Volume-4 , Issue-4 , Apr - 2016 Go Back

A Disturbance tolerant systems is a framework outlined temporary, have the unique features of discontinuous Framework which makes steering quite distinctive from other remote network. Steering misconduct like selfish or malignant hub can cause parcel delay furthermore, modifying parcels in a network. A hub is required to keep a few marked contact record of its past contact based on it the next hub can identify a parcel dropping, although here it may reduce the parcel conveyance proportion furthermore, waste the framework assets such as power furthermore, bandwidth. To reduce this issue we propose a plan as record handler, it is utilized to maintain the entire data about parcel independently furthermore, to give more security furthermore, we introduce RC4 calculation where the message furthermore, the key can be send person to hubs for avoiding misconduct on a network

Disturbance Tolerant Networks, Steering misbehavior, Mitigation

[1] M. Chuah; P. Yang, “Node Density-Based Adaptive Routing Scheme for Disruption Tolerant Networks”, MILCOM 2006 - 2006 IEEE Military Communications conference, Year: 2006, Pages: 1 – 6.
[2] B. Burns; O. Brock; B. N. Levine, “Autonomous enhancement of disruption tolerant networks”, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.Year: 2006, Pages: 2105 – 2110.
[3] Feng Li; Jie Wu” MOPS: Providing Content-Based Service in Disruption-Tolerant Networks”, Distributed Computing Systems, 2009. ICDCS '09. 29th IEEE International Conference on, Year: 2009, Pages: 526 – 533.
[4] Zhong Xu; Yuan Jin; Weihuan Shu; Xue Liu; Junhai Luo, “SReD: A Secure Reputation-based Dynamic Window Scheme for disruption-tolerant networks”, MILCOM 2009 - 2009 IEEE Military Communications Conference, Year: 2009, Pages: 1 – 7.
[5] Joshua Schoolcraft; Keith Wilson” Experimental characterization of space optical communications with disruption-tolerant network protocols”, Space Optical Systems and Applications (ICSOS), 2011 International Conference on, Year: 2011,Pages: 248 – 252.
[6] Qing Ye; Liang Cheng; Mooi Choo Chuah; B. D. Davison, “OS-multicast: On-demand Situation-aware Multicasting in Disruption Tolerant Networks”, 2006 IEEE 63rd Vehicular Technology Conference, Year: 2006, Volume: 1, Pages: 96 – 100.
[7] Yazhou Jiao; Zhigang Jin; Yantai Shu, “Data Dissemination in Delay and Disruption Tolerant Networks Based on Content Classification”, Mobile Ad-hoc and Sensor Networks, 2009. MSN '09. 5th International Conference on, Year: 2009, Pages: 366 – 370.
[8] Marcello Caleffi; Luigi Paura, “Opportunistic Routing for Disruption Tolerant Networks”, Advanced Information Networking and Applications Workshops, 2009. WAINA '09. International Conference on, Year: 2009,Pages: 826 – 831.
[9] Long Zhang; Xianwei Zhou, “A rough set performance evaluation approach for multicast routing strategies in delay and disruption tolerant networks”, Future Information Networks, 2009. ICFIN 2009. First International Conference on, Year: 2009,Pages: 280 – 284.
[10] Mark-Oliver Stehr; Carolyn Talcott” Planning and learning algorithms for routing in Disruption-Tolerant Networks” MILCOM 2008 - 2008 IEEE Military Communications Conference,Year: 2008,Pages: 1 – 8.
[11] Chang-Jun Luo; Ming-Tian Zhou; Zheng-Yin Cao” Disruption-Tolerant Wireless Sensor Networks for Wind Tunnel Monitoring”Apperceiving Computing and Intelligence Analysis, 2008. ICACIA 2008. International Conference on,Year: 2008,Pages: 408 – 411.
[12] Umesh Kumar Singh, Shivlal Mewada, Lokesh Laddhani and Kamal Bunkar, “An Overview & Study of Security Issues in Mobile Ado Networks”, International Journal of Computer Science and Information Security (IJCSIS) USA, Vol-9, No.4, pp (106-111), April 2011.
[13] Yuanyuan Mao; Yang Xia; Zoebir Bong; “Multi-policy link state routing for disruption tolerant networks”, Wireless and Mobile Networking Conference (WMNC), 2013 6th Joint IFIP, Year: 2013, Pages: 1 – 7.
[14] Qinghua Li; Guohong Cao, “Mitigating Routing Misbehavior in Disruption Tolerant Networks”, IEEE Transactions on Information Forensics and Security, Year: 2012, Volume: 7, Issue: 2, Pages: 664 – 675.

S.Dinesh Kumar, G.Sathish Kumar, "Delay Tolerant Network (DTN): A Substitute Result for Efficient Trust Establishment," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.252-257, 2016.

In later years, the popularity of cloud registering innovation is widely grown also, most associations want to use this innovation in their business processes. But on the other hand, the use of this innovation is not easy also, numerous associations are concerned about storing their sensitive data in their data focuses instead of storing them in the cloud capacity centers. In the cloud registering environment, trust, as an arrangement to upgrade the security, has attracted the consideration of researchers. Trust is one of the most imperative ways to improve the dependability of cloud registering assets given in the cloud environment also, has an imperative role in the business environments. Trusting the client to select the suitable source helps in heterogeneous cloud infrastructure. In this paper, we present the trust model based on models of suitable administration quality also, speed of usage for cloud resources. Reproduction results appear that the proposed model compared with comparative models, in expansion to taking into account measures of the quality of service, chooses the most solid source in a cloud environment by taking into account the speed of things.

Cloud Computing; Trust Model; Reliability; Availability; Quality of Service

[1] Wei Ou; Xiaofeng Wang; Wenbao Han; Yongjun Wang, “Research on Trust Evaluation Model Based on TPM”, 2009 Fourth International Conference on Frontier of Computer Science and Technology, Year: 2009, Pages: 593 – 597.
[2] Junshe Wang; Xiaolong Li; Yun Zhang, “Research of P2P Network Trust Model”, Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on, Year: 2013, Volume: 1, Pages: 70 – 73.
[3] Wenzhong Yang; Cuanhe Huang; Bo Wang; Tong Wang; Zhenyu Zhang, “A General Trust Model Based on Trust Algebra”, 2009 International Conference on Multimedia Information Networking and Security, Year: 2009, Volume: 1, Pages: 125 – 129.
[4] Hou Liping; Shi Lei” Research on Trust Model of PKI, “Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on, Year: 2011, Volume: 1, Pages: 232 – 235.
[5] Suhuan Sun; Changwei Zhao; Zhiyong Zhang, “A matrix factorization based trust factors model”, Information and Automation, 2015 IEEE International Conference on, Year: 2015,Pages: 803 – 808.
[6] D. W. Chadwick; A. J. Young; N. K. Cicovic, “Merging and extending the PGP and PEM trust models-the ICE-TEL trustmodel”, IEEE Network, Year: 1997, Volume: 11, Issue: 3,Pages: 16 – 24.
[7] Guangjie Han; Jinfang Jiang; Lei Shu; Mohsen Guizani, “An Attack-Resistant Trust Model Based on Multidimensional Trust Metrics in Underwater Acoustic Sensor Network”, IEEE Transactions on Mobile Computing,Year: 2015, Volume: 14, Issue: 12,Pages: 2447 – 2459.
[8] Umesh Kumar Singh, Shivlal Mewada, Lokesh Laddhani and Kamal Bunkar, “An Overview & Study of Security Issues in Mobile Ado Networks”, International Journal of Computer Science and Information Security (IJCSIS) USA, Vol-9, No.4, pp (106-111), April 2011.
[9] S.Ranjitha and D. Prabakar and S. Karthik, "A Study on Security issues in Wireless Sensor Networks", International Journal of Computer Sciences and Engineering, Volume-03, Issue-09, Page No (50-53), Sep -2015, E-ISSN: 2347-2693
[10] Yanli Yu; Keqiu Li; Yong Zhang; Lianpeng Xu, “A Service Trust Model with Passive Trust”, Network and Parallel Computing, 2008. NPC 2008. IFIP International Conference on, Year: 2008, Pages: 218 – 225.
[11] Wei Ou; Xiaofeng Wang; Wenbao Han; Yongjun Wang, “Research on Trust Evaluation Model Based on TPM”, 2009 Fourth International Conference on Frontier of Computer Science and Technology, Year: 2009, Pages: 593 – 597.
[12] Junshe Wang; Xiaolong Li; Yun Zhang, “Research of P2P Network Trust Model”, Research of P2P Network Trust Model, Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on, Year: 2013, Volume: 1,Pages: 70 – 73.
[13] Wenzhong Yang; Cuanhe Huang; Bo Wang; Tong Wang; Zhenyu Zhang, “A General Trust Model Based on Trust Algebra”, 2009 International Conference on Multimedia Information Networking and Security, Year: 2009, Volume: 1,Pages: 125 – 129.
[14] Hou Liping; Shi Lei, “Research on Trust Model of PKI”, Research on Trust Model of PKI”, Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on, Year: 2011, Volume: 1, Pages: 232 – 235.
[15] Suhuan Sun; Changwei Zhao; Zhiyong Zhang, “A matrix factorization based trust factors model”, Information and Automation, 2015 IEEE International Conference on, Year: 2015, Pages: 803 – 808.
[16] D. W. Chadwick; A. J. Young; N. K. Cicovic, “Merging and extending the PGP and PEM trust models-the ICE-TEL trustmodel”, IEEE Network, Year: 1997, Volume: 11, Issue: 3, Pages: 16 – 24.
[17] Madhavi S. Kukade and Kapil N. Hande, "Analysis of Uniform Distribution of Storage Nodes in Wireless Sensor Network", International Journal of Computer Sciences and Engineering, Volume-02, Issue-01, Page No (43-47), Jan -2014, E-ISSN: 2347-2693
[18] Guangwei Zhang; Jianchu Kang; Rui He, “Towards a trust model with uncertainty for e-commerce systems”, IEEE International Conference on e-Business Engineering (ICEBE'05), Year: 2005, Pages: 200 – 207.
[19] Er. Satish Kumar, "A Study of Wireless Sensor Networks- A Review", International Journal of Computer Sciences and Engineering, Volume-04, Issue-03, Page No (23-27), Mar -2016, E-ISSN: 2347-2693
[20] A. Hemalatha and D. Sasirekha, "A Survey on Energy Efficient Routing Protocols in WSN", International Journal of Computer Sciences and Engineering, Volume-04, Issue-03, Page No (77-81), Mar -2016, E-ISSN: 2347-2693

C.Jasmin Selvi, G.Sathish Kumar, "An Adaptive Double-Quality-Guaranteed (DQG) Scheme based Quality of Service (QOS) in Heterogeneous Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.258-265, 2016.

Cloud Computing is creating as a new computational worldview shift. Hadoop-MapReduce has become a powerful Calculation Model alternately handling huge information on Dispersed thing equipment groups such as Clouds. In all Hadoop implementations, the shortcoming FIFO scheduler is accessible where employments are booked in FIFO request with support alternately other Need based schedulers also. In this paper we study distinctive scheduler changes conceivable with Hadoop and too given some guidelines on how to improve the Planning in Hadoop in Cloud Environments.

Cloud Computing, Hadoop, HDFS, MapReduce

[1] S. Narkhede; T. Baraskar; D. Mukhopadhyay, “Analyzing web application log files to find hit count through the utilization of Hadoop MapReduce in cloud computing environment”, IT in Business, Industry and Government (CSIBIG), 2014 Conference on Year: 2014 Pages: 1 – 7.
[2] Ankita Kadre and S.R Yadav, "A Survey on Map Reduce Algorithm for Big data analysis using Hadoop, Pig and Hive utility tools", International Journal of Computer Sciences and Engineering, Volume-03, Issue-10, Page No (52-57), Oct -2015
[3] D. Garg; K. Trivedi, “Fuzzy K-mean clustering in MapReduce on cloud based hadoop”, Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on Year: 2014 Pages: 1607 – 1610.
[4] Mantripatjit Kaur and Gurleen Kaur Dhaliwal, "Performance Comparison of Map Reduce and Apache Spark on Hadoop for Big Data Analysis", International Journal of Computer Sciences and Engineering, Volume-03, Issue-11, Page No (66-69), Nov -2015
[5] J. George; C. A. Chen; R. Stoleru; G. G. Xie; T. Sookoor; D. Bruno, “Hadoop MapReduce for Tactical Clouds” Cloud Networking (CloudNet), 2014 IEEE 3rd International Conference on Year: 2014 Pages: 320 – 326.

N.Vetrivelan, C.Jasmin Selvi, "A Survey on Enhanced MapReduce Spatial Hadoop in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.266-271, 2016.

In later years ubiquity of private cars is getting urban movement more furthermore, more crowded. As result movement is becoming one of imperative issues in huge cities in all over the world. Some of the movement concerns are congestions furthermore, mischances which have caused a tremendous waste of time, property harm furthermore, ecological pollution. This research paper presents a novel insightful movement organization system, based on Web of Things, which is featured by low cost, high scalability, high compatibility, easy to upgrade, to replace conventional movement organization structure furthermore, the proposed structure can improve street movement tremendously. the high spectral efficiency and the resistance to multi-path fading, cooperative OFDMA becomes a hopeful contestant for high-speed wireless communication networks. The resource allocation in this paper is a mixed integer and continuous variable optimization problem, which is a NP hard problem. We construct a dynamic optimization framework for the RA problem, with the aim to maximize the average utility of all users with multi-service. Particle swarm optimization (PSO), as a population based stochastic optimization technique, is used to solve highly non-linear mixed integer optimization problems. PSO is used to solve the RA problem in OFDMA systems. In this paper, based on MDPSO, we propose a dynamic resource allocation algorithm to find the asymptotic optimal solution for the NP problem. Our proposed dynamic optimization framework for RA by considering three dynamic situations: time-varying fading channel, MSs states change, and relay stations (RSs) states change. The proposed dynamic algorithm achieves the better performance at linear complexity compared to the existing algorithms.

Insightful Traffic; Internet-of-Things; RFID; Remote Sensor Networks; Operator Technology.

[1] Y. W. Pan, A. Nix, and M. Beach, “Distributed Resource Allocation for OFDMA-Based Relay Networks,” IEEE Trans. Veh. Technol., vol. 60, pp. 919-931, Mar. 2011.
[2] D. W. K. Ng and R. Schober, “Resource Allocation and Scheduling in Multi-Cell OFDMA Systems with Decode-and-Forward Relaying,” IEEE Trans. Wireless Commun., vol. 10, pp. 2246-2258, July, 2011.
[3] C. Liu, X. W. Qin, S. H. Zhang, et al., “Proportional-Fair Downlink Resource Allocation in OFDMA-Based Relay Networks,” Journal of Communications and Networks, vol. 13, pp. 633-638, Dec. 2011.
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[5] T. Wang and L. Vandendorpe, “WSR Maximized Resource Allocation in Multiple DF Relays Aided OFDMA Downlink Transmission,” IEEE Trans. Signal Process. vol. 59, pp. 3964-3976, Aug. 2011.
[6] K. Wen-Hsing and L. Wanjiun, “Utility-Based Resource Allocation in Wireless Networks,” IEEE Trans. Wireless Commun, vol. 6, pp. 3600-3606, 2007.
[7] Umesh Kumar Singh, Shivlal Mewada, Lokesh Laddhani and Kamal Bunkar, “An Overview & Study of Security Issues in Mobile Ado Networks”, International Journal of Computer Science and Information Security (IJCSIS) USA, Vol-9, No.4, pp (106-111), April 2011
[8] W. Li, S. Zeng, Z. Jin, Q. Xin and J. Lei, “Particle Swarm Optimization Based Resource Allocation and Adaptive Modulation in Cooperative OFDMA Systems,” in Proc. of IEEE & CIC 2nd International Conference on Communications in China (ICCC), Xi’an, China, Aug. 2013, pp.1-6.
[9] R. Jain, D. M. Chiu, and W. Hawe, “A quantitative measure of fairness and discrimination for resource allocation in shared systems,” Digital Equipment Corp., vol. Hudson, MA, DEC Res. Rep. TR-301, 1984.
[10] V. Erceg, K. V. S. Hari, M. S. Smith, et al., “Channel Models for Fixed Wireless Applications,” Contribution IEEE 802.16.3c-01/29r1, Feb. 2001.
[11] N. Mokari and K. Navaie, “Radio resource allocation in OFDM-based cooperative relaying networks for a mixture of elastic and streaming traffic,” IET Commun., no. April 2010, pp. 1083-1089, 2011.

S.Jayasri, D.Karthika, "Microcontroller Based Traffic and Road Condition Monitoring Alert System Using Internet of Things," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.272-279, 2016.

In remote sensor network, clustering is used as an intense system to achieve scalability, self-organization, power saving, channel access, directing etc.. Lifetime of sensor hubs decides the lifetime of the system and, is crucial for the detecting capability.. Clustering is the key system used to develop the lifetime of a sensor network. Clustering can be used for load balancing to develop the lifetime of a sensor system by reducing vitality consumption. Load balancing utilizing clustering can too increment system scalability. Remote sensor system with the hubs with distinctive vitality levels can prolong the system lifetime of the system and, too its reliability. In this paper we propose a clustering system which will parity the load among the cluster by utilizing some reinforcement nodes. The reinforcement high vitality and, high handling power hubs replace the cluster head after the cluster reaches to its limit. This approach will increment the system lifetime and, will give high throughput.

Remote Sensor Network, Clustering, Reliability, Scalability

[1] Sohail Jabbar ; Ayesha Ejaz Butt, “Threshold based load balancing protocol for energy efficient routing in WSN”, Published in: Advanced Communication Technology (ICACT), 2011 13th International Conference on Date of Conference: 13-16 Feb. 2011 Page(s): 196 – 2011.
[2] Shamneesh Sharma, Dinesh Kumar and Keshav Kishore, "Wireless Sensor Networks- A Review on Topologies and Node Architecture", International Journal of Computer Sciences and Engineering, Volume-01, Issue-02, Page No (19-25), Oct -2013
[3] Hang Qin; Zhongbo Wu, “Analysis and improvement of the Dynamic Load Balancing of Overlay-based WSN”, Published in: Computer and Information Technology, 2008. CIT 2008. 8th IEEE International Conference on Date of Conference: 8-11 July 2008 Page(s): 815 – 820.
[4] R.Nathiya and S.G.Santhi, "Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)", International Journal of Computer Sciences and Engineering, Volume-02, Issue-02, Page No (36-43), Feb -2014
[5] Naveed Ahmed Khan; Kashif Saghar ; Rizwan Ahmad ; Adnan K. Kiani, “Achieving energy efficiency through load balancing: A comparison through formal verification of two WSN routing protocols”, Published in: 2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST) Date of Conference: 12-16 Jan. 2016 Page(s): 350 – 354.
[6] Yi-Jing Chu ; Chu-Ping Tseng ; Kuo-Chi Liao ; Yung-Cheng Wu, “The first order load-balanced algorithm with static fixing scheme for centralized WSN system in outdoor environmental monitoring”, Published in: Sensors, 2009 IEEE Date of Conference: 25-28 Oct. 2009 Page(s): 1810 – 1813.
[7] Sujata Agrawal, K.D. Kulat and M. B.Daigavane, "Evaluation of Routing Algorithm for Ad-hoc and Wireless Sensor Network Protocol", International Journal of Computer Sciences and Engineering, Volume-01, Issue-02, Page No (11-18), Oct -2013
[8] Janos Levendovszky ; Kalman Tornai ; Gergely Treplan ; Andras Olah, “Novel Load Balancing Algorithms Ensuring Uniform Packet Loss Probabilities for WSN”, Published in: Vehicular Technology Conference (VTC Spring), 2011 IEEE 73rd Date of Conference: 15-18 May 2011 Page(s): 1 – 5.
[9] S. Prabhavathi ; A. Ananda Rao ; A. Subramanyam, “Globular topology of large scale WSN for efficient load balancing using multiple sink node”, Published in: Computing, Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference on Date of Conference: 4-6 July 2013 Page(s): 1 – 6.
[10] Pawan Singh Mehra ; M. N. Doja ; Bashir Alam, “Energy efficient self organising load balanced clustering scheme for heterogeneous WSN”, Published in: Energy Economics and Environment (ICEEE), 2015 International Conference on Date of Conference: 27-28 March 2015 Page(s): 1 – 6.

P.Karthika, V.Mathimalar, "An Improved Grid-Based Energy Efficient Load Balanced Clustering Scheme in WSN," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.280-287, 2016.

Data Mining has wide use in numerous fields such as financial, medication, medical research also, among govt. departments. Grouping is one of the widely connected works in Data Mining applications. For the past several years, due to the increment of diverse security problems, numerous conceptual also, practical options to the grouping issue have been proposed under diverse security designs. On the other hand, with the latest reputation of cloud processing, users now have to be capable to delegate their data, in encoded form, as well as the Data Mining undertaking to the cloud. Considering that the information on the cloud is in secured type, current privacy-ensuring grouping strategies are not appropriate. In this paper, we concentrate on fixing the grouping issue over encoded data. In specific, we prescribe a secured k-NN classifier over secured information in the cloud. The proposed convention safeguards the security of information, solace of user’s criticism query, also, disguises the information access styles. To the best of our information, our undertaking is the initially to make a secured k-NN classifier over secured information under the semi-honest model. Also, we empirically evaluate the execution of our proposed convention utilizing a real-world dataset under diverse parameter configurations.

Security, k-NN Classifier, Outsourced Databases, Encryption

[1] R. Vidya Banu; N. Nagaveni, “Preservation of Data Privacy Using PCA Based Transformation, ARTCom '09. International Conference on Year: 2009 Pages: 439 – 443.
[2] Vaishnavi L. Kaundanya; Anita Patil; Ashish Panat, “Performance of k-NN classifier for emotion detection using EEG signals”, Communications and Signal Processing (ICCSP), 2015 International Conference on Year: 2015 Pages: 1160 – 1164.
[3] C. Rodriguez; F. Boto; I. Soraluze; A. Perez, “An incremental and hierarchical k-NN classifier for handwritten characters” Pattern Recognition, 2002. Proceedings. 16th International Conference on Year: 2002, Volume: 3 Pages: 98 – 101.
[4] Mahdi Hasanlou; Farhad Samadzadegan, “Comparative Study of Intrinsic Dimensionality Estimation and Dimension Reduction Techniques on Hyperspectral Images Using K-NN Classifier”, IEEE Geoscience and Remote Sensing Letters Year: 2012, Volume: 9, Issue: 6 Pages: 1046 – 1050.
[5] Ankita Srivastava; M. P. Singh; Prabhat Kumar, “Supervised Semantic Analysis of Product Reviews Using Weighted k-NN Classifier”, Information Technology: New Generations (ITNG), 2014 11th International Conference on Year: 2014 Pages: 502 – 507.
[6] I.Soraluze; C. Rodriguez; F. Boto; A. Perez, “Multidimensional multistage k-NN classifiers for handwritten digit recognition”, Proceedings. Eighth International Workshop on Year: 2002 Pages: 19 – 23.

Dr.V.Maniraj, V.Krishnaveni, "K-Nearest Neighbor Grouping in Excess of Semantically Secure Encryption Interactive Evidence," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.288-291, 2016.

Clients are known to experience troubles in dealing with information retrieval look outputs, particularly if those yields are above a certain size. It has been contended by several analysts that look yield Clustering can help clients in their collaboration with IR frameworks in some retrieval situations, providing them with an review of their results by abusing the topicality information that resides in the yield but has not been used at the retrieval stage. This review might enable them to find applicable records more effortlessly by focused on the most promising clusters, or to use the Groups as a starting-point for question refinement or expansion. In this paper, the results of tests carried out to assess the viability of Clustering as a look yield presentation technique are reported and discussed.

Content Clustering, Pattern Mining, Content Retrieval, Clustering Algorithm

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[3] Beil F., Ester M., and Xu X. (2002) “Frequent term-based text clustering” Proc. 8th ACM SIGKDD Int. Conf. Knowledge Discov. Data Minining, pp. 436–442.
[4] Cheng H., Yan X., Han J., and Hsu C.-W. (2007) “Discriminative frequent pattern analysis for effective classification” Proc. IEEE 23rd Int. Conf. Data Eng., pp. 716–725.
[5] Gao Y., Xu Y., Li Y., and Liu B. (2013) “A two-stage approach for generating topic models” in Advances in Knowledge Discovery and Data Mining, PADKDD’13. New York, NY, USA: Springer, pp. 221–232.
[6] Gao Y., Xu Y., and Li Y. (2013) “Pattern-based topic models for information filtering” in Proc. Int. Conf. Data Min. Workshop SENTIRE, pp. 921–928.
[7] Han J., Cheng H., Xin D., and Yan X. (2007) “Frequent pattern mining: Current status and future directions” Data Min. Knowl. Discov., vol. 15, no. 1, pp. 55–86.
[8] Robertson S., Zaragoza H., and Taylor M. (2004) ‘Simple BM25 extension to multiple weighted fields’ in Proc. 13th ACM Int. Conf. Inform. Knowl. Manag., pp. 42–49.
[9] Anuradha Awachar, Rajashree Bairagi, Vijayalaxmi Hegade and Mahadev Khandagale (2014), "An Overview of Ontology Based Text Document Clustering Algorithms", International Journal of Computer Sciences and Engineering, Volume-02, Issue-02, Page No (60-64
[10] Wang C. and Blei D. M. (2011) “Collaborative topic modeling for recommending scientific articles” in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 448–456.
[11] Xu Y., Li Y., and Shaw G. (2011), “Reliable representations for association rules” Data Knowl. Eng., vol. 70, no. 6, pp. 555–575.
[12] Zaki M. J. and Hsiao C.-J. (2002) “CHARM: An efficient algorithm for closed item set mining.” in Proc. SDM, vol. 2, pp. 457–473.

R.Krithika, G.Sathish Kumar, "Pattern Based Frequent Term Retrieval Search Using Text Clustering," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.292-297, 2016.

Cognitive Radio System has emerged as a arrangement to the growing range scarcity furthermore, inefficiency problems. However, Cognitive Radio Systems face execution furthermore, security bottlenecks due to lack of memory furthermore, boundless computational capabilities. This issue could be solved if we make use of Cloud as a focal element for storing range accessibility data furthermore, handling of the range accessibility data furthermore, correctly map the region of the unauthorized client to that of the accessible range bands. We will be considering only those range groups for correspondence where the primary customers are absent. If the authorized client is detected, we shall vacant that bands, furthermore, move to another unmoving range bands, that matches our requirements. Admittance will be based on FCFS premise furthermore, at the same time the Quality of Administration necessities (in terms of data rate) of the unauthorized customers will satisfied.

Cloud Computing, Cognitive Radio Network, Dynamic Range Access, Hierarchical Access Structure, Range Task Policy.

[1] Farhan Bashir Shaikh; , “Security threats in cloud computing”, Internet Technology and Secured Transactions (ICITST), 2011 International Conference for, Year: 2011, Pages: 214 – 219.
[2] Mohemed Almorsy; John Grundy; Amani S. Ibrahim, “Collaboration-Based Cloud Computing Security Management Framework”, Cloud Computing (CLOUD), 2011 IEEE International Conference on, Year: 2011,Pages: 364 – 371.
[3] Zehua Zhang; Xuejie Zhang, “Realization of open cloud computing federation based on mobile agent”, Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on, Year: 2009, Volume: 3, Pages: 642 – 646.
[4] Wang En Dong; Wu Nan; Li Xu, “QoS-Oriented Monitoring Model of Cloud Computing Resources Availability”, Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on, Year: 2013, Pages: 1537 – 1540.
[5] Dipayan Dev; Krishna Lal Baishnab, “Notice of Violation of IEEE Publication Principles A Review and Research Towards Mobile Cloud Computing”, Mobile Cloud Computing, Services, and Engineering, MobileCloud), 2014 2nd IEEE International Conference on, Year: 2014, Pages: 252 – 256.
[6] Yong Pan; Ning Hu, “Research on dependability of cloud computing systems Reliability”, Maintainability and Safety (ICRMS), 2014 International Conference on, Year: 2014, Pages: 435 – 439.
[7] Aminatul Solehah Idris; Nurhilyana Anuar; Mudiana Mokhsin Misron; Farah Hanim Mohd Fauzi, “The readiness of Cloud Computing: A case study in Politeknik Sultan Salahuddin Abdul Aziz Shah, Shah Alam”, Computational Science and Technology (ICCST), 2014 International Conference on, Year: 2014, Pages: 1 – 5.
[8] Ubaidullah Alias Kashif; Zulfiqar Ali Memon; Abdul Rasheed Balouch; Jamil Ahmed Chandio, “Distributed trust protocol for IaaS Cloud Computing”, 2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST),Year: 2015, Pages: 275 - 279
[9] Qiang Guan; Chi-Chen Chiu; Song Fu, “CDA: A Cloud Dependability Analysis Framework for Characterizing System Dependability in Cloud Computing Infrastructures”, Dependable Computing (PRDC), 2012 IEEE 18th Pacific Rim International Symposium on, Year: 2012, Pages: 11 – 20.
[10] Yanuarizki Amanatullah; Charles Lim; Heru Purnomo Ipung; Arkav Juliandri” Toward cloud computing reference architecture: Cloud service management perspective”,ICT for Smart Society (ICISS), 2013 International Conference on ,Year: 2013, Pages: 1 – 4.
[11] Umesh Kumar Singh, Shivlal Mewada, Lokesh Laddhani and Kamal Bunkar, “An Overview & Study of Security Issues in Mobile Ado Networks”, International Journal of Computer Science and Information Security (IJCSIS) USA, Vol-9, No.4, pp (106-111), April 2011
[12] Sohraab Soltani; Yalin Sagduyu; Yi Shi; Jason Li; Jared Feldman; John Matyjas, “Distributed cognitive radio network architecture, SDR implementation and emulation testbed”, Military Communications Conference, MILCOM 2015 - 2015 IEEE, Year: 2015, Pages: 438 – 443.
[13] Jerome Sonnenberg; David B. Chester; James Schroeder; Keith Olds, “Quantifying the relative merits of genetic and swarm algorithms for network optimization in cognitive radio networks”, MILCOM 2012 - 2012 IEEE Military Communications Conference, Year: 2012, Pages: 1 – 8.
[14] Sohraab Soltani; Yalin E. Sagduyu; Jason H. Li; Jared Feldman; John Matyjas, “Demonstration of plug-and-play cognitive radio network emulation testbed”, Dynamic Spectrum Access Networks (DYSPAN), 2014 IEEE International Symposium on, Year: 2014, Pages: 376 – 377.
[15] Guolin Sun; Guisong Liu; Yi Wang, “SDN architecture for cognitive radio networks”, Cognitive Cellular Systems (CCS), 2014 1st International Workshop on, Year: 2014, Pages: 1 – 5.
[16] J.Nandhini and V.Hamsadhwani, "Improving interference mitigation and priority based channel allocation in cognitive radio networks", International Journal of Computer Sciences and Engineering, Volume-03, Issue-05, Page No (16-20), May -2015
[17] Minho Jo; Longzhe Han; Dohoon Kim; Hoh Peter, “In Selfish attacks and detection in cognitive radio Ad-Hoc networks”, IEEE Network, Year: 2013, Volume: 27, Issue: 3, Pages: 46 - 50,
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[19] Navdeep Kaur Randhawa and Avtar Singh Buttar, "Sensing of Spectrum Holes in Cognitive Radio Networks: A Survey", International Journal of Computer Sciences and Engineering, Volume-02, Issue-08, Page No (28-34), Aug -2014
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T.Nilavazhaki, G.Sathish Kumar, "Spectrum Sensing Cognitive Radio For Opportunistic Access Environments Based Trust Management," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.298-301, 2016.

Right from the Stone Age one law is governing the market for every generation Furthermore that well acknowledged law is “Law of Demand & Supply”. ICT is not far away from this law. It is a well-known fact that when cyber world is marching quickly towards higher form of innovative evolution, Cloud Processing has entered as backbone support to ICT as a tool for better cost performance. It is a platform, which is highly governed by the law of “Demand &Supply”, whether it is Banking, Healthcare or Agriculture, all are in need of this innovation to make their execution as cost powerful as conceivable Furthermore, ubiquitous in functionality. As by the law, Demand Furthermore, Supply both go hand-to-hand. This paper presents some of the demand facets Furthermore, corresponding supply administrations for banking, farming Furthermore, healthcare. It enables programming architect to make the system of any division to build up fully fledged working area in due consideration of cloud computing. Keyword: Banking Furthermore, Financial System (BFS), Cloud Computing, Hospital Administration Information System (HMIS), Information Correspondence Innovation (ICT), SaaS Application, Total Cost of Possession (TCO).

Cloud Computing, Wellbeing Care, Cloud processing in Agriculture, Demand & Supply.

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A.Pavithra, V.Snehalatha, "Medical Analysis Based on Agri And Human Healthcare Under Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.302-305, 2016.

Millimeter-wave correspondence is a promising innovation for future 5G cell systems to give exceptionally high data rate (multi-gigabits-persecond) for portable devices. Empowering D2D interchanges over directional mmWave systems is of critical importance to productively use the extensive bandwidth to increment framework capacity. In this article, the spread highlights of mmWave correspondence and the related impacts on 5G cell systems are discussed. We introduce an mmWave+4G framework engineering with TDMA-based MACINTOSH structure as a candidate for 5G cell networks. We propose an compelling asset sharing plan by permitting non-meddling D2D joins to work concurrently. We too discuss neighbor revelation for continuous handoffs in 5G cell networks. In 4g technology capable to provide speed up to 100mbps but battery uses is more. Fifth generation network provide reasonable broadband wireless connectivity (very high speed). Millimeter wave (mmWave) communication is a hopeful solution for future fifth generation (5G) cellular networks to offer extremely high capability. Here we used 5g being developed to accommodate Qos rate requirements set by further development of existing 4g applications. 5g is a next major phase of mobile telecommunication and wireless system. 10 times more capacity than others. In this project, a playout buffer is used to control and preserve the data playout quality. We formulate the difficult of using dynamically allocated bandwidth to charge the buffer as a Markov decision process (MDP), aiming to exploit data playout quality for all the users moving in the whole coverage zone. The proposed technique on playout quality provisioning is effective for real time video applications of the users with great mobility.

Security, k-NN Classifier, Outsourced Databases, Encryption

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G.Poonguzhali, C.Vidya, "Enabling Device to- Device Communication in Millimeter Wave 5g Cellular Networks," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.306-312, 2016.