Volume-7 , Issue-5 , May - 2019 Go Back

Air pollution has become a great cause of concern nowadays. The worst affected areas are urban environments especially large metropolitan cities, like Delhi. It has adverse impact on the physical and mental health of human beings. In this context, predicting air pollution has become an urgent need of the hour. This would help people to take safety measures as well as government to enact policies to safeguard the citizens. Traditionally, climatologists and meteorologists have relied on physical simulations for weather forecasting. With the advancement in artificial neural network predicting the future values based on previously observed values has become quite popular. This paper focuses on Time series analysis to predict air pollution in Delhi using LSTM, an artificial recurrent neural network architecture. We use LSTM because it can work on sequences of arbitrary length. We have taken a data-centric approach to predict air pollution and used historical weather data of Delhi that includes several weather variables – atmospheric pressure, temperature, rain, wind direction and wind speed etc.

Air Pollution Prediction, RNN, LSTM, Deep Learning

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Shadab Ahmad Ghazali, Raj Kumar, "Predicting Air Pollution in Delhi using Long Short-Term Memory Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.482-486, 2019.

Muscle is an essential organ of the body accountable for movements. EMG has a wide range of research from electrode design to recording methods, analytical methods, and various applications. The aim of this paper is to review EMG to understand and decomposition in a concise manner. Extraction and classification of features are has considered demanding tasks as it allows a consistent assessment of the neuromuscular diseases. This manuscript has described various methods of extraction and classification of features that would help to understand their nature and process of adoption. In the evaluation of EMG signals, a number of analysts had tried their hands, so in this paper, we have tried to integrate best of the best researchers that could be advantageous for further analysis. Comparison of the traditional researchers by J. L. Betthauser et al., O. W. Samuel Zhou Hui et al. and Xiangyang Zhu et al. has been conducted to interpret the optimum techniques for the evaluation Betthauser et al. has shown 89% of accuracy with Enhanced Adaptive Sparse Representation Classification (EASRC) technique, O. W. Samuel Zhou Hui et al. has shown 92% of accuracy with LDA and ANN technique and Xiangyang Zhu has used LDA-CA technique with 91% of accuracy.

Electromyography, Motor Unit Action Potential, Detection, Decomposition, Features, Classifiers

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Reema Jain, Vijay Kumar Garg, "Review of Electromyography Signal with Detection, Decomposition, Features And Classifier Theories," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.487-500, 2019.

The tremendous growth in usage of internet technology has led to the development of various wireless networks. One of the wireless networks that have gathered lot of attention is Wireless Mesh Network (WMN). WMN is being preferred because of its numerous advantages such as better coverage area, communication with other networks, low energy consumption, cost effectiveness, increased network capacity and is compatible with all IEEE 802.11 standards. However there are several challenges degrades the network performance. Normally WMNs adopts shortest path algorithm for route establishment and most of the existing algorithms are not fully accounting the factors that impacts network performance, which in turn introduces unbalanced load distribution issues in the WMN. The design of a novel delay-based link quality metric is given in this paper which utilizes the real-time statistics from the wireless driver to consider the wireless contention, congestion, and channel loss. The proposed delay metric is additive in nature and introduce less routing overhead compared to existing mechanisms. Simulation results illustrate that the proposed DBL-AODV protocol significantly enhances the performance of the network by reducing the routing overhead and the routes having high delay are avoided from the process of packet forwarding compared to the standard protocol.

Delay, Load balancing, AODV,WMN

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Keerthi D S, Shobha Rani A, Basavaraju T G, "Delay-Based Routing Mechanism for Load Balanced Routing in Wireless Mesh Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.501-506, 2019.

Gestures are the most common way of interaction for physically challenged people. Owing to this, many researchers are interested in the direction of automated hand gestures recognition. Major applications include extremely wide range: from sign language to robot control or from virtual reality to intelligent home systems. In addition, these enable deaf and dumb to interface with machine in a more natural way. As a result, immense endeavours have been done in this domain and this article, therefore, reviews the major researches in a comprehensive manner.

Gesture Recognition; Artificial Neural Networks; Computer Vision; Classification

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[37] Suma S L and Sarika Raga,” Real Time Face Recognition of Human Faces by using LBPH and Viola Jones Algorithm” International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.06-10,2018.
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Shaminder Singh, Anuj Kumar Gupta, Tejwant Singh, "Computer Vision based Hand Gesture Recognition: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.507-515, 2019.

In the current years, the broad utilization of cloud computing in IT industry has prompted excessive utilization of energy in the host and subsequently data centers, which obviously, has turned into a matter of thought. To spare energy in cloud, dynamic virtual machine consolidation and power aware mechanisms can be thought of one of the best strategies. In this approach, a portion of the under-stacked physical machines (PMs)are place either into low-control mode or are turned off with the assistance of live relocation of Virtual Machines(VMs). Fault tolerance mechanism with dynamic relocation is proposed through this literature. Proposed work presents a novel approach of conserving energy considering parameters such as fan speed, temperature, power consumption and energy. Fan speed is allocate to each Virtual Machine(VM) along with temperature. Deterioration of virtual machines are detected at distinct level of examination. 1) Fan speed is compared against temperature, In case Fan speed is lower as compared to temperature then VM with temperature rise upon load is detected. 2)Energy consumption is another criteria used to detect deterioration. Deterioration can be detected at any level and if detected, dynamic relocation through Live VM migration is done and progress monitoring mechanism is used to conserve energy. Using the approach energy efficiency is achieved along with reliability. Simulation is conducted in Netbeans with CLoudsim 3.0.3. Proposed approach conserve energy up to 25% .

Fault Tolerance; Energy efficiency; Reliability; Migration

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[3] Sanajay Sharma, “Trends in Server Efficiency and Power Usage in Data Centers” SPEC 2016 Asian Summit
[4] T. Mastelic , A. Oleksiak , H. Claussen , I. Brandic , J.M. Pierson , A. V. Vasilakos, “Cloud Computing: Survey on Energy Efficiency”, ACM Computing Surveys (CSUR), v.47 n.2, p.1-36, January 2015
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[6] INDIA`S ENERGY SCENARIO IN 2020
D. Sun,Guiran, Chang, C. Miao, X. Wang, “Analyzing, modeling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments” Springer Science+Business Media New York 2013 , 21 March 2013
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[9] Rodrigo, N.; Calheiros, R.; Ranjan, A.; Beloglazov, César A. F. De Rose.; Buyya, R., “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms “, In Softw. Pract. Exper on, vol., no., pp.23-50, January 2011.
[10] Amin, Z.; Sethi N.; Singh, H., “Review on Fault Tolerance Techniques in Cloud Computing”, in International Journal of Computer Applications (0975 – 8887 )on, vol. 116, no. 8, pp. 13, 2015.
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Harleen kaur, Kamaljit Kaur, "Energy Aware Load Balancing Fault Tolerant Mechanism for Enhancing Reliability of Cloud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.516-520, 2019.

Docker is a platform for building, deploying and managing various application containers, which are virtualized, on a common operating system. Docker has been widely adopted in every sector. Also, it can be used for the distribution and management of Docker images. Images are nothing but the iso images ie. images of operating systems and enterprise applications. Docker registry is a container which can be used to store images of operating system and enterprise applications. This paper proposes building a private centralized registry for employees of any organization. It includes generating Docker images for Linux platforms and automating image creation with Dockerfiles that will work across platforms. The paper also proposes the development of effective search logic for searching Docker images to get faster and easier outcomes. Also integrating this framework with the existing framework of any organization to improve their work efficiency.

Docker, Docker images, Dockerfile, Docker registry, Continuous integration

[1] “Docker Hub” https://hub.docker.com/
[2] “Docker documentation” https://docs.docker.com/
[3] “Docker Registry” https://docs.docker.com/registry/
[4] “Dockerfiles” https://docs.docker.com/engine/reference/builder/#usage
[5] Antonio Brogi; Davide Neri; Jacopo Soldani: Docker Finder: Multi-attribute Search of Docker Images. In: IEEE International Conference on Cloud Engineering (IC2E), Canada, pp. 273 – 278. (2017).
[6] Wang Kangjin, Yang Yong; Li Ying; Luo Hanmei, Ma Lin: FID: A Faster Image Distribution System for Docker Platform. In: 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W), Tucson, AZ, USA, pp. 191-198. (2017)
[7] Christopher B. Hauser; Jörg Domaschka: ViCE Registry: An image Registry for virtual Collaborative Environment. In: 2017 IEEE 9th International Conference on Cloud Computing Technology and Science, Hong-kong, China, pp. 82 – 89. (2017)
[8] Senthil Nathan; Rahul Ghosh; Tridib Mukherjee; Krishnaprasad Narayanan. CoMICon: A Co-operative Management System for Docker Container Images. In: 2017 IEEE International Conference on Cloud Engineering, Vancouver, BC, Canada, pp. 116 – 126. (2017)
[9] Jeeva Chelladhurai; Pethuru Raj Chelliah; Sathish Alampalayam Kumar. Securing Docker Containers from Denial of Service (DoS) Attacks. In: 2016 IEEE International Conference on Services Computing, San Francisco, CA, USA, pp. 856 – 859. (2016)
[10] Qiang Liu; Wei Zheng; Ming Zhang; Yuxing Wang; Kexun Yu. In: Docker-based Automatic Deployment for Nuclear Fusion Experimental Data Archive Cluster. IEEE Transactions On Plasma Science. pp. 1281 – 1284. (2018)
[11] Abdulrahman Azab. Enabling Docker Containers for High-Performance and Many-Task Computing. In: 2017 IEEE International Conference on Cloud Engineering, Vancouver, BC, Canada, pp. 279 – 285. (2017)
[12] Bin Xie, Guanyi Sun, Guo Ma. Docker Based Overlay Network Performance Evaluation in Large Scale Streaming System. In: 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi`an, China, pp. 366 – 369. (2016)
[13] Orest Lavriv, Bohdan Buhyl, Mykhailo Klymash, Ganna Grynkevych. Services continuous integration based on modern free infrastructure. In: 2nd International Conference on Advanced Information and Communication Technologies (AICT), Ukraine, pp. 150 – 153. (2017)
[14] Enrico Bacis, Simone Mutti, Steven Capelli, Stefano Paraboschi. DockerPolicyModules: Mandatory Access Control for Docker containers. In: IEEE Conference on Communications and Network Security (CNS), Italy, pp. 749-750. (2015)

Sanskruti Shrawane, Medha Shah, "Continuous Integration and end-to-end Automation Framework Deployment using Docker," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.521-525, 2019.

the objective of this work is to study the impact of feature level fusion and synthesis of classifiers for gender classification using fingerprints. Initially, feature level fusion of Multi-Block Projection Profiles features and Segmentation based Fractal Texture Analysis (SFTA) features are extracted for a single instance of fingerprints. Further, along with the feature level fusion and synthesis of classifiers on fingerprint have been piloted and the experiments are conducted accordingly on four different Homologous fingerprint databases. The results reveal that feature level fusion with synthesis of classifiers greatly improves the efficiency of gender classification over the non-fused and single classifier and outperforms the earlier reported techniques.

Gender Identification, Biometrics, Fingerprint, SFTA, MBPP, KNN,SVM and Decision Tree classifier

[1] Naveen Kumar Jain, Sunil Sharma, Anurag Paliwal., A Real-Time Approach To Determine The Gender Using Fingerprints”, IJAIR ISSN: 2278-7844, pp:229-233, 2012.
[2] Gnanasivam .P, and Dr. Muttan S, “Fingerprint Gender Classification Using Wavelet Transform and Singular Value Decomposition”, International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012.
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[5] A. S. Falohun, O.D.Fenwa, F. A. Alala,” A Fingerprint-based Age and Gender Detector System using Fingerprint Pattern Analysis ”, International Journal of Computer Applications (0975 – 8887) Vol.4, pp.136 –140, February 2016.
[6] Caifeng Shan and Shaogang Gong and McOwan, Peter W,” Learning Gender from Human Gaits and Faces”, IEEE Conference on Advanced Video and Signal Based Surveillance Washington, DC, USA.,ISBN 978-1-4244-1695-0., pp.:505-510,2007.
[7] Pragya Bharti, Dr. C. S. Lamba,” DWT-Neural Network based Gender Classification”, International Journal of Digital Application & Contemporary research, Volume 2, Issue 8, March 2014.
[8] Tapia J.E., Perez C.A., Bowyer K.W. (2015) “Gender Classification from Iris Images Using Fusion of Uniform Local Binary Patterns. In: Agapito L., Bronstein M., Rother C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science, vol. 8926,2014. Springer, Cham
[9] Manish Verma and Suneeta Agarwal.’’ Fingerprint Based Male - Female Classification. ’’ in Proceedings of the international workshop on computational intelligence in security for information systems (CISIS’08), Genoa, Italy, pp.251 – 255, 2008.
[10] Rafik Djemili ; Hocine Bourouba ; Mohamed Cherif Amara Korba,” A speech signal based gender identification system using four classifiers”, 2012 International Conference on Multimedia Computing and Systems ,ISBN no.978-1-4673-1520-3, 2012.
[11] Abhijit Patil, Kruthi R and Shivanand Gornale, " Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.5, pp. 34-43, 2019.DOI: 10.5815/ijigsp.2019.
[12] S.S Gornale ,Abhijit Patil and Kruti R ,” Fusion Of Gabor Wavelet And Local Binary Patterns (LBP) Features Sets For Gender Identification Using Palmprints”, International journal of Imaging Science and Engineering,Vol.10,Issue No.2,April2018.
[13] Pallavi Chand, Shubhendu Kumar Sarangi, “A Novel Method for Gender Classification Using DWT and SVD Techniques”, International Journal of Computer Technology & Applications, Vol 4 (3),pp.445-449, June 2013.
[14] S.S Gornale and Kruti R “Fusion of Fingerprint and Age Biometric for Gender Classification Using Frequency and Texture Analysis” Signal & Image Processing: An International Journal (SIPIJ) Vol.5, No.6, pp.: 76-85 December 2014. DOI: 10.5121/sipij.2014.5606,ISSN 0976-710X ( online); 2229-3922(print).
[15] S. S. Gornale, Mallikarjun Hangarge, Rajmohan Pardeshi, Kruthi R“ Haralick Feature Descriptors for Gender Classification Using Fingerprints: A Machine Learning Approach “,IJARCSSE Vol. 5,Issue 9,September-2015.
[16] S. S. Gornale, Geetha D, Kruthi R “Analysis of a fingerprint image for gender classification using spatial and frequency-domain analysis”, American International Journal of Research in Science, Technology, Engineering and Mathematics”,ISSN 2328-3491, ISSN:2328-3580, ISSN (CD-ROM): 2328-3629, pp.46-50, 2013.
[17] S. S. Gornale, “Fingerprint-Based Gender Classification for Biometric Security: A State-Of-The-Art Technique”,International Journal of Research in Science, Technology, Engineering & Mathematics ISSN 2328-3491, pp. 39-49 Dec-2014.
[18] S .S Gornale and Abhijit Patil,” Statistical Features Based Gender Identification Using SVM”, International Journal for Scientific Research and Development, issue 8, pp.:241-244, ISSN: 2321 0613, 2016.
[19] Shivanand Gornale, Abhijit Patil and Veersheety C. “Fingerprint-Based Gender Identification Using DWT and Gabor Filters”, International Journal of Computer Applications Vol.152 Issue 4, pp.34-37.2016.
[20] Shivanand Gornale, Basavana M, and Kruti R, “Fingerprint-Based Gender Classification Using Local Binary Pattern”, International Journal of Computational Intelligence and Research Vol.13 Issue 2, pp. 261-272,2017.
[21] Prabha,S Jitendra, and P Rajmohan,”Fingerprint-based Automatic Human Gender Identification”, International Journal of Computer Applications ISSN 0975 – 8887, Vol-170 Issue-7, July 2017 .
[22] S .S Gornale, Abhijit Patil and Mallikarjun Hangarge and R Pardesi. (2019) Automatic Human Gender Identification Using Palmprint. In: Luhach A., Hawari K., Mihai I., Hsiung PA., Mishra R. (eds) Smart Computational Strategies: Theoretical and Practical Aspects. Springer, Singapore. ISBN: 978-981-13-6295-8, pp.: 49-58, Vol.22 March 2019.DOI: https://doi.org/10.1007/978-981-13-6295-8_5.
[23] S .S Gornale, Abhijit Patil and Mallikarjun Hangarge ”Binarized Statistical Image Feature set for Palmprint based Gender Identification”, Book of abstract- International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND-2019) pp:61 3-4 march 2019 NIT Kurukshetra India.
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[31] KVK Multimodal Database, Dept. of C.S and I.T,Aurangabad Maharashtra,India.

Kruti R, Abhijit Patil, Shivanand Gornale, "Fusion of Features and Synthesis Classifiers for Gender Classification using Fingerprints," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.526-533, 2019.

Different techniques like mark, content passwords, PINs and so on have been utilized in the past to confirm people`s personality. In any case, biometric distinguishing proof framework, which uses the special natural characteristics of people like voice, face, fingerprints, palm and iris to perceive the concerned individual, has totally changed the image of verification based frameworks. It has given secure technique to validation as natural personalities of an individual can`t be fashioned. Biometric ID framework coordinates the interesting organic characteristics of people with their information put away as biometric formats. Be that as it may, the natural qualities of individuals changes with age which prompts corruption in the exhibition of biometric confirmation framework and along these lines furnishes an overwhelming assignment to manage. In this paper we investigate age initiated changes in the iris of human eye and analyze how these progressions and other potential causes influence the exhibition of iris based biometric framework.

Ageing, Biometrictemplate, Iris, Biometricauthenticationsystem, Pupil

[1]E. WOLFF, Anatomy of the eye and orbit, H.K. Lewis, London, UK, 7th edition 1976.
[2]Daugman, John. History and Development of Iris Recognition URL: http://www.cl.cam.ac.uk/users/jgd1000/history.html(19 February, 2002)
[3] A. Poursaberi and B.N. Araabi, “iris recognition for partially occluded images: methodology and sensitivity Analysis” Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007, Article ID 36751.
[4] A. Poursaberi and B.N. Araabi, “Half eye wavelet based method for iris recognition” proceeding of the 2000 5th International Conference on Intelligent System Design and Applications(ISDA’05).
[5] A.S Narote, S.P. Narote, M.B Kokare, L.M. Waghmare, ”An Iris Recognition Based on Dual Tree Complex Wavelet Transform” published in the proceedings of IEEE International Conference TENCON 2007, Taiwan during Oct. 30-Nov. 02,2007.
[6] L.M. Waghmare, S.P. Narote, A.S. Narote, “Biometric Personal Identification Using IRIS”, Proceedings of International Conference on Systemic, Cybernetics and Informatics, ICSCI – 2006, Pentagon Research Centre – Hyderabad, pp.679-682, Jan 2006.
[7]Sandipan P. Narote, Abhilasha S. Narote and Laxman M. Waghmare, “Iris based recognition system using Wavelet transfer”, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.11, November 2009.
[8]D.E Benn, M.S. Nixon and J.N. Carter, “Robust eye Extraction using H.T. “AVBPA’99.
[9]Pitfalls in Studying “Big Data” From Operational Scenarios URL: http://csis.pace.edu/-ctappert/papers/prpceedings/2016BTAS/Papers/PitfallsInStudyingBigdata.pdf

Deepak Kumar, Raj Kumar, "Analysing the effects of ageing on Iris based Biometric Identification System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.534-537, 2019.

Today sensing resources are widely increased in terms of vehicles and it affects the cloud computing systems. This technology is used for predicting traffic and for road safety. These systems usually share resources and collaborate with sensing devices for processing data and propagate results. In this paper we proposed Vehicular cloud based fault tolerance mechanism that considers cost matrix and dynamic fault tolerance. The allocation of resources depends critically on the cost associated with virtual machine. It considers exponential residency of VC and execution time along with bandwidth utilization. Bandwidth consumption and cost of execution is reduced greatly by the effect of proposed mechanism.

Vehicular Cloud, Cloud computing, Fault tolerance, Resource scheduling

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[2] B. Brik, N. Lagraa, N. Tamani, and A. Lakas, “Renting out Cloud Services in Mobile Vehicular,” Res. Gate, no. December, 2018.
[3] M. R. Jabbarpour, A. Marefat, A. Jalooli, and H. Zarrabi, “Correction to : Cloud-based vehicular networks : a taxonomy , survey , and conceptual hybrid architecture Could-based vehicular networks : a taxonomy , survey , and conceptual hybrid architecture,” Wirel. Networks, no. November, 2017.
[4] R. Yu, X. Huang, J. Kang, J. Ding, S. Maharjan, S. Gjessing, and Y. Zhang, “Cooperative resource management in cloud-enabled vehicular networks,” IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7938–7951, 2015.
[5] T. Mori, Y. Utsunomiya, X. Tian, and T. Okuda, “Queueing theoretic approach to job assignment strategy considering various inter-Arrival of job in fog computing,” 19th Asia-Pacific Netw. Oper. Manag. Symp. Manag. a World Things, APNOMS 2017, pp. 151–156, 2017.
[6] K. Zheng, H. Meng, P. Chatzimisios, L. Lei, and X. Shen, “An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems,” IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7920–7928, 2015.
[7] K. Zhang, Y. Mao, S. Leng, Q. Zhao, L. Li, and X. Peng, “Energy-efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks,” IEEE Access, vol. 3536, no. c, pp. 1–10, 2016.
[8] W. Zhang, Z. Zhang, and H. Chao, “Cooperative Fog Computing for Dealing with Big Data in the Internet of Vehicles : Architecture and Hierarchical Resource Management,” IEEE Access, no. December, pp. 60–67, 2017.
[9] J. Fan, R. Li, and X. Zhang, “Research on fault tolerance strategy based on two level checkpoint server in autonomous vehicular cloud,” Proc. 2017 IEEE 7th Int. Conf. Electron. Inf. Emerg. Commun. ICEIEC 2017, no. 61363079, pp. 381–384, 2017.
[10] Y. Sharma, B. Javadi, W. Si, and D. Sun, “Reliability and energy efficiency in cloud computing systems: Survey and taxonomy,” J. Netw. Comput. Appl., vol. 74, pp. 66–85, 2016.
[11] H. S. Y. Lin, “EAFR: An Energy-Efficient Adaptive File Replication System in Data-Intensive Clusters,” IEEE Trans. Parallel Distrib. Syst., pp. 1017–1030, 2017.
[12] B. Shrimali and H. Patel, “Performance Based Energy Efficient Techniques For VM Allocation In Cloud Environment,” IEEE Access, pp. 477–486, 2017.
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[14] H. M.-R. Mahdi Ghamkhari, “Energy and Performance Management of Green Data Centers: A Profit Maximization Approach,” IEEE Trans. Smart Grid, pp. 1017–1025, 2017.
[15] M. Salehi, M. K. Tavana, S. Rehman, S. Member, M. Shafique, and A. Ejlali, “Two-State Checkpointing for Energy-Efficient Fault Tolerance in Hard Real-Time Systems,” pp. 1–12, 2016.
[16] S. Ben Alla and A. Ezzati, “Hierarchical adaptive balanced energy efficient routing protocol (HABRP) for heterogeneous wireless sensor networks,” Ieee, 2011.
[17] P. Handa, B. Singh Sohi, and N. Kumar, “Energy efficient hybrid routing protocol for underwater acoustic sensor network,” 2016 Int. Conf. Electr. Electron. Optim. Tech., pp. 2573–2578, 2016.
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Jaspreet Singh, Kamaljit Kaur, "Dynamic Fault Tolerance Job Allocation Mechanism to Conserve Resources in Vehicular Cloud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.538-547, 2019.

In this paper we make a comparative study of the overheads of locks and STM by taking different practical synchronization problems as examples to understand why the performance of STM is worse than that of locks. Overhead is the combination of excess or indirect computation time, memory, bandwidth, or other resources that are required to perform a specific task. While executing parallel programs whenever any lock or STM function is called it takes some time and also occupies some space. The total time taken by all the lock or STM calls of the program is the total lock or STM time overhead of that program. The total space occupied by all the lock or STM calls of the program is the total lock or STM space overhead of that program. The flexible approach is an approach of programming with STM by which STM has been made more user-friendly and by which execution time of STM has been reduced. We make a study of the overheads of the flexible approach also. We found that the time and space overheads of STM are higher than that of locks. The time and space overheads of the Flexible Approach were less than those of STM but higher than those of locks.

Multiprocessing, Parallel Processing, Locks, Software Transactional Memory, Overheads

[1] Cornelia Cecilia Eglantine,“Overhead(Computing)”, published by TypPress, 2012
[2] Yang Ni, Vijay Menon, Richard L. Hudson, Ali-Reza Adl-Tabatabai, J. Eliot, B. Moss, Bratin Saha, Antony L. Hosking, Tatiana Shpeisman, “Open Nesting in Software Transactional Memory”, In the Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming, pp. 68-78, 2007
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[6] Sandhya S.Mannarswamy, Ramaswamy Govindarajan, “Variable Granularity Access Tracking Scheme for Improving the Performance of Software Transactional Memory”, In the Proceedings of International Conference on Parallel Architectures and Compilation Techniques, pp. 232-242, 2011
[7] Ryan Saptarshi Ray, “STM:Lock-Free Synchronization”, Special Issue of IJCCT, ISSN (ONLINE): 2231 – 0371, ISSN (PRINT): 0975 – 7449, Volume- 3, Issue-2, pp. 19-25, 2012
[8] Anupriya Chakraborty, Sourav Saha, Ryan Saptarshi Ray, Utpal Kumar Ray,“ Lock-Free Readers/Writers”, International Journal of Computer Science Issues (IJCSI), ISSN (PRINT): 1694 – 0814, ISSN (ONLINE): 1694 – 0784, Volume- 10, Issue-4, No-2, pp. 180-186, 2013
[9] Venkatakash Raj Rao Jillelamudi, Sourav Mukherjee, Ryan Saptarshi Ray, Utpal Kumar Ray,“ Lock-Free Dining Philosopher”, International Journal of Computer and Communication Technology (IJCCT), ISSN (PRINT): 0975 – 7449, Volume- 4, Issue-3, pp. 54-58, 2013
[10] Rup Kamal, Ryan Saptarshi Ray, Utpal Kumar Ray, Parama Bhaumik, “Cigarette Smokers’ Problem with STM”, International Journal of Computer and Communication Technology (IJCCT), ISSN (PRINT): 0975 – 7449, Volume- 4, Issue-3, pp 63-68, 2013
[11] Ryan Saptarshi Ray, Parama Bhaumik, Utpal Kumar Ray, “Flexible Programming Approach using STM”, International Journal of Computer Sciences and Engineering (IJCSE) E-ISSN:2347-2693 Volume- 6, Issue-7, pp. 349-353, 2018
[12] Nilam Choudhary, Shikhar Agarwal, Geerija Lavania, “Smart Voting System through Facial Recognition”, International Journal of Scientific Research in Computer Sciences and Engineering (ISSN: 2320-7639) Volume- 7, Issue-2, pp. 7-10, 2019
[13] S. JabeenBegum, B. Swaathi, “A Survey for identifying Parkinson’s disease by Binary Bat Algorithm”, International Journal of Scientific Research in Computer Sciences and Engineering (ISSN: 2320-7639) Volume- 7, Issue-2, pp. 17-23, 2019

Ryan Saptarshi Ray, Parama Bhaumik, Utpal Kumar Ray, "A Study and Analysis of Lock and STM Overheads," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.548-556, 2019.