IJETSI

Title: PROPOSING NEW ROUTING PROTOCOL BASED ON CHAOS ALGORITHM
Authors: Ali Majdi
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Ali Majdi: Department of Computer Engineering at Bilkent University, Ankara, Turkey

References [1] Singh, and A. Nagaraju, “Low latency and energy efficient routing-aware network coding-based data transmission in multi-hop and multi-sink” WSN. Ad Hoc Networks, 107, p. 102182, 2020. [2] A.Malar, M. Kowsigan, N. Krishnamoorthy, S. Karthick, E. Prabhu, and K. Venkatachalam, “Multi constraints applied energy efficient routing technique based on ant colony optimization used for disaster resilient location detection in mobile ad-hoc network”. Journal of Ambient Intelligence and Humanized Computing, 12(3), pp.4007-4017;2021. [3] M. Khorraminia, Z. Lesani, M. Ghasvari, L. Rajabion, Mehdi Darbandi, A. Hassani, “A Model for Assessing the Impact of Cloud Computing on the Success of Customer Relationship Management Systems”; Published by International Journal of Digital Policy, Regulation and Governance (ISSN: 2398-5038), 2019. https://doi.org/10.1108/DPRG-03-2019-0016. [4] K.A. Darabkh, O.M. Amro, R.T. Al-Zubi, and H.B. Salameh, Y”et efficient routing protocols for half-and full-duplex cognitive radio Ad-Hoc Networks over IoT environment”. Journal of Network and Computer Applications, 173, p.102836 ;2021. [5] S. Norozpour “Proposing New Method for Clustering and Optimizing Energy Consumption in WSN”; Published by International Journal of Talent Development & Excellence (ISSN: 1869-0459), Vol. 12, No. 3S, PP. 2631-2643, 2020. [6] Mehdi Darbandi; “Proposing New Intelligent System for Suggesting Better Service Providers in Cloud Computing based on Kalman Filtering”; Published by HCTL International Journal of Technology Innovations and Research, (ISSN: 2321-1814), Vol. 24, Issue 1, PP. 1-9, Mar. 2017, DOI: 10.5281/Zenodo.1034475. [7] D.G. Zhang, Y.Y. Cui, and T. Zhang, “New quantum-genetic based OLSR protocol (QG-OLSR) for Mobile Ad hoc Network”. Applied Soft Computing, 80, pp.285-296; 2019. [8] N.C. Singh, and A. Sharma, Resilience of mobile ad hoc networks to security attacks and optimization of routing process. Materials Today: Proceedings ;2020. [9] M.Darbandi; “Proposing New Intelligence Algorithm for Suggesting Better Services to Cloud Users based on Kalman Filtering”; Published by Journal of Computer Sciences and Applications (ISSN: 2328-7268), Vol. 5, Issue 1, 2017; PP. 11-16; DOI: 10.12691/JCSA-5-1-2; USA. [10] C.R. da Costa Bento, and E.C.G. Wille, “Bio-inspired routing algorithm for MANETs based on fungi networks”. Ad Hoc Networks, 107, p.102248 ;2020. [11] M.A. Gawas, and S. Govekar, “State-of-art and open issues of cross-layer design and QOS routing in internet of vehicles”. Wireless Personal Communications, 116(3), pp.2261-2297;2021. [12] Norozpour, S. (2021). On Comparison of Different Image Segmentation Techniques. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(11), 4659-4663. [13] Norozpour, S. N., Momenzadeh, M., &Abolhasani, A. (2021). Proposing new system for handling business data systems with more functionality and usability. NexoRevistaCientífica, 34(02), 835-847. [14] Norozpour, S. (2021). On e-Learning Difficulties Worldwide Faced Throughout the COVID-19. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal\| NVEO, 9751-9760. [15] Norozpour, S. (2021). Simulation of the Relation Between the Number of COVID-19 Death Cases as a Result of the Number of Handwashing Facilities by Using Artificial Intelligence. Artificial Intelligence for COVID-19, 1-10. [16] C.V. Subbaiah, and G. Kannayaram, “Heuristic ant colony and reliable fuzzy QoS routing for mobile ad hoc network”. Journal of Ambient Intelligence and Humanized Computing, pp.1-12 ;2021. [17] K.R. Venugopal, T. Shiv Prakash, and M. Kumaraswamy, “QoS Routing Algorithms for Wireless Sensor Networks” (pp. 1-165). Springer; 2020) [18] M. Rathee, S. Kumar, A.H. Gandomi, K. Dilip, B. Balusamy, and R. Patan, “Ant colony optimization based quality of service aware energy balancing secure routing algorithm for wireless sensor networks”. IEEE Transactions on Engineering Management, 68(1), pp.170-182 ;2019. [19] U. Baroudi, M. Bin-Yahya, M. Alshammari, and U. Yaqoub, “Ticket-based QoS routing optimization using genetic algorithm for WSN applications in smart grid”. Journal of Ambient Intelligence and Humanized Computing, 10(4), pp.1325-1338 ;2019. [20] M.Darbandi; “Kalman Filtering for Estimation and Prediction Servers with Lower Traffic Loads for Transferring High-Level Processes in Cloud Computing”; Published by HCTL International Journal of Technology Innovations and Research, (ISSN: 2321-1814), Vol. 23, Issue 1, pp. 10-20, Feb. 2017, DOI: 10.5281/Zenodo.345288. [21] Ali, M.O.E., Aldegheishem, A., Lloret, J. and Alrajeh, N.: A QoS-Based routing algorithm over software defined networks. Journal of Network and Computer Applications, p.103215; 2021 [22] S. Haghgoo, M. Hajiali, A. Khabir, “Prediction and Estimation of Next Demands of Cloud Users based on their Comments in CRM and Previous usages”, International IEEE Conference on Communication, Computing & Internet of Things; Feb. 2018, Chennai. DOI: 10.1109/IC3IoT.2018.8668119. [23] N. Varyani, Z.L. Zhang, M. Rangachari, and D. Dai, LADEQ: “A fast Lagrangian relaxation based algorithm for destination-based QoS routing”. In 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (pp. 462-468). IEEE; 2019. [24] Rostamzadeh, F. Canavero, F. Kashefi and Mehdi Darbandi, “Automotive AM-Band Radiated Emission Mitigation Techniques, a Practical Approach”, International IEEE Symposium on Electromagnetic Compatibility; Aug. 2012, Pittsburgh, USA. DOI: 10.1109/ISEMC.2012.6351791. [25] M. Oche, A.B. Tambuwal, C. Chemebe, R.M. Noor, and S. Distefano, VANETs “QoS-based routing protocols based on multi-constrained ability to support ITS infotainment services”. Wireless Networks, 26(3), pp.1685-1715 ;2020. [26] Chen, R. Bai, J. Li, Y. Liu, N. Xue, and J. Ren, “A multiobjective single bus corridor scheduling using machine learning-based predictive models”. International Journal of Production Research, pp.1-16 ;2020. [27] G. Mirjalily, M. Asgarian, and Z.Q. Luo, Interference-“Aware NFV-enabled Multicast Service in Resource-Constrained Wireless Mesh Networks”. IEEE Transactions on Network and Service Management;2021. [28] Papanna, N., Reddy, A.R.M. and Seetha, M.: EELAM: Energy efficient lifetime aware multicast route selection for mobile ad hoc networks. Applied Computing and Informatics, 15(2), pp.120-128 ;2019 [29] Mehdi Darbandi, M. Abedi; “involving Kalman filter technique for increasing the reliability and efficiency of cloud computing”, International Conference on Scientific Computing 2012; Los Vegas, USA. [30] F. Kashefi “Perusal about influences of Cloud Computing on the processes of these days and presenting new ideas about its security”, International IEEE Conf. AICT., Dec. 2011, Baku, Azerbaijan. DOI: 10.1109/ICAICT.2011.6111007. [31] P. Shahbazi “New Novel idea for Cloud Computing: How can we use Kalman filter in security of Cloud Computing”, International IEEE Conf. AICT.; Oct. 2012, Georgia, Tbilisi. DOI: 10.1109/ICAICT.2012.6398466. [32] S.A. Alghamdi, “Cuckoo energy-efficient load-balancing on-demand multipath routing protocol”. Arabian Journal for Science and Engineering, pp.1-15 ;2021. [33] Y.H. Robinson, S. Balaji, and E.G. Julie, PSOBLAP: “particle swarm optimization-based bandwidth and link availability prediction algorithm for multipath routing in mobile ad hoc networks”. Wireless Personal Communications, 106(4), pp.2261-2289 ;2019. [34] Q. Zhang, L. Ding, and Z. Liao, “A novel genetic algorithm for stable multicast routing in mobile ad hoc networks”. China Communications, 16(8), pp.24-37 ;2019. [35] S.S. Benazer, M.S. Dawood, G. Suganya, and S.K. Ramanathan, “Performance analysis of modified on-demand multicast routing protocol for MANET using non forwarding nodes”. Materials Today: Proceedings, 45, pp.2603-2605 ;2021. [36] N.S. Farheen, and A. Jain, “Improved Routing in MANET with Optimized Multi path routing fine tuned with Hybrid modeling”. Journal of King Saud University-Computer and Information Sciences;2020. [37] D.M. Babu, and M Ussenaiah,. CS?MAODV: “Cuckoo search and M?tree?based multiconstraint optimal Multicast Ad hoc On?demand Distance Vector Routing Protocol for MANETs”. International Journal of Communication Systems, 33(16), p.e4411; 2020.
Abstract: In MANET, the multicast routing is considered as a non-deterministic polynomial (NP) complexity, it contains assorted objectives and restrictions. In the multicast issue of MANET, the Quality of services (QoS) based upon cost, delay, jitter, bandwidth, these are constantly deemed as multi-objective for intending multiple cast routing protocols. Conversely, mobile node has finite battery energy and the lifetime of network depending on its mobile node battery energy. If the mobile node has high battery energy consumption, it automatically reduces the network’s life time because of their route breaks. Alternatively, node’s battery energy has to be consumed to ensure higher level quality of services in multicast routing for transmitting the accurate data anywhere and anytime. Therefore, the network lifespan is deemed as multi-objective in the multicast routing (MR) problem. To overcome these issues, QoS and Network Lifetime Aware Reliable Multicast Routing Protocol are proposed by applying Chaos integrated Cuckoo Search Rider Optimization Algorithm for effectual data transmission in MANET (QOS-MRP-CSROA-MANET). The proposed method is the joint execution of both the Cuckoo Search Algorithm with chaos theory (Chaotic-CSA) and Rider Optimization (ROA) algorithm and hence it is called as Chaotic-CSA-ROA, which is utilized to solve the MR problem of MANET. Here, the MR problem of MANET have five objectives, viz cost, delay, jitter, bandwidth, network lifetime are optimized with the help of Chaotic-CSA-ROA. Then the proposed method is simulated in NS2 simulator for validating the performance of the proposed QOS-MRP-CSROA-MANET system. Here, evaluation metrics, via delay, delivery ratio, drop, Network lifespan, overhead and throughput are analyzed with node, rate and speed. The proposed QOS-MRP-CSROA-MANET provide higher throughput in node as 32.9496% and 65.5839%, higher throughput in rate as 16.6049% and 30.4654%, higher through put in speed as 10.1298% and 7.0825%, low packet drop in node as 63.7313% and 52.2255%, low packet drop in rate as 51.5528% and 25.6220%, low packet drop in speed as 18.0857% and 24.5953% compared with existing methods, like QOS aware of multicast routing protocol using particle swarm optimization algorithm in MANET (QOS-MRP-PSOA-MANET) and QOS aware of multicast routing protocol using genetic algorithm in MANET (QOS-MRP-GA-MANET) respectively.

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