An Investigation Study on QoS and Traffic Aware Job Scheduling Techniques with Big Data
Research Paper | Journal Paper
Vol.5 , Issue.11 , pp.1-7, Nov-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i11.17
Abstract
Big Data Analytics (BDA) applications are new software application for processing large amount of data to collect the hidden value. Big data is defined as a datasets whose size is beyond the capability of usual relational databases to collect, direct and handle the data with lesser latency. Most of the recent research works aimed to reduce the traffic and workload for convalescing the quality of services in big data. In recent times, many research works are carried out for getting better the performance of regression and classification process during the data access from the big data. However, the job completion time and memory space complexity remained challenging issue. Our main objective is to reduce the space complexity and time complexity during the data accessing from big data. In order to reduce the job completion time and memory space consumption, many existing techniques are reviewed. The key objective of the research is to increase performance of traffic aware job scheduling techniques with minimal space and time complexity. In this paper, review of various existing job scheduling techniques is carried out. The study and analysis about the performance of three existing techniques in terms of their space and time complexity is measured as the number of user requests increases and a comparison of the results between these techniques is carried out. Limitations of existing techniques are also discussed.
Key-Words / Index Term
Big Data Analytics, Relational databases, Quality of services, Regression, Classification
References
[1] Peng Li, Song Guo, Toshiaki Miyazaki, Xiaofei Liao, Hai Jin, Albert Y. Zomaya,Kun Wang, “Traffic-aware Geo-distributed Big Data Analytics with Predictable Job Completion Time”, IEEE Transactions on Parallel and Distributed Systems, Volume 28, Issue 6, Pages 1785 – 1796, 2017.
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[6] Bogdan Nicolae, Carlos H. A. Costay, Claudia Misalez, Kostas Katrinis and Yoonho Park, “Leveraging Adaptive I/O to Optimize Collective Data Shuffling Patterns for Big Data Analytics”, IEEE Transactions on Parallel and Distributed Systems, Volume 28, Issue 6, Pages 1663 – 1674, June 2017
[7] Carlos Ordonez, Yiqun Zhang and Wellington Cabrera, “The Gamma Matrix to Summarize Dense and Sparse Data Sets for Big Data Analytics”, IEEE Transactions on Knowledge and Data Engineering, Volume 28, Issue 7, Pages 1905 – 1918 July 2016,
[8] Kun Wang, Huining Li, Yixiong Feng, and Guangdong Tian, “Big Data Analytics for System Stability Evaluation Strategy in the Energy Internet”, IEEE Transactions on Industrial Informatics, Volume 13, Issue 4, Pages 1969 – 1978, August 2017
[9] L. U. Laboshin, A. A. Lukashin and V. S. Zaborovsky, “The Big Data Approach to Collecting and Analyzing Traffic Data in Large Scale Networks”, Procedia Computer Science, Elsevier, Volume 103, Pages 536-542, 2017.
[10] Mohd Usama, Mengchen Liu and Min Chen, “Job schedulers for Big data processing in Hadoop environment: Testing real-life schedulers using benchmark programs”, Digital Communications and Networks, Elsevier, Pages 1-14, August 2017.
[11] E. Sivaraman, Dr.R.Manickachezian, “High Performance and Fault Tolerant Distributed File System for Big Data Storage and Processing Using Hadoop”, IEEE Xplore Digital Library, DOI: 10.1109/ICICA.2014.16, E-ISBN: 978-1-4799-3966-4
[12] Sapinderjit Kaur, Kirandeep Kaur, Amit.Chhabra, “Parallel Job Scheduling Using Grey Wolf Optimization Algorithm For Heterogenous Multi-Cluster Environment”, International Journal of Computer Science and Engineering Vol.5 , Issue.10 , pp.44-53, Oct-2017
[13] S.Hemalatha, Dr.R.Manickachezian, “Implicit Security Architecture Framework in Cloud Computing Based on Data Partitioning and Security Key Distribution”, International Journal of Emerging Technologies in Computational and Applied Sciences, pp. 76-81, ISSN: 2279-0055, Feb. 2013.
Citation
C.R. Durga devi, R. Manicka Chezian, "An Investigation Study on QoS and Traffic Aware Job Scheduling Techniques with Big Data," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.1-7, 2017.
Energy Aware Congestion Adaptive Reactive Routing Protocol with Link Quality Monitoring
Research Paper | Journal Paper
Vol.5 , Issue.11 , pp.8-15, Nov-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i11.815
Abstract
In MANETs, packet delivery from source to destination is a complex task as many factors affect the network performance. The major factors in routing are node mobility, node energy and congestion in the wireless bandwidth limited channel and battery operated nodes with dynamic topology. This paper proposes a cross-layer based feedback among different layers, namely physical, MAC and network to pass the link information from physical to network layer. The Cross-Layer design approach helps checking the link condition as well as energy and congestion status to select the optimal path between source and destination. The proposed energy aware congestion adaptive reactive routing protocol with link monitoring (EACARP-LM) also finds alternate path proactively before the link breaks due to mobility, becomes heavily congested or an intermediate node exhaust its battery power. The protocol uses energy monitor, congestion monitor and link monitor for monitoring the respective parameter during the node operations. Based on these monitors the node takes decision for transmitting the packets and selecting a new route for packet delivery. This cross-layer design approach is implemented in Network simulator (NS2 simulator) and its performance is compared with the traditional AODV routing protocol, which indicates the better QoS parameters and increased network life time.
Key-Words / Index Term
Cross-Layer, Energy aware, Congestion adaptive, Link monitoring, EACARP-LM
References
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[9] V. Srivastava and M. Motani, “Cross layer design: a survey and the road ahead”, IEEE Commun Mag , vol. 43. No. 12, pp 112-9,2005.
[10] M. Conti, G. Maselli and G. Turi, “Cross-layering in mobile ad-hoc network design”, IEEE Comput Soc, pp 48-51, Feb. 2004.
[11] T. Issariyakul and E. Hossain, Introduction to Network Simulator NS2, Springer, 2008.
[12] B.Ramchandran and S. shanmugavel, ”Received Signal Strength-based Cross-layer design for Mobile Ad Hoc Networks”, IETE Technical Review, Vol. 25, no. 4, pp. 192-200, JUL-AUG 2008.
[13] S. Kumar S, A. Grace Selvarani, "Improving Energy Efficiency by Using Tree-Based Routing Protocol for Wireless Sensor Network", International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.201-206, 2015.
[14] F. Xie, L. Du, Y. Bai and L. Chen, “Energy Aware Reliable Routing Protocol for Mobile Ad-Hoc Networks”, IEEE Communication Society, WCNC proceedings, pp. 4313-4317, 2007.
[15] T. S. Kumaran and V. Sankaranarayanan, “Early congestion detection and adaptive routing in MANET”, Egyptian Informatics Journal, Elsevier, vol. 12, no. 3, pp. 165-175, Nov. 2011.
[16] A. Nedumaran and V. Jeyalakshmi, “CAERP: A Congestion and Energy Aware Routing Protocol for Mobile Ad Hoc Network”, Indian Journal Of Science And Technology, vol. 8, no. 35, 2015.
[17] E. Natarajan and L. Devi, “Cross layer based energy aware routing and congestion control algorithm in MANET”, International Journal of Computer Science and Mobile Computing, vol. 3, no 10, pp. 700-9, 2014.
[18] P.K.Suri, M.K.Soni and P. Tomar, “QoS enabled power aware routing protocol (QEPAR)”, International Journal of Engineering Science and Technology, vol. 2, no.9, pp 4880-4885, 2010.
Citation
J.K. Singh, M. Kalla, "Energy Aware Congestion Adaptive Reactive Routing Protocol with Link Quality Monitoring," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.8-15, 2017.
Analysis of Speed, Accuracy of Deep Learning using Gini index, HSM based fuzzy decision trees
Research Paper | Journal Paper
Vol.5 , Issue.11 , pp.16-24, Nov-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i11.1624
Abstract
Deep Learning has gained tremendous importance due to its advancement in various fields of text mining, speech recognition, computer vision, natural language processing etc. The weights of the input layer attributes and the series of hidden layers of deep learning plays a dominant role in its classification speed and accuracy. The weight adjustment algorithm for the Deep Learning is proposed in this paper. Generally, the weights can be determined by mathematical techniques, can be suggested by the domain experts or by considering random weights. In this proposed work, the weights of a neural network are computed mathematically by constructing the fuzzy decision tree. It is proposed to use the maximum heterogeneous split measure(HSM) value of the attribute of the fuzzy decision tree as the weight of the corresponding attribute for the weight adjustment algorithm to classify using neural networks. Fast classification and accuracy is achieved with the computed HSM weights of the deep learning which outperforms when compared with the fuzzy decision tree classifiers. The same work was carried out using the least value of gini index. And in this paper the classification speed, accuracy is compared by considering the gini index and HSM based fuzzy decision trees and analyzed the results.
Key-Words / Index Term
Deep Learning, Heterogeneous split measure, gini index, weight, fuzzy, Decision trees, Classification Accuracy
References
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Citation
S.V.G.Reddy, K.Thammi Reddy, V.Valli Kumari, "Analysis of Speed, Accuracy of Deep Learning using Gini index, HSM based fuzzy decision trees," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.16-24, 2017.
Blackhole Attack and its effect on VANET
Research Paper | Journal Paper
Vol.5 , Issue.11 , pp.25-32, Nov-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i11.2532
Abstract
VANET is one type of adhoc networks which established among vehicles equipped with communication facilities. In VANETs vehicles are equipped with device known as on Board Unit (OBU) through which it can communicate with other vehicles as well as with infrastructure which is known as Road Side Unit (RSU). VANET can be used for different applications like Life-Critical Safety Applications, Safety warning Applications, Electronic Toll Collections, Internet Access in vehicles, Vehicle Group Communications, Roadside Services Finder and many more. Various researchers are proposing different solutions in VANET for Protocols, Effective Service Parameters, Smart Network challenges and lot more by seeing the usefulness of VANETs in various applications but security is a main issue for adopting VANET as a life critical solution. Security is the major concern for various VANET applications where a wrong, replicated or delayed messages may directly or indirectly affect the human lives. Many of the applications require a high level of security in VANET adoption. Security Attacks on VANET can be categories in Routing Attack, Monitoring Attack, Social Attack, Timing Attack, Application Attack and Network Attack. In this paper we focused on Routing Attack detection, specifically Blackhole Attack detection methodologies. We have proposed four different approaches for Blackhole attack detection: Neighborhood based, Sequence Number based, Packet Drop rate based and Cluster Forming based. We analyze the performance of the proposed approaches for different scenario with comparative analysis.
Key-Words / Index Term
Blackhole Attack Detection, AODV, Neighborhood based blackhole detection, Sequence Number based blackhole detection, Packet Drop rate based blackhole detection, Cluster Forming based blackhole detection
References
[1]. Zeadally, S., Hunt, R., Chen, YS. et al. Telecommun Syst (2012) 50: 217. doi:10.1007/s11235-010-9400-5
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[3]. S. Verma, B. Mallick and P. Verma, "Impact of gray hole attack in VANET," 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp. 127-130.
http://doi.org/10.1109/NGCT.2015.7375097
[4]. J. Li, H. Lu and M. Guizani, "ACPN: A Novel Authentication Framework with Conditional Privacy-Preservation and Non-Repudiation for VANETs," in IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 4, pp. 938-948, April 2015.
doi:10.1109/TPDS.2014.2308215
[5]. A. D. Patel and K. Chawda, "Blackhole and grayhole attacks in MANET," International Conference on Information Communication and Embedded Systems (ICICES2014), Chennai, 2014, pp. 1-6.
doi:10.1109/ICICES.2014.7033859
Meenakshi Jamgade and Vimal Shukla , "Comparative on AODV and DSR under Black Hole Attacks Detection Scheme Using Secure RSA Algorithms in MANET", International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.145-150, 2016.
[6]. Pradeep Kumar Sharma, Shivlal Mewada and Pratiksha Nigam, "Investigation Based Performance of Black and Gray Hole Attack in Mobile Ad-Hoc Network", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.4, pp.8-11, 2013.
[7]. R. Kumari, P. Nand, "Performance Analysis of Existing Routing Protocols", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.47-50, 2017.
[8]. Umesh Kumar Singh, Jalaj Patidar and Kailash Chandra Phuleriya, "On Mechanism to Prevent Cooperative Black Hole Attack in Mobile Ad Hoc Networks", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.11-15, 2015.
[9]. Bissmeyer, N.; Schroder, K.H.; Petit, J.; Mauthofer, S.; Bayarou, K.M., "Experimental analysis of misbehavior detection and prevention in VANETs," in Vehicular Networking Conference (VNC), 2013 IEEE , vol., no., pp.198-201, 16-18 Dec. 2013
[10]. Bala, Anu; Bansal, M.; Singh, J., "Performance Analysis of MANET under Blackhole Attack," in Networks and Communications, 2009. NETCOM `09. First International Conference on ,IEEE vol., no., pp.141-145, 27-29 Dec. 2009
[11]. Wazid, M.; Katal, A.; Singh Sachan, R.; Goudar, R.H.; Singh, D.P., "Detection and prevention mechanism for Blackhole attack in Wireless Sensor Network," in Communications and Signal Processing (ICCSP), 2013 International Conference on , IEEE vol., no., pp.576-581, 3-5 April 2013
[12]. Junhai Luo; Mingyu Fan; Danxia Ye, "Black hole attack prevention based on authentication mechanism," in Communication Systems, 2008. ICCS 2008. 11th IEEE Singapore International Conference on , vol., no., pp.173-177, 19-21 Nov. 2008.
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Citation
Ajay N. Upadhyaya, J.S. Shah , "Blackhole Attack and its effect on VANET," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.25-32, 2017.
Efficient Processing and Optimization of Queries with Set Predicates using Filtered Bitmap Index
Research Paper | Journal Paper
Vol.5 , Issue.11 , pp.33-39, Nov-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i11.3339
Abstract
Query optimization is a common task performed by database administrators and application designers in order to tune the overall performance of the database system. In several applications, the currently available Database Management System is inadequate to support the comparison between the group of tuples with their attributes and values. Currently, databases are used in almost all corporate and business applications that handle a huge amount of data. The complex SQL queries consist of scalar-level operations are often formed to obtain even very simple set-level semantics. Such queries are not only difficult to write but also challenging for a database engine to optimize. To overcome this problem, in this paper we developed an effective algorithm using Filtered Bitmap Index Approach for processing queries with set predicates. It eliminates the necessity of processing the entire Bitmap array index for the required tables and speeds up the query processing significantly. Experimental results show that our approach outperforms the existing algorithm to process queries with set predicates.
Key-Words / Index Term
Bitmap array Index, Set predicates, Set-level semantics, SQL, Filtered Bitmap index, Processing queries, Optimizing queries
References
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[20] Hui Zhao, Shuqiang Yang, Zhikun Chen, Songcang Jin, Hong Yin and Long Li, ”MapReduce model-based optimization of range queries”, Published in 2012, 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012).
[21] Bin He,Hui-l Hsiao, Member IEEE, Ziyang Liu ,Yu Huang,and Yi Chen,Member,IEEE, “Efficient Iceberg Query Evaluation Using Comressed Bitmap Index”, IEEE Transactionson K1nowledge and Data Engineering , Vol. 24, No. 9, SEPTEMBER 2012.
[22] Davide Martinenghi and Marco Tagliasacchi, ” Cost-Aware Rank Join with Random and Sorted Access”, Published in the IEEE Transactions On Knowledge And Data Engineering, VOL. 24, NO. 12, DECEMBER 2012.
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Citation
A.Regita Thangam, S.John Peter, "Efficient Processing and Optimization of Queries with Set Predicates using Filtered Bitmap Index," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.33-39, 2017.
Design of Arbitrary Image Slicer in Execution of Steganography
Research Paper | Journal Paper
Vol.5 , Issue.11 , pp.40-43, Nov-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i11.4043
Abstract
A new coding technique is projected in this paper. Steganography and visual Cryptography is employed to achieve the security. The secret colour image is hidden behind a canopy image. The steganographic image is currently sliced into multiple slices and transmitted in an open system setting. At the receiver side, the received slices are organised in such a way that to come up with the original image, that has the secret colour image hidden in it. This can be done by Visual Cryptography. Currently Steganography is employed on this image to get the secret image.
Key-Words / Index Term
Cryptography, Steganography, secret image
References
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Citation
A.Balasubramani, Chdv. Subba Rao, "Design of Arbitrary Image Slicer in Execution of Steganography," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.40-43, 2017.
Data Mining Approach for Feature Reduction Using Fuzzy Association Rule
Research Paper | Journal Paper
Vol.5 , Issue.11 , pp.44-49, Nov-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i11.4449
Abstract
Data mining is an upgrading technology for knowledge extraction in many fields like medical, educational, industrial, etc. Extracting an important data from large database is most vital factor. Data extraction processwere done through many techniques like feature extraction, prediction, classification, etc. for our research analyses prediction of data mining helps a lot for accessing useful information. In this paper we focused on road traffic dataset and we used fuzzy data extraction for membership function by using FCM. For the knowledge extraction process here we implemented the correlation and coefficient algorithm for road traffic dataset and attribute reduction were done by using Genetic algorithm and finally with the help of A-Priori algorithm we generate the rule for the mining the associate object for feature reduction.
Key-Words / Index Term
Data Mining, Prediction, Feature Reduction, Fuzzy, Association Rule and Rule Generation
References
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Citation
Siji P D, M.L.Valarmathi, "Data Mining Approach for Feature Reduction Using Fuzzy Association Rule," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.44-49, 2017.
Fuzzy based Sybil attack detection in Wireless Sensor Network
Research Paper | Journal Paper
Vol.5 , Issue.11 , pp.50-56, Nov-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i11.5056
Abstract
Wireless Sensor Networks (WSNs) are mostly vulnerable to the various attacks. The performance of the wireless sensor networks plays vital role but attacks degrades the performance. One of the attacks is the Sybil attack, in which a malicious node creates a huge number of fake identities in the network. The study indicates that the UWB ranging-based Sybil attack detection in wireless sensor network has better results but it can be improved further by utilizing the optimistic decision making technique. This research work mainly focus on the wireless sensor network which use fuzzy membership function for Sybil attack detection which further improves the WSNs. This proposed technique provides 95% accuracy and higher the value of F-measure and lower the false probability rate and error rate.
Key-Words / Index Term
Wireless Sensor Network, Sybil attack, Fuzzy membership function
References
[1] Panagiotis Sarigiannidis, “Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information”, Elsevier, June 2015.
[2] Manju V C, “Sybil attack prevention in Wireless Sensor Network”, IJCNWMC 2014.
[3] N. M. Saravana Kumar, “Signature Based Vulnerability Detection Over Wireless Sensor Network for Reliable Data Transmission”, Springer 2014.
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[11] H. S. Chiu and K. S. Lui, “DePHI: detection mechanism for ad hoc wireless networks” in Proceedings of the IEEE 1st International Symposium on Wireless Pervasive Computing, pp. 1–6, January 2006.
[12] T. Hayajneh, “DeWorm: a simple protocol to detect attacks in wireless ad hoc networks” in Proceedings of the 3rd IEEE International Conference on Network and System Security (NSS ’09), pp. 73– 80, Gold Coast, Australia, October 2009.
[13] Reza Rafeh, “Detecting Sybil Nodes in Wireless Sensor Networks using Two-hop Messages”, Indian Journal of Science and Technology, Vol 7(9), 1359–1368, September 2014.
[14] R. Amuthavalli, “Detection and prevention of Sybil attack in wireless sensor network employing random password comparison method" Journal of Theoretical & Applied Information Technology, Vol. 67, No. 1, 2014.
[15] Wei Shi, Sanyang Liu, “A Light-weight Detection Mechanism against Sybil Attack in Wireless Sensor Network”, KSII Transactions of Internet ad Information Systems Vol. 9, NO. 9, September 2015.
[16] Imran Makhdoom, "A novel code attestation scheme against Sybil Attack in Wireless Sensor Networks" , National Software Engineering Conference (NSEC), IEEE 2014.
[17] T.G. Dhanalakshmi, “Safety concerns of Sybil attack in WSN”, IEEE 2014.
[18] Rupinder Singh, “TBSD: A Defend Against Sybil Attack in Wireless Sensor Networks”, IJCSNS 2016.
[19] A. V. Vibi, “Detection of Sybil attack using neighboring node messaging using wireless sensor network” International Journal of Advanced Technology in Engineering and Science, Volume No. 3, Issue No. 3, ISSN (online): 2348 – 7550, March 2015.
[20] Udaya Suriya Raj Kumar Dhamodharan and Rajamani Vayanaperumal, “Detecting and preventing Sybil attacks in wire-less sensor networks using message authentication and passing method," The Scientific World Journal, 2015.
[21] Prabhjot Kaur, “Review Paper of Detection and Prevention of Sybil Attack in WSN Using Centralized IDs”, International Journal of Engineering Science and Computing, July 2016.
[22] A. B. Karuppiah, "A Novel Energy-Efficient Sybil Node Detection Algorithm for Intrusion Detection System in Wireless Sensor Networks", 3rd International Conference on Eco-friendly Computing and Communication Systems (ICECCS) IEEE 2014.
[23] T. G. Dhanalakshmi, "Safety concerns of Sybil attack in WSN”, International Conference on Science Engineering and Management Research (ICSEMR), IEEE 2014.
[24] Rupinder Singh, “A Novel Sybil Attack Detection Technique for Wireless Sensor Networks”, Advances in Computational Sciences and Technology ISSN 0973-6107 Vol.10, No. 2, pp. 185-202, 2017.
[25] Singh R, “Sybil Attack Countermeasures in Wireless Sensor Network”, International Journal of Computer Networks and Wireless Communications, Vol. 6, No. 3, May2016.
Citation
Palak, "Fuzzy based Sybil attack detection in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.50-56, 2017.
Life time improvement with hybrid clustering in Mobile Sensor Networks
Research Paper | Journal Paper
Vol.5 , Issue.11 , pp.57-63, Nov-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i11.5763
Abstract
In mobile sensor networks (MSN) handling dynamically changing topology in clustered MSNs is an important challenge as it affects the cluster formation. If re cluster triggering is performed at predetermined time or rounds without considering nodes position and stability of original cluster structure, leads to unnecessary energy usage in a dynamic topology. Life time of MSN can be extended by efficient re cluster triggering techniques. In this work cluster head (CH) rotation and re cluster triggering carried out either locally or globally based on changing position of CH and backup-CH (BU-CH) or residual energy. CH, one BU-CH are identified in every cluster, CH hands over its role to BU-CH without altering cluster structure in case it moves out of cluster boundary. In the absence of both within core boundaries then either local or global re clustering triggered. Reclustering and CH rotation are triggered when CH or BU-CH nodes surpass their boundaries and not by predetermined time or rounds as in most works. This work is a hybrid, distributed, topology driven clustering technique with minimum overheads, reduced reclustering in turn prolongs network lifetime.
Key-Words / Index Term
MSN, Re cluster triggering, Hybrid re clustering, Global and local reclustering, Energy efficient
References
[1] Morteza M. Zanjireh and Hadi Larijani "A Survey on Centralised and Distributed Clustering Routing Algorithms for WSNs,"IEEE 81st Vehicular Technology Conference , Glasgow, pp.1-6,2015.
[2] A. Rana, M. Bala, Varsha, "Performance Analysis of Energy Efficient Clustering Protocol in WSN", International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.1-5, 2017.
[3] Davood Izadi, Jemal Abawajy, and Sara Ghanavati,"An Alternative Clustering Scheme in WSN", IEEE Sensors Journal, Vol: 15, Issue: 7, pp.4148 - 4155, 2015.
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[5] Manju Bhardwaj, "Faulty Link Detection in Cluster based Energy Efficient Wireless Sensor Networks", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.1-8, 2017.
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[7] Manju Bhardwaj, "Faulty Link Detection in Cluster based Energy Efficient Wireless Sensor Networks", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.1-8, 2017..
[8] H. J.De Silva; S. Gamwarige; E. C. Kulasekere,"Energy expenditure of global reclustering and local delegation in Wireless Sensor Networks", Seventh International Conference on Wireless And Optical Communications Networks , Sri Lanka, 6-8 Sept. 2010.
[9]Peyman Neamatollahi; Hoda Taheri; Mahmoud Naghibzadeh; Saeid Abrishami, "A distributed clustering scheme for wireless sensor networks",6th Conference on Information and Knowledge Technology , Iran ,pp.20-24,2014.
[10] B. Ramesh, K. Nandhini ," Clustering Algorithms – A Literature Review", International Journal of Computer Sciences and Engineering, Vol-5, Issue-10, PP.302-306,2017.
[11 ] Mona Nasseri ; Junghwan Kim ; Robert Green ; Mansoor Alam,"Identification of the Optimum Relocalization Time in the Mobile Wireless Sensor Network Using Time-Bounded Relocalization Methodology", IEEE Transactions on Vehicular Technology ,pp.344-357 ,2017.
[12] Yihui Li ; Gaoxi Xiao ; Gurpreet Singh ;Rashmi Gupta, "Algorithms for finding best locations of cluster heads for minimizing energy consumption in wireless sensor networks", Wireless Networks (10220038), Vol 19, Issue 7,pp.1755–1768, 2013.
[13] Walaa Abdellatief ;Osama Youness ;Hatem Abdelkader ;Mohee Hadhoud" Global distributed clustering technique for randomly deployed wireless sensor networks",12th International Computer Engineering Conference ,Egypt ,pp.8-13,2016.
[14] B A Mohan ; H Sarojadevi, "A hybrid approach for data collection using multiple mobile nodes in WSN", IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology, India,pp.711-714, 2016.
[15] Ashanie Guanathillak, Kithsiri Samarasinghe" Energy Efficient Clustering Algorithm with Global & Local Re-clustering for Wireless Sensor Networks," International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering ,vol.7,no.7,pp.816-823, 2013.
Citation
Suma G, M Siddappa, "Life time improvement with hybrid clustering in Mobile Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.57-63, 2017.
Review Paper on Graph Based Approach for Mining Health Examination Records Using Views
Review Paper | Journal Paper
Vol.5 , Issue.11 , pp.64-67, Nov-2017
CrossRef-DOI: https://doi.org/10.26438/ijcse/v5i11.6467
Abstract
Answering Queries using Views is proven as an effective technology for querying real life graphs. Real life graphs are really large, so if a query arises from such graph it’s a troublesome process. Answering using views is an easy method. When SHG health algorithm is combined with answering queries using views, we can analyze the medical data and based on that data we can predict whether a health examination participant is at risk, if yes what the key associated disease category is. This helps to predict the risks at an early stage. Medical data are usually large and distributed. So we use efficient algorithms like maximally contained rewriting, Minimal containment along with the SHG algorithm to analyze medical data. Semi supervised Heterogeneous algorithm is an efficient algorithm. Maximally contained rewriting algorithm helps to find an approximate answer to the query even if it is not contained in the views.
Key-Words / Index Term
Pattern containment,SHG,Minimal Containment
References
[1] A.Y. Halevy’s, “Answering queries using views: A Survey, “VLDBJ, vol.10, no.4, pp-270-294 2001.
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[11] M. Muralidharan, V.Valli Mayil, "A Study of Natural Language Processing Procedures", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.300-304, 2017.
Citation
Reshma Ravi, Remya R, "Review Paper on Graph Based Approach for Mining Health Examination Records Using Views," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.64-67, 2017.