Volume-8 , Issue-2 , Apr 2020, ISSN 2321-3256 (Electronic Version) Go Back

• Open Access   Article

  Reduce Impact Blackhole Attack in AODV Protocol

  Z. Sh Alnadhery, S. M. Baneamoon

  Research Paper | Journal (IJSRNSC)

  Vol.8 , Issue.2 , pp.1-7, Apr-2020

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  Abstract
  One of the most famous technologies in the networks is MANET technology, which is used in medical, military and other fields. MANET is a group of nodes that connect between them without the access point. AODV protocol is not provided with protection mechanisms because the primary purpose of this protocol is to quickly deliver packets to the destination. This is the main reason for its being attacked by malicious nodes. One of these attacks is a blackhole attack where the malicious node sends a fake message to the source that the shortest path to the destination passes through this malicious node. Then, the packets are dropped, which reduces the effectiveness and performance of AODV. In this paper, we proposed a SPAODV method to protect from blackhole attack and reduce its impact. In the SPAODV method, after the source node receives an RREP message, CHECKVERIFY will be sent to all proposed routes to confirm the route to the destination by the source node. The desired destination is the only node that can confirm the validity of the path by sending VERIFY to the source. Results of simulation by using NS2 showed that the SPAODV method better protection and better performance in AODV under blackhole attack networks compared to an EAODV method and a MAODV method.

  Key-Words / Index Term
  AODV, SPAODV, Blackhole attack, Routing Overhead, NS2.

  References
  [1] D. Gorine and R. Saleh, “Performance Analysis of Routing Protocols in MANET Under Malicious Attacks,” International Journal of Network Security & Its Applications (IJNSA), Vol.11, No.2, pp.1-12, 2019.
  [2] A. R. Mahlous, “The Effectiveness of Mobile Ad Hoc Routing Protocols Under a Gray Hole Attack,” International Journal on Information Technologies & Security, Vol.11, No.1, 2019.
  [3] R. Kumari, P. Nand, “Performance Analysis of Existing Routing Protocols,” Int. Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.47-50, 2017.
  [4] Ch. Panos, Ch. Ntantogian, S. Malliaros and Ch. Xenakis, “Analyzing, Quantifying, and Detecting the Blackhole Attack in Infrastructure-less Networks,” Computer Networks, Vol.113, pp.94-110, 2016.
  [5] A. ِA. Chavan, D. S. Kurule and P. U. Der, “Performance Analysis of AODV and DSDV Routing Protocol in MANET and Modifications in AODV against Blackhole Attack,” In the Procedia Computer Science, Vol.79, pp. 835-844, 2016.
  [6] R. Rana and R. Kumar, “Performance Analysis of AODV in Presence of Malicious Node,” Acta Electronica Malaysia (AEM), Zibeline International Publishing, Vol.3, No.1, pp.1-5, 2019.
  [7] M. Salehi, A. Boukerche and A. Darehshoorzadeh, “Modeling and Performance Evaluation of Security Attacks on Opportunistic Routing Protocols for Multihop Wireless Networks,” Ad Hoc Networks, Vol.50, pp.88-101, 2016.
  [8] S. Ali, “Enhanced Virtual Private Network Authenticated Ad Hoc on Demand Distance Vector Routing,” International Journal of Innovative Engineering and Management Research, Vol.7, Issue.12, pp.190-197, 2018.
  [9] M. Ebrahimi and S. Jamali, “Securing AODV Routing Protocol Against the Blackhole Attack Using Firefly Algorithm,” International Journal of Applied Operational Research, Vol.6, No.4, pp.53-64, 2016.
  [10] A. Sharma and P.K. Johari, “Eliminating Collaborative Blackhole Attack by Using Fuzzy Logic in Mobile Ad-hoc Network,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.34-41, 2017.
  [11] T. Delkesh and M. A. Jamali, “EAODV: Detection and Removal of Multiple Blackhole Attacks Through Sending Forged Packets in MANETs,” Journal of Ambient Intelligence and Humanized Computing, Vol.10, No.5, pp.1897-1914, 2018.
  

  Citation
  Z. Sh Alnadhery, S. M. Baneamoon, "Reduce Impact Blackhole Attack in AODV Protocol," International Journal of Scientific Research in Network Security and Communication, Vol.8, Issue.2, pp.1-7, 2020

• Open Access   Article

  Queuing System on Service Interruption and Standby Server

  S. Maragathasundari, M. Manimala and Abhishikth Lingisetty

  Research Paper | Journal (IJSRNSC)

  Vol.8 , Issue.2 , pp.8-12, Apr-2020

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  Abstract
  This article deals with a Queuing model in which the presence of customers follows a Poisson distribution. Customers show up exclusively. Service rendered in a single stage follows a general scattering. Interruption happens capriciously in the server. At the point when the server gets barged in on, it gets into a Repair methodology where it is carried out in two stages vital and expanded fix arrange. To keep up a key good ways from the long queue, during the hour of extended fix process a standby server is given. This Queuing issue is enlightened by valuable variable system and the line execution measures are deduced. The model is especially pushed by numerical examination and graphical delineation.

  Key-Words / Index Term
  Compulsory repair, Extended optional repair, Service interruption, Standby server.

  References
  [1] Atencia,I. “A discrete time queuing system with server breakdowns and changes in the repair times”,Ann.oper.Res. 2015,235,37-49.
  [2] Khalaf.R.F, Madan.K.C and Lucas.C.A, “An M[x]/G/1 Queue with Bernoulli schedule, General vacation times, random breakdowns, general and general repair times”, Applied mathematical sciences, Vol.5, No.1, pp.35-51,2011.
  [3] Karthikeyan.K and Maragathasundari.S, “Batch arrival of two stages with standby server during general vacation time & general repair time”, International Journal of Mathematical archive, Vol.6, No.4, pp.43-48, 2015.
  [4] Kavitha.K and Maragathasundari.S, “Analysis of Non Markovian queue with restricted admissibility and optional types of repair”, International journal of scientific research and management studies, Vol.2, No.5, pp.244-252, 2012
  [5] Madan.K.C, “An M/G/1 queue with second optional service”, Queuing system, 34(2000),37-46.
  [6] Maragathasundari. S (2019), Queuing system modeling for supermarkets, International Journal ofInnovative Technology and Exploring Engineering ,ISSN: 2278-3075, Volume-9, Issue-2S2,pp.591-597, December 2019.
  [7] Maragathasundari.S and Karthikeyen.K, “A study on M/G/1 Queuing system with extended vacation, random break downs and general repair”, International journal of Mathematics and its Applications,vol.3,pp.43-49,2015.
  [8] Rajadurai.P and Chandrasekaran,V.M, “A study on M/G/1 feedback retrial queue with subject to server breakdown and repair under multiple working vacation policy”, AlexandrianEngineering Journal, vol.57,pp.947-962,2018.
  [9] Thiruvengadam.K, Queuing with breakdown, Operations Research, vol.11,pp.62-71,1963.
  [10] Zhang.M and Liu.Q, “An M/G/1 queue with server breakdown, working vacations and vacation interruption”, Operations Research, vol.52,pp.256-276,2015.
  

  Citation
  S. Maragathasundari, M. Manimala and Abhishikth Lingisetty, "Queuing System on Service Interruption and Standby Server," International Journal of Scientific Research in Network Security and Communication, Vol.8, Issue.2, pp.8-12, 2020

• Open Access   Article

  Network Traffic Control Using AI

  N. SelvaKumar, M. Rohini, C. Narmada, M. Yogeshprabhu

  Research Paper | Journal (IJSRNSC)

  Vol.8 , Issue.2 , pp.13-21, Apr-2020

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  The core of next generation 5G wireless network is heterogeneous network. The existing traditional 4G technology approaches are centrally managed and reactive conception-based network which needs additional hardware for every update and when there is a demand for the resources in the network. To reduce traffic in 4G network techniques like Port based, payload and feature based approach are used. They do not provide efficient resource management and it does not classify all type of network traffic effectively. 5G helps in giving solution to the problem of 4G network using prediction and traffic learning to increase performance and bandwidth. Heterogeneous network provides more desirable Quality of Service (QOS) and explores the resources of the network explicitly. The assortment of heterogeneous network brings difficulty in traffic control of the network. The problem in heterogeneous network is network traffic which cannot be controlled and managed due to different protocols and data transfer rate. The key problem of heterogeneous network is to achieve intelligent and efficient internet traffic control. The upcoming 5G heterogeneous network cannot be fulfilled until Artificial Intelligence is deployed in the network. This work aims to explore artificial intelligence inspired network traffic scheme for reducing traffic in a heterogeneous network. In this work the proposed system consists of clustering of traffic data set, statistical feature extraction of the network data, prediction of network traffic and classification of network traffic. The clustering of network data is implemented using Enhanced Density-based spatial clustering of applications with noise (DBSCAN) algorithm. The statistical feature of the network data is extracted and they are given as input into the Modified Backpropagation model for prediction and regression tree method is used for classification of network traffic. Finally caching and pushing of network is included to make use of the network resource effectively and also to provide finer Quality of Service (QOS) in a network.

  Key-Words / Index Term
  5GNetwork; Artificial Intelligence; Machine Learning; Data Clustering; Statistical Feature; Traffic Prediction; Traffic classification.

  References
  [1] L. C. I, S. Han, Z. Xu, et al, New paradigm of 5G wireless internet [J], IEEE Journal on Selected Areas in Communications,Vol.34, No.3,474-482,2016.
  [2] R. Li, Z. Zhao, X. Zhou, et al, Intelligent 5G: when cellular networks meet artificial intelligence[J], IEEE Wireless Communications, PP(99) ,2-10,2017
  [3] S. Dzulkifly, L. Giupponi, F. Sai, et al, Decentralized Q learning for uplink power control[C], IEEE, International Workshop on Computer Aided Modelling and Design of Communication Links and Networks, IEEE, 54-58, 2015.
  [4] M. Shafi et al.,“5G: A Tutorial Overview of Standards, Trials,Challenges, Deployment, and Practice,” IEEE JSAC, vol. 35,no. 6, , pp. 1201–21. June 2017
  [5] D. Soldani, A. Manzalini, Horizon 2020 and beyond: on the5G operating system for a true digital society [J], IEEE Vehicular Technology Magazine, Vol.10, No.1, 32-42,2015.
  [6] Y. Zhang et al., “Home M2M Networks: Architectures, Standards, and QoS Improvement,” IEEE Commun. Mag.,vol. 49, no. 4, Apr. 2011, pp. 44–52.
  [7] Chen Z, Wen J, Geng Y. Predicting future traffic using hidden markov models. In: Proceddings of 24th IEEE International Conference on
  Network Protocols (ICNP). IEEE; 2016. p. 1–6.
  [8] I. Portugal, P. Alencar, and D. Cowan, “The Use of MachineLearning Algorithms in Recommender Systems: A Systematic Review,” Expert Systems with Applications, vol. 97, May 2018, pp. 205–27.
  [9] S. Maharjan et al., “Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach,” IEEE Trans. Smart Grid, vol. 4, no. 1, 2013, pp. 120–32.
  [10] G. Alnwaimi, S. Vahid, and K. Moessner, “Dynamic heterogeneous learning games for opportunistic access in LTE-based macro/femtocell deployments,” IEEE Transactions on Wireless Communications, vol. 14, no. 4, pp. 2294–2308, 2015.
  [11] D. D. Nguyen, H. X. Nguyen, and L. B. White, “Reinforcement Learning with Network-Assisted Feedback for Heterogeneous RAT Selection,” IEEE Transactions on Wireless Communications, vol. 16, no. 9, pp. 6062–6076, 2017.
  [12] Rathidevi M and Dr. S.V . Sudha, “ Implementing Artificial Neural Network in Enhanced Micro Blog for Social Network, E Commerce and News Channels,” on International Research Journal in Advanced Engineering and Technology, Vol 3 Issue 3, ISSN: 2454-4744, E- ISSN: 2454-4752, pp. 2527-2533, 2017.
  [13] U. Challita, L. Dong, and W. Saad, “Deep learning for proactive resource allocation in LTE-U networks,” in European Wireless 2017- 23rd European Wireless Conference, 2017.
  [14] R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” Tech. Rep., 2013.
  [15] C. Jiang, H. Zhang, Y. Ren, Z. Han, K. C. Chen, and L. Hanzo, “Machine Learning Paradigms for Next-Generation Wireless Networks,” IEEE Wireless Communications, 2017.
  [16] T. E. Bogale, X. Wang, and L. B. Le, “Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks,” ITU Special Issue: The impact of Artificial Intelligence (AI) on communication networks and services., vol. 1, 2018
  [17] G. Villarrubia, J. F. De Paz, P. Chamoso, and F. D. la Prieta, “Artificial neural networks used in optimization problems,” Neurocomputing, vol. 272, pp. 10–16, 2018.
  [18] B. Bojovic´, E. Meshkova, N. Baldo, J. Riihijärvi, and M. Petrova, “Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments,” Eurasip Journal on Wireless Communications and Networking, vol. 2016, no. 1, 2016.
  [19] E. Balevi and R. D. Gitlin, “Unsupervised machine learning in 5G networks for low latency communications,” 2017 IEEE 36th International Performance Computing and Communications Conference, IPCCC 2017, vol. 2018-Janua, pp. 1–2, 2018.
  [20] Sadasivam Vijayakumar Sudha and Keppanagowder Thanushkodi, “ A Genetic Based Neuro Fuzzy Technique for Process Grain Sized Scheduling of Parallel Jobs,” Journal of Computer Science, pp. 48-54, ISSN 1549-3636, Science Publication 2012.
  [21] U. Challita, L. Dong, and W. Saad, “Deep learning for proactive resource allocation in LTE-U networks,” in European Wireless 2017- 23rd European Wireless Conference, 2017
  
  [22] R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” Tech. Rep., 2013
  
  [23] Q. V. Le, N. Jaitly, and G. E. Hinton Google, “A Simple Way to Initialize Recurrent Networks of Rectified Linear Units,” Tech. Rep., 2015.
  [24] G. Alnwaimi, S. Vahid, and K. Moessner, “Dynamic heterogeneous learning games for opportunistic access in LTE-based macro/femtocell deployments,” IEEE Transactions on Wireless Communications, vol. 14, no. 4, pp. 2294–2308, 2015.
  [25] D. D. Nguyen, H. X. Nguyen, and L. B. White, “Reinforcement Learning with Network-Assisted Feedback for Heterogeneous RAT Selection,” IEEE Transactions on Wireless Communications, vol. 16, no. 9, pp. 6062–6076, 2017.
  [26] M. S. Parwez, D. B. Rawat, and M. Garuba, “Big data analytics for user-activity analysis and user-anomaly detection in mobile wireless network,” IEEE Transactions on Industrial Informatics, vol. 13, no. 4, pp. 2058–2065, 2017.
  [27] S.V. Sudha, K. Thanushkodi, “ Use of Neural Network with Hybrid Intelligent Optimization Techniques for Weight Determination in Parallel Job Scheduling,” Scientific Research and Essays, Vol.7(26), pp.2310-2333, 12 July 2012.
  [28] U. Challita, L. Dong, and W. Saad, “Deep learning for proactive resource allocation in LTE-U networks,” in European Wireless 2017- 23rd European Wireless Conference, 2017.
  [29] L.-C. Wang and S. H. Cheng, “Data-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approach,” IEEE Transactions on Network Science and Engineering, vol. 4697, no. c, pp. 1–1, 2018.
  [30] S. P. Sotiroudis, K. Siakavara, and J. N. Sahalos, “A Neural Network Approach to the Prediction of the Propagation Path-loss for Mobile Communications Systems in Urban Environments,” PIERS Online, vol. 3, no. 8, pp. 1175–1179, 2007.
  [31] T. M. Mitchell, Machine Learning, 1st ed. McGraw-Hill Science/Engineering/Math, 1997.
  [32] T. Zhou, L. Chen, and J. Shen, “Movie Recommendation System Employing the User-Based CF in Cloud Computing,” Proc. 2017 IEEE Int’l. Conf. Computational Science and Engineeringand Embedded and Ubiquitous Computing, vol. 2. 2017, pp. 46–50.
  [33] P. U. Stanford Vision Lab, Stanford University, “ImageNet.” [Online]. Available: http://image-net.org/about-overview
  [34] J. Erman, A. Mahanti, M. Arlitt, I. Cohen, and C. Williamson, “Semisupervised network traffic classification,” ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) Performance Evaluation Review, vol. 35, no. 1, pp. 369–370, 2007
  [35] A. Moore and K. Papagiannaki, “Toward the accurate identification of network applications,” in Proc. Passive and Active Measurement Workshop (PAM2005), Boston, MA, USA, March/April 2005.
  [36] A. Madhukar and C. Williamson, “A longitudinal study of P2P traffic classification,” in 14th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, September 2006.
  [37] S. Sen, O. Spatscheck, and D. Wang, “Accurate, scalable in network identification of P2P traffic using application signatures,” in WWW2004, New York, NY, USA, May 2004.
  [38] J. Erman, A. Mahanti, and M. Arlitt. Byte Me: A Case for Byte Accuracy in Traffic Classification. In ACM SIGMETRICS MineNet Workshop, June 2007
  [39] V. Paxson, “Empirically derived analytic models of wide-area TCP connections,” IEEE/ACM Transactions on Networking, vol. 2, no. 4, pp. 316–336, 1994.
  [40] C. Dewes, A. Wichmann, and A. Feldmann, “An analysis of Internet chat systems,” in ACM/SIGCOMM Internet Measurement Conference 2003, Miami, Florida, USA, October 2003.
  [41] K. Claffy, “Internet traffic characterisation,” PhD Thesis, University of California, San Diego, 1994.
  [42] T. Lang, G. Armitage, P. Branch, and H.-Y. Choo, “A synthetic traffic model for Half-life,” in Proceedings of Australian
  

  Citation
  N. SelvaKumar, M. Rohini, C. Narmada, M. Yogeshprabhu, "Network Traffic Control Using AI," International Journal of Scientific Research in Network Security and Communication, Vol.8, Issue.2, pp.13-21, 2020

• Open Access   Article

  A Voice Signal Interpreter using Machine Learning Techniques

  Rosy Mishra, Y. Sowjanya, Sushanta Meher, Mousumi Meher

  Research Paper | Journal (IJSRNSC)

  Vol.8 , Issue.2 , pp.22-27, Apr-2020

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  Abstract
  As we all know computer generated speech in immense now a days. In the current era of IT usage of computer generated speech is in great demand where as these speeches are still not human, and it`s not easy on the ears. The production quality system like Google Assistant, Apple Siri, Amazon Alexa, or Bixby as different from what a human generated speech would sound like. Many advances have been made in speech synthesis using neural network for easy accessing. Prime goal of this paper is to create an interpreter (Machine translator), to translate text to speech using a neural network. In this paper the main objective is to combine two techniques to produce a new product i.e. a new version of the translator. An artificial communicator is being developed to communicate with the universal language that is English for communication in between System & humans. These two technologies are used to link with each other & competence thoroughly. The purpose of this Application software is to permit different picture to convert its text in a language which is displayed in vocalizations. And it also permits for weakening vision in worst case seen in fields in picture format to understand language. A skill is being generated in machine through software for the Suitableness of humans for easy accessing.

  Key-Words / Index Term
  Neural network, machine translator, Text to speech Translator, perceptron model

  References
  [1] Wouter Gevaert, Georgi Tsenov, Valeri Mladenov, Senior Member, IEEE, “Neural Networks used for Speech Recognition” in the Journal of Automatic Control, University of Belgrade, vol. 20:1-7, 2010©.
  [2] Malti Bansal, Shivam Sonkar “Text Image to Speech Conversion using Matlab and Microsoft SAPI” International Journal of Electronics, Electrical and Computational System IJEECS ISSN 2348-117X Volume 6, Issue 11 November 2017.
  [3] Mohd Bilal Ganai1, Er Jyoti Arora2, “Implementation of Text to Speech Conversion Technique” in International Journal of Innovative Research in Computer and Communication Engineering Vol. 3, Issue 9, September 2015.
  [4] Chaw Su Thu Thu1, Theingi Zin 2, “Implementation of Text to Speech Conversion” in International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181Vol. 3 Issue 3, March – 2014.
  [5]1N.Swetha,2K.Anuradha“Text-to-Speech Conversion” in International Journal of Advanced Trends in Computer Science and Engineering, Vol.2 , No.6, Pages : 269-278 (2013)Special Issue ( ICETEM) 2013 ISSN 2278-3091.
  [6] Jisha Gopinath1, Aravind S2, Pooja Chandran3, “Text to Speech Conversion System using OCR” in International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 1, January 2015.
  [7] Rubi Debnath, Vivek Hanumante, Disha Bhattacharjee, Deepti Tripathi, Sahadev Roy, “Multilingual Speech Translator using MATLAB” in International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO) – 2015.
  [8] Barik R.C., Mishra R. (2016) “Comparative Analogy on Classification and Clustering of Genomic Signal by a Novel Factor Analysis and F-Score Method”. In: Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, Vol 394. Springer, New Delhi.
  [9] R. C. Barik, R. Pati and H. S. Behera,“ Robust signal processing compression for clustering of speech waveform and image spectrum”, IEEE International Conference on Communication and Signal Processing, April 2-4, 2015, India.
  [10] Nisha Agrawal , Sanjukta Urma , Sonam Padhan , Ram Ch. Barik, “Indian Agro Based Pest Region Detection by clustering and Pseudo- Color Image Processing” in International Journal of Engineering Research & Technology (IJET) ISSN:2278-0181.
  

  Citation
  Rosy Mishra, Y. Sowjanya, Sushanta Meher, Mousumi Meher, "A Voice Signal Interpreter using Machine Learning Techniques," International Journal of Scientific Research in Network Security and Communication, Vol.8, Issue.2, pp.22-27, 2020

• Open Access   Article

  Birds’ Eye View on Mobile Number Portability

  M. Satya Prasad

  Review Paper | Journal (IJSRNSC)

  Vol.8 , Issue.2 , pp.28-35, Apr-2020

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  Abstract
  This paper presents about Mobile Number Portability, its Challenges, elements, types and terminology. It also explains in detail various technical steps involved between the Mobile service providers during the process of MNP. It also illustrates the advantages, disadvantages and problems related to MNP for the customer, Telecom Service Provider and the GOI in the process of implementation.

  Key-Words / Index Term
  Telecom Regulatory Authority (TRAI), Mobile number portability (MNP), Mobile Number Portability Service Provider (MNPSP), Licensed Service Area (LAS), Closed User Group (CUG), Mobile identity numbers (MINs), Home Location Registers (HLR), Visiting Location Registers (VLR), Donor Operator (DO), Licensed Service Area (LAS), .. more

  References
  [1]. Buehler, S., Dewenter, R., & Haucap, J. (2006). “Mobile Number Portability in Europe.” SSRN Electronic Journal 30(7):385-399 • July 2005
  [2]. Dick, A.S. & Basu, Kl. (1994). Customer Loyalty: “Toward Integrated Conceptual” Journal of the academy of marketing 1994
  [3]. Gans, J.S., King, S.P. & Woodbridge, G. (2001). Numbers to the People: Regulation Handbook
  [4]. Ownership and Local Number Portability. Information Economics and Policy. Telecommunications Policy. Horrocks, J. (2007c).
  [5]. Lessons from other countries. Workshop on Implementing Mobile Number Portability, Islamabad, Pakistan PTA August 22-24, 2007
  [6]. Keynote Capitals Research. (2009). Mobile number portability in India. Stock Markets Review- A Case Study
  [7]. Katka, K. (2004, November Mobile number portability - Time for more port authority.
  [8]. Melody, W.H. (1999). Telecom Reform: Progress and Prospects. Telecommunications Policy, 23(1)
  [9]. Lago, (2007). Number Portability: Worldwide Implementation Experience. Paper
  [10]. Smura, T. (2004). Mobile Number Portability-Case Finland. Mimeo, Networking Paper in Global Journal of Business Research 8(4):99 - 110 • January 2014
  [11]. Laboratory, Helsinki University of Technology, 2004. Taaffe, J. (2004). Leader: A tale of two services. Total Telecom. ISSN Volume 10, Issue 1, 2015
  [12]. Wieland, K. (2007). NTT DoCoMo feels margin squeeze. Global News Analysis, Telecommunications International, June. Paper in SSRN -2010
  [13]. BSNL Technical Literature on MNP for Figures
  

  Citation
  M. Satya Prasad, "Birds’ Eye View on Mobile Number Portability," International Journal of Scientific Research in Network Security and Communication, Vol.8, Issue.2, pp.28-35, 2020