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Fetal brain magnetic resonance imaging (MRI) is an essential and trivial task to analyze and detect the growth of baby brain abnormalities and possibilities of diseases related to the brain. This Paper starts with different perception and view of different elder’s analysis and techniques such as morphological, voxel classification, Richardson LucyDeconvolution Method, diffusion-weighted and fast furious transform with Fetal Brain MRI. Finally, concluded with the development trend of automated image segmentationtechniques of fetal brain MRI imagesand their comparison.

Image Segmentation, fetal brain MRI, Morphological, Voxel Classification,Richardson Lucy Deconvolution Method, Diffusion-Weighted, Fast Furious Transform

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S. Vijayalakshmi, N. Suresh Kumar, "A Review on Fetal Brain Structure Extraction Techniques from Human MRI Images", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.239-242, 2018.

Evaluation of the Hippocampus structure is extremely important step regarding the precaution, detection as well as identification of numerous brain upheavals owing to the implication of the complex structural changes of the HC in those disorders. In this paper, a new hybrid method for segmenting the HC from MRI brain images is introduced by using clustering method Fuzzy C-Means which is very sensitive to noise. So prior to segmentation, pre-processing is done to make the image free of noise as well as with prominent Region of Interest. To segment HC, image features are computed to validate the slice to identify whether it comprises of Hippocampus or not. Enhancement techniques based on morphological operations and filters are applied to make a image clear. Finally FCM clustering method is used to get the HC from binary image. The result of the above said procedure ideally extracts the hippocampus. For Quantitative analysis of proposed method Dice and Jaccard parameters are used.

Alzheimer’s disease, segmentation, FCM clustering, hippocampus, image features

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[9] Csernansky J.G.,Wang, L., Swank J., Miller J.P., Gado M., McKeel D., Miller M.I., MorrisJ.C., “Preclinical detection of Alzheimer`s disease: hippocampal shape and volume predict dementia onset in the elderly”, Neuroimage vol.25, pp. 783–792.2005.
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[13] Penn Hippocampus Atlas www.nitrc.org/projects/pennhippoatlas/
[14] G.L.N. MURTHY1 and B. ANURADHA2, “Edge Enhanced Fuzzy C Means Algorithm for Hippocampus Segmentation and Abnormality Identification”, Vol. 10(4), 1747-1755,2017.
[15] K.Somasundaram, T. Genish, “Segmentation of Hippocampus from Human Brain MRI using Mathemaical Morphology and Fuzzy Logic”,International Conference on Emerging Trends in Science , Engineering and Technology, PP 269-273, 2012.
[16] K.Somasundaram and s.vijayalakshni, “Hippocampus segmentation Based on Prior shape model with region growing from high-resolution MRI of post-mortem samples”,Volume 3, No. 4, PP.320-324, July-Augest2012.
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S. Vijayalakshmi, Savita, "Fuzzy C-Means Based Automated Technique for Hippocampus Segmentation", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.243-247, 2018.

With the heterogenous data generated from large volumes of sensor networks, internet, telecommunications, current data becomes huge on big data. To handle these types of data efficient query processing techniques are necessary. As data keep on changing dynamically, an efficient clustering and indexing method is needed for continuously processing the data streams. Dynamic data can be partitioned into number of clusters, then followed by indexing. This project uses a new index structure called adaptive clustering, which is a combination of cluster and block based techniques, for processing data streams like stock market data .The incoming data which is dynamically entering is first clustered and later indexed using adaptive techniques. Experimental analysis will be made with the existing techniques in terms of space, cost, scalability and rate of retrieval.

Internet, Adaptive Indexing, Review

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P.K.Usha Rani, K. Reddy Madhavi, "Review on Adaptive Indexing Method for Effective Retrieval of streaming Data", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.248-250, 2018.

A simple method to extract brain portion from T1-Weighted Magnetic Resonance Image (MRI) of human head scans is proposed in this article. The proposed method employs mean filter and morphological operations. This method is experimented on five volumes of normal T1-W MRI of human head scans taken from the Internet Brain Segmentation Repository (IBSR). The chosen method gives comparable results with the existing popular methods such as Brain Extraction Tool (BET) and Brain Surface Extractor (BSE). The performance of the proposed method is evaluated using Jaccard (J) similarity index and Dice coefficient (D) and a corresponding mean value of 0.936 and 0.965 is obtained.

Magnetic Resonance Image (MRI), Brain Extraction, Mean filter, Morphological Operations and Connected Component analysis.

[1] T. Kalaiselvi, “Brain Portion Extraction and Brain Abnormality Detection from Magnetic Resonance Imaging of Human Head Scans”, Pallavi publications India pvt.ltd, Erode. ISBN 978-93-80406-76-3, 2011.
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K. Ezhilarasan, K. Somasundaram, T. Kalaiselvi, "A simple method for automatic brain extraction from T1-W Magnetic Resonance Images (MRI) of human head scans", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.251-256, 2018.

Digital Mammogram is widely accepted as a common modality of breast cancer detection. Image enhancement techniques play a significant role in subjectively altering the brightness and contrast of an image. These methods, despite the reported merits, suffer from over-enhancement, which has adverse effect in segmentation as well as feature extraction. This paper presents a new enhancement algorithm that enhances a digital mammogram, based on the iterative partition of its histogram pattern. The proposed Modified-Dualistic Sub-Image Histogram Equalization (M-DSIHE) method primarily partitions the original histogram into two, using the mid-point of the active dynamic intensity range of the input image. Then the partitioned histograms are iteratively divided and the histogram equalization is applied on the each partitioned histogram. This M-DSIHE has the advantage of enhancing the contrast of the original image, with due brightness preservation. The recorded results of the M-DSIHE method is observed to have an edge over the competitive methods, in terms of the quantitative and qualitative metrics.

Mammogram enhancement, Histogram, Histogram Equalization, Contrast Enhancement, Mammogram Segmentation, DSIHE

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[11] B. C. Patel, G. R. Sinha, “Gray level clustering and contrast enhancement (GLC–CE) of mammographic breast cancer images“,CSI Transactions on ICT, Vol.2, Issue.4, pp.279-286, 2015.
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[13] B Gupta and M Tiwari, “A tool supported approach for brightness preserving contrast enhancement and mass segmentation of mammogram images using histogram modified grey relational analysis “,Multidimensional Systems and Signal Processing, Vol.28, Issue.4, pp.1549–1567, 2017.
[14] N. Kharel, A. Alsadoon, P. W. C. Prasad, “Early diagnosis of breast cancer using contrast limited adaptive histogram equalization (CLAHE) and Morphology methods”, In the Proceeding of the 2017 IEEE International Conference on Information and Communication Systems (ICICS 2017),Irbid, Jordan, pp.120-124, 2017.
[15] P. Shanmugavadivu, K. Balasubramanian, “Thresholded and Optimized Histogram Equalization for contrast enhancement of images“, Computers and Electrical Engineering, Vol. 40, Issue.3, pp. 757-768, 2014.
[16] P. Shanmugavadivu, S. G. L. Narayanan, “Psychoanalysis of characteristic contrast enhancement of digital mammogram image”, In the Proceeding of the 2017 Second IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT 2017), Coimbatore, India, pp.1-4, 2017.

S. Dhamodharan, P. Shanmugavadivu, "Brightness Preserving Contrast Enhancement of Digital Mammogram using Modified-Dualistic Sub-Image Histogram Equalization", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.257-261, 2018.

The rapid growth of technology needs massive data storage for online transmission. Medical data occupies huge space for storage which has to be reduced for effective transmission, specifically for telemedicine applications. This emphasises the need of image compression which creates extra storage space to store more data. This paper presents a novel implementation of vector quantization using spiral scan order for enhancing the quality and compression as well. The standard metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Compression Ratio (CR) and Bit Rate (BR) are used to evaluate the performance of the proposed method. Experimental results demonstrate the merits of the proposed method in terms of compression metrics than other existing methods

Spiral Scan Order, Image Compression, Vector Quantization

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[3] M.Mary Shanthi Rani, Residual Vector Quantization Based Iris Image Compression, International Journal of Computational Intelligence Studies, Inderscience Publishers,2014,3(4), pp.329-334.
[4] Zoran H.Peric, Nikola Simic and Milan S. Savic , Image Coding Algorithm Based on Modified Block Truncation Coding and Delta Modulation, IEEE, TELSIKS , pp.199-202, 2017.
[5] Yongseok Jin, and Hyuk-Jae Lee, A Block-Based Pass-Parallel SPIHT Algorithm, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, No. 7, July 2012.
[6] M. Mary Shanthi Rani, Adaptive Classified Pattern Matching Vector Quantization Approach for compressing images, The 2009 International Conference on Image Processing, Computer Vision & Pattern Recognition Proceedings, Las Vegas, USA., 2009, pp.532-538.
[7] M.Mary Shanthi Rani, P.Chitra and K.Mahalakshmi , A Novel Approach of Vector Quantization using Particle Swarm Optimization Algorithm for Generating Efficient Codebook, International Journal of Advanced Research in Computer Science, 2017, Vol.8, Issue 9, 294-298.
[8] M. Mary Shanthi Rani, P. Chitra and R.Vijayalakshmi, Image Compression Based On Vector Quantization Using Novel Genetic Algorithm For Compressing Medical Images, International Journal of Computer Engineering and Applications, Volume XII, Issue I, 2018,pp. 104-114.
[9] M.Mary Shanthi Rani and P. Chitra, A Novel Hybrid Method of Haar-Wavelet and Residual Vector Quantization for Compressing Medical Images, Proceedings of IEEE International Conference on Advances in Computer Applications (ICACA) @ Bharathiar University- Coimbatore, 2016, 1, pp.321-326, [DOI: 10.1109/ICACA.2016.7887974].
[10] M.Mary Shanthi Rani and P.Chitra, A Hybrid Medical Image Coding Method based on Haar Wavelet Transform and Particle Swarm Optimization Technique , International Journal of Pure and Applied Mathematics, 118(8), pp. 3056-3067,2018.
[11] P.Chitra and M.Mary Shanthi Rani, Modified Haar Wavelet based Method for compressing medical images, International Journal of Computer Application (2250-1797) Issue 8 Volume 1, pp. 243-251, 2018.
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[16] Ivana Pace and Francis Zarb, A Comparison of Sequential and Spiral Scanning Techniques in Brain CT, Radiologic Technology, Volume 86, Number 4, pp.1-6, March/April 2015.
[17] Pasumpon Pandian, A. and S.N. Sivanandam, Hybrid Algorithm for Lossless Image Compression using Simple Selective Scan order with Bit Plane Slicing, Journal of Computer Science , Vol. 8 (8), pp.1338-1345, 2012.
[18] Afshan Mulla, Namrata Gunjikar and Radhika Naik, Comparison of Different Image Compression Techniques, International Journal of Computer Applications (0975 – 8887) Volume 70– No.28, May 2013.
[19] J. Papitha, G. Merlin Nancy and D. Nedumaran, “Compression Techniques on MR Image – A Comparative Study”, International conference on Communication and Signal Processing, April 3-5, 2013.

P. Chitra, M. Mary Shanthi Rani, "Spiral Scan Order based Vector Quantization for Compressing Medical Test Image", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.262-265, 2018.

In medical imaging, segmentation of skull from MRI Brain images is an important task in detecting and diagnosis of brain related diseases. Due to homogeneous nature of intensities, segmenting brain and non brain tissues became an challenging task. In this paper, we proposed a method using Outlier Detection based Morphological Operations (ODMO) to segment brain from MRI head scans. First, we used outlier detection to find the threshold value followed by the morphological operations and Largest Connected Component (LCC) to extract the brain. We tested our method from the images obtained from Internet Brain Segmentation Repository (IBSR) and real time images from SBC Scan, Dindigul. In order to estimate the results of our proposed method, similarity measures and overlapping measures like Jaccard (J), Dice (D), Sensitivity (S) and Specificity (SP) are used and it is compared with manual segmented images. Our proposed technique yields exceeding results when compared with existing standard techniques such as Brain Extraction Tool (BET) and Brain Surface Extractor (BSE).

Brain Extraction, Outlier Detection, Morphological Operations, MRI Brain Image

[1] K. Somasundaram, P. Kalavathi, "Brain Segmentation in Magnetic Resonance Human Head Scans using Multi-Seeded Region Growing", Imaging Science Journal, Vol. 62, No. 5, pp. 273 - 284, 2014.
[2] K. Somasundaram, P. Kalavathi, "A Hybrid Method for Automatic Skull Stripping of Magnetic Resonance Images (MRI) of Human Head Scans", IEEE Xplore Digital Library, pp. 1-5, 2010.
[3] K. Somasundaram, P. Kalavathi, "A Novel Skull Stripping Technique for T1-weighted MRI Human Head Scans", ACM Digital Library, pp. 1 - 8, 2012.
[4] K. Somasundaram, P. Kalavathi, "Contour-Based Brain Segmentation Method for Magnetic Resonance Imaging Human Head Scans", Journal of Computer Assisted Tomography, Vol. 37, No. 3, pp. 353 - 368, 2013.
[5] K. Somasundaram, P. Kalavathi, "Skull Stripping of MRI Head Scans based on Chan-Vese Active Contour Model", International Journal of Knowledge Management & e-learning, Vol. 3, No. 1, pp. 7 - 14, 2011.
[6] SM. Smith, "Fast Robust Automated Brain Extraction", Human Brain Mapping Vol. 17, pp. 143 - 155, 2002.
[7] DW. Shattuck , SR. Sandor-Leahy, KA. Schaper, DA. Rottenberg, RM. Leahy, "Magnetic resonance image tissue classification using a partial volume model", NeuroImage, Vol. 13, No. 5, pp. 856 – 876, 2001.
[8] J. Gao, and M. Xie. “Skull Stripping MR Brain Images using Anisotropic Diffusion Filtering and Morphological Processing,” International Symposium on Computer Network and Multimedia Technology, Wuhan, Vol. 1. pp. 1-4, 2009.
[9] G. J. Park and C. Lee, “Skull Stripping Based on Region Growing for Magnetic Resonance Images,” Neuroimage, Vol. 47, No. 4, pp. 1394-1407, 2009.
[10] S. Sadananthan , W. Zheng , M. Chee, V. Zagorodnov, "Skull stripping using graph cuts", NeuroImage, Vol. 49, No. 1, pp. 225–239, 2010.
[11] A. H. Zhuang, D. J. Valentino and A. W. Toga, “Skull Stripping Magnetic Resonance Images using a Model-based Level Sets,” NeuroImge, Vol. 32, No. 1, pp. 79-92, 2006.
[12] JE. Iglesias, CY. Liu, PM. Thompson, Z. Tu, "Robust brain extraction across datasets and comparison with publicly available methods", IEEE Trans Med Imaging Vol. 30, No. 9, pp.1617–1634, 2011.
[13] S. Bauer, T. Fejes, M. Reyes," A skull-stripping filter for ITK", Insight Journal, 2012
[14] X. Tao and C. M. Chang, “A Skull Stripping Method using Deformable Surface and Tissue Classification,” Proc. SPIE, Vol. 7, pp. 623-630, 2010.
[15] W. Zhao, M. Xie, J. Gao and T. Li, “A Modified Skull Stripping Method Based on Morphological Processing,” Second International Conference on Computer Modeling and Simulation, Sanya, Hanna, Vol. 1, pp. 159-163, 2010.
[16] M. Prastawa, E. Bullitt, S. Ho, G. Gerig, "A Brain Tumor Segmentation Framework based on Outlier Detection", Medical Image Analysis, Elsevier,Vol. 8, pp. 275 - 283, 2004.
[17] P. Kalavathi, V.B. Surya Prasath, " Methods on Skull Stripping of MRI Images - A Review", Journal of Digital Imaging, Springer, 2015.
[18] K. Singh, S. Upadhyaya, "Outlier Detection : Applications and Techniques", International Journal of Computer Science, Vol. 9, No. 3, pp. 307 - 323, 2012.
[19] M. Sonka, V. Hlavac and R. Boyle, "Image Processing Analysis, and Machine Vision," Second Edition, Thomson Learning, Singapore, 1999.
[20] R.C. Gonzalez and R.E. Woods, "Digital Image Processing", (Addison-Wesley Publishing Company, Reading, MA), 1992.
[21] IBSR data set available online: http://www.cma. mgh.harvard.edu/ ibsr/index.html.
[22] SBC Scans, Dindigul, State of Tamilnadu, India.
[23] http://sve.bmap.ucla.edu/instructions/m
[24] P.Shantha kumar, P.Ganesh kumar, " Performance Analysis of brain tumor diagnosis based on soft computing technique", American journal of applied sciences, Vol. 11, No. 2, pp. 329-336, 2014.

T.Priya, P. Kalavathi, "Brain Extraction from MRI Human Head Scans using Outlier Detection based Morphological Operations", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.266-273, 2018.

The need of face recognition exponentially progresses due to its applications in law and enforcement, commercial to security systems. The existing face recognition systems efficiently accomplish this task in the constrained environment. However, implementation of face recognition system in unconstrained environment still remains as a challenge. There major issues in unconstrained environment are facial expression, illumination and partial occlusion. The collection and collation of enormous individuals’ data such as profile images of employee of an organization or citizens of a province for the creation of public dataset itself is highly complex. This paper presents a generic framework to construct an annotated database, especially for the partially occluded face recognition system. This Annotated Database Construction Framework (ADCF) has the provision to store the profile images and the facial components, along with the respective feature vectors. This ADCF felicitates the logical calibration of database contents to address the need of Partially Occluded Face Recognition System, with reference to the probe image. Hence, ADCF offers better scope for the researcher to precisely validate the authenticity of any face recognition system even with occlusion.

Annotated database; Partial occlusion, face recognition

[1] R. Elmasri and S. B. Navathe, “Database Systems Models, Languages, Design and Application Programming”, Pearson Publication, India, pp. 1-27, 2014.
[2] R. J. Muller, “Database Design for Smarties using UML for Data Modeling”, Morgan Kaufmann Publication, USA, pp. 55-73, 2000.
[3] M. Morrison and J. Morrison, “Database Driven Web Sites”, Course publication, Canada, pp. 1-14, 2000.
[4] A. Silberschatz, H. F. Korth and S. Sudarshan, “Database System Concepts”, McGraw Hill Publication, Singapore, pp. 201-261, 2016. ISBN: 007-124476X.
[5] R. Elmasri and S. B. Navathe, “Fundamentals of Database Systems”, Addison-Wesley Pearson Publication, Boston, pp. 516-528, 2011. ISBN 13: 978-0-136-08620-8.
[6] P. Shanmugavadivu and A. Kumar, “Rapid Face Detection and Annotation with Loosely Face Geometry”, In the Proceedings of the 2016 IEEE 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, India, pp.594-597, 2016.
[7] P. Shanmugavadivu and A. Kumar, “Human skin detection in digital images using multi colour scheme system”, In the Proceedings of the IEEE 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimal, India, pp.1-6, 2017.
[8] A. Kumar and P. Shanmugavadivu, “Partially Occluded Face Recognition using Dynamic Time Wrapping”, unpublished.

A. Kumar, P. Shanmugavadivu, "Annotated Database Construction Framework for Partially Occluded Face Recognition", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.274-277, 2018.

Morphometric analysis was carried out to estimate the characteristics of a watershed. Earlier this analysis was laborious and time consuming as they were performed manually. The advent of satellite image and Geographical Information System (GIS) had made the analysis easier. For the present study Shanthanavardhani watershed located in Dindigul District, Tamil Nadu, India was selected. The geographical area of the watershed was 71.85 Km2. Morphometric parameters were analysed in three different aspects viz., linear, relief and areal. The watershed falls in 5th basin order, the basin relief is 0.82 Km, ruggedness number is 2.19. The value of drainage density, bifurcation ratio and stream frequency of the watershed is 2.68, 3.48 and 5.07 respectively. The output of the analysis can be used for further study such as identification sites for constructing water harvesting structure, estimation of soil erosion etc.

GIS, satellite image, watershed, morphometric parameters, drainage density

[1] Tribhuvan, P.R. and Sonar, M.A. “ Morphometric Analysis of a Phulambri River Drainage Basin (Gp8 Watershed), Aurangabad District (Maharashtra) using Geographical Information System”, Cloud Publications, International Journal of Advanced Remote Sensing and GIS, Vol. 5, Issue 6, pp. 1813-1828, 2016.
[2] P T Aravinda, H B Balakrishna, “Morphometric Analysis of Vrishabhavathi Watershed using Remote Sensing and GIS”, International Journal of Research in Engineering and Technology, Vol. 2, Issue. 8, pp 514-522, 2013.
[3] Farrukh Altaf, Gowhar Meraj, and Shakil A. Romshoo, “Morphometric Analysis to Infer Hydrological Behaviour of Lidder Watershed, Western Himalaya, India”, Hindawi Publishing Corporation, Geography Journal, Vol. 2013, Issue , pp 1-14, 2013.
[4] Itnal M P, Chandan Kumar B and A G Ugarkar, “Morphometric Analysis and Mini Watersheds Prioritization of Doddahalla Watershed, Ghataprabha River Sub-Basin, Karnataka, South India”, International Journal of Earth Sciences and Engineering, Vol. 8, issue 3, pp 91-98, 2015.
[5] Kuldeep Pareta, Upasana Pareta, “Quantitative Morphometric Analysis of a Watershed of Yamuna Basin, India using ASTER (DEM) Data and GIS” International Journal of Geomatics and Geosciences, Volume 2, issue 1, pp 248- 269, 2011.
[6] Strahler, A.N. “Quantitative Geomorphology of Drainage basins and Channel Networks; Handbook of applied hydrology”, McGraw-Hill Book Cooperation, New York., pp. 4-76. 1964
[7] Horton, R.E., “Erosional Development of Streams and their Drainage Basins: Hydrophysical Approach to Quantitative Morphology”’ Geological Society of America Bulletin, Vol. 56, Pp.275-370, 1945.
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[10] Rekha V. B, George A. V and Rita M, “Morphometric Analysis and Micro-watershed Prioritization of Peruvanthanam Sub-watershed, the Manimala River Basin, Kerala, South India”, Environmental Research, Engineering and Management, Vol. 3. Issue 57, pp 6-14, 2011.
[11] Pandey, V. K., Pandey, A. and Panda, S.N., “Watershed management using remote sensing and GIS – a case study of Banikdih watershed (Eastern India)” Asian Journal of Geoinformatics. Vol.7, Issue 1 , pp 3-16, 2007
[12] Ashish Pandey, S. Behra, R.P. Pandey and R.P. Singh, “Application of GIS for watershed prioritization and management - A Case Study”, International Journal of Environmental Science Development & Monitoring, Vol. 2, Issue 1, pp. 25-42, 2011.

N.D. Mani, A. Rajeshwari, "Morphometric Analysis of Shanthanavardhini Watershed using GIS", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.278-282, 2018.

The Internet of Things (IoT), with its ability to collect data using sensors and store the voluminous data over the cloud has become the de facto standard in building up smart homes and smart cities. The routing protocols are used in the network layer and they play the pivotal role. They perform the intelligent task of forwarding and routing. If the routing is not done properly then there will be a heavy loss and retransmission of the packets, that would cost more power, memory, bandwidth and procession capacity. Therefore, the routing protocols used in the regular networks cannot be used efficiently in IoT. IPv6 routing protocol for Low power and lossy networks (RPL) has become the favourite routing protocol of Internet of Things. There are several metrics used in the RPL to determine the path cost and to help to connect the nodes with each other. The performance quality of RPL can be analysed and measured from the factor that how best it works utilizing the resources like energy, memory, bandwidth etc. The quality of services parameters like packet delivery ratio, network convergence time, remaining energy, latency and control traffic overhead are analysed to measure the performance of RPL. The Cooja simulator running over the Contiki Sensor OS is chosen as an ideal platform due to its special feature of supporting the cross-level simulation. The open source network analyser Wireshark used in Contiki OS also helps in the process of performing the protocol analysis.

Internet of things, Routing, Low power and lossy networks, RPL, QoS for RPL, Cooja Simulator, Wireshark

[1] C. Thomson, I. Wadhaj, I. Romdhani and A. Al-Dubai, "Performance evaluation of RPL metrics in environments with strained transmission ranges," 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), Agadir, pp. 1-8, 2016.
[2] Yaqoob et al., "Internet of Things Architecture: Recent Advances, Taxonomy, Requirements, and Open Challenges," in IEEE Wireless Communications, Vol. 24, No. 3, pp. 10-16, June 2017.
[3] A.W. Atalay, “Design and Analysis of Routing Protocol for IPv6 Wireless Sensor Networks”, Diss. University of Pisa, Italy, pp. 5-30, 2015
[4] B. Daniel., “A Performance Evaluation of RPL with Variations of the Trickle Algorithm”, Diss. Worcester Polytechnic Institute, pp. 6-20, 2016.
[5] W. Mardini, M.Ebrahim, M.Al-Rudaini, “Comprehensive Performance Analysis of RPL Objective Functions in IoT Networks”, International Journal of Communication Networks and Information Security (IJCNIS), Vol. 9, No. 3, pp. 323-332, Dec 2017.
[6] H. Ali, “A Performance Evaluation of RPL in Contiki”. Master’s Thesis, Blekinge Institute of Technology, Sweden, (ELnaz), pp. 5-85, 2012.
[7] “Critical Evaluation of RPL Routing Protocol for WBAN”, Chapter 4, India, pp. 79-100
[8] T. Winter, P. Thubert, A. Brandt, J. Hui, R. Kelsey, P. Levis, K. Pister, R. Struick, J. Vasseur, R. Alexander, “RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks”, IETF RFC 6550, 2012
[9] J. P. Vasseur, M. Kim, K. Pister, N. Dejean, and D. Barthe, “Routing Metrics Used for Path Calculation in Low-Power and Lossy Networks”, RFC 6551, Internet Engineering Task Force RFC 6551, March 2012.
[10] P. Thubert (Ed), “Objective Function Zero for the Routing Protocol for Low-Power and Lossy Networks (RPL)”, RFC 6552, IETF, 2012.
[11] O. Gnawali, P. Levis, "The Minimum Rank with Hysteresis Objective Function", IETF RFC 6719, 2012
[12] F. Osterlind, A. Dunkels, J. Eriksson, N. Finne and T. Voigt, "Cross-Level Sensor Network Simulation with COOJA," Proceedings. 2006 31st IEEE Conference on Local Computer Networks, Tampa, FL, pp. 641-648, 2006.
[13] T. Mehmood, “COOJA Network Simulator: Exploring the Infinite Possible Ways to Compute the Performance Metrics of IOT Based Smart Devices to Understand the Working of IOT Based Compression & Routing Protocols”, Cornell University, Islamabad, pp. 1-7, 2007.
[14] A. Orebaugh, G.Ramirez, J. Burke, L.Pesce, J. Wright, G.Morris, “Wireshark & Ethereal Network Protocol Analyser Toolkit”, Syngress Publishing, Inc., Rockland, pp. 13-53, 2007.
[15] M. Asif, S. Khan, R. Ahmad, M. Sohail and D. Singh, "Quality of Service of Routing Protocols in Wireless Sensor Networks: A Review," in IEEE Access, vol. 5, pp. 1846-1871, 2017.
[16] M. Vučinić, B. Tourancheau and A. Duda, "Performance comparison of the RPL and LOADng routing protocols in a Home Automation scenario," 2013 IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, pp. 1974-1979, 2013.
[17] N. Pradeska, Widyawan, W. Najib and S. S. Kusumawardani, "Performance analysis of objective function MRHOF and OF0 in routing protocol RPL IPV6 over low power wireless personal area networks (6LoWPAN)," 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, pp. 1-6, 2016.
[18] S. A. Jyothi, A. Singla, P. B. Godfrey and A. Kolla, "Measuring and Understanding Throughput of Network Topologies," SC16: International Conference for High Performance Computing, Networking, Storage and Analysis, Salt Lake City, UT, pp. 761-772, 2016.
[19] I. N. R. Hendrawan and I. G. N. W. Arsa, "Zolertia Z1 energy usage simulation with Cooja simulator," 2017 1st International Conference on Informatics and Computational Sciences (ICICoS), Semarang, pp. 147-152, 2017.
[20] Nguyen Thanh Long, N. De Caro, W. Colitti, A. Touhafi and K. Steenhaut, "Comparative performance study of RPL in Wireless Sensor Networks," 2012 19th IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT), Eindhoven, pp. 1-6, 2012.

A.S. Joseph Charles, P. Kalavathi, "QoS Measurement of RPL using Cooja Simulator and Wireshark Network Analyser", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.283-291, 2018.