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• Open Access   Article

  A Case Study on Brain Tumor Segmentation Using Content based Imaging

  D. Sherlin, D. Murugan

  Research Paper | Journal (IJSRNSC)

  Vol.6 , Issue.3 , pp.1-5, Jun-2018

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  Abstract
  Brain tumors are getting to be noticeably basic in the current world for which determination of the sickness is essential as the early forecast of the malady will spare the Life. There are different kinds of tumors are there out of which gliomas are the most well-known and forceful, as the life expectancy of the individual will be decreased. For such situation culminate design about the treatment is the great move keeping in mind the end goal to enhance the patient`s life. Tumor appraisal should be possible through different ways though MRI (Magnetic Resonance Imaging) is the for the most part utilized method. Be that as it may, the disadvantage in the MRI is the measure of information produced which will set aside gigantic measure of opportunity to recognize to affirm the nearness of tumor and correct area of tumor. To stay away from this and to enhance the precision of the fragmented tumors, another method has been proposed which is Content Based Imaging Technique. In this strategy there are three distinct stages are performed specifically pre-handling, arrangement and post-preparing. The first information picture is contrasted and the arrangement of database pictures and substance are contrasted with partitioned the brain tumor as it were. Likewise the sort of tumor will likewise be distinguished alongside the precision rate.

  Key-Words / Index Term
  Brain, Braintumor, Brain tumor Segmentation, Gliomas, Malignant, Content based imaging

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  Citation
  D. Sherlin, D. Murugan, "A Case Study on Brain Tumor Segmentation Using Content based Imaging," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.1-5, 2018

• Open Access   Article

  Iterative Vessel Segmentation with Stopping Criterion for Fundus Imagery

  J. Kanimozhi, P. Vasuki, S. Mohamed Mansoor Roomi

  Research Paper | Journal (IJSRNSC)

  Vol.6 , Issue.3 , pp.6-12, Jun-2018

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  Abstract
  Vessel segmentation in fundus images plays vital role in diagnosing and treating patients in Ophthalmology. This proposed vessel segmentation algorithm consists of three stages to improve the lower contrast fundus images includes enhancement followed by thresholding and segmentation. Adaptive histogram equalization method is used to enhance the input image. From the enhanced image the major vessel are extracted by thresholding using gray thresh method. The new vessel pixels are identified iteratively using region growing method in which a new stopping criterion is introduced to improve the accuracy. The proposed method outperforms than the existing method of iterative vessel segmentation which achieves 3% greater in accuracy.

  Key-Words / Index Term
  Contrast enhancement; Histogram equalization; Segmentation; Stopping criterion; Accuracy; ROC

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  Citation
  J. Kanimozhi, P. Vasuki, S. Mohamed Mansoor Roomi, "Iterative Vessel Segmentation with Stopping Criterion for Fundus Imagery," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.6-12, 2018

• Open Access   Article

  SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement

  A. Shakin Banu, P.Vasuki, S. Mohamed Mansoor Roomi, A. Yusuf Khan

  Research Paper | Journal (IJSRNSC)

  Vol.6 , Issue.3 , pp.13-17, Jun-2018

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  Abstract
  Synthetic Aperture Radar Image Classification is one of the four problem domains in the field of Automatic Target Recognition. This paper proposes Wavelet transform based Euclidean distance with Shanon Index measurement to classify the SAR image, which consists of three steps including preprocessing, feature extraction and classification. Preprocessing removes the speckle noise present in the image. Daubechies wavelet is used to extract the features by obtaining the approximated image. Finally, the classification process is completed using Euclidian distance with the help of Shannon index measurement. The performance of the existing system is compared with the existing Maximum Likelihood Classifier in terms of accuracy and an accuracy of 95.3% is achieved in classification for the proposed method.

  Key-Words / Index Term
  SAR, image classification, feature extraction, Daubechies Wavelet, Euclidean distance and Shannon index measurement

  References
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  Citation
  A. Shakin Banu, P.Vasuki, S. Mohamed Mansoor Roomi, A. Yusuf Khan, "SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.13-17, 2018

• Open Access   Article

  Adaptive Vector Quantization for Improved Coding Efficiency

  S. Vimala, P. Uma, S. Senbagam

  Research Paper | Journal (IJSRNSC)

  Vol.6 , Issue.3 , pp.18-22, Jun-2018

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  Abstract
  In this paper, we propose a novel method of improving the initial codebook for Vector Quantization (VQ) to compress still images. VQ is a simple and efficient compression technique which comprises of three phases: 1. Codebook Generation 2. Index Map Generation and 3. Image Reconstruction. Default codebook is generated first and is improved by refining the representative vectors called the code vectors. The codebook optimization technique proposed in this paper improves the quality of reconstructed images to a greater extent where the average bpp is decreased to a value of 0.79 which is a significant improvement. Benchmark images such as Lena, Kush, Cameraman, Barbara are tested with the proposed technique and this technique produces better results.

  Key-Words / Index Term
  Image Compression, Vector Quantization, Index Map, bpp, Still Image

  References
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  Citation
  S. Vimala, P. Uma, S. Senbagam, "Adaptive Vector Quantization for Improved Coding Efficiency," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.18-22, 2018

• Open Access   Article

  Input Analysis for Accreditation Prediction in Higher Education Sector by Using Gradient Boosting Algorithm

  A.Deepa, E. Chandra Blessie

  Research Paper | Journal (IJSRNSC)

  Vol.6 , Issue.3 , pp.23-27, Jun-2018

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  Abstract
  The main objective of this paper is to analyze the input criteria using Gradient Boosting Algorithm to predict the NBA accreditation strategy with generating solutions and suggestions to the institutions on that specific point. The NBA (The National board of accreditation) is formed by the prestigious AICTE (All India Council of Technical Education).The Aim of the council is to evaluate technical institutions periodically and inspecting programs basis according to specified norms and standards as per the council. Now a day’s colleges are feeling more prestigious to get the NBA accreditation. In order to get the accreditation college need to pass in various conditions like Overall Infrastructure, Academic Process, Result outcome and etc. These conditions have sub categories with points. The institutions need to meet out the allotted points to get a NBA Accreditation. This paper uses a Machine Learning Algorithm namely Gradient Boosting which can be used for the prediction of the status of the institutions and it considers the Input criteria and only one sub point within it that is the Student Intake Procedure. Before accreditation team’s arrival for inspection it checks some specific points in these criteria on a sample data and suggests the weak points and provides solutions for improvement.

  Key-Words / Index Term
  NBA, NAAC, AICTE, Machine Learning, ANN, Gradient Boosting Algorithm

  References
  [1] Higher education, high-impact research, and world university rankings”: A case of India and comparison with china.
  [2] Dr.E Chandra Blessie, Deepa A,’Bharatiar University’, Big Data Analytics For Accreditation In Higher Education Sector”, IJCSIT, Volume 8, Issue 3 May 2017.
  [3] Dr.E Chandra Blessie, Deepa A,’Bharatiar University’, “Comparison Of Big Data Tools For Accreditation In Higher Education Sector”, 5th international Conference on ‘Contemporary Issues And Challenges In Agriculture, Managemnt, And Information Technology’ in srilanaka
  [4] Alan Olinsky Bryant University, USA Kristin Kennedy ,Bryant University, USA Bonnie Brayton Kennedy, ‘Assessing Gradient Boosting in the Reduction of Misclassification Error in the Prediction of Success for Actuarial Majors ‘. 2012 CS-BIGS .
  [5] S.B.Kotsiantis Department of Computer Science and Technology University of Peloponnese, Greece End of Karaiskaki, 22100, Tripolis GR. ‘Supervised Machine Learning: A Review of Classification Techniques’. Informatica 31 (2007) 249-268 249.
  [6] A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation”, Published in: Autonomic Computing, 2006, ICAC 06. International Conference on Autonomic Computing.

  Citation
  A.Deepa, E. Chandra Blessie, "Input Analysis for Accreditation Prediction in Higher Education Sector by Using Gradient Boosting Algorithm," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.23-27, 2018

• Open Access   Article

  Wireless Mesh Network Link Failure Issues and Challenges: A Survey

  Rucha Pawar, Vailshali Munguwadi, Pranav Lapsiwala

  Survey Paper | Journal (IJSRNSC)

  Vol.6 , Issue.3 , pp.28-36, Jun-2018

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  Abstract
  Wireless Mesh network gives the new definition in wireless broadband technology with the advantage of various enhanced features such as self organising, self-configuring and self-healing which helps to significantly reduce the complexity, cost and enhances network capacity, connectivity and flexibility. WMN integration with multiple mesh client and ad-hoc network demands reliability.This article depicts the issue of frequent link failure in WMNs and their effects on throughput, congestion and energy consumption. As compared to conventional ad-hoc network, wireless mesh network covers large geographical region. Wireless network uses fidelity standard for media access and communication that is highly immune to noise and interference. Therefore, link and route maintenance is always a challenge for the researchers. This paper presents study of WMN different characteristics, link failure issues, advantages and its drawback. Such a mesh network can be used in the development of IoT, Smart Cities applications, electronic noise system and etc.

  Key-Words / Index Term
  Wireless mesh network; Link failure; Link quality; WMN Architecture

  References
  [1] Ian F. Akyildiz , Xudong Wang, Weilin Wang.” Wireless mesh networks: a survey “,Computer Networks Journal (Elsevier),Vol. 47, Issue 4,15 March 2005.
  [2] Hongqiang Zhai and Yuguang Fang. “Physical Carrier Sensing and Spatial Reuse in Multirate and Multihop Wireless Ad Hoc Networks” In Proc. of the IEEE INFOCOM ’06, April 2006.
  [3] Jinyang Li, Charles Blake, Douglas S. J. De Couto, Hu Imm Lee, and Robert Morris. “Capacity of Ad Hoc Wireless Networks” In Proc. of theACM Mobi-Com ’01, pages 61–69, July 2001.
  [4] Yan Zhang, Jijun Luo, Honglin Hu, December 13 2006 by Auerbach Publications, “wireless mesh networking architecture protocol and standards”[book]
  [5] www. meshdynamics.com/military-mesh-networks.html
  [6] Usman Ashraf , Slim Abdellatif, Guy Juanole “Route Maintenance in IEEE 802.11 wireless mesh networks” Computer Communication Journal (Elsevier), Pages 1604-1621,vol.34, Issue:13,February 2011
  [7] Liang-Bi Chen, Bo-Chuan Cheng ,You-Chiun Wang, Katherine Shu-Min Li , Jing-Jou Tang ”An efficient fault tolerance path finding algorithm for improving the robustness of multichannel wireless mesh networks” : Consumer Electronics (ICCE), IEEE International Conference, march 2016.
  [8] Djohara Benyamina,Abdelhakim Hafid, Michel Gendreau“Wireless mesh network planning: A multi objective solution“ broadband communication networks and systems, international conference,sept 2008
  [9] Chun Tung Chou, Archan Misra “Low Latency Multimedia Broadcast in Multi-Rate Wireless Meshes” IEEE Workshop on Wireless Mesh Networks (WIMESH) Conference , sep 2005.
  [10] Sarra Mamechaoui, Fedoua Didi and Guy Pujolle “A survey on energy efficiency for wireless Mesh network” International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.2, March 2013.
  [11] Anis Ouni, Hervé Rivano , Fabrice Valois , Catherine Rosenberg “Energy and Throughput Optimization of Wireless Mesh Network with Continuous Power Control” IEEE Transactions on Wireless Communications ,Volume: 14, Issue: 2, Feb. 2015
  [12] Kusuma, Umapathi G R “Implementation of Optimization of Latency in Wireless Mesh Network” International Journal of Computer Science and Information Technologies, Vol. 5 , 2014, Pg No.6010-6015
  [13] Yashpal Rohilla, Preeti Gulia” A comparative study of wireless mesh and adhoc network : A Cross layer design approach ” International Journal on Computer Science and Engineering, Vol. 4, 06 June 2012.
  [14] Jangeun Jun, Mihail L. Sichitiu “MRP: Wireless mesh networks routing protocol”,Elsevier, Computer Communications,2008.
  [15] Kyu-Han Kim, Kang G. Shin.,“Self Reconfigurable Wireless Mesh Network”, IEEE Transactio ns on networking, vol.19, Issue:2, April 2011.
  [16] Krishna N. Ramachandran, Elizabeth M. Belding, Kevin C. Almeroth, Milind M. Buddhikot “Interference-Aware Channel Assignment in Multi-Radio Wireless Mesh Networks”25 IEEE conference,vol 5122,2008
  [17] U. Ashraf, S. Abdellatif, G. Juanole, Efficient route maintenance in wireless mesh networks, Wireless Computing, IEEE Conference, ISWPC Santorini, Greece, 2008.

  Citation
  Rucha Pawar, Vailshali Munguwadi, Pranav Lapsiwala, "Wireless Mesh Network Link Failure Issues and Challenges: A Survey," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.28-36, 2018

• Open Access   Article

  Comprehensive Approach in Studying the Behaviour of Contiki RPL Protocol in Diverse Data Transmission Ranges

  P. Manickam, V. Muthu Ganeshan, M. Girija

  Research Paper | Journal (IJSRNSC)

  Vol.6 , Issue.3 , pp.37-42, Jun-2018

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  Abstract
  Internet of Things (IoT) is a collection of interconnected wireless objects. These objects are smart objects which are communication and auto computing devices. All these devices are uniquely identifiable and addressable and able to send data to other objects. With rapid growth of wireless and electronic objects, IoT brings the new dimension to the world to communicate. The Routing Protocol for Low Power and Lossy Networks (RPL) is the standard routing protocol of Network layer for Internet of Things (IoT). This paper presents the concepts of RPL routing protocol and its working mechanisms. Also, this paper provides the simulation results of RPL protocol using Cooja Simulator.

  Key-Words / Index Term
  DAO, DIO,DIS,OF and Rank

  References
  [1] T. Winter, P. Thubert, J. Vasseur, et al., “RPL: routing protocol for low power and lossy networks”, RFC6550,2012
  [2] M. Friedemann and F. Christian “From the Internet of Computers to the Internet of Things”, 2010.
  [3] Tie Qiu, Amr Tolba, “An efficient routing protocol for emergency response Internet of Things”, Journal of Network and Computer Applications, Vol.72, Sep. 2016, pp 104-112.
  [4] Thubert, P. RPL Objective Function Zero. RFC 6552. https://datatracker.ietf.org/doc/rfc6552/ (accessed on 31 July 2015).
  [5] Z. Tao and L. Xianfeng “Evaluating and Analyzing the Performance of RPL in Contiki”, 2014.
  [6] K. C. Lee, R. Sudhaakar, L. Dai, S. Addepalli, and M. Gerla, “RPL under mobility,” in IEEE Consumer Communications and Networking Conference (CCNC), 2012 (IEEE, 2012), pp. 300–304.
  [7] G. Montenegro, and C. Schumacher, “IPv6 over lowpower wireless personal area networks (6LoWPANs): overview, assumptions, problem statement, and goals,” 2007.
  [8] www.contiki-os.org/start.html
  [9] J. Vasseur, M. Kim, K. Pister, N. Dejean, and D. Barthel, “Routing metrics used for path calculation in low-power and lossy networks,” RFC 6551,2012.
  [10] P. Thubert, “Objective Function Zero for RPL,” RFC 6552, vol. 33, pp.3–8, 2012.
  [11] J. W. Hui, “RFC6553 Option for Carrying RPL Information in Data-plane Diagrams,” RFC 6553, vol. 33, pp. 3–8, 2012.
  [12] V. Hui, J., Vasseur, J. P., Culler, D., & Manral, “An IPv6 Routing Header for Source Routes with the Routing Protocol for Low-Power and Lossy Networks (RPL),” Saudi Med J, vol. 33, pp. 3–8, 2012.S. Balandin, S. Andreev and Y. Koucheryavy. “Internet of things, smart spaces, and next generation networking”, Berlin: Springer. 2013.
  [13] A. Dunkels, J. Eriksson, N. Finne, and N. Tsiftes. “Powertrace: Network level power profiling for low-power wireless networks”. March 2011.
  [14] B. Sharkawy, A. Khattab, and K. M. F. Elsayed, “Fault-tolerant rplthrough context awareness” , IEEE World Forum on Internet of Things (WF-IoT), March 2014, pp. 437–441.
  [15] Z. Tao and L. Xianfeng “Evaluating and Analyzing the Performance of RPL in Contiki”, 2014.
  [16] L. B. Saad, C. Chauvenet, and B. Tourancheau, “Simulation of therpl routing protocol for ipv6 sensor networks: two cases studies,”Int. Conference on Sensor Technologies and Applications SENSORCOMM 2011. IARIA, 2011.
  [17] O. Gaddour, A. Koubaa, and M. Abid, “Quality-of-service aware routing for static and mobile ipv6-based low-power and lossy sensor networks using rpl,” Ad Hoc Networks, vol. 33, pp. 233–256, 2015.
  [18] The Internet Engineering Task Force (IETF), 2010. < www.ietf.org>
  [19] Routing Over Low Power and Lossy Networks (ROLL), 2004.
  [20] Tripathi, J. de Oliveira, J. Vasseur, Applicability study of RPL with local repair in smart grid substation networks, in: Smart Grid Communications (SmartGridComm), First IEEE International Conference on, November 2010, pp. 1–6.
  [21] JP. Vasseur, M. Kim, K. Pister, N. Dejean, and D. Barthel., “Routing Metrics Used for Path Calculation in Low-Power and Lossy Networks.”, RFC 6551 , March 2012.
  [22] O. Gnawali and P. Levis, “The Minimum Rank with Hysteresis Objective Function”, RFC 6719 , September 2012.

  Citation
  P. Manickam, V. Muthu Ganeshan, M. Girija, "Comprehensive Approach in Studying the Behaviour of Contiki RPL Protocol in Diverse Data Transmission Ranges," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.37-42, 2018

• Open Access   Article

  A Survey on Load Balancing Schemes in RPL based Internet of Things

  A. Sebastian, S. Sivagurunathan

  Survey Paper | Journal (IJSRNSC)

  Vol.6 , Issue.3 , pp.43-49, Jun-2018

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  Abstract
  Internet of Things technology is a collection of sensors and actuators which gathers data from physical environment that can be stored and processed to generate actuating information. Physical environment data such as temperature, pressure, humidity, pollution and any valuable parameters related to human body, machine, etc. have great value for automation, fault detection and timely remedy. Thus, IoT networks have given rise to Smart Cities, Smart Health, Smart Transport Logistics, Smart Production and Supply chain management, Smart Home and many more. For IoT deployments, ROLL-WG has standardized Routing Protocol for Low Power and Lossy Networks (RPL) for urban environment (RFC 5548), home automation (RFC 5826), industrial control (RFC 5673) and building automation (RFC 5867). RPL is a destination vector protocol for low power devices which is designed to address the needs of constrained IoT environment. In RPL, nodes organize themselves by forming a Destination Oriented Directed Acyclic Graph (DODAG) rooted towards the sink. RPL uses Objective Functions (ETX & Hop Count) to optimize path selection. Many newObjective Functions for IoT applications are suggested by researchers for path optimization. In most cases, RPL is efficient in providing fast network convergence. However, path optimization and network performance are affected by Load Balancing problem. In this article, we survey existing load balancing schemes in RPL based Internet of Things. We also list out load balancing metrics and challenges in RPL with reference to load imbalance.

  Key-Words / Index Term
  Internet of Things, RPL, Load Balancing schemes, Load Balancing Metrics

  References
  [1] S. Sivagurunathan et el., Internet of Things for developing smart sustainable cities (SSC): A security Perspective, Connectivity Frameworks for Smart Devices, Computer Communications and Networks (Z. Mahmood (ed), DOI 10.1007/978-3-319-33124-9_13 307, 2016.
  [2] Z. Sheng, S. Yang, Y. Yu, A. Vasilakos, J. McCann, and K. Leung, “A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities,” IEEE Wirel. Commun., vol. 20, no. 6, pp. 91–98, 2013.
  [3] B. G. Mamoun Qasem, Ahmed Al-Dubai, Imed Romdhani, “Load Balancing Objective Function in RPL”, ROLL – WG INTERNET DRAFT, pp. 1–10, 2017.
  [4] R. Jadhav, “Optimization of Parent node selection RPL based Natworks”, ROLL-WG INTERNET DRAFT, pp. 1–11, 2017.
  [5] M. R. Palattella et al., “Standardized protocol stack for the internet of (important) things,” IEEE Commun. Surv. Tutorials, vol. 15, no. 3, pp. 1389–1406, 2013.
  [6] O Gnawali, P Levis, “The Minimum Rank with Hysteresis Objective Functione”, RFC, pp. 1–13, 2012.
  [7] H.-S. Kim, H. Kim, J. Paek, and S. Bahk, “Load Balancing under Heavy Traffic in RPL Routing Protocol for Low Power and Lossy Networks,” IEEE Trans. Mob. Comput., vol. 1233, no. c, pp. 1–1, 2016.
  [8] Marwa Mamdough et al, “RPL Load balancing via minimum degree spanning tree”, IEEE transaction, 2016
  [9] X. Liu, J. Guo, G. Bhatti, P. Orlik, and K. Parsons, “Load Balanced
  Routing for Low Power and Lossy Networks.”
  [10] Quan Le, Thu Ngo-Quynh, Thomas Magedanz et al, “RPL based multipath Routing protocols doe Internet of Things”, IEEE Xplore, 2014
  [11] Minkeun Ha, Kiwoong Kwon, Daeyoung Kim, Peng-Yong Kong, “Dynamic and Distributed Load Balancing Scheme in Multi- gateway based 6LoWPAN”, IEEE International Conference on Green Computing, 2015
  [12] O. Iova, F. Theoleyre, and T. Noel, “Using multiparent routing in RPL to increase the stability and the lifetime of the network,” Ad Hoc Networks, vol. 29, no. February, pp. 45–62, 2015.
  [13] D. T. Delaney, L. Xu, and G. M. P. O’Hare, “Spreading the load in a tree type routing structure,” Proc. - Int. Conf. Comput. Commun. Networks, ICCCN, 2013.
  [14] M. N. Moghadam and H. Taheri, “High throughput load balanced multipath routing in homogeneous wireless sensor networks,” 22nd Iran. Conf. Electr. Eng. ICEE 2014, no. Icee, pp. 1516–1521, 2014.
  [15] S. Surendran and A. N. Jadhav, “Maximizing Network Lifetime by Using Smart Cluster Head Selection,” IOSR J. Electron. Commun. Eng. Ver. III, vol. 10, no. 6, pp. 2278–2834, 2015.
  [16] S. K. Baji Baba and K. R. R. Mohan Rao, “Improving the network life time of a wireless sensor network using the integration of progressive sleep scheduling algorithm with opportunistic routing protocol,” Indian J. Sci. Technol., vol. 9, no. 17, 2016.
  [17] O. Iova, F. Theoleyre, and T. Noel, “Exploiting multiple parents in RPL to improve both the network lifetime and its stability,” IEEE Int. Conf. Commun., vol. 2015–Septe, pp. 610–616, 2015.
  [18] B. Djamaa, M. Richardson, B. Djamaa, and M. Richardson, “The Trickle Algorithm : Issues and Solutions The Trickle Algorithm : Issues and Solutions,” no. January, 2015.
  [19] M. Michel, S. Duquennoy, B. Quoitin, and T. Voigt, “Load-balanced data collection through opportunistic routing,” Proc. - IEEE Int. Conf. Distrib. Comput. Sens. Syst. DCOSS 2015, pp. 62–70, 2015.
  [20] X. Yang, J. Guo, P. Orlik, K. Parsons, and K. Ishibashi, “Stability metric based routing protocol for low-power and lossy networks,” 2014 IEEE Int. Conf. Commun., pp. 3688–3693, 2014.
  [21] P. Karkazis, I. Papaefstathiou, L. Sarakis, T. Zahariadis, T. H. Velivassaki, and D. Bargiotas, “Evaluation of RPL with a transmission count-efficient and trust-aware routing metric,” 2014 IEEE Int. Conf. Commun. ICC 2014, pp. 550–556, 2014.
  [22] C. Paper, S. California, D. Gon, G. U. Federal, and S. California, “ALABAMO : A LoAd BAlancing MOdel for RPL ALABAMO : A LoAd BAlancing MOdel for RPL,” no. May, 2016.
  [23] S. Jawaligi and G. S. Biradar, “Reliability and load sensitive data gathering protocol for WSNs using single mobile sink node,” Proc. IEEE Int. Conf. Emerg. Technol. Trends Comput. Commun. Electr. Eng. ICETT 2016, 2017.
  [24] P. Kuila and P. K. Jana, “Energy Efficient Load-Balanced Clustering Algorithm for Wireless Sensor Networks,” Procedia Technol., vol. 6, pp. 771–777, 2012.
  [25] A. Sebastian and S. Sivagurunathan, “Multi Sink RPL based Internet of Things for Emergency Response in Smart Cities,” International Journal of Pure and Applied Mathematics, vol. 118, no. 18, pp. 2875–2881, 2018.
  [26] A. Sebastian and S. Sivagurunathan, “Bandwidth Allocation based Load Balancing for RPL (BA-LBRPL),” International Journal of Computer Sciences and Engineering, Vol. 2, pp. 76-80, 2018.
  

  Citation
  A. Sebastian, S. Sivagurunathan, "A Survey on Load Balancing Schemes in RPL based Internet of Things," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.43-49, 2018

• Open Access   Article

  A Nature Inspired Optimal Path Finding Algorithm to Mitigate Congestion in WSNs

  G.P. Sunitha, B.P. Vijay Kumar, S.M. Dilip Kumar

  Research Paper | Journal (IJSRNSC)

  Vol.6 , Issue.3 , pp.50-57, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrnsc/v6i3.5057

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  Abstract
  In resource constrained wireless sensor networks, congestion control is an extremely important issue that need to be addressed. The individual capacities of the channels are exceeded by the bulk traffic and creates adverse effects on the performance of the network. Therefore, to resolve the congestion problems in wireless sensor network the challenge lies in developing more sophisticated routing techniques which are able to fairly deliver the data between source and destination with minimum consumption of energy and reduced congestion. In the recent times, various swarm intelligence based routing approaches are proposed that aided in congestion detection and control mechanisms. Most of them are found to be with lower convergence rate. Therefore, a nature inspired hierarchical routing technique which aims to reduce congestion and energy consumption with network longevity and faster convergence rate is proposed. In this technique, a static partition of the target area based on node density is done to optimize energy efficiency. Firefly behavior based routing is modeled to select the optimal path for data transmission. This approach is concerned with exploiting global behavioral patterns emerging from local interactions. The proposed technique aims to minimize congestion by applying network load balance.

  Key-Words / Index Term
  Congestion, optimal path, Energy

  References
  [1] Imran Ali Khan, Syed Afsar Shah,Babar Nazir, “Congestion control algorithms in wireless sensor networks: Trends and opportunities”, Journal King Saud Universit,y Vol.29, Issue.3, pp.236-245, Elsevier 2017.
  [2] Muhammed Saleem, Gianni A Di Caro, Muddassar Farooq “Swarm Intelligence based routing protocol for wireless sensor networks: Survey and future directions”, Information Sciences, Vol.181, Issue 20, pp.4597-4624, 2011.
  [3] Guo,Wenjing and Zhang wei “A survey on intelligent routing protocols in wireless sensor networks”,Journal of Network and Computer Applications, Vol.38, pp.185-201, Elsevier 2014.
  [4] Mukhdeep Singh Manshalia, Myank Dave and Satya Bir Singh “Computational intelligence for congestion control ad quality of service improvement in wireless sensor networks”, Transaction on Machine Learning and Artificial Intelligence, Vol.5, Issue 6, pp.21, 2017.
  [5] Jia Dongyao, Zou Shengxiong Li Meng and Zhu Huaihua “Adaptive multi-path routing based on an improved leapfrog algorithm”, Information Sciences, Vol.367, pp.615-629, 2016.
  [6] Pavlos Antoniou, Andreas Pitsillides, Tim Blackwell, Andries Engelbrecht and Loizos Michael. “Congestion control in wireless sensor networks based on bird flocking behavior”, Computer Networks Vol.57, Issue 5, pp.1167-1191, 2013.
  [7] Praveen Lalwani, Isha Ganguli and Haider Banka “FARW: Firefly algorithm for routing in wireless sensor networks”, In Recent Advances in Information Technology(RAIT), pp.248-252, IEEE 2016.
  [8] Mukhdeep Singh Manshalia, Myank Dave and Satya Bir Singh, “Congestion control in wireless sensor networks based on bioluminescent firefly behavior”, Wireless Sensor Networks Vol.7, Issue 12, pp.149, 2015.
  [9] Jiun-Huei Ho, Hong-Chi Shih, Bin-Yih Liao and Shu-Chuan Chu “ A ladder diffusion algorithm using ant colony optimization for wireless sensor networks”, Information Sciences, Vol.192, pp.204-212, 2012.
  [10] Ado Adamou Abba Ari, Blaise Omer Yenke,Nabila Labraoui,Irepran Damakoa and Abdelhak Gueroui, “A power efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence based approach”, Journal of Network and Computer Applications, Vol.69, pp.77-97, 2016
  [11] Hameed Orojloo and Abolfazl T Haghighat “A tabu search based routing algorithm for wireless sensor networks”, Wireless Networks, Vol.22, Issue 5, pp.1711-1724, 2016.
  [12] G P Sunitha, S M Dilip Kumar and B P Vijaya Kumar “Energy balanced zone based routing protocol to mitigate congestion in wireless sensor networks”, Wireless Personal Communications, Vol.97, Issue 2, pp.2683-2711, Springer 2017.
  [13] G Yogarajan and T Revathi,“Nature inspired discrete firefly algorithm for optimal mobile data gathering in wireless sensor networks”, Wireless Networks, pp.1-15, Springer 2017.
  Jati Gilang Kusuma and others,“Evolutionary discrete firefly algorithm for travelling salesman problem”, In Adaptive and Intelligent Systems, pp.393-403, Springer 2011.
  

  Citation
  G.P. Sunitha, B.P. Vijay Kumar, S.M. Dilip Kumar, "A Nature Inspired Optimal Path Finding Algorithm to Mitigate Congestion in WSNs," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.50-57, 2018

• Open Access   Article

  Separating Moving Objects from Stationary Background using Dynamic Mode Decomposition

  Athisha S., Keerthi Krishnan K., Sreelekshmi P.S.

  Research Paper | Journal (IJSRNSC)

  Vol.6 , Issue.3 , pp.58-64, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrnsc/v6i3.5864

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  Abstract
  Real-time background/foreground separation of a video is necessary for detecting an object, identifying, tracking vehicle, as well as recognizing objects. Several algorithms were already found for background initialization and foreground detection. Recent examinations have presented a robust method, Dynamic Mode Decomposition (DMD) for separating video frames into a background (low-rank) model and foreground (sparse) segments. The full video stream first converted to frames and applied DMD on each frame for the separation of background/foreground objects. Since the method uses full video frames it had more computational complexity and difficult to analyze. For a better solution, this paper shows that the large continuous video stream first converted to frames and each frame breaks into segments and then DMD applied on the segment where the moving object or foreground is obtained. The issue using DMD is the burden of working with large information and now it can be easy work using the segmented video frames. The strength of DMD is demonstrated using a publicly available Scene Background Initialisation (SBI) dataset. The objective of this work is to obtain a background/foreground model from a video sequence where the background is filled with a number of foreground objects with less complexity. Finally compared the accuracy parameters between different background/foreground separation methods with DMD and shows the performance of DMD_Segmented is much better.

  Key-Words / Index Term
  Background/Foreground Separation, SBI Dataset, Dynamic Mode Decomposition (DMD), Segmentation

  References
  This paper has been demonstrated that the method of dynamic mode decomposition, typically used for evaluating the dynamics of complex systems, can be used for background/foreground separation in videos with visually appealing results and excellent computational efficiency. The issue using DMD is a load of working with an excess of information and it can be easy work using the segmented video frames. This work obtained a background/foreground model from a video sequence where the background is filled with a number of foreground objects with less complexity and compared the results of DMD with other methods.
  
  REFERENCES
  
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  Citation
  Athisha S., Keerthi Krishnan K., Sreelekshmi P.S., "Separating Moving Objects from Stationary Background using Dynamic Mode Decomposition," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.58-64, 2018