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Wireless sensor networks (WSN) present wide-ranging variety of real time applications for advance purposes. WSN can collect and process enormous amount of data from environment like weather, pollution, traffic conditions, industrial process monitoring, and condition based maintenance. But due to lower sensing range of these networks, dense networks are required, this bring the necessity to attain a well-organized medium access (MAC) protocol subject to power constraints. In this paper, Sensor – MAC protocol have been simulated for demonstrating saving in the energy consumption from different sources of energy waste like idle listening, collision, overhearing and control overhead.

WSN, MAC, Idle listening, Latency analysis, Sleep-wake up cycle

[1] A. Cerpa, J. Elson, M. Hamilton, J. Zhao, Habitat monitoring: application driver for wireless communications technology, ACM SIGCOMM’2000, Costa Rica, April ‘01

[2] Deepak Sharma, “An overview of Wireless Sensor Networks” International Journal of Enhanced Research in Management & Computer Applications, Vol. 4 Issue 4, April-2015,pp(47-51) ISSN: 2319-7471.
[3] Medium Access Control With Coordinated Adaptive Sleeping for Wireless Sensor Networks Wei Ye, Member, IEEE, John Heidemann, Member, IEEE, and Debor IEEE/ACM transactions on networking, vol. 12, no. 3, June 2004 ah Estrin, Fellow, IEEE

[4] K.A. Delin, R.P. Harvey, N.A. Chabot, S.P. Jackson, Mike Adams, D.W. Johnson, and J.T. Britton, “Sensor Web in Antarctica: Developing an Intelligent, Autonomous Platform for Locating Biological Flourishes in Cryogenic Environments,” 34th Lunar and Planetary Science Conference, 2003.
[5] D.C. Steere, et al., “Research Challenges in Environmental Observations and Forecasting Systems,” Proc. ACM/IEEE Int. Conf. Mobile Computing and Networking (MOBICOMM), 2000, pp. 292-299.
[6] K.A. Delin, S.P. Jackson, D.W. Johnson, S.C. Burleigh, R.R. Woodrow, M. Mc Auley, J.T. Britton, J.M. Dohm, T.P.A. Ferr., Felipe Ip, D.F. Rucker, and V.R. Baker, “Sensor Web for Spatio-Temporal Monitoring of a Hydrological Environmental,” 35th Lunar and Planetary Science Conference, League City, TX, 2004.
[7] K. Lorincz, D. Malan, Thaddeus R. F. Fulford-Jones, A. Nawoj, A. Clavel, V. Shnayder, G.Mainland, S. Moulton,and M. Welsh, “Sensor Networks for Emergency Response: Challenges and Opportunities”, Special Issue on Pervasive
[8] Payal, Deepak Sharma, Suresh Kumar, “Performance Evaluation of Reactive Routing Protocols Using IEEE 802.15.4 Application in Designed Wireless Sensor Network.”International Journal of Computer Sciences and Engineering. Vol.6. Issue 4, pp (90-96) March 2018. ISSN 2347-2693.doi: 10.26438/ijcse/v6i4.9096.

Rajbir Singh, "Evaluation of Energy Saving Medium Access Control Protocol for Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.514-520, 2018.

An emotion is a strong feeling deriving from one`s mood, circumstance, or relationships with others. They include the perception, response and interpretation of the feelings related to the experience of any specific situation. Emotions are the ones which connect the feelings, actions and thoughts. When we interact with other people, it is important to give cues to help them understand how we are feeling. Text analysis is one of the techniques, which helps us to understand a subject in a proper way through keywords used by him/her. By studying the keywords, we can paint a picture of the writer’s emotional fears, honesty, outlays, mental state and many other personality characteristics. In this paper we aim to analyze the keywords to determine the person’s emotion levels by using the keyword based method for emotion recognition.This will help to identify people with stress and depression and the ones who need counseling to come out of such emotions.

Emotions , Emotion recognition, Text, Keywords, Optical Character Recognition

[1] Prof. S.V. Kedar, Dr. D. S. Bormane , “Automatic Emotion Recognition: A Systematic Review” , IETE, India, 2016.
[2] Shiv Naresh Shivhare, Shakun Garg, Anitesh Mishra, “Emotion Finder: Detecting Emotion From Blogs and Textual Documents” , International Conference on Computing, Communication and Automation, India, 2015.
[3] Edward Chao-Chun Kao, Ting-Hao Yang, Chang-Tai Hsieh, Von-Wun Soo, ”Towards Text-based Emotion Detection A Survey and Possible Improvements ”, International Conference on Information Management and Engineering, Taiwan, 2009.
[4] C.-H. Wu, Z.-J. Chuang and Y.-C. Lin, “Emotion Recognition from Text Using Semantic Labels and Separable Mixture Models,” ACM Transactions on Asian Language Information Processing (TALIP), vol. 5, issue 2, pp. 165-183, 2006.
[5] Sheeba Grover , Dr. Amandeep Verma, “Design for Emotion Detection of Punjabi Text using Hybrid Approach”, India
[6] D. S´anchez, M.J. Mart´ın-Bautista, I. Blanco, “Text Knowledge Mining: An Alternative to Text Data Mining”, in IEEE International Conference on Data Mining Workshops, 2008.
[7] Shiv Naresh Shivhare, Saritha Khethawat, “Emotion Detection from Text”, Second International Conference on Computer Science, Engineering and Applications (CCSEA-2012), India, 2012, ISBN: 978-1-921987-03-8.
[8] R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, “Recognition of Emotional States in Natural human-computer interaction,” in IEEE Signal Processing Magazine, vol. 18(1), 2009.

Mohit Kumavat, Amrita Kumbhar, S.V. Kedar, Suraj Menon, Vineet Prasad, Omkar Parit, "An approach to predict emotional state using printed document," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.521-524, 2018.

Today the Internet may be a very important a part of each day’s life, and lots of information is generated. Discovering data from vast quantity of information manually may be very difficult, oft impossible. Researchers in cyber security face increasing amounts of knowledge and it`s evident that additional powerful tools area unit required to handle. Cyber bullying indirectly, direct attacks (messages sent on to their children), and cyber bullying (with or while not the victim`s information to help fellow cyber bully others) are two types. Indirectly concerned in cyber bullying, harassment adults actually because it`s too dangerous.

Sentiment analysis, opinion mining, Support Vector Machine, Term Frequency, TF-IDF

[1] Ana Kovaevi “Cyberbullying detection using web content mining”, 22nd Conference on Telecommunications forum TELFOR, IEEE Nov. 2014.
[2] Amrita Mangaonkar, Allenoush Hayrapetian, Rajeev Raje, “,IEEE International Conference on Electro/Information Technology (EIT), May 2015.
[3] Paridhi Singhal and Ashish Bansal, “Improved Textual Cyberbullying Detection Using Data Mining” International Journal of Information and Computation Technology, Vol 3, Number 6, pp. 569-576, pp 569-576.
[4] Pokharkar Anuja, Shelake Shubham, Kate Nalini, Murbade Arun, “Protective Shield for Social Networks to Defend Cyberbullying and Online Grooming Attacks”, Proceedings of 40th IRF International Conference, Oct 2015. [5] Divyashree, Vinutha H, Deepashree N, “An Effective Approach for Cyberbullying Detection and Avoidance”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 4, April 2016.
[6] Karthik Dinakar Roi Reichart Henry Lieberman, “Modeling the Detection of Textual Cyberbullying” , Fifth International AAAI Conference on Weblogs and Social Media, Julay 2011
[7] K. Nalini Dr. L. Jaba Sheela, “A survey on Datamining in Cyber Bullying” , International Journal on Recent and Innovation Trends in Computing and Communication, Vol: 2 Issue: 7, pp 1865-1869, July 2014
[8] Vinita Nahar, Xue Li, Chaoyi Pang, “An Effective Approach for Cyberbullying Detection”, Communications in Information Science and Management Engineering,Vol. 3 Iss. 5, pp238-247, May 2013.

A. Bichhwe, R. Khatri, "Identify Cyber Bulling words using Clustering for Social Media," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.525-528, 2018.

An object whose behaviour is found to be different from others in a dataset is said to be an outlier. The existing outlier detection algorithms are able to detect outliers only in static datasets, but are found to be inappropriate, when it comes to dynamic datasets where data arrive continuously in a stream-lined fashion viz., sensor data. To deal with such steam data, Association rule mining serves as a best technique, where frequent item sets are internally evaluated from the data, in an iterative fashion. Outlier detection techniques for static datasets include cluster analysis, where clusters are being generated from the data using k-Means clustering to discover outliers. In this paper, we propose two different approaches for outlier detection. One uses association rule based technique on dynamic datasets and the other uses K-means clustering and distance based approach on static datasets to prune local outliers. Experiments are conducted on different variants of static and dynamic datasets to detect the deviant objects (outliers) effectively in fewer computations.

outlier, static data, dynamic data, association rule mining, cluster analysis

[1] Li-Jen Kao, Yo-Ping Huang, “Association rules based algorithm for identifying outlier transactions in data stream,” IEEE International Conference on Systems, Man, and Cybernetics, Oct. 14-17, 2012.
[2] J.H. Chang and W.S. Lee, “Finding recent frequent item sets adaptively over online data streams,” in Proceedings of the 9th ACM SIGKDD, Washington, DC, USA, pp.487-492, August 2003.
[3] E.M. Knorr and R.T. Ng, “Algorithms for mining distance-based outliers in large databases,” In Proceedings 24th International Conference on Very Large Data Bases, VLDB, pp. 392-403, 1998.
[4] P. Rajendra, D. Jatindra Kumar, N. Sukumar, “An outlier detection method based on clustering,” International Conference on Emerging Applications of Information Technology, 2011.
[5] F. Angiulli, S. Basta, and C. Pizzuti, “Distance-based detection and prediction of outliers,” IEEE Transactions on Knowledge and Data Engineering, 18:145-160, 2006.
[6] F. Angiulli and C. Pizzuti, “Fast outlier detection in high dimensional spaces,” In PKDD ’02: Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 15-26, 2002.
[7] F. Angiulli and C. Pizzuti, “Outlier mining in large high-dimensional data sets,” IEEE Transactions on Knowledge and Data Engineering, 17:203-215, 2005.
[8] M.M. Breunig, H.-P. Kriegel, R.T. Ng, and J. Sander, “LOF: identifying density-based local outliers,” SIGMOD Rec., 29(2):93-104, 2000.
[9] K. Zhang, M. Hutter, and H. Jin, “A new local distance-based outlier detection approach for scattered real-world data,” In PAKDD ’09: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp.813-822, 2009.
[10] K. Narita and H. Katigawa, “Outlier detection for transactional Databases using association rules,” in Proceedings of the 9th International Conference on Web-Age Information Management, Zhangjiajie, Hunan, pp. 373-380, July 2008.
[11] R.S. Walse, G.D. Kurundkar, P.U. Bhalchandra, “A Review: Design and Development of Novel Techniques for Clustering and Classification of Data”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol. 06, pp. 19-22, Jan-2018.
[12] Namrata Ghuse, Pranali Pawar, Amol Potgantwar, “An Improved Approach For Fraud Detection in Health Insurance Using Data Mining Techniques”, International Journal of Scientific Research in Network Security and Communication, vol. 5, issue 5, June-2017.

C. Leela Krishna, C. Kala Krishna, "Outlier Detection Using Association Rule Mining and Cluster Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.529-533, 2018.

Energy efficient routing is a one of the major trusted area in wireless sensor networks (WSNs). The wireless sensor network composed of a large number of sensor nodes which has limited energy resource. Nodes in networks are basically battery operated and thus have access to limited amount of energy. The sensor nodes are working through the battery, energy saving becomes more vital issue in WSNs. The lack of energy can lead to a link failure during an active communication session, which affects the throughput and energy wastage. The routing algorithms assure the concept of energy saving without affecting the Quality of Service (QoS) Parameters like Throughput, End to End Delay, Overhead and Packet Delivery Ratio. In the existing system AODV (Ad-hoc On-demand Distance Vector Routing) is implemented. The AODV Protocol is combination of DSR and DSDV Protocols. The proposed work is to implement LEACH (Low Energy Adaptive clustering Hierarchy) algorithm in modified AODV (MAODV) Protocol which decrease the system delay, overhead and increase the system throughput and packet delivery ratio. Simulation is performed using NS2 and results shows that the proposed system is better than the existing system. The proposed system energy consumption is decreased by 47% compared to the existing system.

Wireless Sensor Networks (WSNs), AODV, MAODV, LEACH Algorithm

[1] P. Praneeth, J. Anuradha, “Compromising AODV for better performance and improve energy efficiency in AODV”, In the proceedings of the 2017 6th National Conference on Technology and Management (NCTA) , Srilanka, pp.201-204, 2017.
[2] R. Balakrishna, U. Rajeswar Rao, “Performance issues on AODV and AOMDV for MANETS”, International Journal of ComputerScience and Information Technologies, Vol.1 (2), India, pp.38-43, 2010.
[3] R. Sharma, C.K. Jha, M. Sharma, “A Comparative Simulation Based Analysis of DSR and DSDV Routing Protocols”, IEEE IMPACT3 978-1-4799-1205-6/13/$31.00 ©, India, pp.36-40, 2013.
[4] S.Vanthana, “Comparative Study of Proactive and Routing Protocols Using NS2” World Congress on Computing and Communication Technologies, Tiruchippalli, pp.275-279, 2014.
[5] Dan N. Galatchi, Roxana A. Zoican, “Flat Routing Protocols for Ad Hoc Mobile Wireless Networks”, TELSIKS 978-1-4577-2019-2/11/$26.00 ©2011 IEEE, Serbia, pp.669-672, 2011.
[6] O. Pankaj, G. Vivekkumar, “Simulation and Comparison of AODV and AOMDV Routing Protocols in MANET”, International Journal of Engineering Resersh and Technology, Vol.3, Issue. 9, pp.745-749, 2014.
[7] Mr. Sachin Sarode, Ms. Apeksha Sakhare , “Review on LEACH: A Protocol for Energy EfficientWireless Sensor Network”, In the proceeding of the 2015 2nd International Conference On Electronics And Communication System, India, pp.1021-1026, 2015.
[8] Krishna kumar A, Dr. Anuratha V, “An Energy-Efficient Cluster Head Selection of LEACH Protocol for Wireless Sensor Networks”, In the proceeding of the 2017 International Conference on Nextgen Electronic Technologies, India, pp.57-61, 2017.
[9] G. Reenkamal Kaur, Ch. Priya, S. Monika “Study of LEACH Protocol for Wireless Sensor Network”, In the proceeding of the 2014 International Conference on Communication, Computing & Systems, India, pp.196-198, 2014.

K. Mounika, Ch. Rambabu, V.V.K.D.V. Prasad, "Improving the Network Life Time of Wireless Sensor Network using MAODV Protocol with LEACH Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.534-538, 2018.

A low-cost data acquisition system, for use in sensing applications, is presented here. The system uses Arduino UNO board to implement data acquisition strategy and to interface analog sensor data from signal processing unit, to PC for further processing. A fiber optic loop serves as a sensor for the system. Python programming is used to process the incoming digital data and provide the required graphical-interface. The graphical data provided by the system is stored separately in a spreadsheet, which can be later used for processing and analyzing. The results obtained by the system are linear and stable.

Data Acquisition (DAQ) System, Arduino UNO, Universal Serial Bus (USB), Python programming

[1] Maurizio Di Paolo Emilio, “Data Acquisition Systems – From Fundamental to Applied Design” Springer publication, New York, pp. 1-2, 2013.
[2] Data Acquisition Tutorial :: Radio-Electronics.com (http://www.radio-electronics.com/info/t_and_m/data-acquisition/data-acquisition.php), visited on 8th March 2018.
[3] Goswami, T. Bezboruah and K. C. Sarma, “Design of an Embedded system for Monitoring and Controlling Temperature and Light”, International Journal of Electronic Engineering Research, Vol 1, No 1, pp. 27-36, 2009.
[4] H. K. Singh, R. Gogoi, and T. Bezboruah, “Design Approach for a Web-based Data Acquisition and Control System,” International Conference of Internet Computing, pp 16-19, 2009.
[5] D. K. Fisher, P. J. Gould, “Open-Source Hardware is a Low-Cost Alternative for Scientific Instrumentation and Research,” Modern Instrumentation, vol 1, pp. 8-20, April 2012.
[6] M. Feuntes, M. Vivar, J. M. Burgos, J. Aguilera and J. A. Vacas, “Design of an accurate, low-cost autonomous data logger for PV monitoring using Arduino that compiles with IEC standards”, Journal of Solid Energy Materials and Solar Cells, Elsevier, pp. 529-543, 2014.
[7] G. Lockridge, B. Dzwonkowski, R. Nelson and S. Powers, “Development of a Low-cost Arduino based Sonde for Coastal Applications”, Journal of Sensors (MDPI), pp. 1-16, 2016.
[8] A. Gonzalez, J. L. Olazagoitan and J. Vinolas, “A Low Cost Data acquisition System for Automobile Dynamic Applictions,” Journal of Sensors (MDPI), pp. 1-20, 2018.
[9] Teli Saraswati and C. Mani, “Smart Real-time Embedded Arduino based Data Acquisition System,” International Journal of Research in Engineering and Technology, pp. 258-262, 2015.
[10] Python programming language by Tutorial point, simply easy learning, (https://www.tutorialspoint.com/python/python_tutorial.pdf), accessed on 20 the March 2018.

Pratiksha Sarma, Hidam Kumarjit Singh, Tulshi Bezboruah, "A Real-Time Data Acquisition System for Monitoring Sensor Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.539-542, 2018.

Tuberculosis (TB) is an infectious bacteria based disease which spreads at a high rate with person to person interaction and can even lead to death. In this paper, we have proposed a health care system for prevention and control of spreading of tuberculosis with the help of radio frequency based Internet of thing (IOT) sensor devices, fog computing, mobile phones and cloud computing. In the initial stage the cloud is used to classify the user using the decision tree on the basis of their infection, and then the alerts and monitoring is done via the fog layer. The Radio frequency based sensors devices present for sensing the proximity interactions between the users, automatically providing alert to the user about the presence of any infected individual in their proximity and this proximity data is used for the creation of Temporal Network Graphs at a local level so that the spread can be controlled easily at the local level itself thus making it easy to figure the spread patterns and mass spreader. The analysis of different metric of temporal graphs are calculated and the real time based alert generation makes the healthcare system even better.

FOG healthcare, RFID sensors, proximity contacts, temporal network graphs, Tuberculosis.

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Palvi Mahajan, Amit Chhabra, Keshav Dhir, "Cloud, Fog and IOT based framework for the spread control of TB," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.543-550, 2018.

Cloud industry has reached a critical mass in the past few years, with many cloud service providers fielding competing services. Despite the competition, some of the security mechanisms offered by the services to be similar, indicating that the cloud industry has established several “best-practices,” while other security mechanisms vary widely, indicating that there is also still room for innovation and experimentation. The cloud industry had grown in the fast few years, with so many providers focusing on cloud services. Besides huge competition the security mechanisms that is provided by all these vendors had shown many good practices but that paves a way for many new innovative experiments. This paper mainly focusses on improving the security mechanism against DDOS attacks. With the existing system we are not able to predict the magnitude of DDOS attack as the causes vary across different situation. So, resolving this security issue becomes much more complex in real time situation. One important reason for DDOS attacks can be because of fake users creating spoofed request. Apart from that there are also additional attacks which are made within cloud environment and outside cloud environment, so security mechanisms must be tightened. There is also some hidden pattern which prevails on user surfing through websites based on their frequency and content visited which is also required to establish furthermore security based on user behavior. The aim of the paper is to predict the magnitude of DDOS attack which is bonded with a two-fold solution 1. Capturing trust rating for a user visiting a website considering his frequency and website safety ranking based on a Scaled User Rating Algorithm. 2. Considering parameters that helps in figuring DDOS attack pattern based on both internal and external attacks within the cloud environment. The aims defined in this paper help us in figuring out a malicious behavior of user based on his surfing pattern and contents that he had referred, which in turn help us in expecting a possible DDOS attack. In addition to that we are also trying to find possible parameters that could be a reason for DDOS attack analyzing threats that had happened within and across cloud environments in the past.

Cloud Computing; Data Privacy; Data Protection; Security; Virtualization; Monitoring; Deep Learning; Predictive Analytics; Scaled User Rating

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[2] LI Jun-Jian, Li-Qin, “User’s Behavior Trust Evaluate Algorithm Based OnCloud Model” IEEE Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control, 2015
[3] Xiaoming Ye, Xingshu Chen, Haizhou Wang, XuemeiZeng, Guolin Shao, Xueyuan Yin, and Chun Xu, “An Anomalous Behavior Detection Model in Cloud Computing”Special Issue On Information Security ,Volume 21, Number 3, June 2016
[4] Xin Lu, Cheng du, China; Yue Xu, Cheng du, China “An User Behavior Credibility Authentication Modelin Cloud Computing Environment”. IEEE International Conference on Information Technology and Electronic Commerce, 2014.
[5] Mahesh Babu, Mary SairaBhanu, “Analyzing User Behavior Using KeyStroke Dynamicsto Protect Cloud from Malicious Insiders” IEEE International Conference on Cloud Computing in Emerging Markets, 2014
[6] Lucky Nkosi, Paul TarwireyiMathew O Adigun, “Detecting a Malicious Insider in the CloudEnvironment Using Sequential Rule Mining”, IEEE International Conference on Adaptive Science and Technology, 2014
[7] Ngugi, Benjamin, Beverly K. Kahn, and Marilyn Tremaine. "Typing biometrics: impact of human learning on performance quality. " Journal of Data and Information Quality (JDIQ) 2. 2 (2011): 11
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[11] P. Rutravigneshwaran, "A Study of Intrusion Detection System using Efficient Data Mining Techniques", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.6, pp.5-8, 2017

Thiruchendhil Arasu, E. George Dharma Prakash Raj, Murali Krishna, "Scaled User Rating Algorithm to Perform Behavioral Analysis for Cloud Secure 360," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.551-559, 2018.

One of the important parameter in bearing manufacturing industry is high quality products with high precision, good surface finish and accuracy. To achieve production targets and quality standards in bearing manufacturing industry, one of the key department is maintenance, which maintain machines to perform up to its highest capabilities. Reliability is the quality of being trustworthy or performing consistently well. Reliability is also key tool to measure maintenance system performance and capability. Under reliability there are few pillars which decide reliability score. Some of that key pillars are computerized maintenance management system (CMMS), planned maintenance, redesigning, roles and responsibilities. In particular pillars also specific tasks are perform to increase reliability score. In this paper Failure mode effect analysis is proposed for grinding machines to detect critical machines and increase reliability of maintenance activities on these machines. CMMS is one of the main measures for reliability score. CMMS helps to reduce data gathering. It helps for data analyzing also. CMMS is tool that can use for maintenance planning as well. In this work, CMMS is used for improving reliability of maintenance activities.

CMMS, FMEA, Maintenance, Reliability

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[7] V. Mauro, C. Vittorio and A. L. VitoIntrona, "Optimization of photovoltaic maintenance plan by means of a FMEA approach based on real data," Energy Conversion and Management, vol. 152, pp. 1-12, 2017.
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A.S. Patel, P. J. Bagga, A.M. Sankhla, Tirth Goswami, "Maintenance Reliability Using Computer Managed Maintenance System in Bearing Manufacturing Industry," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.560-566, 2018.

With the fast expansion in wide area networks leading to availability of low cost fundamental computational resources, the popularity of computational grids has increased. Effective load balancing and scheduling are the key concerns for meeting QoS requirements of users for computational grids. Fuzzy Logic contributes to handle the uncertainties involved in processors’ load and tasks’ execution length during scheduling decisions to ensure a better load balancing in distributed systems. In an effort to enhance the previously proposed and implemented dynamic load balancing algorithms for hierarchical and distributed computational grids viz. DLBCGBH – H / D, Fuzzy based Min – Max Scheduling (FMiMaS) is proposed in this paper which when integrated with the Local scheduling proposed in DLBCGBH – H / D, devises its enhanced version viz. ‘Hierarchical with Fuzzy’ & ‘Distributed with Fuzzy’ approaches based on Hybrid Scheduling. It is implemented using GridSim 4.0 and the comparison of simulation results with DLBCGBH – H / D and Built-in Space Shared utility of GridSim 4.0 demonstrate tremendous improvements in terms of the performance metrics viz. Average Consumed Time, Average Processing Cost and Average Waiting Time.

Computational Grid, Distributed, Hierarchal, Fuzzy Logic, Space Shared, Load Balancing, Scheduling, Binary Heaps, Fuzzy Min – Max Scheduling, Hybrid Scheduling, FMiMaS

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A. Kumar, H. Pathak, "Fuzzy Min – Max Scheduling (FMiMaS) for Computational Grids," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.567-575, 2018.