and M.-S. Alouini, “Terahertz communication: The opportunities of wireless technology beyond 5G,” in Proc. 2018 International Conference on Advanced Communication Technologies and Networking, Apr. 2018, pp. 1–5.
XIII. J. Ni, X. Lin, and X. S. Shen, “Efficient and secure service-oriented authentication supporting network slicing for 5G-enabled IoT,” IEEE JSAC, vol. 36, no. 3, Mar. 2018.
XIV. J. Zhu, “Capacity-power consumption and energy- efficiency evaluation of green wireless networks,” China Communications, vol. 9,no. 2, 2012, pp. 13–21.
XV. K. Kotobi and S. G. Bilen, “Secure blockchains for dynamic spectrum access: A decentralized database in moving cognitive radio networks enhances security and user access,” IEEE Veh. Technol. Mag., vol. 13, no. 1, pp. 32–39, Mar. 2018. doi:10.1109/MVT.2017.2740458.
XVI. M. H. Bhuyan, D. K. Bhattacharyya and J. K. Kalita, “Network Anomaly Detection: Methods, Systems and Tools,” in IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303-336, First Quarter 2014.
XVII. M. T. Hossan, M. Z. Chowdhury, M. Shahjalal, and Y. M. Jang, “Human bond communication with head-mounted displays: scope,challenges, solutions, and applications,” IEEE Communications Magazine, vol. 57, no. 2, pp. 26-32, February 2019.
XVIII. R. Henry, A. Herzberg, and A. Kate, “Blockchain access privacy: challenges and directions,” IEEE Security & Privacy, vol. 16, no. 4, pp. 38-45, Jul./Aug. 2018.
XIX. R. Li, “Towards a New Internet for the Year 2030 and Beyond,” in Third Annual ITU IMT-2020/5G Workshop and Demo Day, 2018.
XX. R. Tripathi, S. Vignesh, V. Tamarapalli and D. Medhi, “Cost Efficient Design of Fault Tolerant Geo-Distributed Data Centers,” inIEEE Transactions on Network and Service Management, vol. 14, no. 2, pp. 289-301, June 2017, doi: 10.1109/TNSM.2017.2691007.
XXI. S. Ali, N. Rajatheva, and W. Saad, “Fast uplink grant for machine type communications: Challenges and opportunities,” IEEE Comm. Mag., vol. 57, no. 3, Mar. 2019.
XXII. T. Aste, P. Tasca, and T. Di Matteo, “Blockchain technologies: the foreseeable impact on society and industry,” Computer, vol. 50, no. 9, pp. 18-28, 2017.
XXIII. Tallapally Sampath Kumar, B. Manjula. : ‘EFFICIENT MULTI-LEVEL ENCRYPTION PROCEDURE FOR CLOUD SECURITY’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-6, June (2020) pp 402-410. DOI : 10.26782/jmcms.2020.06.00031.

COMPARATIVE ANALYSIS OF PREDICTION TECHNIQUES ON THE BASIS OF TELECOM CUSTOMER CHURN

Authors:

Yasser Khan, Zeeshan Rasheed, Naeem Ahmed, Minhaj Ullah, Malik Taimur Ali, Farrukh Hassan, Sheeraz Ahmed

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00002

Abstract:

Telecommunication customer churn is considered as major cause for dropped revenue and customer baseline of voice, multimedia and broadband service provider. There is strong need on focusing to understand the contributory factors of churn. Now considering factors from data sets obtained from Pakistan major telecom operators are applied for modeling. On the basis of results obtained from the optimal techniques, comparative technical evaluation is carried out. This research study is comprised mainly of proposition of conceptual frame work for telecom customer churn that lead to creation of predictive model. This is trained tested and evaluated on given data set taken from Pakistan Telecom industry that has provided accurate & reliable outcomes. Out of four prevailing statistical and machine learning algorithm, artificial neural network is declared the most reliable model, followed by decision tree. The logistic regression is placed at last position by considering the performance metrics like accuracy, recall, precision and ROC curve. The results from research has revealed main parameters found responsible for customer churn were data rate, call failure rate, mean time to repair and monthly billing amount. On the basis of these parameter artificial neural network has achieved 79% more efficiency as compare to low performing statistical techniques.

Keywords:

Artificial Neural Network,Prediction,Churn management,Telecom Churn,

Refference:

I. Asmaa Jamal Awad, Ahmed Abdulrasool Ahmed, Osamah Abdallatif. : ‘ESTIMATION TYPES OF FAILURE FOR THERMO-ELECTRIC UNIT BY USING ARTIFICIAL NEURAL NETWORK (ANN)’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-7, July (2020) pp 47-69. DOI : 10.26782/jmcms.2020.07.00005
II. Al-Weshah, Ghazi A., Excimirey Al-Manasrah, and Manar Al-Qatawneh. “Customer relationship management systems and organizational performance: Quantitative evidence from the Jordanian telecommunication industry.” Journal of Marketing Communications 25, no. 8 (2019): 799-819.
III. Bhattacharyya, Jishnu, and Manoj Kumar Dash. “Investigation of customer churn insights and intelligence from social media: a netnographic research.” Online Information Review (2020).
IV. DARMA, Jufri, Azhar SUSANTO, Sri MULYANI, and Jadi SUPRIJADI. “The Role of Top Management Support in the Quality of Financial Accounting Information Systems.” Journal of Applied Economic Sciences 13, no. 4 (2018).
IV. Dridi, Amna, Mohamed Medhat Gaber, R. Muhammad Atif Azad, and Jagdev Bhogal. “Scholarly data mining: A systematic review of its applications.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (2020)

V. Gordini, Niccolò, and Valerio Veglio. “Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry.” Industrial Marketing Management 62 (2017): 100-107.
VI. Idris, Adnan, Aksam Iftikhar, and Zia ur Rehman. “Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO under sampling.” Cluster Computing 22, no. 3 (2019): 7241-7255.
VII. Jere, Mlenga G., and Alick Mukupa. “Customer satisfaction and loyalty drivers in the Zambian mobile telecommunications industry.” Journal of Business and Retail Management Research 13, no. 2 (2018).
VIII. Khdour, Naser, and Atef Al-Raoush. “The impact of organizational storytelling on organizational performance within Jordanian telecommunication sector.” Journal of Workplace Learning (2020).
IX. Lee, Hyunsong, Hyunhong Choi, and Yoonmo Koo. “Lowering customer’s switching cost using B2B services for telecommunication companies.” Telematics and Informatics 35, no. 7 (2018): 2054-2066.
X. Mashchak, Nataliia, and Oksana Dovhun. “Modern Marketing and Logistics Approaches in the Implementation of E-Commerce.” In Integration of Information Flow for Greening Supply Chain Management, pp. 375-391. Springer, Cham, 2020.
XI. Muthu, BalaAnand, C. B. Sivaparthipan, Gunasekaran Manogaran, Revathi Sundarasekar, Seifedine Kadry, A. Shanthini, and Antony Dasel. “IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector.” Peer-to-peer networking and applications 13, no. 6 (2020): 2123-2134.
XII. Özata, Hatice Işık, Önder Demir, and Buket Doğan. “Analysis of Patents in Cyber Security with Text Mining.” International Journal of Computer Theory and Engineering 13, no. 1 (2021).
XIII. Pant, Laxmi Prasad, and Helen Hambly Odame. “Broadband for a sustainable digital future of rural communities: A reflexive interactive assessment.” Journal of Rural Studies 54 (2017): 435-450.
XIV. Rachid, Ait Daoud, Amine Abdellah, Bouikhalene Belaid, and Lbibb Rachid. “Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context.” International Journal of Electrical and Computer Engineering 8, no. 4 (2018): 2367.
XV. Stiassny, Alfred, Agnes Somosi, and Krisztina Kolos. “Enhancing customer retention in case of service elimination? An empirical investigation in telecommunications.” (2019).
XVI. Taniguchi, Tadanari. “Self-organizing map analysis and classification of consumption trends of foreigners visiting Japan using a questionnaire survey.” Journal of Global Tourism Research 3, no. 2 (2018).
XVII. Thuethongchai, Nopsaran, Tatri Taiphapoon, Achara Chandrachai, and Sipat Triukose. “Adopt big-data analytics to explore and exploit the new value for service innovation.” Social Sciences 9, no. 3 (2020): 29.
XVIII. Van den Poel, Dirk, and Bart Lariviere. “Customer attrition analysis for financial services using proportional hazard models.” European journal of operational research 157, no. 1 (2004): 196-217.
XIX Wang, Li, Chaochao Chen, Jun Zhou, and Xiaolong Li. “Time-sensitive Customer Churn Prediction based on PU Learning.” arXiv preprint arXiv:1802.09788 (2018).
XX. Yasser Khan, Shahryar Shafiq, Sheeraz Ahmed, Nadeem Safwan, Mehr-e-Munir, Alamgir Khan. : ‘Factors affecting Service Quality, Customer Satisfaction and Customer Churn in Pakistan Telecommunication Services Market’. J. Mech. Cont.& Math. Sci., Vol.-14, No.-4, July-August (2019) pp 576-594. DOI : 10.26782/jmcms.2019.08.00048

WITHDRAWN: IMPROVEMENT IN SIGNAL QUALITY THROUGH MEDIAN BASE FILTERING

Authors:

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00003

Abstract:

Check for Withdrawn Notice

Keywords:

Refference:

View Download

A NEW EVOLUTIONARY METHOD TO PARAMETERS AND ORDERS IDENTIFICATION AND SYNCHRONIZATION OF CHAOTIC FRACTIONAL-ORDER SYSTEMS

Authors:

Ali Soleimanizadeh, Mohammad Ali Nekui

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00004

Abstract:

System identification is an important task in the control theory. Classical control theory is usually known for integer-order processes. Nowadays real processes are fractional order usually. According to a large number of fractional-order systems, identification of these systems is so important. This paper aims to evaluate an improved Biogeography-based Optimization (BBO) approach to estimate the parameters and orders of fractional-order systems. After that, a method based on this algorithm has been introduced to synchronization of chaotic systems. Results show that the proposed scheme has high accuracy.

Keywords:

Fractional-order system,System identification,Biogeography-based Optimization,

Refference:

I. Al_ A, Modares H. System identification and control using adaptive particle swarm optimization. Applied Mathematical Modelling. 2011 Mar 1;35(3):1210-21.
II. Antonik P, Gulina M, Pauwels J, Massar S. Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography. Physical Review E. 2018 Jul 24;98(1):012215.
III. Behinfaraz R., Badamchizadeh M. New approach to synchronization of two different fractional-order chaotic systems, International Symposium on Artificial Intelligence and Signal Processing (AISP), 2015, 149-153.
IV. Behinfaraz, Reza, and Mohammad Ali Badamchizadeh. Synchronization of different fractional-ordered chaotic systems using optimized active control. Modeling, Simulation, and Applied Optimization (ICM- SAO), 2015 6th International Conference on. IEEE, 2015.
V. Bouzeriba A. Fuzzy Adaptive Controller for Synchronization of Uncertain Fractional-Order Chaotic Systems. In Advanced Synchronization Control and Bifurcation of Chaotic Fractional-Order Systems 2018 (pp. 190-217). IGI Global.
VI. D.Simon, “Biogeography-based optimization” ,IEEE Trans. on Evo. Com. vol.12,pp.702-713, 2008.
VII. Doye IN, Salama KN, Laleg-Kirati TM. Robust fractional-order proportional-integral observer for synchronization of chaotic fractional-order systems. IEEE/CAA Journal of Automatica Sinica. 2019 Jan;6(1):268-77.
VIII. H. Ma M. Fei, Z. Ding, J. Jin, “Biogeography-based optimization ensemble of migration models for global numerical optimization”, Proc. IEEE Congress on Evolutionary Computation, June 2012.
IX. H. Ma, D. Simon, “Blended Biogeography-based optimization for constrained optimization”, Evolutionary Comp., Vol. 24, pp. 517-525,2011.
X. Hartley TT, Lorenzo CF. Fractional-order system identification based on continuous order-distributions. Signal processing. 2003 Nov 1;83(11):2287-300.
XI. I. Podlubny, “The Laplace Transform Method for Linear Differential Equations of the Fractional Order”, UEF-02-94, The Academy of Sciences Institute of Experimental Physics, Kosice, Slovak Republic, 1994.
XII. Johnson T, Husbands P. System identification using genetic algorithms. In International Conference on Parallel Problem Solving from Nature 1990 Oct 1 (pp. 85-89). Springer, Berlin, Heidelberg.
XIII. Kazemi A, Behinfaraz R, Ghiasi AR. Accurate model reduction of large scale systems using adaptive multi-objective particle swarm optimization algorithm. 2017 International Conference on In Mechanical, System and Control Engineering (ICMSC), 2017 May 19 (pp. 372-376).
XIV. Kilbas AA, Srivastava HM, Trujillo JJ. Theory and applications of fractional differential equations . Elsevier Science Limited; 2006.
XV. L . Dorcak , “Numerical Methods for Simulation the Fractional-Order Control Systems”, UEF SAV, The Academy of Sciences Institute of Experimental Physics, Kosice, Slovak Republic, 1994.
XVI. Li C, Peng G.”Chaos in Chens system with a fractional order” Chaos Solitons Fract 2004; 22:44350
XVII. Ouannas A, Grassi G, Azar AT. Fractional-Order Control Scheme for QS Chaos Synchronization. InInternational Conference on Advanced Machine Learning Technologies and Applications 2019 Mar 28 (pp. 434-441). Springer, Cham.
XVIII. Ouannas A, Odibat Z. Reduced-Increased Synchronization Between Fractional Chaotic Systems with Different Dimensions and Orders. Available at SSRN 3274053. 2018 Jun 20.
XIX. P. J. Torvik, R.L. Bagley, “On the appearance of the fractional derivative in the behaviour of real mate- rials”, Transactions of the ASME, vol. 51, June 1984, pp. 294-298.
XX. Pham VT, Ouannas A, Volos C, Kapitaniak T. A simple fractional-order chaotic system without equi- librium and its synchronization. AEU-International Journal of Electronics and Communications. 2018 Mar 1;86:69-76.
XXI. Pillai N, Schwartz SL, Ho T, Dokoumetzidis A, Bies R, Freedman I. Estimating parameters of nonlinear dynamic systems in pharmacology using chaos synchronization and grid search. Journal of Pharmacoki – netics and Pharmacodynamics. 2019:1-8.
XXII. Reza Behinfaraz , Mohammadali Badamchizadeh, Amir Rikhtegar Ghiasi, An adaptive method to parameter identification and synchronization of fractional-order chaotic systems with parameter uncertainty, Applied Mathematical Modelling, Volume 40, Issues 78, April 2016, Pages 4468-4479.
XXIII. Sanaullah Mastoi, Wan Ainun Mior othman, Umair Ali, Umair Ahmed Rajput, Ghulam Fizza. : ‘NUMERICAL SOLUTION OF THE PARTIAL DIFFERENTIAL EQUATION USING RANDOMLY GENERATED FINITE GRIDS AND TWO-DIMENSIONAL FRACTIONAL-ORDER LEGENDRE FUNCTION’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-6, June (2021) pp 39-51. DOI : 10.26782/jmcms.2021.06.00004.
XXIV. Singh S, Azar AT, Vaidyanathan S, Ouannas A, Bhat MA. Multi switching Synchronization of Com- mensurate Fractional Order Hyperchaotic Systems Via Active Control. In Mathematical Techniques of Fractional Order Systems 2018 (pp. 319-345).
XXV. Sontakke Bhausaheb, Rajashri Pandit : ‘NUMERICAL SOLUTION OF TIME FRACTIONAL TIME REGULARIZED LONG WAVE EQUATION BY ADOMINAN DECOMPOSITION METHOD AND APPLICATIONS’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-2, February (2021) pp 48-60. DOI : 10.26782/jmcms.2021.02.00005.
XXVI. Vaseghi B, Pourmina MA, Mobayen S. Finite-time chaos synchronization and its application in wireless sensor networks. Transactions of the Institute of Measurement and Control. 2018 Sep;40(13):3788-99.

A NEW CONSTRUCTION OF OS OF SUBALGEBRAS AND INVARIANT SOLUTION OF THE BLACK-SCHOLES EQUATION

Authors:

Zahid Hussain, Sadaqat Hussain, Suhail Abbas, Shams-ur-Rehman, Shahid Hussain

DOI NO:

https://doi.org/10.26782/jmcms.2021.09.00005

Abstract:

In this manuscript, the Lie group technique is applied to construct a new OS and invariant solutions of a one-dimensional LA, which describes the symmetries properties of a nonlinear Black-Scholes model. The structure of LA depends on one parameter. We have shown a novel way to construct the so-called OS of subalgebras of the Black-Scholes equation by utilizing the given symmetries. We transform the symmetries of the Black-Scholes equation into a simple ordinary differential equation called the Lie equation, which provides us a way through which to construct a new optimal scheme of subalgebras of the Black-Scholes through applying the concept of LE. The OS which consists of minimal representatives is utilized to develop the invariant solution for the Black-Scholes equation. The fundamental use of the Lie group analysis to the differential equation is the categorization of group invariant solutions of differential equations via OS. Finally, we have utilized the OS to construct the invariant solution of the Black-Scholes equation.

Keywords:

Black-Scholes Equation,Generators,LE,OS,Invariant solution,

Refference:

I. A.K. Yadav,. And, A.T Ali,. “An OS and Invariant solution of Dark Energy Models in cylindrically symmetric space-time”, The European Physical Journal plus, Eur. Phys. J. Plus, pp. 129-179 (2014).
II. A.P. Coggeshall and Meyer-ter-Vehn, “Group Invariant solutions and OSs for multidimensional hydrodynamics”, J. Math. Phys 33,( 1994).
III. A.P. hupakin. “OS of sub algebras of one solvable algebra L7”, Lie Groups and Their Applications 1 (1994).
IV. C.A. Pooe. Mahomed F.M. and Wafo Soh C. Invariant Solutions of the Black- Scholes Equation, Math. & Compt. Apps 8, p p. 63- 70 (2003).
V. F. Black. and M. Scholes, “The pricing of options and corporate liabilities”, J. Pol. Econ. 81, pp. 637 – 659 (1973).
VI. G. Baumann, “Symmetry Analysis of Differential Equations with Mathematica. Telos, Springer Verlag”, New York, (2000).
VII. G.W. Bluman,.and S.C. Anco,. “Symmetry and Integration Methods for Differential Equations”, Springer-Verlag, New York (2002).
VIII. J. Stampfli, and Victor, “The Mathematics of finance, in: Brooks/ Cole series in Advance Mathematics, Brooks/ cole, Pacific Grove”, CA, Modeling and Hedging, (2001).
IX. L. A. Bordag. “Option-valuation in illiquid markets: invariant solutions to a nonlinear model in mathematical Control Theory and Finance”, eds.A. Sarychev, M. Guerra and M. R. Grossinho, 81, pp.72-94, “Springer”,2008.
X. L.V Ovsyannikov,. “Group analysis of differential equations”, Nuaka, Moscow, 1978. English transl., ed W.F. Ames, Academic Press, New York (1982).
XI. L.V. Ovsyannikov ,“Group properties of differential equations”, Siberian Branch, USSR Academy of Sciences, Novosibirsk (1962).
XII. Mohammad Asif Arefin, Biswajit Gain, Rezaul Karim, Saddam Hossain. : ‘A COMPARATIVE EXPLORATION ON DIFFERENT NUMERICAL METHODS FOR SOLVING ORDINARY DIFFERENTIAL EQUATIONS’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-12, December (2020) pp 1-11. DOI : 10.26782/jmcms.2020.12.00001.
XIII. N. H. Ibragimov. “CRC Handbook of Lie group analysis of differential equations. Volume1,2,3 CRC Press”, Boca Raton, Ann Arbor, London, Tokyo, (1994,1995,1996).
XIV. N.H. bragimov. “Exercises for courses based on Lie group analysis”, ALGA Publication, Karlskrona (2008).

XV. N.H. Ibragimov, “Analytical Methods in Mathematical Modelling”, ALGA Publication Karlskrona (2008).
XVI. N.H. Ibragimov, “Elementary Lie group analysis and ordinary differential equations”, John Wiley and Sons, Chichester (1999).
XVII. N.H. Ibragimov, “Transformation Groups Applied to Mathematical Physics CRC Press”, Boca Raton, F.L, (1985).
XVIII. N.H. bragimov, “A practical course in differential equations and mathematical modelling”, Third Edition, ALGA Publication, Karlskrona (2006)
XIX. Ovsyannikov,. OS of subalgebras, Lie Groups and Their Applications 1 (1994).
XX. P. J. Olver,. “Applications of Lie Groups to Differential Equations”, GTM 107, Second edn., Springer-Verlag (1993).
XXI. P. Wilmot, S. Howison, and Dewynne, “The Mathematics of financial Derivatives”, Cambridge University Press, Cambridge, (1997).
XXII. R. K, Gazizov. and , N.H Ibragimov1, “Lie symmetry analysis of differential equations in finance”, Nonlinear Dynamics, 17, pp. 387 – 407(1998).
XXIII. Sivaram. B. : ‘COMPARATIVE STUDY OF SOLUTION METHODS OF NON-HOMOGENEOUS LINEAR ORDINARY DIFFERENTIAL EQUATIONS WITH CONSTANT COEFFICIENTS’. J. Mech. Cont.& Math. Sci., Vol.-16, No.-1, January (2021) pp 1-18. DOI : 10.26782/jmcms.2021.01.00001.