Review on Software Analysis & Design Tools
Review Paper | Journal Paper
Vol.8 , Issue.1 , pp.115-119, Jan-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i1.115119
Abstract
Software analysis and design includes all activities, which help the transformation of requirement specification into implementation. Requirement specifications specify all functional and non-functional expectations from the software. These requirement specifications come in the shape of human readable and understandable documents, to which a computer has nothing to do. Software analysis and design is the intermediate stage, which helps human-readable requirements to be transformed into actual code.
Key-Words / Index Term
Software Analysis, DFD, HIPO
References
[1] Cameron, J.R., An overview of JSD, IEEE Transactions on Software Engineering, Vol. SE-12, No.2, February, pp 222–240. 1986.
[2] Richard Fairley ,Software Engineeering Concepts ,Tata Mcgraw Hill.
[3] Pankaj Jalote , An Integrated Approach to Software engineering, Narosa Publication.
[4] Diethelm, I., L. Geiger, and A. Zundorf “Teaching Modeling with Objects First,” WCCE 2005, 8th World Conference on Computers in Education, Cape Town, South Africa, 2005.
[5] Whitgift, David, “Methods and Tools for Software Configuration Management”, J. Wiley, 1991.
[6] Coad Peter and Edward Yourdan, Object-Oriented Design, 1991.
[7] Software Management Guide, Vol. I, Software Technology Support Center, p. 23, October 1993.
[8] Dyer, Mike, “he Cleanroom Approach to Quality Software Development”, 1993.
[9] Blum Bruce I., “Software Engineering: A holistic View”, 1992.
[10] Booch, Grady, Software Engineering with Ada, p. 25, 1994.
Citation
Bindia Tarika, "Review on Software Analysis & Design Tools," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.115-119, 2020.
Ensuring Secured Multicast Group Communications for Wireless Sensor Networks using Efficient Key Distribution Schemes
Research Paper | Journal Paper
Vol.8 , Issue.1 , pp.120-126, Jan-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i1.120126
Abstract
Multicast is the only prominent method for transmitting data from a single source to several known destinations. More than ever, in wireless sensor networks, with the help of unguided medium, a single transmission able to be received by all nodes within a transmission range. A wireless sensor network (WSN) are spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. For that reason, the multicast in wireless networks is anticipated to lay concrete on the way for efficient group communications, by which many group-based applications, such as charged video on demand or video conferencing, can be commercialized. In WSNs security, the key management problem is one of the most important and the most fundamental aspects. To attain security in wireless sensor networks, it is significant to be able to encrypt and authentication messages among sensor nodes. Before doing so, keys for performing encryption and authentication must be agreed upon by the communication nodes among the WSN. Nevertheless, due to the resource constrains on the sensor nodes, many key agreement mechanisms used in general networks, such as Diffie-Hellman and other public-key based schemes , are not feasible in sensor networks.
Key-Words / Index Term
WSN, Multicast Rekeying, key tree, Accuracy, Computation and Communication time
References
[1] Canetti, Garay, Itkis, Micciancio, Naor, Pinkas, “Multicast Security: A Taxonomy and Some Efficient Constructions,” Proc. IEEE INFOCOM, pp. 708-716, 1999.
[2] Caronni, Waldvogel, Sun, Plattner, “Efficient Security for Large and Dynamic Multicast Groups,” Proc. IEEE Seventh Int’l Workshops Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 376-383, 1998.
[3] Chang, Engel, Kandlur, Pendarakis, Saha, “Key Management for Secure Lnternet Multicast Using Boolean Function Minimization Techniques,” Proc. IEEE INFOCOM, pp. 689-698, 1999.
[4] Fan, Judge, Ammar, “HySOR: Group Key Management with Collusion-Scalability Tradeoffs Using a Hybrid Structuring of Receivers,” Proc. 11th Int’l Conf. Computer Comm. and Networks, pp. 196-201, 2002.
[5] Liu and Yang, “Collusion-Resistant Multicast Key Distribution Based on Homomorphic One-Way Function Trees,” IEEE Trans. Information Forensics and Security, vol. 6, no. 3, pp. 980-991, Sept. 2011.
[6] Mittra, “Iolus: A Framework for Scalable Secure Multicasting", Proc. of ACM SIGCOMMi`97, 277-288, Sep. 1997.
[7] Moyer, Rao and Rohatgi, "A Survey of Security Issues in Multicast Communications", IEEE Network Magazine, Vol. 13, No.6, March 1999, pp. 12-23.
[8] Paul Judge and Mostafa Ammar, "Security Issues and Solutions in Multicast Content Distribution: A Survey", IEEE Network, February 2003, pp 30-36.
[9] Perrig, Song, Tygar, “ELK, a New Protocol for Efficient Large-Group Key Distribution,” Proc. IEEE Symp. Security and Privacy, pp. 247-262, 2001.
[10] Peter Kruus and Joseph Macker, “Techniques and issues in multicast security," MILCOM98,1998.
[11] Sherman and McGrew, “Key Establishment in Large Dynamic Groups Using One-Way Function Trees,” IEEE Trans. Software Eng., vol. 29, no. 5, pp. 444-458, May 2003.
[12] Waldvogel, Caronni, Dan, Weiler, Plattner,“The VersaKey Framework: Versatile Group Key Management,” IEEE J. Selected Areas in Comm., vol. 17, no. 9, pp. 1614-1631, Sept.1999.
[13] Wallner, Harder and Agee, "Key Management for Multicast: Issues and Architectures", Internet Draft (work in progress), draft-wallner-key-arch-01.txt, Sep. 15, 1998.
[14] Wallner, Harder, Agee, “Key Management for Multicast: Issues and Architectures,” Internet Draft, Internet Eng. Task Force, 1998.
[15] Wong, Gouda and Lam, "Secure Group Communications Using Key Graphs", Proc.ACM SIGCOMM`98, Sep. 1998.
[16] Wong, Gouda, Lam, “Secure Group Communications Using Key Graphs,” IEEE-ACM Trans. Networking, vol. 8, no. 1, pp. 16-30, Feb. 2000.
[17] Zhou and Huang, “An Optimal Key Distribution Scheme for Secure Multicast Group Communication,” Proc. IEEE INFOCOM, pp. 1-5, 2010.
[18] MacWilliams , . Sloane, The Theory of Error Correcting Codes. North-Holland Math. Library, 1977.
[19] Bloemer, Kalfane, Karpinski, Karp, Luby, Zuckerman, “An XOR-Based Erasure-Resilient Coding Scheme,” Technical Report TR-95-048, Int’l Computer Science Inst., Aug. 1995.
[20] Yu, Sun, Liu, “Optimizing the Rekeying Cost for Contributory Group Key Agreement Schemes”, IEEE Trans. on Dependable and Secure Computing, vol. 4, no. 3, pp. 228 – 242, 2007.
Citation
S. Sasikala Devi, "Ensuring Secured Multicast Group Communications for Wireless Sensor Networks using Efficient Key Distribution Schemes," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.120-126, 2020.
A Technique To Improve MAC In WSN With Clock Synchronization
Research Paper | Journal Paper
Vol.8 , Issue.1 , pp.127-131, Jan-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i1.127131
Abstract
The wireless sensor network is the type of network, which is used to sense the environmental conditions like temperature, pressure etc. This type of network is generally deployed on the far places like oceans, forests and deserts, in such places, it is very difficult to recharge or replace battery of these sensor nodes. To reduce energy consumption of sensor nodes various techniques has been applied so far. Among these, one is by using LEACH protocol used for clustering in which cluster heads are selected on the basis of distance and energy. Then various modes are applied on this protocol such as active, sleep and ready mode. Nodes will go into the sleep, active and ready based on the priority of request for the channel access. The main problem exists in this is of clock synchronization due to which packet loss happened in the network which reduce network performance. In this paper, further enhancement will be proposed in the existing protocol by synchronizing the clocks of sensor nodes based on time lay technique. Then the proposed technique will be implemented in simulated in NS2. The graphical result will show that proposed technique performs better than existing protocol in terms of throughput, packet loss, energy, overhead, delay.
Key-Words / Index Term
Wireless Sensor Network (WSN), Clock synchronization , energy consumption
References
[1] Bharathidasan, A., & Ponduru, V. A. S. “ Sensor networks: An overview.”, Department of Computer Science, University of California, Davis, CA, 95616, 2002
[2] Somani, A. K., Kher, S., Speck, P., & Chen, J. “Distributed dynamic clustering algorithm in uneven distributed wireless sensor network.”, Technical Reports [DCNL-ON-2006-005], Iowa State University, 2006
[3] Mamalis, B., Gavalas, D., Konstantopoulos, C., & Pantziou, G, “ Clustering in wireless sensor networks. RFID and Sensor Networks: Architectures, Protocols, Security and Integrations”, Y. Zhang, LT Yang, J. Chen, eds, 324-353, 2009
[4] Dahnil, D. P., Singh, Y. P., & Ho, C. K., “ Energy-efficient cluster formation in heterogeneous Wireless Sensor Networks: A comparative study.”, In Advanced Communication Technology (ICACT), 2011 13th International Conference on (pp. 746-751). IEEE., Feb (2011)
[5] Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. ,” Wireless sensor networks: a survey”, Computer networks, 38(4), 393-422, 2002
[6] Bin-fang Cao, Jian-qi Li, Li Wang and Wen-Hu Wang”Energy optimized approach based on clustering routing protocols for wireless sensor networks”,IEEE,pp. 3710-3715,2013
[7] Wang, Y., & Guo, S. “Optimized energy-latency cooperative transmission in duty-cycled wireless sensor networks”, In Mechatronics and Automation (ICMA), 2013 IEEE International Conference on (pp. 185-190). IEEE., 2013
[8] Neamatollahi, P., Taheri, H., Naghibzadeh, M., & Yaghmaee, M. ,”A hybrid clustering approach for prolonging lifetime in wireless sensor networks”, In Computer Networks and Distributed Systems (CNDS), 2011 International Symposium on (pp. 170-174). IEEE, Feb 2011
[9] Nikodem, M., & Wojciechowski, B.,”Upper Bounds on Network Lifetime for Clustered Wireless Sensor Networks” In New Technologies, Mobility and Security (NTMS), 2011 4th IFIP International Conference on (pp. 1-6). IEEE, Feb 2011.
[10] Sultan, M., and Hwang, I., “Modulation-Aware Energy Balancing in Hierarchical Wireless Sensor Networks”, EURASIP Journal on Wireless Communications and Networking, pp. 355-359, 2007
[11] Anand, D., G., Chandrakanth, H., G., and Giriprasad, M., N., D., “An Energy Efficient Distributed Protocol For Ensuring Coverage And Connectivity (E3c2) Of Wireless Sensor Networks”, International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol. 3, No.1., 2012
[12] Sheikh Tahir Bakhsh, Rayed AlGhamdi, Abdulrahman H. Altalhi, Sabeen Tahir and Muhammad Aman Sheikh, “Adaptive Sleep Efficient Hybrid Medium Access Control algorithm for next generation wireless sensor networks” , EURASIP Journal on Wireless Communications and Networking, vol.84, pp.1-15, 2017.
Citation
Neetu, Bharti Duhan, "A Technique To Improve MAC In WSN With Clock Synchronization," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.127-131, 2020.
Approaches to Block Rumors in Social Networks: A Review
Review Paper | Journal Paper
Vol.8 , Issue.1 , pp.132-136, Jan-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i1.132136
Abstract
As Online Social Networks have become the integral part of our lives, the pros and cons of using them have been reflected in the society. On one hand, these online networks are the easiest ways to connect with your peers, communities and good for social and professional collaborations; on the other hand, they are the most vulnerable means of spreading rumors, threats and gossips within no time. There are several information diffusion algorithms using which the rumors can be shared on these mediums. The ways companies target and increase their customer base and sales using these diffusion algorithms, similarly rumors and gossips among the communities can also be shared. Some algorithms are deterministic and some are stochastic in nature. In this paper, we have reviewed the methods for spreading and blocking the rumors and compared them in the context of dynamic social networks. We have categorized the approaches on the basis of various measures and analysed their behavioural differences. The impact of several social parameters have also been studied to find the factors which are preferable to block the rumors.
Key-Words / Index Term
Social Networks, Information Diffusion, Rumor Blocking, Dynamic Graphs, Anti-rumors
References
[1] Harris, Lisa, and Alan Rae. "Social networks: the future of marketing for small business." Journal of business strategy 2009.
[2] Watts, Duncan J. "The “new” science of networks." Annu. Rev. Sociol. 30 (2004): 243-270.
[3] Verbeke, Wouter, David Martens, and Bart Baesens. "Social network analysis for customer churn prediction." Applied Soft Computing 14 (2014): 431-446.
[4] Centola, Damon. "The spread of behavior in an online social network experiment." science 329, no. 5996 (2010): 1194-1197.
[5] Ghosh, Rumi, and Kristina Lerman. "Predicting influential users in online social networks." arXiv preprint arXiv:1005.4882 (2010).
[6] Bargar, Alicia, Stephanie Pitts, Janis Butkevics, and Ian McCulloh. "Challenges and Opportunities to Counter Information Operations Through Social Network Analysis and Theory." In 2019 11th International Conference on Cyber Conflict (CyCon), vol. 900, pp. 1-18. IEEE, 2019.
[7] Ohara, Kouzou, Kazumi Saito, Masahiro Kimura, and Hiroshi Motoda. "Critical Node Identification based on Articulation Point Detection for Uncertain Network." International Journal of Networking and Computing 9, no. 2 (2019): 201-216.
[8] Yan, Ruidong, Yi Li, Weili Wu, Deying Li, and Yongcai Wang. "Rumor blocking through online link deletion on social networks." ACM Transactions on Knowledge Discovery from Data (TKDD) 13, no. 2 (2019): 1-26.
[9] Zhao, Yuxin, Shenghong Li, and Feng Jin. "Identification of influential nodes in social networks with community structure based on label propagation." Neurocomputing 210 (2016): 34-44.
[10] Ma, Ling-ling, Chuang Ma, Hai-Feng Zhang, and Bing-Hong Wang. "Identifying influential spreaders in complex networks based on gravity formula." Physica A: Statistical Mechanics and its Applications 451 (2016): 205-212.
[11] Zubiaga, Arkaitz, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter. "Detection and resolution of rumours in social media: A survey." ACM Computing Surveys (CSUR) 51, no. 2 (2018): 1-36.
[12] Robert H. Knapp. "A psychology of rumor. " Public Opin. Q. 8, 1 (1944), 22–37.
[13] Zubiaga, Arkaitz, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Peter Tolmie. "Analysing how people orient to and spread rumours in social media by looking at conversational threads." PloS one 11, no. 3 (2016).
[14] Domingos, Pedro, and Matt Richardson. "Mining the network value of customers." In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 57-66. 2001.
[15] Kempe, David, Jon Kleinberg, and Éva Tardos. "Maximizing the spread of influence through a social network." In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 137-146. 2003.
[16] Dekker, A.H., 2008. "Centrality in social networks: Theoretical and simulation approaches." Proceedings of SimTecT (2008), pp.12-15.
[17] Bonacich, Phillip. "Power and centrality: A family of measures." American journal of sociology 92, no. 5 (1987): 1170-1182.
[18] Kaur, Harneet, and Jing He. "Blocking negative influential node set in social networks: from host perspective." Transactions on Emerging Telecommunications Technologies 28, no. 4 (2017): e3007.
[19] Arazkhani, Niloofar, Mohammad Reza Meybodi, and Alireza Rezvanian. "Influence Blocking Maximization in Social Network Using Centrality Measures." In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), pp. 492-497. IEEE, 2019.
[20] Budak, Ceren, Divyakant Agrawal, and Amr El Abbadi. "Limiting the spread of misinformation in social networks." In Proceedings of the 20th international conference on World wide web, pp. 665-674. ACM, 2011.
[21] Tong, Guangmo, Weili Wu, Ling Guo, Deying Li, Cong Liu, Bin Liu, and Ding-Zhu Du. "An efficient randomized algorithm for rumor blocking in online social networks." IEEE Transactions on Network Science and Engineering (2017).
[22] Dey, Paramita, and Sarbani Roy. "Centrality based information blocking and influence minimization in online social network." In 2017 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 1-6. IEEE, 2017.
[23] M. Kimura, K. Saito, and H. Motoda, “Minimizing the spread of contamination by blocking links in a network.” in AAAI, vol. 8, 2008, pp. 1175–1180.
[24] E. B. Khalil, B. Dilkina, and L. Song, “Scalable diffusion-aware optimization of network topology,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014, pp. 1226–1235.
[25] H. Tong, B. A. Prakash, T. Eliassi-Rad, M. Faloutsos, and C. Faloutsos, “Gelling, and melting, large graphs by edge manipulation,” in Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 2012, pp. 245–254.
[26] Kuhlman, Chris J., Gaurav Tuli, Samarth Swarup, Madhav V. Marathe, and S. S. Ravi. "Blocking simple and complex contagion by edge removal." In 2013 IEEE 13th International Conference on Data Mining, pp. 399-408. IEEE, 2013.
[27] Yao, Qipeng, Chuan Zhou, Linbo Xiang, Yanan Cao, and Li Guo. "Minimizing the negative influence by blocking links in social networks." In International conference on trustworthy computing and services, pp. 65-73. Springer, Berlin, Heidelberg, 2014.
[28] He, Jing, Hongyu Liang, and Hao Yuan. "Controlling infection by blocking nodes and links simultaneously." In International workshop on internet and network economics, pp. 206-217. Springer, Berlin, Heidelberg, 2011.
[29] S. Wang, X. Zhao, Y. Chen, Z. Li, K. Zhang, and J. Xia, “Negative influence minimizing by blocking nodes in social networks.” In AAAI (Late-Breaking Developments), 2013, pp. 134–136.
[30] Tong, Guangmo, Weili Wu, Shaojie Tang, and Ding-Zhu Du. "Adaptive influence maximization in dynamic social networks." IEEE/ACM Transactions on Networking (TON) 25, no. 1 (2017): 112-125.
[31] Tripathy, R.M., Bagchi, A. and Mehta, S., 2010, October. "A study of rumor control strategies on social networks." In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 1817-1820).
[32] Tripathy, R.M., Bagchi, A. and Mehta, S., 2013. "Towards combating rumors in social networks: Models and metrics." Intelligent Data Analysis, 17(1), pp.149-175.
[33] L. Fan, Z. Lu, W. Wu, B. Thuraisingham, H. Ma, and Y. Bi, "Least cost rumor blocking in social networks," in Distributed Computing Systems (ICDCS), 2013 IEEE 33rd International Conference on. IEEE, 2013, pp. 540–549.
[34] Nguyen, N.P., Yan, G., Thai, M.T. and Eidenbenz, S., 2012, June. "Containment of misinformation spread in online social networks." In Proceedings of the 4th Annual ACM Web Science Conference (pp. 213-222).
[35] Santhoshkumar, S. and Babu, L.D., 2019. "An Effective Rumor Control Approach for Online Social Networks." In Information Systems Design and Intelligent Applications (pp. 63-73). Springer, Singapore.
Citation
P. K. Tiwari, M. K. Singh, A.K. Bharti, "Approaches to Block Rumors in Social Networks: A Review," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.132-136, 2020.
Load Balancing Issues and Techniques In Cloud Computing
Review Paper | Journal Paper
Vol.8 , Issue.1 , pp.137-140, Jan-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i1.137140
Abstract
Cloud computing is growing rapidly due to its attractive features. Load on cloud is escalating rapidly due to the increase of new applications. An efficient use of cloud depends on several aspects such as security, speed, privacy etc. Load balancing ensures that every device and processor perform equal number of tasks in equal amount of time. Load balancing contribute to a reduction of resource consumption, set failover, enable expandability, avoid bottleneck and over-provisioning etc. So, this paper discusses basics of load balancing, issues, challenges, benefits, metrics, types and various techniques of load balancing in cloud computing.
Key-Words / Index Term
Resource Allocation, Cloud Computing, Load Balancing, Virtual Machines, CPU, QoS
References
[1] P. Mell and T. Grance.” The NIST definition of cloud computing,” National Institute of Standards and Technology, Information Technology Laboratory, Version 15, Oct. 7, 2009.
[2] I. Foster, Y. Zha, I. Raicu and S. Lu,” Cloud computing and grid computing 360-degree compared,” 2008 Grid computing environments workshop, Austin, TX,2008, pp. 1-10.
[3] M. Ahmed, A. Sina, R. Chowdhury et all,” An advanced survey on cloud computing and state-of-the-art research,” 2012 International Journal of Computer Science,2012 Volume 9.
[4] R. Panwar, B. Mallick,” Load balancing in cloud computing using dynamic load management algorithm”,2015 International Conference on Green Computing and Internet of Things (ICGCIoT),2015, pp.773-778.
[5] S. Katoch and J. Thakur,” Load balancing algorithms in cloud computing environment: a review,” 2014 International Journal on Recent and Innovation Trends in Computing and Communication, Volume 2, Issue 8, Aug 2014, pp.2151-2156.
[6] L. Mukati, A. Upadhyay,” A survey on static and dynamic load balancing algorithms in cloud computing,” 2019 International conference on Recent Advances in Interdisciplinary Trends in Engineering & Applications (RAITEA), Indore,2019.
[7] S. Joshi and U. Kumari,” Load balancing in cloud computing: Challenges & issuies,”2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida,2016, pp.120-125.
[8] K. Li, G. Xu, G. Zhao,Y. Dong and D. Wang ,” Cloud task scheduling based on load balancing ant colony optimization,” 2011 Sixth Annual Chinagrid Conference, Liaoning, 2011,pp. 3-9.
[9] A. T. Velte, T. J. Velte, R. Elsenpeter, “Cloud Computing: A Practical Approach, “Tata McGraw Hill ,2010.
[10] M. Randles, D. Lamb and A. Bendiab, “A comparative Study into distributed load balancing algorithms for cloud computing,”2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (WALNA), Perth,WA, April 2010, pp.551-556.
[11] HC. Hseih, ML. Chiang,,”The incremental load balance cloud algorithm by using dynamic data deployment,” 2019 Journal of Grid Computing ,Volume 17,Issue 3,September 2019,pp.553-575.
[12] H. Shoja, H. Nahid and R. Azizi,”A comparative survey on load balancing algorithms in cloud computing,”2014 Fifth International Conference on Computing Communications and Networking Technologies(ICCCNT), Hefei,2014, pp. 1-5.
[13] R. Achar, P.S. Thilagam,N. Soans ,P.V. Vikyath, S. Rao and A.M. Vijeth,” Load balancing in cloud based on live migration of virtual machines,” 2013 Annual IEEE India Conference(INDICON),Mumbai,2013,pp. 1-5.
[14] E. Gupta, V. Deshpande,” A technique based on ant colony optimization for load balancing in cloud data centre,” 2014 International Conference on Information Technology (ICIT), Bhubaneswar,2014, pp. 12-17.
[15] S. Aslanzadeh and Z. Chaczko,” Load balancing optimization in cloud computing: applying endocrine-particle swarm optimization,”2015 IEEE International Conference on Electro/Information Technology (EIT), Dekalb, IL,2015, pp. 165-169.
[16] F. Vhansure ,A. Deshmukh, and S.Sumathy,”Load balancing in multi cloud computing environment with genetic algorithm”,2017 IOP Conference Series: Materials Science and Engineering,2017,pp.1-10.
[17] W. Sun, Z. Ji,J. Sun ,N. Zhang and Y. Hu, ”SAACO: A self-adaptive ant colony optimization in cloud computing,” 2015 IEEE Fifth International Conference On Big Data and Cloud Computing,Dalian,2015,pp. 148-153.
[18] S. G. Domanal and G.R.M. Reddy,” Load balancing in cloud computing using modified throttled algorithm,” 2013 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), Bangalore, 2013, pp.1-5.
[19] K.R. Babu and P. Samuel,” Enhanced bee colony algorithm for efficient load balancing and scheduling in cloud,”2016 6th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA), Kochi,2016,pp.67-78.
[20] K.R.R Babu and P. Samuel, “Service-level agreement-aware scheduling and load balancing of tasks in cloud,”2019 Software: Practice and Experience,2019, pp.1-18.
[21] R.G. Rajan and V. Jeyakrishnam , ”A survey on load balancing in cloud computing environments,” 2013 International Journal of Advanced Research in Computer and Communication Engineering,vol.2,Issue no. 12,2013,pp.4726-4728.
[22] S. Aslam and M. A. Shah,” Load balancing Algorithms in Cloud Computing: A survey of modern techniques,” 2015 National Software Engineering Conference (NSEC), Rawalpindi,2015, pp.30-35.
[23] G. Fan, L. Chen, H. Yu and D. Liu,” Formally modelling and analysing cost-aware job scheduling for cloud data center,”2018 Software: Practice and Experience, Volume 48, Issue 9,2018, pp.1536-1559.
[24] J. M. Shah, S. Pandya, N. Joshi, K. Kotecha and D.B. Choksi,” Load balancing in cloud computing: Methodological survey on different types of algorithm,” 2017 International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, 2017, pp. 100-107.
[25] Z. N. Rashid,S. R. M.Zebari , K.H. Sharif and K . Jacksi,” Distributed Cloud Computing and Distributed parallel computing: A Review,” 2018 International Conference on Advance Science and Engineering (ICOASE), Duhok,2018, pp.167-172.
[26] N. AI. Sallami, S. AI. Alousi,” Load balancing with neural network”,2014 International Journal of Advanced Computer Science and Applications,2014, pp.138-145.
[27] A.S. Milani, N.J. Navimipour,” Load balancing mechanisms and techniques in the cloud environments: Systematic literature review and future trends”,2016 Journal of Network and Computer Applications,2016, pp.86-98.
[28] J. Acharya, M. Mehta and B. Saini,” Particle swarm optimization Based load balancing in Cloud Computing,” 2016 International Conference on Communication and Electronics Systems (ICCES), Coimbatore,2016, pp.1-4.
[29] V.N. Volkova, L.V. Chemenkaya, E.N. Desyatirikova, M. Hajali and A. Osama,” Load balancing in cloud computing,”2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow,2018, pp.387-390.
[30] A. Vig , R.S. Kushwah and S.S. Kushwah ,”An Efficient Distributed Approach for Load Balancing in Cloud Computing,”2015 International conference on Computational Intelligence and Communication Networks(CICN),Jabalpur,2015,pp.751-755.
[31] C. Fancy, M. Pushpalatha and Pushpa,” Experimentation of traditional load balancing algorithms in software defined network,” 2019 International journal of Recent Technology and Engineering,2019, pp.527-532.
Citation
Roopali Gupta, Meenu Dave, "Load Balancing Issues and Techniques In Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.137-140, 2020.
Data Classification Approach For Text Analysis and Its Ambiguity
Review Paper | Journal Paper
Vol.8 , Issue.1 , pp.141-145, Jan-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i1.141145
Abstract
Sentiment analysis or opinion mining is one of the fastest growing fields with its demand and potential benefits that is increasing every day. With the onset of the internet and modern technology, there has been a vigorous growth in the amount of data. Each individual is able to express his/her own ideas freely on social media. All of this data can be analysed and used in order to draw benefits and quality information. In this paper, the focus is on cyber-hate classification based on for public opinion or views, since the spread of hate speech using social media can have disruptive impacts on social sentiment analysis. In particular, here proposing a modified fuzzy approach with two stage training for dealing with text ambiguity and classifying three type approach positive, negative and neutral sentiment, and compare its performance with those popular methods as well as some existing fuzzy approaches.
Key-Words / Index Term
Ambiguity, cyber hate, fuzzy, Sentiment analysis
References
[1] R. Zhao, A. Zhou, and K. Mao, “Automatic detection of cyber bullying on social networks based on bullying features,” in the Proceedings of the 17th International Conference on Distributed Computer Network, pp. 4-7, Jan. 2016.
[2] K. Reynolds, A. Kontostathis, and L. Edwards, “Using machine learning to detect cyberbullying,” in Proceedings of 10th International Conference on Machine Learning Application, Honolulu, USA, pp. 241-244,Dec. 2011.
[3] J. H. Park and P. Fung, “One-step and two-step classification for abusive language detection on Twitter,” in Proc. 1st Workshop Abusive Lang. Online, Vancouver, BC, Canada, pp. 41–45,Aug 2016.
[4] H. Mubarak, K. Darwish, and W. Magdy, “Abusive language detection on Arabic social media,” in Proceeding of 1st Workshop Abusive Lang. Online, Vancouver, BC, Canada, pp. 52–56,Aug 2017.
[5] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” in Proceedings of Conference Empirical Methods Natural Lang. Process. (ACL), pp. 79-86,2002.
[6] C. Jefferson, H. Liu, and M. Cocea, “Fuzzy approach for sentiment analysis,” in Proc. IEEE International Conference on Fuzzy Syst., Naples, Italy, pp. 1–6,July 2017.
[7] P. Burnap and M. L. Williams, “Cyber hate speech on Twitter: An application of machine classification and statistical modeling for policy and decision making,” Policy Internet, vol. 7, issue 2, pp. 223–242, 2015.
[8] P. Burnap and M. L. Williams, “Us and them: Identifying cyber hate on Twitter across multiple protected characteristics,” EPJ Data Sci., vol. 5, Issue 1, pp. 11-15, 2016.
[9] A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, “Sentiment analysis of Twitter data,” in Proceedings of Workshop Language on Social Media, Stroudsburg, PA, USA, pp. 30–38,June 2011.
[10] I. Kwok and Y. Wang, “Locate the hate: Detecting tweets against blacks,” in Proceedings of 27th AAAI Conference on Artificial Intelligence, Bellevue, WA, USA, pp. 1621–1622,July 2013.
[11] A. Mahmud, K. Z. Ahmed, and M. Khan, “Detecting flames and insults in text,” in Proeeding of 6th International Conference on Natural Language Processing., Gothenburg, Sweden, pp. 25–27, Aug 2008.
[12] B. Gambäck and U. K. Sikdar, “Using convolutional neural networks to classify hate-speech,” in Proc. 1st Workshop Abusive Lang. Online, Vancouver, BC, Canada, Aug, pp. 85–90,Aug 2017.
[13] D. Chandran, K. A. Crockett, D. Mclean, and A. Crispin, “An automatic corpus based method for a building multiple fuzzy word dataset,” in Proceedings of IEEE International Conference on Fuzzy Syst., Istanbul, Turkey, pp. 1–8, Aug 2015.
[14] I. B. Sassi, S. B. Yahia, and S. Mellouli, “Fuzzy classification-based emotional context recognition from online social networks messages,” in Proc. IEEE Int. Conf. Fuzzy Syst., Naples, Italy, pp. 1–6,July 2017.
[15] S. Asur and B. Huberman, “Predicting the future with social media,” in International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010, vol. 1, pp. 492–499,2010.
[16] D. Hardt and J. Wulff, “What is the meaning of 5 *’s? An investigation of the expression and rating of sentiment,” in Proceedings of KONVENS 2012, J. Jancsary, Ed. OGAI, pATHOS 2012 workshop,pp. 319-326, 2012.
[17] H. Chen, P. De, Y. Hu, and B.-H. Hwang, “Sentiment revealed in social media and its effect on the stock market,” in Statistical Signal Processing Workshop (SSP), 2011 IEE, pp.25-28, 2011.
[18] S. P. Robertson, “Changes in referents and emotions over time in election-related social networking dialog,” in System Sciences (HICSS), on 44th Hawaii International Conference, pp. 1–9, 2011.
[19]M. Salath´e and S. Khandelwal, “Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control,” PLoS computational biology, vol. 7, no. 10, 2011.
[20] T. Menezes, C. Roth, J.-P. Cointet, “Finding the semantic-level precursors on a blog network.” IJSCCPS, vol. 1, pp. 115–134, 2011.
[21] Han Liu, Pete Burnap, Wafa Alorainy, Matthew L.Williams, ”A fuzzy approach to text classification with two stage training for ambiguous instances”, in proceeding of IEEE Transactions On Computational Social Systems ,vol. 6,no.2 ,pp. 227-240,2019.
[22] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra,”A review: Design and development of novel techniques for clustering and classification of data”, International journal of scientific research in computer sciences and engineering,Vol.06, Special Issue.01 , pp.19-22, Jan-2018.
[23]V.K.Jin, N.Tripathi,”Speech feature analysis and biometric person identification in multilingual environment”, International journal of scientific research in network security and communication,Vol.6, Issue 1,pp.7-11, Feb 2011.
[24]Bindushree V,Rashmi G.R, Uma H.R, ”Analysis of text recognition with data mining technique”, International journal of scientific research in computer science and engineering,Vol.7,Issue.6,pp. 40-42,Dec 2019.
Citation
Supriya M. Yawalkar, A.S. Kapse, "Data Classification Approach For Text Analysis and Its Ambiguity," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.141-145, 2020.
Smart Home Design Using IoT
Survey Paper | Journal Paper
Vol.8 , Issue.1 , pp.146-150, Jan-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i1.146150
Abstract
Energy saving has become prime concern everywhere. When we talk about it, the first thing that comes in mind is Smart Grid. Smart Grids the perfect solution for saving and optimizing the energy usage. IoT do not need any introduction in the current scenario. IoT technology helps in checking the proper usage of energy. In this paper, the focus is given on the design of smart home that will use smart grid coupled with IoT technology. The working of this system, protocols used by this system and challenges of the same is discussed in this paper.
Key-Words / Index Term
Smart Grid, IoT, Smart home
References
[1].Adi Candra Swastika, Resa Pramudita, Rifqy Hakimi “IoT based smart grid system design for smart homes”, 2017, IEEE.
[2]. T. D.Atmaja, D. R. Saleh, “Cloud Computing untuk Mendukung Aplikasi Smart grid “ , Konferensi Teknologi Informasi dan Komunikasi untuk Indonesia, PP. 158-163, 2011
[3]. M. Yun, B. Yuxin," Research on the Architecture and Key Technology of Internet of Things (loT) Applied on Smart grid” , International Conference on Advances in Energy Engineering, PP. 69 - 72 , 2010.
[4]. Maninder Kaur, Sheetal Kalra, “A Review on IOT Based Smart grid”, International Journal of Energy, Information and Communications, Vol.7, Issue 3 , pp.11-22, 2016
[5]. HomePlug Support for IEEE Standards. [Online]. Available: https://www.lora-alliance.org/What-Is-LoRa/Technology
[6]. S.K. Viswanath, C. Yuen, W. Tushar, W.T. Li, C.K. Wen, K. Hu, C. Chen, X. Liu, “System Design of The Internet of Things for Residential Smart Grid”, IEEE Wireless Communications, Vol. 23, Oct. 2016.
[7]. Prof. Martin Djamin, Ir., M.Sc., Ph.D., APU dkk, " Teknologi Smart grid Untuk Smart City ", BPPT Press, 2012
Citation
Raushan Kashypa, "Smart Home Design Using IoT," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.146-150, 2020.
Counter-Terrorism and Crime Detection Using Hybrid Approach of Data Mining, NLP and GEO-Spatial Social Media Analytics
Research Paper | Journal Paper
Vol.8 , Issue.1 , pp.151-158, Jan-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i1.151158
Abstract
Crime, an unlawful act, causes terror and threat to our society and is a major concern for national security as well as international security. However, very negligible work has been done to develop models and methods to hold an active collaboration between counter terrorism and criminal investigation systems. The need is felt to develop a system that collects as well as categorise the data on crimes along with an analysis of crime affected areas identification. In this study, an efficient crime investigation system is proposed in which fuzzy rules and k mean clustering algorithm is employed to identify and detect crime affected region along with showing it on the map. The study of Data Mining and NLP is incorporated for crime detection and prevention with an aim to provide a safer society to live.
Key-Words / Index Term
Counter terrorism; Crime detection; Social Media; Data mining; Geo-Spatial; NLP
References
[1] Q.Rossy, O.Ribaux, “ A collaborative approach for incorporating forensic data into crime investigation using criminal intelligence analysis and visualisation” Forensic Science and Justice, Vol. 54 , 2014 , pp. 146-153.
[2] Q.Rossy, S.Ioset, D. Dessimoz, and O. Ribaux, “Integrating forensic information in a crime intelligence database,” Forensic Sci. Int., vol. 230, no. 1–3, pp. 137–146, 2013.
[3] Q. Zhang, L. Cheng, and R. Boutaba, “Cloud computing : state-of-the-art and research challenges,” pp. 7–18, 2010.
[4] S. H. Liao, P. H. Chu, and P. Y. Hsiao, “Data mining techniques and applications - A decade review from 2000 to 2011,” Expert Syst. Appl., vol. 39, no. 12, pp. 11303–11311, 2012.
[5] J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2001.
[6] E. R. Groff and N. G. La Vigne, “Forecasting the Future of Predictive Crime Mapping,” Crime Prev. Stud., vol. 13, pp. 29–57, 2002.
[7] W. Gorr, A. Olligschlaeger, and Y. Thompson, “Short-term forecasting of crime,” Int. J. Forecast., vol. 19, no. 4, pp. 579–594, 2003.
[8]G. Oatley, “Crimes analysis software : ‘ Pins in Maps ’, clustering and Bayes net prediction, ” Expert Systems with Applications , vol. 25, no. March, pp. 569–588, 2016.
[9]T. H. Grubesic, “On the application of fuzzy clustering for crime hot spot detection,” J. Quant. Criminol., vol. 22, no. 1, pp. 77–105, 2006.
[10] S. V. Nath, “Crime Pattern Detection Using Data Mining,” Web Intell.Intell.Agent Technol. Work. 2006. WI-IAT 2006 Work. 2006 IEEE/WIC/ACM Int. Conf., vol. 1, no. 954, pp. 41–44,2006.
[11] K. Stoffel, P. Cotofrei, and D. Han, “Fuzzy Clustering based Methodology for Multidimensional Data Analysis in Computational Forensic Domain,” Itcf 2010, vol. 4, pp. 400–410, 2011.
[12] A. Juyal, O. Gupta, “A review on clustering techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, 2014.
[13] T.Moon, S.Heo, S.Lee, “Ubiquitous crime prevention system for a safer city”, Environmental Sciences, Vol. 22, pp. 288-301,2014.
[14] S. K. Sood, “Dynamic Resource Provisioning in Cloud based on Queuing Model,” vol. 2, no. 4, 2013. [15] CASAGRAS, RFID and the inclusive model for the NLP report, EU Project Number 216803, pp 16–23, 2011.
[16]J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “NLP( IoT ): A vision , architectural elements , and future directions,” vol. 29, pp. 1645–1660, 2013.
[17]E. Borgia, “The NLP vision : Key features , applications and open issues,” vol. 54, pp. 1–31, 2014.
[18]J. Byun, A. Nasridinov, Y. Park*," NLP for Smart Crime Detection", Contemporary Engineering Sciences, Vol. 7, no. 15, pp.749 - 754, 2014.
[19]N. Dlodlo, P. Mbecke, M. Mofolo, and M. Mhlanga, “The NLP in community safety and crime prevention for South Africa,” Innov. Adv. Comput. Informatics, Syst. Sci. Netw. Eng.,pp.531–537,2015.
[20] S. A. Alvi, B. Afzal, G. A. Shah, L. Atzori, and W. Mahmood, “Ad Hoc Networks Internet of multimedia things : Vision and challenges,” Ad Hoc Networks, vol. 33, pp. 87–111, 2015.
[21] V. Spicer, J. Song, P. Brantingham, A. Park, and M. A. Andresen, “Street pro fi le analysis : A new method for mapping crime on major roadways,” Appl. Geogr., vol. 69, pp. 65–74, 2016
Citation
Pallavi, Rajeev Kumar Bedi, Sunil Kumar Gupta, "Counter-Terrorism and Crime Detection Using Hybrid Approach of Data Mining, NLP and GEO-Spatial Social Media Analytics," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.151-158, 2020.
Cloud Computing Towards Rural Development in India
Review Paper | Journal Paper
Vol.8 , Issue.1 , pp.159-165, Jan-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i1.159165
Abstract
A technical popular expression is cloud computing in the precedent period that gives a change in outlook where calculation power, stockpiling and system assets are given as an administration. Rather than purchasing costly equipment and programming which needs establishment, setup, upkeep; the cloud computing encourages the use of cloud application and foundations dependent on compensation as you go plot. Provincial populace being most of Indian populace has the capability of making India a financial superpower and a created nation yet in the present situation this greater part is totally unaware of the power and ability of Information innovation in progress of business openings and work in light of the gigantic expenses brought about on foundation, programming and so on. This paper examines way through which the `cloud computing` worldwide can assist the provincial populace in conquering these obstacles and which will at last lead to country zone advancement and a general monetary advancement of the country.
Key-Words / Index Term
Cloud Computing, Rural, Agricultural, SaaS, Platform, Service
References
[1] Dr C. Chandramouli, Registrar General & Census Commissioner, India. censusindia.gov.in/2011-prov-results/india/Rural_Urban_2011.pdf.
[2] Madanmohan Rao., Internet growth, impacts and success, yourstory.com/2015/02/internet-india-2018.
[3] S. K. Choudhary, R.S Jadoun, H. L Mandoriya, “Role of Cloud Computing Technology in Agriculture Fields”, Computer Engineering and Intelligent Systems, Vol.7, No.3, 2016, pp. 1-7.
[4] Kamna Choubisa, “Cloud Computing & Rural Development”, International Journal of Information Technology and Knowledge Management, 2012, Volume 6, No. 1, pp. 98-100.
[5] Nirvikar Singh “ICTs and Rural Development in India” Uni of California, October 2006.
[6] John Prakash Veigas, Nithin Kumar Heraje, “Cloud Computing Adoption and its Impact in India”, International Research Journal of Engineering and Technology (IRJET), Volume: 06 Issue: 06 | June 2019, pp. 3128-3130.
[7] http://www.nytimes.com/2008/01/07/business/worldbusiness/07ihtcar.1 9051152.html?_r=0.
[8] Yanxin Zhu , Di Wu and Sujian Li, “Cloud Computing and Agricultural Development of China: Theory and Practice” IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 1, No 1, January 2013.
[9] Rakesh Patel , Mili. Patel, Lecturer, Department of Information Technology, Kirodimal Institute of Technology Raigarh, “Application of Cloud Computing in Agricultural Development of Rural India”, International Journal of Computer Science and Information Technologies, Vol. 4 (6), 2013.
[10] Ranjit Panigrahi, M. K. Ghose, Moumita Pramanik , “Cloud Computing:A New Era of Computing in the Field of Information Management. ”,
[11] Mitsuyoshi Hori, Eiji Kawashima, Tomihiro Yamazaki, “Application of Cloud Computing in the Field of Agriculture and Prospects in Other Fields ”.
[12] R. Piplode, P. Sharma and U.K. Singh, “
Study of Threats, Risk and Challenges in Cloud Computing”, Isroset-Journal (IJSRCSE), Vol. 1, Issue. 1, pp. 26-30, Jan-2013.
[13] M. Mittal, P. Sharma and P. K. Gehlot, “
A Comparative Study of Security Issues& Challenges of Cloud Computing”, Isroset-Journal (IJSRCSE), Vol. 1, Issue. 5, pp. 9-15, Sep-2013.
Citation
A.K. Gupta, R.K. Dwivedi, K. Mukharjee, "Cloud Computing Towards Rural Development in India," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.159-165, 2020.
An Efficient Multipath Packet Forwarding (MPF) Protocol with Modified Dijkstra Path Searching Algorithm in Underwater Wireless Sensor Network
Research Paper | Journal Paper
Vol.8 , Issue.1 , pp.166-173, Jan-2020
CrossRef-DOI: https://doi.org/10.26438/ijcse/v8i1.166173
Abstract
Underwater Wireless Sensor network is an emerging to be a promising technology in unveiling the mysteries of the marine life and other underwater applications. Routing in underwater wireless sensor networks differs from routing in global wireless sensor networks. However, distinctive features of UWSNs like multipath propagation delay, limited bandwidth, and energy constraints. This paper presents a Multipath Packet Forwarding (MPF) protocol with modified Dijkstra path searching algorithm is used for dynamic data transmission in this work. The proposed Multipath Packet Forwarding protocol is used for multiple paths which loops free and disjoint in message communication. In addition, during the path selection of the best shortest path forwarder node, the robust weight function parameters make it more efficient to consume minimum energy. The experimental result analysis indicates that Multipath Packet Forwarding (MPF) protocol with modified Dijkstra path searching algorithm has very good adaption ability to the UWSN in terms of Throughput, Packet Delivery Ratio (PDR), Packet Loss Ratio and Energy consumption.
Key-Words / Index Term
Underwater Sensor Network, Routing, WSN, Dijkstra, Multi-path
References
[1] Akyildiz I. F., Pompili D., and Melodia T., “Underwater acoustic sensor networks: research challenges,” Ad hoc networks, vol. 3, no. 3, pp. 257–279, 2005.
[2] Aziz A.A., Sekercioglu Y.A., Fitzpatrick, P., et al.: “A survey on distributed topology control techniques for extending the lifetime of battery powered wireless sensor networks”, IEEE Commun. Surv. Tutorials, 2013, 15, (1), pp. 121–144.
[3] Han G., Jiang J., Zhang C., Duong T. Q., Guizani M., and Karagiannidis G. K., “A survey on mobile anchor node assisted localization in wireless sensor networks.” IEEE Communications Surveys and Tutorials, vol. 18, no. 3, pp. 2220–2243, 2016.
[4] Jiang P., Wang X., and Jiang L., “Node deployment algorithm based on connected tree for underwater sensor networks”, Sensors, 2015, 15, (7), pp. 16 763–16 785.
[5] Kilfoyle D. B., and Baggeroer A. B., “The state of the art in underwater acoustic telemetry,” IEEE Journal of oceanic engineering, vol. 25, no. 1, pp. 4–27, 2000.
[6] Llor J., and Malumbres M.P., “Underwater wireless sensor networks: how do acoustic propagation models impact the performance of higher-level protocols?”, Sensors, 2012, 12, (2), pp. 1312–1335.
[7] Lee E., Park S., Yu F., and Kim S.-H., “Data gathering mechanism with local sink in geographic routing for wireless sensor networks,” IEEE Trans. Consum. Electron., vol. 56, no. 3, pp. 1433–1441, Aug. 2010.
[8] Muhammad Khalid, Yue Cao , Naveed Ahmad, Waqar Khalid, Piyush Dhawankar, "Radius-based multipath courier node routing protocol for acoustic communications", IET Wireless Sensor Systems, 2018, Vol. 8 Iss. 4, pp. 183-189.
[9] Tuna G. and Gungor V.C., “A survey on deployment techniques, localization algorithms, and research challenges for underwater acoustic sensor networks”, Int. J. Commun. Syst., 2017, pp. 1–21.
[10] Xu G., Shen W. and Wang X., “Applications of wireless sensor networks in marine environment monitoring: a survey”, Sensors, 2014, 14, (9), pp. 16 932–16 954.
[11] Zenia N.Z., Aseeri M., and Ahmed M.R., “Energy-efficiency and reliability in mac and routing protocols for underwater wireless sensor network: a survey”, J. Netw. Comput. Appl., 2016, 71, pp. 72–85.
Citation
P. Devi, T. Ramesh, "An Efficient Multipath Packet Forwarding (MPF) Protocol with Modified Dijkstra Path Searching Algorithm in Underwater Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.166-173, 2020.