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Wireless sensor networks are composed of many cheap sensor nodes with limited sensing, computation, and communication capabilities. These networks have a variety of applications in both military and civilian usage, including battlefield surveillance, target tracking, environmental and health care monitoring, wildfire detection, and traffic regulation. Because of the need to low deployment cost of wireless sensor networks, sensor nodes have simple hardware and this leads severe resource constraints. Bearing in mind the limited resources of sensor nodes, it is critical to minimize the amount of data transmission to improve the average sensor lifetime and the overall bandwidth utilization. The process of summarizing and combining sensor data, which is used to reduce the amount of data transmission in the network, is referred to as data aggregation. Adopting an appropriate data aggregation is significantly important for improving the data accuracy, latency, fault-tolerance, and security. This paper reviews the data aggregation protocols in wireless sensor networks based on the existing research. The study can provide future research directions in this area.

Wireless Sensor Network, Data Aggregation

[1] I. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, "Wireless sensor networks: a survey", Computer Networks, vol. 38, no. 4, pp. 393-422, 2002.
[2] K. Sohrabi, J. Gao, V. Ailawadhi and G. Pottie, "Protocols for self- organization of a wireless sensor network", IEEE Personal Communications, vol. 7, no. 5, pp. 16-27, 2000.
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[6] I. Akyildiz and I. Kasimoglu, "Wireless sensor and actor networks: research challenges", Ad Hoc Networks, vol. 2, no. 4, pp. 351-367, 2004.
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[8] Z. Gui, Y. Fan, “An Application of Wireless Sensor Networks in Health Care Setting, Part I”, International Journal of Soft Computing and Engineering, vol. 3. no.4, pp.163-166, 2013.
[9] Z. Gui, Y. Fan, “An Application of Wireless Sensor Networks in Health Care Setting, Part II”, International Journal of Soft Computing and Engineering, vol. 3. no.4, pp.167-171, 2013.
[10] J. Chang and P. Ju, "An energy-saving routing architecture with a uniform clustering algorithm for wireless body sensor networks", Future Generation Computer Systems, vol. 35, pp. 128-140, 2014.
[11] C. Intanagonwiwat, D. Estrin, R. Govindan, J. Heidemann, Impact of network density on data aggregation in wireless sensor networks, in: Proceedings of the 22nd International Conference on Distributed Computing Systems, Vienna, Austria, pp. 457–458, 2002.
[12] C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann and F. Silva, "Directed diffusion for wireless sensor networking", IEEE/ACM Transactions on Networking, vol. 11, no. 1, pp. 2-16, 2003.
[13] B. Krishnamachari, D. Estrin, S. Wicker, The impact of data aggregation in wireless sensor networks, in: Proceedings of the 22nd International Conference on Distributed Computing Systems Workshops, Vienna, Austria, pp. 575–578, 2002.
[14] M. Ding, X. Cheng, G. Xue, Aggregation tree construction in sensor networks, in: Proceedings of the 58th IEEE Vehicular Technology Conference, Florida, USA, pp. 2168–2172, 2003.
[15] R. Cristescu, B. Beferull-Lozano, M. Vetterli, “On network correlated data gathering”, in: Proceedings of the 23rd Annual Joint Conference of the IEEE Computer and Communications Societies, Hong Kong, China, pp. 2571–2582, 2004.
[16] S. Madden, M. Franklin, J. Hellerstein and W. Hong, "TAG", ACM SIGOPS Operating Systems Review, vol. 36, no., p. 131, 2002. [17] B. Zhou, L. Ngoh, B. Lee and C. Fu, "HDA: A hierarchical data aggregation scheme for sensor networks", Computer Communications, vol. 29, no. 9, pp. 1292-1299, 2006.
[18] M. Lee, V.W.S. Wong, An Energy-Aware Spanning Tree Algorithm for Data AggrAegation in Wireless Sensor Networks, in IEEE Pacific Rim Conference on Communications, Computers and signal Processing, BC, Canada, 2005.
[19] S. Lindsey, C. Raghavendra and K. Sivalingam, "Data gathering algorithms in sensor networks using energy metrics", IEEE Transactions on Parallel and Distributed Systems, vol. 13, no. 9, pp. 924-935, 2002.
[20] G. Di Bacco, T. Melodia, F. Cuomo, “A MAC Protocol for DelayBounded Applications in Wireless Sensor Networks”, In Proc of ThirdAnnual Mediterranean AdHoc Networking Workshop, Bodrum, Turkey, pp. 208-220, 2004.
[21] N. Nokhanji and Z. Hanapi, "A Survey on Cluster-based Routing Protocols in Wireless Sensor Networks", Journal of Applied Sciences, vol. 14, no. 18, pp. 2011-2022, 2014.
[22] Y. Kim, E. Jung and Y. Park, "A Radio-Aware Routing Algorithm for Reliable Directed Diffusion in Lossy Wireless Sensor Networks", Sensors, vol. 9, no. 10, pp. 8047-8072, 2009.
[23] W. Heinzelman, A. Chandrakasan and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks", IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp. 660-670, 2002.
[24] O. Younis and S. Fahmy, "HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks", IEEE Transactions on Mobile Computing, vol. 3, no. 4, pp. 366-379, 2004. [25] Y. Yao and J. Gehrke, "The cougar approach to in-network query processing in sensor networks", ACM SIGMOD Record, vol. 31, no. 3, p. 9, 2002.
[26] S. Chatterjea, P. Havinga, A dynamic data aggregation scheme for wireless sensor networks, in: Proceedings of the Program for Research on Integrated Systems and Circuits, Veldhoven, Netherlands, 2003.
[27] V. Mhatre and C. Rosenberg, "Design guidelines for wireless sensor networks: communication, clustering and aggregation", Ad Hoc Networks, vol. 2, no. 1, pp. 45-63, 2004.
[28] P. Popovski F.H.P. Fitzek, H. Yomo, T.K. Madsen, R. Prasad, N.J. Vej, MAC-Layer Approach for Cluster-Based Aggregation in Sensor Networks, in International Workshop on Wireless Ad-Hoc Networks, Oulu, Finland, 2004.
[29] S. Pattem, B. Krishnamachari and R. Govindan, The impact of spatial correlation on routing with compression in wireless sensor networks", ACM Transactions on Sensor Networks, vol. 4, no. 4, pp. 1- 33, 2008.
[30] A. Nasridinov, S. Ihm and Y. Park, "Skyline-Based Aggregator Node Selection in Wireless Sensor Networks", International Journal of Distributed Sensor Networks, vol. 9, no. 9, p. 356194, 2013.

O. Pourgalehdari, M. Salari, "A Review on Data Aggregation Protocols in Wireless Networks," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.50-56, 2017.

An emerging technology aiming to connect the surrounding environmental things to the network and making the access to the same in a very easy way is Internet of Things- IOT. The paper focuses on the same technology which is useful for identifying, disseminating the known facts and connect the entire world under a single system. The work here creates an android application for providing a parking solution to smart cities. This literature aims to resolve the parking issues and other related problems as traffic, pollution, over fuel consumption etc. by public involvement. This application holds good for both public and government as well which in turn helps decrease congestions on roads.

Internet of Things (IOT), Smart Parking System (SPS) , Parking, Security, Addressability, M-M communication

[1] L. A.A. Iera, G. Morabito, “The Internet of things: a survey,” Computer Networks”, vol. 54, no. 15, pp. 2787-2805, 2010.
[2] K.Karimi, G. Atkinson, “What the Internet of Things (IoT) Needs to Become a Reality”, White Paper, Free Scale and ARM, 2013.
[3] M. Albano, A. Brogi, R. Popescu, M. Diaz, and J. A. Dianes, “Towards secure middleware for embedded peer-to-peer systems: Objectives and requirements” in RSPSI ’07:
[4] T. Taleb and A. Kunz, “Machine Type Communications in 3GPP Networks: Potential, Challenges, and Solutions,” IEEE Communication. Mag.
[5] S. Sharma, Chhatarpal, R. Harijan: “Survey on Internet of Things and Design for a Smart Parking Area.” International Journal of Inventive Engineering and Sciences (IJIES), ISSN: 2319–9598, Volume-2 Issue-9, 2014
[6] S. V. Reve, S. Choudhri “Management of Car Parking System Using Wireless Sensor Network International Journal of Emerging Technology and Advanced Engineering”, ISSN 2250-2459, Volume 2, Issue 7, 2012
[7] M. Ahmed, W. Guang “Study on Automated Car Parking System” International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 3 Issue 1, January – 2014
[8] Pala Z, Inanc N. “Smart Parking Applications Using RFID Technology” Published in: RFID Eurasia, 2007 1st Annual Date of Conference: 5-6 Sept. 2007 ISBN: 978-975-01566-0-1 INSPEC Accession Number: 9776964 Publisher: IEEE
[9] Hangzhou, Z. China “Wireless Mobile-Based Shopping Mall Car Parking System (WMCPS)” Dec. 6, 2010 to Dec. 10, 2010 ISBN: 978-0-7695-4305-5

A. Joshi, K.S. Kharade, V.S. Patil, D.A. Kulkarni , "IoT Based Smart Parking for Metro Cities," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.57-59, 2017.

The MANET is the self-configuring network in which mobile nodes can join or leave the network any time. Due to its decentralized nature routing is the major concern or major issue on MANET. In this work properties of reactive, proactive and hybrid is highlighted and discuss in terms of discussion or concern. In this work best performing AODV protocol for path establishment for improved using bio inspire techniques.

Mobile Ad Hoc Networks, Routing Protocol, AODV, DSDV, DSR

[1] G.V. Kumar, Y.V. Reddyr, Dr.M. Nagendra, “Current Research Work on Routing Protocol for MANET: A Literature Survey”, in : International Journal on Computer Science and Engineering (IJCSE), Vol. 2(3), 2013.
[2] L. Pal, P. Sharma, N. Kaurav and S.L. Mewada, "Performance Analysis of Reactive and Proactive Routing Protocols for Mobile Ad-hoc –Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1(1), pp.1-4, 2013 .
[3] Internet Engineering Task Force, “MANET working group charter Available“from: IETF MANET group Character Sector , 2010.
[4] K. Majumder and S.K. Sarkar, “Performance analysis of AODV and DSR Routing Protocols in Hybrid Network Scenario”, Proc. IEEE transactions on networking, pp. 1-4 , 2009.
[5] K. Pandey, A. Swaroop, “A Comprehensive Performance Analysis Of Proactive, Reactive and Hybrid MANETs Routing Protocols”, International Journal of computer Science Issues, Vol. 8 (6), pp. 432-441, 2011.
[6] N. Surayati, M. Usop, A. Abdullah, “Performance Evaluation of AODV, DSDV & DSR Routing Protocol in Grid Environment”, International Journal of Computer Science and Network Security (IJCSNS), Vol. 9, pp. 191-196, 2009.
[7] S.A. Ade1, P.A. Tijare, “Performance Comparison of AODV, DSDV, OLSR and DSR Routing Protocols in MANET”, International Journal of Information Technology and Knowledge Management, Vol. 2, pp. 545-548, 2010.
[8] S. Mohseni, R. Hassan, A. Patel, and R. Razali, “Comparative Review Study of Reactive and Proactive Routing Protocols in MANETs”, in: 4th IEEE International Conference on Digital Ecosystems and Technologies 2010 IEEE, pp- 304-309, 2010 .
[9] M. Nachammai, N. Radha, "Survey on Black Hole and Gray Hole Attacks in MANET", International Journal of Computer Sciences and Engineering, Vol.4(5), pp.66-70, 2016.
[10] S. Mawada, U.K. Singh and P. Sharma, “A novel security based model for wireless mesh networks”, Int. J. Sci. Res. Network Security and Communication, Vol.1(1), pp.11-15, 2013.
[11] X. Hong, K. Xu, M. Gerla, “ Scalable routing protocols for mobile ad hic networks”, in: Network IEEE, Vol. 16, Issue 4, july 2002, pp. 11-21.
[12] Arma Amir Mehdi, “Performance Evaluation with Throughput, Packet Delivery on Routing Protocols in MANETs” , in: International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6(2), 2016.
[13] V.G. Babu , "Efficient Mobility Using Multicast Routing Mechanisms", International Journal of Computer Sciences and Engineering, Vol.1(3), pp.39-46, Nov -2013
[14] D. Tamrakar, S. Bhattacharya and S. Jain, "A Scheme to Eliminate Redundant Rebroadcast and Reduce Transmission Delay Using Binary Exponential Algorithm in Ad-Hoc Wireless Networks", International Journal of Scientific Research in Network Security and Communication, Vol.2(2), pp.1-5, 2014.
[15] R. Parasher, Y. Rathi, “A_AODV: A Modern Routing Algorithm for Mobile Ad-Hoc Network” in: International Research Journal of Engineering and Technology (IRJET) Vol 2(1), e-ISSN: 2395 -0056, p-ISSN: 2395-0072, 2015.
[16] R. Pandey,S. Solanki, R. Dubey, “Improved Performance of AODV Routing Protocol with Increasing Number of Nodes using Traveling Salesman Problem” ,International Journal of Computer Applications , Vol. 98, pp. 0975 – 8887, 2014.
[17] M. Vajed, S. Jamali, “Performance Comparison of AODV, DSDV, DSR and TORA Routing Protocols in MANETs” in: International Research Journal of Applied and Basic Sciences.Vol. 3(7), pp. 1429-1436, 2012.

J. Kaur, G.Singh, "MANET Routing Protocols: A Review," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.60-64, 2017.

This research paper is aimed at the detection of dengue disease using Artificial Neural Network (ANN). Necessary data was collected from Jalpaiguri Sadar Hospital for training the net. An ANN was designed which detects dengue disease. North Bengal region was aimed for the analysis.

Disease Detection, Artificial Neural Network

[1] B. Cetiner, M. Sari, H. Aburas, “Recognition of dengue disease patterns using artificial neural networks”, 5th International Advanced Technologies Symposium (IATS’09), May 13-15, 2009, Karabuk, Turkey.
[2] F. Elijorde, D. Clarite, B. Gerardo, Y. Byun, “Tracking and prediction of dengue outbreak using cloud-based services and artificial neural network”, International Journal of Multimedia and Ubiquitous Engineering Vol.11, No.5 (2016), pp.355-366 .
[3] F. Gharehchopogh, M. Molany, F. DabaghchiMokri, “Using artificial neural network in diagnosis of thyroid disease”: a case Study, International Journal Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013.
[4] R. Dey, V. Bajpai, G. Gandhi, B. Dey, “Application of Artificial Neural Network (ANN) technique for Diagnosing Diabetes Mellitus”, the Third international Conference on Industrial and Information Systems(ICIIS), PP.1-4, IEEE, Kharagpur, India,2008..
[5] F.S. Gharehchopogh, Z.A. Khalifelu, “Neural Network Application in Diagnosis of Patient: A CaseStudy”, International Conference on Computer Networks and Information Technology (ICCNIT), PP. 245 – 249, Abbottabad, Pakistan, 2011.Study”, International Conference on Computer Networks and Information Technology (ICCNIT), PP. 245 – 249, Abbottabad, Pakistan, 2011.
[6] L Fausett, “Fundamentals of Neural Networks”, Pearson Publisher. pp.1-11, 1993.

P. Saha, R. Mandal, "Detection of Dengue Disease Using Artificial Neural Networks," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.61-64, 2017.

Mobile ad hoc network is also known as MANET. MANET is a collection of wireless device which dynamically form a network topology without any pre-existing network infrastructure. In MANET, mobile node can move freely from one place to another and thus network topology is kept changing every time because of self-organization and self-configuration. In MANET, nodes can communicate with each other without any centralized devices and they will be able to exchange information between themself. All nodes act as router between itself to receive packet and forward to itss destination. In order to facilitate nodes to communicate each other in network, they use Routing Protocol. The main purpose of Routing protocol is to facilitate communicate between nodes and to forward packet to destination accurately. There are difference types of Routing protocol used in MANET some of them are DSDV and AODV which will be discussed.

MANET, DSDV, AODV

[1] R.L. Biradar, “Survey Paper on MANET’s”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3(2), pp.1375-1382, 2015.
[2] K.A Talwar, S.M. Benakappa, B.N. Yuvaraju, “A Survey: Routing Protocols in MANETs”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2(7), pp. 4969-4973, 2014.
[3] H. Bakht, “Survey of Routing Protocols for Mobile Ad-hoc Network”, International Journal of Information and Communication Technology Research, Vol. 1(6), pp.258-270, 2011.
[4] Aarti, S.S. Tyagi, “Study of MANET: Characteristics, Challenges, Application and Security Attacks”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3(5), pp.252-257, 2013.
[5] S.S Dhenakaran, A. Parvathavarthini, “An Overview of Routing Protocols in Mobile Ad-Hoc Network” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3(2),pp.251-259, 2013.
[6] V.G. Muralishankar, E.G.D.P Raj, “Routing Protocols for MANET: A Literature Survey”, International Journal of Computer Science and Mobile Applications, Vol. 2(3), pp. 18-24, 2014.
[7] M.R. Kumar, N. Geethanjali, “A Literature Survey of Routing Protocols in MANETs”, International Journal of Science and Research, Vol. 2(4), pp. 33-40, 2013.
[8] R. Patel, Anjuman Ranavadiya, Shreya Patel, “A Survey Paper on AODV Routing Protocol for MANET”, International Journal for Scientific Research & Development, Vol. 21(10), pp. 141-145, 2014
[9] R.R. Ravi, V. Jayanthi, “A Survey of Routing Protocol in MANET”, International Journal of Computer Science and Information Technologies, Vol. 5 (2), pp. 1984-1988, 2014.
[10] S.R. Kumar, N. Thillaiarasu, “A Survey of Secure Routing Protocols of Mobile Ad Hoc Network” International Journal of Computer Science and Engineering, Vol. 2(2), pp.39-44, 2015.
[11] H. Kaur, “A Survey on Manet Routing Protocols”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 5(1), pp.511-514, 2015.
[12] K.S. Bhavsar, “Comparison of DSDV and AODV Routing Protocol in Mobile Ad hoc Network” , International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 6(3), pp. 404-411, 2016.
[13] A.K. Gupta, H. Sadawarti, A.K. Verma, “Review of Various Routing Protocols for MANETs”, International Journal of Information and Electronics Engineering, Vol. 1(3), pp.251-259, 2011.
[14] G.V. Kumar, Y.V. Reddyr, M. Nagendra, “Current Research Work on Routing Protocols for MANET: A Literature Survey”, International Journal on Computer Science and Engineering Vol. 02(03), pp.706-713, 2010.

P.W. Dkhar, R. Khongthaw, "Survey Paper on DSDV and AODV Routing Protocol of MANET," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.69-74, 2017.

The Agricultural sector is a very important one in the developing countries. In agriculture domain it is very difficult to predict the price of the vegetable, so making use of the prediction technique like neural networks the price is predicted. In this paper a prediction model is established with the help of Adaptive neuro-fuzzy inference system and compares the result with other models. The result for the proposed prediction model is more efficient and accurate than other neural network models for predicting the price of the vegetables.

Data mining, Back-Propagation neural network (BPNN),Redial basis Function (RBF), ANFIS, Vegetible Price

[1] Guo Qiang, LUO Chang-shou, WEI Qing-feng .,“Prediction and research on vegetable price based on genetic algorithm and Neural network model” , Asia Agricultural Research 3(5):148-150 2011.
[2] N. Hemageetha ,G.M. Nasira, “Analysis of the Soil Data Using Classification Techniques for Agricultural Purpose “, International Journal of Computer Sciences and Engineering Vol-4 Issue 6, PP 118-122 , 2016.
[3] K. G. Akintola ,B.K. Alese and A.F. Thompson., “Timeseries forecasting with neural network –a case study of stock price of intercontinental bank Nigeria” IJRRAS Dec2011.
[4] Chapgshou Luo, Qingfeng Wei, Liying Zhou, Jungeng Zhang and R. Suien Sun, “Prediction of vegetable price based on Neural Network and Genetic Algorithm”. IFIP AICT 346, PP. 672-681 © Springer link 2011.
[5] G.M. Nasira and N. Hemageetha, “Vegetable price prediction using data mining classification technique” , International Conference on pattern Recognition, Informatics and Medical Engineering (PRIME 2012), PP. 99-102 ISBN No:978-1-4673-1037-6.
[6] T. Jayalskshmi, A. Santhakumar, “Statistical Normalization and Back propogation for classification” International Journal of computing Theory nad Engineering, Vol 3- No 1 Feb2011.
[7] V. Vaidhehi ,”The roll of Dataset in training ANFIS system for course Advisor”, International Journal of Innovation research in Advanced Engineering vol 1 Issus 6 July2014.
[8] G.M. Nasira and N. Hemageetha, “Forecasting Model for Vegetable Price Using Back Propagation Neural Network” International Journal of Computational Intelligence and Informatics,Vol. 2: No. 1, pp.110—115, Sep 2012.
[9] N. Hemageetha and G.M. Nasira, “Redial bassis function model for Vegetable Price Prediction “ International Conference on pattern Recognition, Informatics and Medical Engineering (PRIME 2013), PP. 424—428 ISBN No:978-1-4673-5843-9.

N. Hemageetha, G.M. Nasira , "Vegetable Price Prediction using Adaptive Neuro-Fuzzy Inference System," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.75-79, 2017.

Present day, cities are not restricted to only constructions and physical infrastructures. They are embracing knowledge based system extending from sensor networks to public databases. Big data is a very valuable resource to make cities extra advanced, intellectual, and unified. Cities are responsible for maintaining a safe and secure place for the public. Big data and technologies are revolutionizing how cities can locate, mitigate and prevent safety issues. This Project involves design and implementation of an application based interface for the Crime Dataset of the city. The application will help public as well as researchers to find safe and habitable locations in development of Smart cities.

Smart City Project,Challenges,Assessment

[1] The Govt. of Hong Administrative Region, “Research Report on Smart City”, September 2015.
[2] S. Chainey and J. Ractcliffe, “GIS and Crime Mapping”, Wiley, 2005.
[3] N. Barberies, A. Shleifer and R. Vishny, “A Model of Investor Sentiment”, Journal of Financial Economics, 49:307-243, 1998.
[4] Senate Department for Urban Development and the
Environment, “Smart City Strategy Berlin”, 21 April
2015.
[5] K. R. Kunzmann, “Smart cities: A new paradigm of urban development”, Crios, vol. 4, no. 1, pp. 9–20, 2014.
[6] A. Bartoli, J. Hern´andez-Serrano, M. Soriano, M. Dohler, A. Kountouris, D. Barthel, “Security and privacy in your smart city”, in Proceedings of the Barcelona Smart Cities Congress, 2011.
[7] A. S. Elmaghraby and M. M. Losavio, “Cyber security challenges in smart cities: Safety, security and privacy”, Journal of Advanced Research, vol. 5, no. 4, pp. 491–497, 2014.
[8] R. Kitchin, “The real-time city? big data and smart urbanism”, GeoJournal, vol. 79, no. 1, pp. 1–14, 2014.
[9] C. Schmitt, “Security and privacy in the era of big data”, 2014.
[10] M. Sen, A. Dutt, S. Agarwal, and A. Nath, “Issues of privacy and security in the role of software in smart cities”, in Communication Systems and Network Technologies (CSNT), 2013 International Conference on. IEEE, 2013, pp. 518–523.
[11] A. P. A. Ling and M. Masao, “Selection of model in developing information security criteria on smart grid security system”, in Parallel and Distributed Processing with Applications Workshops (ISPAW), 2011 Ninth IEEE International Symposium on. IEEE, 2011, pp. 91–98.
[12] K. Su, J. Li, and H. Fu, “Smart city and the applications”, in Electronics, Communications and Control (ICECC), 2011 International Conference on. IEEE, 2011, pp. 1028–1031.
[13] Semantic, “Transformational smart cities: cyber security and resilience”, 2010.
[14] Zanella, N. Bui, A. P. Castellani, L. Vangelista, and M. Zorzi,“Internet of things for smart cities”, IEEE Internet of Things Journal, 2014.
[15] Q. Xiao, C. Boulet, and T. Gibbons, “Rfid security issues in military supply chains,” in Availability, Reliability and Security 2007 ARES 2007”, The Second International Conference on. IEEE, 2007, pp. 599– 605.
[16] D. Jiang and C. ShiWei, “A study of information security for m2m of iot”, in Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on, vol. 3. IEEE, 2010, pp. V3–576.
[17] C. Hennebert and V. Berg, “A framework of deployment strategy for hierarchical wsn security management”, in Data Privacy Management and Autonomous Spontaneus Security. Springer, 2012, pp. 310–318.
[18] Stephen Goldsmith, “Digital Transformations: Wiring
he Responsive City- Predictive Tools for Public Safety”,
2014, “http://datasmart.ash.harvard.edu/news/article
/predictive tools-for-public-safety-506.
[19] Kevin Ebi, “A day without serious crime? Cities reap real benefits from predictive policing”, smartcities council.com/crime-benefitspredictive-policing.pdf, January 2015
[20] “Survey Report on City of Chicago Data Portal”, data.cityofchicago .org/ Public-Safety / Crimes-2001/ ijzp-q8t2.pdf”, 2001.
[21] “Whet Moser Crime Follows Temperature in Chicago”, “chicagomag.com/Chicago-MagazineCrime-FollowsTemperature-In-Chicago.pdf, May 2013
[22] “Laura Bliss In Chicago Air Pollution Could Be Pushing Up Crime”,citylab.com/housingin-chicago-air-pollution could-be-pushing-upcrime.pdf, Dec 2015
[23] “Kevin Ebi How Durham, N.C. fights crime with data and wins”, smartcitiescouncil.com/article/how-durham-nc-fights-crime-data.pdf, 2014

A.P. Khan, G.S. Patil, R. Chaudhari, N.P. Patil, "A Survey on Smart City Crime Awareness," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.80-83, 2017.

Wireless sensor networks are consisting of several sensor nodes that collect information from their surroundings and then transmit to the end user. In wireless sensor networks, the battery is the main issue. Network lifetime of network depend upon the sensor’s communication. Various methods and techniques are used to enhance the network lifetime. Some authors’ focuses on the mobile sink which is used to reduce the energy consumption. The low energy adaptive clustering hierarchy (LEACH) is an effective algorithm where all the nodes within the cluster send their respective data to the local cluster head.[1] Here, the cluster head selection is done by the technique neural network (NN) in the area of 150*150, 200*200 and 250*250, 350*350, 450*450 which provides greater functionality in the homogeneous WSNs.

Wireless sensor network, battery, Network lifetime, clustering, LEACH, mobile sink ,neural network

[1] S. Mottaghi and M. R. Zahabi, “Optimizing LEACH clustering algorithm with mobile sink and rendezvous nodes,” AEU-International J. Electron. Commun., Vol. 69(2), pp. 507–514, 2015.
[2] K. Hussain, A. H. Abdullah, K. M. Awan, F. Ahsan, A. Hussain, and others, “Cluster head election schemes for WSN and MANET: a survey,” World Appl. Sci. J., Vol. 23(5), pp. 611–620, 2013.
[3] Y. Gu, Y. Ji, J. Li, and B. Zhao, “ESWC: efficient scheduling for the mobile sink in wireless sensor networks with delay constraint,” IEEE Trans. Parallel Distrib. Syst., Vol. 24(7), pp. 1310–1320, 2013.
[4] W. Liang, J. Luo, and X. Xu, “Prolonging network lifetime via a controlled mobile sink in wireless sensor networks,” in Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, pp. 1–6, 2010.
[5] W. Liu, K. Lu, J. Wang, G. Xing, and L. Huang, “Performance analysis of wireless sensor networks with mobile sinks,” IEEE Trans. Veh. Technol., Vol. 61(6), pp. 2777–2788, 2012.
[6] M. Sharma and A. K. Shaw, “Transmission time and throughput analysis of EEE LEACH, LEACH and direct transmission protocol: a simulation based approach,” Adv. Comput., Vol. 3(6), pp. 75, 2012.
[7] R. Munjal and B. Malik, “Approach for improvement in LEACH protocol for wireless sensor network,” 2012 Second International Conference on in Advanced Computing & Communication Technologies (ACCT), , pp. 517–521, 2012.
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I. Singh, Pooja, Varsha, "A Comparative Analysis of Optimizing Leach Clustering Algorithm with Mobile Sink in WSN," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.84-91, 2017.

Data mining is part of the interdisciplinary field of knowledge discovery in databases. Data Mining research began in 1980 and has grown rapidly in 1990s.Specific methods developed in disciplines such as artificial intelligence, machine learning and pattern recognition used in data mining. Data mining has been introduced in various sectors. key functional area of mining technology of the World Wide Web Recently mining techniques applied to the data in the field of criminal law, but that digital forensics. Examples can be found misleading to establish criminal identity criminal groups involved in illegal activities and much more. the politics of data mining technology typically generate a summary of large amounts of data. Digital Forensics is the area of research discoveries and advanced tip. Canvass search field, and digital forensic applications are developing rapidly with the economic giant digital information. law enforcement and military agencies have confidence in digital forensics today. Since the age of information is the speed of thought and data stored in digital form, the need for an accurate intellectual interception, and timely decision errors close to zero digital data processing cores issue. This article will research focusing on the role of data mining techniques for digital forensics. also identifies how data-mining techniques can be applied in the field, a digital forensic forensic examiner to take the next step in the process, which is cost-effective digital program is a crime.

Data Recovery, Forensic exploratory, Digital Forensic

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S. Umar, A. Praveen, S. Gouse, N. Deepthi, "Imminent accession of Artificial Intelligence based Forensic Exploratory with Data Mining Analysis," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.92-95, 2017.

Multiple sequence alignment is a very useful tool[18]. It is used to solve different Biological sequence alignment problem like DNA and Protein sequences[3]. There are many ways to solve multiple sequence alignment problems. Dynamic programming method is used to produce MSA directly[23]. Nowadays, progressive alignment approach and iterative approach are the important methods to solve MSA problems. This paper discussion is about some progressive alignment and iterative Multiple Sequence Alignment algorithm methods and compare their performances.

Progressive MSA, DNA, Progressive Alignment

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R. Karmakar, T.K. Sadhu, A. Hazra, S. Sahana, S. Karmakar, "A Comparative Study of Multiple Sequence Alignments," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.96-100, 2017.