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Social network analysis is no more only bound to studying the relationship between the actors to discover the hidden trends and find scope in them but there is a darker side as well. The deep ocean of network has allowed people to counter attack on different countries via acting from various locations by issuing fake identities. The cycle of events that take place with the evil intentions to disrupt and cause fear not only physically but mentally too is referred to terrorism. SNA allows this act to counter prevent and associate the links by identifying the intentions and working of the actors. Various studies have been conducted after 9/11 attack and people all over the world are monitored so as to track the status and if any conviction is predicted, the complete network is examined. The sole purpose of these studies is to prevent thwarting and prepare oneself to counter terrorism. This paper outlines certain methods that recognize the terrorist activities which need to be monitored so as to detect the pre-event occurrence and counter attack on their planning.

Social Network Analysis, Counter Terrorism, Information Analysis, Intelligence analysis

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[24] Clifford Weinstein, William Campbell, Brian Delaney, Gerald O’Leary.” Modeling and Detection Techniques for Counter-Terror Social Network Analysis and Intent Recognition” IEEE. 978-1-4244-2622-5 (2009) . pp. 1-16.
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A. Srivastava, A. Pillai, D. J. Gupta, "Social Network Analysis as Counter Terrorism Tool," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.655-661, 2018.

Recent technological advancements like internet of things, home automation, disaster management etc shed light on wireless sensor networks. Thousands of micro sensor nodes are geographically located to capture a remote environment. The communication and computing process consume more energy from the nodes. Prolong usage of same path to transfer data may lead to decrease of the residual energy in the sensor nodes, thus causes the node to death. Power conservation in the nodes is the major concern in hostile environments. Clustering is an energy saving scheme that enables the nodes to communicate data to the nominated cluster heads, from where the cluster heads communicate the gathered data to the sink. This gain focus on two factors of increasing the node life time, namely - the selection of cluster head that has more residual energy and the periodic change of cluster heads. This paper surveys on various energy-aware clustering schemes involved in routing data from the sensor nodes to sink.

Wireless Sensor Networks, Energy Consumption, Clustering, Cluster head selection, Routing protocols

[1] Akylidiz, I., Su,W., Subramaniam, S., and E.Cayrici, “A survey on sensor networks”, IEEE Communications Magazine, Volume: 40 Issue: 8, August 2002, pp.102-114.
[2] Akkaya, K. and Younis, M., “A survey of Routing Protocols in Wireless, Sensor Networks”, Elsevier Ad Hoc Network Journal, 2005, pp 325-349.
[3] Al-Karaki, J.N.; Kamal, A.E., “Routing techniques in wireless sensor networks: A survey”, IEEE Wirel. Commun. 2004, 11, 6–28.
[4] Chong, C.Y. and Kumar, S. P., “Sensor networks: Evolution, opportunities, and challenges”, Proceedings of the IEEE 91(8), 2003, pp. 1247-1256.
[5] Ye, M.; Li, C.; Chen, G.; Wu, J., ”EECS: An Energy Efficient Clustering Scheme in Wireless Sensor Networks”, in Proceedings of the 24th IEEE International Performance, Computing, and Communications Conference (IPCCC), Phoenix, AZ, USA, 7–9 April 2005; pp. 535–540.
[6] 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.
[7] Ding, P.; Holliday, J.; Celik, A, “Distributed Energy Efficient Hierarchical Clustering for Wireless Sensor Networks”, in Proceedings of the 8th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Marina Del Rey, CA, USA, 8–10 June 2005; pp. 322–339.
[8] Murugunathan, S.D.; Ma, D.C.F.; Bhasin, R.I.; Fapajuwo, A.O., “A Centralized Energy-Efficient Routing Protocol for Wireless Sensor Network”, IEEE Radio Commun. 2005, 43, S8–S13.
[9] U. Korupolu, S. Kartik, GK. Chakravarthi, “An Efficient Approach for Secure Data Aggregation Method in Wireless Sensor Networks with the impact of Collusion Attacks”, Isroset-Journal (IJSRCSE) Vol.4 , Issue.3 , pp.26-29, Jun-2016.
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[12] S.D.N. Hayath Ali, M. Giri, “A Study on Current Challenging Issues and Optimal Methods for Video Streaming over Heterogenous Wireless Network,” IJSRNSC, Vol.6 , Issue.2 , pp.40-44, Apr-2018.
[13] B. Warneke, M. Last, B. Liebowitz, Kristofer, and S. Pister, “Smart Dust: Communicating with a Cubic Millimeter Computer,” Computer Magazine, vol. 34, no. 1, pp. 44–51, Jan 2001.
[14] Loscri, V.; Morabito, G.; Marano, S., “A Two-Level Hierarchy for Low-Energy Adaptive Clustering Hierarchy”, in Proceedings of the 2nd IEEE Semiannual Vehicular Technology Conference, Dallas, TX, USA, 25–28 September 2005; pp. 1809–1813.
[15] Ye, M.; Li, C.; Chen, G.; Wu, J., “EECS: An Energy Efficient Clustering Scheme in Wireless Sensor Networks” in Proceedings of the 24th IEEE International Performance, Computing, and Communications Conference (IPCCC), Phoenix, AZ, USA, 7–9 April 2005; pp. 535–540.
[16] S.D.N. Hayath Ali1 , M. Giri, “A Study on Current Challenging Issues and Optimal Methods for Video Streaming over Heterogenous Wireless Network”, IRSRNSC, Vol.6 , Issue.2 , pp.40-44, Apr-2018.

A. Gopi Saminathan, V. Nivedhitha, "A Comprehensive Review on Cluster based Energy Efficient Routing Protocols in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.662-668, 2018.

Machine learning has developed with huge information innovations and best outline to make new opportunity for data science in the multi-disciplinary agriculture domain. In this paper, we present a far reaching review of research devoted to applications of machine learning in agrarian generation frameworks. The works broke down were classified in (a)crop management, including applications on yield expectation, infection identification, weed discovery, crop quality, and species acknowledgment; (b) domesticated animals management, including applications on creature welfare and animals generation; (c) water management; and (d) soil management. The review reports the application of machine learning techniques with artificial neural networks, Bayesian networks, support vector machines and k-means. The filtering and classification of the reviewed articles show how farming will benefit from machine learning advancements. By applying machine learning to sensor information, cultivate administration frameworks are advancing into ongoing artificial insight empowered projects that give rich proposals also, bits of knowledge for agriculturist choice help and activity.

Machine learning, Artificial Intelligence, Big data, Artificial neural networks, Bayesian networks, Support vector machines and K-means

[1] Meena Kushwaha and D. V. R. Raghuveer, "Survey of Impact of Technology on Effective Implementation of Precision Farming in India," International Journal on Recent and Innovation Trends in Computing and Communication, vol. 5, no. 6, p. 11, 2017.
[2] T. Ranjeet and L. Armstrong, “An artificial neural network for predicting crops yield in Nepal”, Proceedings of the 9th Conference of the Asian Federation for Information Technology in Agriculture “ICT’s for future Economic and Sustainable Agricultural Systems”, Perth, Australia, pp.376-386, 2014.
[3] S. Jabjone and S. Wannasang, "Decision Support System Using Artificial Neural Network to Predict Rice Production in Phimai District, Thailand," International Journal of Computer and Electrical Engineering, vol. 6, no. 2, pp. 162-166, 2014.
[4] N. Gandhi, L. J. Armstrong, and O. Petkar, “PredictingRice crop yield using Bayesian networks,” 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 795–799, 2016.
[5] S. Dahikar and S. Rode, "Agricultural Crop Yield Prediction Using Artificial Neural Network Approach", International Journal of Innovative Research in Electrical, Electronics, Instrumentation, and Control Engineering, vol. 2, no. 1, 2014.

[6] D. Jiang, X. Yang, N. Clinton, and N. Wang, "An artificial neural network model for estimating crop yields using remotely sensed information," International Journal of Remote Sensing, vol. 25, no. 9, pp. 1723-1732, 2004.
[7] M. Kaul, R. L. Hill, and C. Walthall, "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, vol. 85, no. 1, pp. 1-18, 2005.
[8] K. Movagharnejad and M. Nikzad, "Modeling of tomato drying using the artificial neural network," Computers and Electronics in Agriculture, vol. 59, no. 1-2, pp. 78-85, 2007.

[9] M. R. O`Neal, B. A. Engel, D. R. Ess, and J. R. Frankenberger, "AE—Automation and Emerging Technologies," Biosystems Engineering, vol. 83, no. 1, pp. 31-45, 2002.
[10] K. Ostad-Ali-Askari, M. Shayannejad, and H. Ghorbanizadeh-Kharazi, "Artificial neural network for modeling nitrate pollution of groundwater in the marginal area of Zayandeh-Rood River, Isfahan, Iran," KSCE Journal of Civil Engineering, vol. 21, no. 1, pp. 134-140, 2016.

[11] J. Y. Shin, M. Ajmal, J. Yoo, and T.-W. Kim, "A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook," Advances in Meteorology, vol. 2016, pp. 1-10, 2016.
[12] Aalders, “Modeling land-use decision behavior with Bayesian belief networks”, Ecology and Society, vol.13, no. 1, pp. 16, 2008.
[13] G. C. Chunguang Bi "Bayesian Networks Modeling for Crop Diseases," IFIP International Federation for Information Processing 2011, p. 8, 2011.
[14] M. A. E. Forio et al., "Bayesian belief network models to analyze and predict ecological water quality in rivers," Ecological Modelling, vol. 312, pp. 222-238, 2015.

[15] N. Gandhi, L. Armstrong, and O. Petkar, "Predicting Rice Crop Yield using Bayesian Networks", In Proceedings of IEEE 5th International Advances in Computing, Communications, and Informatics, E-ISBN: 978-1-5090-2029-4, 2016.

[16] S. Madadgar and H. Moradkhani, "Spatio-temporal drought forecasting within Bayesian networks," Journal of Hydrology, vol. 512, pp. 134-146, 2014.
[17] S. Maiti and R. K. Tiwari, "A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction," Environmental Earth Sciences, vol. 71, no. 7, pp. 3147-3160, 2013.
[18] G. S. Nearing, H. V. Gupta, and W. T. Crow, "Information loss in approximately Bayesian estimation techniques: A comparison of generative and discriminative approaches to estimating agricultural productivity," Journal of Hydrology, vol. 507, pp. 163-173, 2013.

[19] C. Perez-Ariza, A. Nicholson and M. Flores, "Prediction of coffee rust disease using Bayesian networks", In Proceedings of the Sixth European Workshop on Probabilistic Graphical Models, pp. 259-266, 2012.

[20] J. M. Quinn, R. M. Monaghan, V. J. Bidwell, and S. R. Harris, "A Bayesian Belief Network approach to evaluating complex effects of irrigation-driven agricultural intensification scenarios on future aquatic environmental and economic values in a New Zealand catchment," Marine and Freshwater Research, vol. 64, no. 5, 2013.
[21] A. Sharma and M. Goyal, "Bayesian network model for monthly rainfall forecast", 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 241-246, 2015.

[22] C.-J. Du and D.-W. Sun, "Pizza sauce spread classification using color vision and support vector machines," Journal of Food Engineering, vol. 66, no. 2, pp. 137-145, 2005.

[23] V. Anandhi and R. Chezian, “Support Vector Regression in forecasting”, International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 10, pp. 4148-4151, 2013.
[24] S. Brdar, D. Culibrk, B. Marinkovic, J. Crnobarac and V. Cmojevic, “Support Vector Machines with Features Contribution Analysis for Agricultural Yield Prediction”, 2011
[25] K. Brudzewski, "Classification of milk by means of an electronic nose and SVM neural network," Sensors and Actuators B: Chemical, vol. 98, no. 2-3, pp. 291-298, 2004.
[26] S. Fagerlund, "Bird Species Recognition Using Support Vector Machines," EURASIP Journal on Advances in Signal Processing, vol. 2007, no. 1, 2007.
[27] A. Bharadwaj, S. Dahiya and R. Jain, "Discretization based Support Vector Machine (D-SVM) for Classification of Agricultural Datasets", International Journal of Computer Applications, vol. 40, no. 1, pp. 8-12,2012.

[28] N. Gandhi, L. Armstrong, O. Petkar and A. Tripathy, "Rice Crop YieldPrediction in India using Support Vector Machines", In Proceedings of IEEE The 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2016.

[29] B. Huang, C. Xie, and R. Tay, "Support vector machines for urban growth modeling," GeoInformatica, vol. 14, no. 1, pp. 83-99, 2009.
[30] Y. Karimi, S. O. Prasher, R. M. Patel, and S. H. Kim, "Application of support vector machine technology for weed and nitrogen stress detection in corn," Computers and Electronics in Agriculture, vol. 51, no. 1-2, pp. 99-109, 2006.
[31] G. Camps-Valls, L. Gomez-Chova, J. Calpe-Maravilla, E. Soria-Olivas, JD. Martin-Guerrero and J. Moreno, “Support vector machines for crop classification using hyperspectral data”, Lect Notes CompSci, 2652, pp. 134–141, 2003.
[32] B. S. Shedthi, S. Shetty, and M. Siddappa, “Implementation and comparison of K-means and fuzzy C-means algorithms for agricultural data,” 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), 2017.
[33] Stewart, J., Stewart, R., & Kennedy, S. (2017). Dynamic IoT management system using K-means machine learning for precision agriculture applications. Proceedings of the Second International Conference on Internet of things and Cloud Computing - ICC `17.
[34] P. K. Dr. SUJATHA S, "Smart Farming using K-means Clustering and SVM Classifier in Image Processing," International Journal of Science, Engineering and Technology Research, vol. 6, no. 11, p. 5, 2017.

[35] G. Chen, Y. Yang, H. Guo, X. Sun, H. Chen, and L. Cai, "Analysis and Research of K-means Algorithm in Soil Fertility Based on Hadoop Platform," Computer and Computing Technologies in Agriculture VIII IFIP Advances in Information and Communication Technology, pp. 304–312, 2015.

[36] J. Baghel and P. Jain, “Disease Detection in Soya Bean using K-Means Clustering Segmentation Technique,” International Journal of Computer Applications, vol. 145, no. 9, pp. 15–18, 2016.
[37] A. Khatra, "Yellow Rust Extraction in Wheat Crop-based on Color Segmentation Techniques," IOSR Journal of Engineering, vol. 3, no. 12, pp. 56–58, 2013.

[38] A. Tellaeche, X.-P. Burgosartizzu, G. Pajares, and A. Ribeiro, “A Vision-Based Hybrid Classifier for Weeds Detection in Precision Agriculture Through the Bayesian and Fuzzy k-Means Paradigms,” Advances in Soft Computing Innovations in Hybrid Intelligent Systems, pp. 72–79, 2007.
[39] T Williams and Dr. P. ShanthiBala, "To Recognize the Crop Growth Rate in Agricultural Land By Using K-Means Clustering Algorithm and Contrast Enhancement Algorithm," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3, no. 5, pp. 41–47, 2018.

[40] J. Tang, D. Wang, Z. Zhang, L. He, J. Xin, and Y. Xu, "Weed identification based on K-means feature learning combined with convolutional neural network," Computers and Electronics in Agriculture, vol. 135, pp. 63-70, 2017.
[41] R. K N, C. N, O. S N, M. B. Meenavathi, and R. V, "Detection of Rows in Agricultural Crop Images Acquired by Remote Sensing from a UAV," International Journal of Image, Graphics and Signal Processing, vol. 8, no. 11, pp. 25-31, 2016.

Ankita Bissa, Meena Kushwaha, Mayank Patel, "A Survey of Machine Learning Appliacations In Decision Making To Improve Farming," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.669-683, 2018.

This is a Application of Big data analytic. In this concept all Tamil news are stored into Big data analytical tools such as R Tool, MangoDB, Hadoop etc. The News is stored in the form of text, Photos, videos, audio etc. The News is gathered from all daily Tamil news paper, Epaper, Ebook and Social Media Content. Then this News will be analysed in various Angle. Moreover Government Policies, Plan, Advertisement etc. will be stored into the big data Tool. This information is stored into Date wise, month wise and year wise. The news will be classified into various types such as political, Sports, International, national, Local news etc. This is stored into the current details as well as past details. So, we can analysis the every news which is arising in the current problem. Now days Social Networks impact all areas of society. So, the Social Media information’s are stored into big data tools. The news is collected over 100 years. So, all the information’s are digitized. This proposed project is stored information’s of Old literatures of Tamil. Moreover the proposed project stored information’s of archaeological information like stone inscription, Palm leaf etc. So, this project consists of all details of Tamil. So, we can store and analyse information’s. Moreover it will be stored into Cloud Computing Environment. So, we can access anywhere else. The proposed project is stored all type of Tamil news come from various sources. Now days all the daily are converted into pdf format. So, that will be stored into Big Data Tool. The Proposed Project Consist of old literature of Tamil, Tamil Stone Age arts, palm leaf letters etc.

Bigdata Analytic, Cloud Computing, MangoDB, Hadoop, R Tool

[1]. Facebook 2017 Statistics Report.
[2]. Social Media growth statistical report.
[3]. Big Data Analytics, Tools and Technology for effective Planning by Arun k.Somani, Ganesh Chandra Deka.
[4]. Internet of Things, A Hands on Approach by Arshdeep Bahga,vijay Madisetti

B. Anandakumar ,R. Manimegalai, "Integrated Tamil News Storage and Analysis Using Big Data Analytics," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.684-686, 2018.

In this paper, we design a public key encryption scheme, is based on the composite residuosity classes and having the property as randomization, also called as non- determinism. The non-deterministic nature of this cryptosystem produces dissimilar ciphertexts for a given same plaintext character on each iteration. The intractability of factorization of this scheme is achieved through the concept of composite residuosity class problem. The key involved in the encryption procedure of the scheme uses the credentials like unique identities of the sender to ensure the authenticity of the user. These identities, are also called attributes of the user, include email, Social Security Number (SSN) or Aadhaar number etc. While encrypting the message, the sender will use any of his identity value as a key in the procedure. The recipient can calculate this attribute value in the decryption procedure.

Attributes, Composite residuosity classes, Decryption, Encryption, and Intractability

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[10] Sarita Sharma, Rakesh Gaherwal, “Comparative Study and Analysis of Unique Identification Number and Social Security Number”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.27-30, 2017.

Sumalatha Gunnala, D.S.R. Murthy, Shirisha Kakarla, "An Attribute Involved Non deterministic Cryptosystem using Composite Residuosity Class Problem," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.687-690, 2018.

The cloud computing is gaining the popularity rapidly due to its esteem services benefits and high computational demand of social, business, web and scientific applications. The cloud datacenters across the world are consuming high volume of energy thus affecting the environment also. The resource allocation in cloud is a key factor to achieve energy efficiency. In this paper, we reviewed the energy concept and different kinds of resource allocation schemes being used in cloud datacenters and went on to derive a taxonomical classification of these strategies based upon various metrics.

Energy efficiency, QoS, Virtual Machine, Datacenters, Cloud Service Provider, Resource Allocation

[1] Yue Gao, “An Energy and Deadline a Aware Resource Provisioning, Scheduling and Optimization Framework for Cloud Systems”, International conference on Hardware/Software Codesign and system synthesis (CODES+ISSS), Montreal, QC, Canada 2013
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[3] Zoltan Adam “Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines on cloud data center” Vol 51, pages 1-6, 2015
[4] S Ali “Profit-aware DVFS enabled resource management of IaaS cloud”. Vol. 10, Issue 2, pp-237-247, 2013
[5] Nasrin Ak., “Energy aware resource allocation of cloud data center: review and open issues” Vol. 19. Issue 3, pp-1163-1182, 2016
[6] Abdul Hameed “A Survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems”. Vol. 98. Issue 7, pp-751-774, 2016
[7] Anton Beloglazov: A taxonomy and survey of energy efficientDCs and cloud computing systems.” Adv comput, Vol.82, pp :47-111, 2011
[8] Anton Beloglazov, Rajkumar,“Energy Efficient Resource Management in Virtualized Cloud Data Centers” International Conference on Cluster, Cloud and Grid Computing, Melbourne, VIC, Australia 2010
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[13] Tarandeep Kaur and Inderveer Chana, “Energy Efficiency Techniques in Cloud Computing: A Survey and Taxonomy”, Vol. 48, Issue 2, pp 46, 2015.
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[22] Satveer , Mahendra Singh Aswal “A Comparative Study of Resource Allocation Strategies for a Green Cloud”, International Conference on Next Generation Computing Technologies (NGCT-2016) Dehradun, India, 14-16 October 2016
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[24] Yousafzail,“Cloud Resource Allocation Schemes:Review, Taxonomy, and Opportunities”, Knowl Inf Syst, Vol. 50, Issue-2 pp :347–381, 2017
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[62] Dabiah Ahmed, “Energy-aware Virtual Machine Consolidation for Cloud Data” International Conference on Utility and Cloud Computing Centers” London UK , 2014
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Satveer, Mahendra Singh, "A Taxonomy and Survey of Energy Efficient Resource Allocation Schemes for Cloud Datacenter," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.691-998, 2018.

Decision-making in requirements engineering plays a vital role in building quality software. Significant research is being applied in the requirements engineering field towards finding the reasons for high failure rates in software development. However, the industry still fails to produce quality requirements. Based on our literature review, we identifying that major contributing factor in getting a low rate of success is due unclear and imprecise requirements. In this paper, we proposed a novel fuzzy based fishbone method for decision making in Goal Oriented Requirements Engineering. It facilitates active stakeholder involvement in decision making process by integrating GORE with existing approaches in requirements engineering with respect to decision making. The main objective of this work is to present a formal framework to aid the decision making in a software development process, with ambiguous and vague data. GORE lays focus on the activities before the formulation of software system requirements. Finally, the proposed method improves the quality of decision making system and obtains high-quality products along with finer productivity.

Requirements Engineering, Fuzzy set theory, Fishbone, GORE, Software quality

[1] Emilio Insfran, Gary Chastek, Patrick Donohoe and Julio César Sampaio,” Requirements engineering in software product line engineering”, Requirements Engineering, Volume 19, Issue 4, pp 331–332,2014.
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[10] Metin Dag˘deviren, Serkan Yavuz, Nevzat Kılınç,”Weapon selection using the AHP and TOPSIS methods under fuzzy environment”, Expert Systems with Applications 36 8143–8151, 2009.
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[12] Wang, J., Liu, S. Y., & Zhang, J.,” An extension of TOPSIS for fuzzy MCDM based on vague set theory”, Journal of Systems Science and Systems Engineering, 14, 73-84, 2005.

Jameela Bano, L. S. S. Reddy, Hedi Khammari, "A Fuzzy based Fishbone Method for Goal-Oriented Requirements Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.699-704, 2018.

Intelligent Transportation system is a potential system which uses Vehicular Ad hoc Networks (VANET) with internet for its safety driven applications. VANET is infrastructure less and the vehicles form a self-structured network. During data forwarding, the vehicles undergo congestion which is a vital issue related to VANET. To overcome this the data has to be propagated in a particular path in optimized fashion . Routing protocols play a vital role for the propagation of data among nodes. This paper reveals the general idea of position routing protocols with much emphasize on Non delay routing protocols. Various parameters such as packet delivery ratio , end to end delay , throughput and cost were analyzed using hybrid NDTN routing protocols. It also focuses on various aspects of VANET like architecture, an overview of routing protocols, position based routing protocols, classification and related works contributed by various researchers.

Beacon,Overlay,End to End delay, Packet Delivery ratio

[1] Wenshuang Liang1, Zhuorong Li1, Hongyang Zhang1,Yunchuan Sun2, and Rongfang Bie1,”Vehicular Ad Hoc Networks: Architectures,Research Issues, hallenges and Trends “ Springer International Publishing Switzerland 2014.
[2] Hannes Hartenstein,University of Karisruhe,Kenneth .P.Laberteaux,Toyota Technical center, ” A Tutorial Survey on Vehicular Adhoc Networks” ,IEEE Communication Magazine June 2008
[3] Neha goel ,Isha Dyani,Gaurav sharma,.”A Study of Position based VANET routing protocol”, IEEE 2016
[4] Balasubramani, Karthikeyan.L Deepalakshmi.V. “Comparison study on Non Delay Tolerant Routing protocols in vehicular networks “ , Science Direct-Proceedia -2015
[5] R.Brendha, Dr.V.Sinthu Janita Prakash,”A Survey on Routing protocols for Vehicular Ad Hoc Networks”,2017,International conference on Advanced computing and communication(ICACCS 2017)
[6] Zineb Squali Hussaini,Imane Ziami- ‘Improvement of GPSR protocol by using Future position estimation of participating nodes in VANET’, IEEE 2016, pg 87-94.
[7] Rewadkar, Dharmpal Doye, (2017) “FGWSO-TAR:Fractional Glowworm swarm optimization for traffic aware routing in urban VANET”, International Journal of Communication Systems Volume 31, Issue 1,Wiley Online Library.
[8] Moez Jerbi* Rabah Merdah, “GyTAR: Improved Greedy Traffic Aware Routing Protocol for Vehicular Ad Hoc Networks in City Environments” ,Proceeding of the 3rd international workshop on Vehicular Adhoc networks ISBN: 1-59593-540-1.

[9] Konstantinos Katsaros, Mehrdad Dianati, “CLWPR - A Novel Cross-Layer Optimized Position Based Routing Protocol for VANETs”, Vehicular Networking Conference (VNC), 2011 IEEE.
[10] Anant Ram ,Manas Kumar Mishra ,”Density Aware Position Based Routing (DAPBR) protocol for VANET “, 2016 pages 142-146,IEEE Conference.
[11] Gen Li,Maode Ma ,(2015), “Adaptive fuzzy multiple attribute decision routing in VANET”- Wiley Online Library https:/doi.org/10.1002/dac.3014.
[12] Abdel muttlib Ibrahim Abdalla Ahmed,Abdullah gani,(2017), “Intersection Based Distance and Traffic Aware Routing (IDTAR) protocol for Smart Vehicular Communication”, IEEE 2017. 13th International Wireless Communications and Mobile Computing Conference (IWCMC) Year: 2017 Pages: 489 – 493.
[13] Harinder Kaur, Meenakshi ,” Analysis of VANET Geographic Routing Protocols on Real City Map”, 2017 2nd IEEE International Conference On Recent Trends in Electronics Information & Communication Technology (RTEICT), May 19-20, 2017, India
[14] Neha Goel,Isha Dhyani,Gaurav sharma-“An Acute Position Based VANET routing protocol”- IEEE 2016, International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE) Year: 2016, Pages: 139 – 144 .
[15] Nizar Alsharif Xuemin Shen “iCAR-II: Infrastructure-Based Connectivity Aware Routing in Vehicular Networks”, Page(s): 4231 – 4244,IEEE 2016
[16] Ahmed Nazar Hassan, Omprakash Kaiwartya , ”Inter Vehicle Distance Based Connectivity Aware Routing in Vehicular Adhoc Networks“ -Springer Science+Business Media,LLC 2017.
[17] Michele Rondinone , Javier Gozalvez ,” Contention-based forwarding with multi-hop connectivity awareness in vehicular ad-hoc networks”,Elsevier Computer Networks ,Volume 57, Issue 8, 4 June 2013, Pages 1821-1837
[18] Kamlesh Kumar Rana, Sachin tripathi, Ram Shringer Raw, “Analytical Analysis of Improved Directional Location Added Routing Protocol for VANETs”, Springer Science+Business Media,LLC2017 DOI 10-1007 /11277-017-4980-y.
[19] Raj K Jaiswal, “Predicted Position Based Routing Protocol Using Kalman Filter for Vehicular Ad-hoc Network”, ICDCN `17 Proceedings of the 18th International Conference on Distributed Computing and Networking.

S. Suguna Devi, A. Bhuvaneswari, "An Ethical Survey on Non-Delay Tolerant Routing Protocols for VANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.705-711, 2018.

Vehicular Adhoc Networks is an emerging technology for the communication of vehicles with the support of Intelligent Transportation Systems(ITS) to avoid fatal collisions and crashes on road and to ensure the road safety among the vehicles. The vehicles can be communicated through a wireless medium. Due to the rapid movement of vehicles and change in the topology leads to some message overheads with delays. Hence connectivity among the vehicles disappears and there is a need to identify an optimal path to be identified from source to destination for effective routing. A cluster will be formed to achieve a better link stability between the nodes and able to forward data to other members in case of any emergency situation. The safety message will be disseminated to other nodes in a limited short span of time. This paper gives a review on Vehicular Adhoc Networks(VANET) with its architecture, applications and various strategies which can be applied to form a cluster that yields high performance and evaluated with Quality of Service parameters.

VANET, ITS, Cluster, Routing Protocols, DSR

[1]. Advanced Technologies for Intelligent Transportation Systems- Marco Picone,Stefano Busanelli,Michelle Amoretti,Francesco Zanichelli,Gianluigi Ferrari.
[2]. Saleh Yousefi,Mahmoud Siadat Mousavi and Mahmood Fathy,”Vehicular Ad Hoc Networks(VANETs):Challenges and Perspectives”, IEEE ITS Telecommunication Proceedings, pp.761-766, 2006.
[3]. Poonam Dhamal,Uma Nagaraj and Disha Devotale,”Study of various routing protocols in VANET”,Elixir Adoc Network,Vol.37A, pp.4033- 4039,2011.
[4]. Vehicular Networks-From Theory to Practices-.Stephen Olariu,Michele C.Weigle.
[5]. Samira Harrabi,Ines Ben Jaffar and Khaled Ghedira,”Novel Optimized Routing Scheme for VANETs”,Elsevier ScienceDirect Procedia Computer Science,Vol.98, pp.32-39,2016.
[6]. Jaskaran Preet Singh and Rasmeet S.Bali,”A Hybrid Backbone based Clustering Algorithm for Vehicular Adhoc Networks”, Elsevier ScienceDirect Procedia Computer Science,Vol.46,pp.1005-1013,2015.
[7]. Sourav Chhabra,Rasmeet Singh Bali and Neeraj Kumar,”Dynamic Vehicle Ontology Based Routing for VANETs”,Elsevier ScienceDirect Procedia Computer Science,Vol.57,pp.789-797,2015.
[8]. Ahmad Abuashour and Michel Kadoch,”Performance Improvement of Cluster-Based Routing Protocol in VANET”,IEEEAccess,Vol:5,2017,pp:15354-15368.
[9]. Haigang Gong,Nianbo Liu,Lingfei Yu and Chao Song,”An Efficient Data Dissemination Protocol with Roadside Parked Vehicles’Assistance in Vehicular Networks”,Hindawi Publishing Corporation International Journal of Distributed Sensor Networks,Vol.2013,pp. 1- 12,2013.
[10]. Eduardo Cambruzzi,Jean-Marie Farines,Werner Kraus and Raimundo Macedo,”A Cluster Management System for VANETs”,Springer Science+Business Media,Vol.14,pp.115-126,2016.
[11]. Yen-Wen Lin,Hao-Chun Weng,Tsung-Han Lee and Shan-Yin Hou,”An Adaptive Clustering Scheme for Improving the Scalability in Intelligent Transportation Systems, Hindawi Publishing Corporation International Journal of Distributed Sensor Networks,Vol.2013,pp.1-18,2013.
[12]. Raghavendra Pal,Nishu Gupta,Arun Prakash and Rajeev Tripathi,”Adaptive Mobility and Range Based Clustering Dependent MAC Protcol for Vehicular Ad Hoc Networks”,Springer Science+Business Media,2017.
[13]. A.Malathi and Dr.N.Sreenath,”An Efficient Clustering Algorithm for VANET”,International Journal of Applied Engineering Research,Vol.12,pp.2000-2005,2017.
[14]. Samo Vodopivec,Janez Bester and Andrej Kos,”A Multihoming Clustering Algorithm for Vehicular Ad Hoc Networks”, Hindawi Publishing Corporation International Journal of Distributed Sensor Networks,Vol.2014,pp. 1-8,2014.
[15]. Mazen Alowish,Yasuhiro Takano,Yoshiaki Shiraishi and Masakatu Morii,”Performance Evaluation of Cluster Based Routing Protocol for VANETs”,Journal of Communications,Vol.12,pp.137-144,2017.
[16]. Mengying Ren,Lyes Khoukhi,Houda Labiod,Jun Zhang and Veronique Veque,”A Mobility based Scheme for Dynamic Clustering in Vehicular Ad-hoc Networks(VANETs)”,Vehicular Communications,2016.
[17]. Waqar Farooq,Muazzam Ali Khan and Saad Rehman,”A Novel Real Time Framework for Cluster Based Multicast Communication in Vehicular Ad Hoc Networks”, Hindawi Publishing Corporation International Journal of Distributed Sensor Networks,Vol.2016,pp. 1-18,2016.

D. Radhika, A. Bhuvaneswari, "A Review on Cluster Based Routing Protocols in Vehicular Adhoc Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.712-718, 2018.

Nowadays a newborn child as a immature death is increasing due to not proper monitoring in incubator by the people. if automatic control and monitor system is needed for the hospital to avoid the new born child death. The proposed work is design a new device to monitor baby condition and send alert call to parents, nurses and doctors. The arduino based New Born child incubator helps to all peoples, the cost this proposed works very less than today’s New Born child incubator which are used in big hospital. So, everyone which belongs to economical backward also use of it. This proposed work not only used for monitoring and controlling the temperature but also provide number of advantages such as controlling humidity, monitoring weight, detection of sound etc.

Component, Formatting, Style, Styling, Insert (key words)

[1] Med A.Z.,Elyes F., and Abdelkader M., “American Journal of Engineering and Applied Sciences”, Application of Adaptive Predictive Control to a Newborn Incubator, 4 (2): 235-243, ISSN 1941-7020, 2011.
[2] Joshi N.S., Kamat R. K., andGaikwad P. K., “International Journalof Advanced Computer Research”, Development of Wireless Monitoring System for Neonatal Intensive Care Unit, (ISSN-print): 2249-7277, ISSN (online: 2277-7970) , Volume-3, Number-3 , Issue-11, September-2013.
[3] Olson K.R., and Caldwell A.C. , “Engineering in Medicine and Biology Society (EMBC)”, Designing an early stage prototype using readily available material for a neonatal incubator for poor settings, 2010 Annual International Conference of the IEEE , pp. 1100 – 1103, 2010.
[4] Kumar P., AkshayNaregalkar K., Thati A., SamaA., “International Journal of Application or Innovation in Engineering & management”, Real Time Monitoring And Control Of Neonatal Incubator Using Lab VIEW,ISSN 2319 – 4847, Volume 2, Issue 4, April 2013.
[5] Paradiso R., Loriga G., and Taccini N., “Information Technology inBiomedicine”, A wearable health care system based on knitted integrated sensors, IEEE Transactions on, vol. 9, pp. 337-344, 2005.

R. Kalai Magal , "A New Device to Monitors New Born Child Health Conditions in an Incubator with Wireless Technology," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.719-722, 2018.