Volume-5 , Issue-11 , Nov - 2017 Go Back

In India energy crisis has become one of the primary concern for its development and economic growth. The gap between power demand of users and its supply is incrementing day by day. Moreover, a large portion of the power plants depend on petroleum derivative and have the danger of being phased out in future. In this paper, we consider these issues by adjusting the power supply and demand, concentrating fundamentally on Disseminated Energy Resources (DER). During off peak hour’s residual energy from DER will be stored in the proposed storage arrangement. The proactive consumers (pro-summer) in the demand side will have the scope to sell this stored energy to the national grid during peak hours in a proposed smart bidirectional network. Finally, grid monitoring and metering interface with an advanced control mechanism has been developed, which is expected to increase the extendibility of the pro-summer to handle their energy usage and costs. We have proposed a mechanism known as Tensor Decomposition (TD), in which we combined the smart meter rating produced from different sources into single dimension using MATLAB and finally applied Principle Component Analysis (PCA) to determine the error rate as compared to that of actual rate. Result obtained after applied the TD mechanism over Smart Grid (SG) data set are encouraging as compared to other preexisting approaches.

Smart Grid; Principle Component Analysis; Tensor Decomposition; Energy

[1]. Owais Khan, Kumaran Vijayasankar,Ramanuja Vedantham, Routing overhead optimization in smart grid networks", International Symposium on Power Line Communications and its Applications (ISPLC), pp. 1-5, 2015.
[2]. Lanchao Liu, Zhu Han, Multi-block ADMM for big data optimization in smart grid", International Conference on Computing, Networking and Communications (ICNC), pp. 1-5, 2015.
[3]. Foroogh Sedighi, Mahshid Helali Moghadam, Integration of heterogeneous data sources in smart grid based on summary schema model", 12th International Conference on Innovations in Information Technology (IIT), pp. 1-6, 2016.
[4]. Chan Loong Kwong, Chan Yan Tim, Leung Chun Kit, Ko Yik Yan, From great to excellence - approaching to total condition based management of power transformer under smart grid operations, 10th International Conference on Advances in Power System Control, Operation & Management (APSCOM 2015), pp. 1-6, 2015.
[5]. Jiajia Yang, Junhua Zhao, Fengji Luo, Fushuan Wen, Zhao Yang Dong, Decision Making for Electricity Retailers: A Brief Survey", IEEE Transactions on Smart Grid, PP(99), pp. 1-5, 2017.
[6]. Jozi, A., Pinto, T., Praa, I., Silva, F., Teixeira, B., Vale, Z, Wang and mendel’s fuzzy rule learning method for energy consumption forecasting considering the influence of environmental temperature", In: Global Information Infrastructure and Networking Symposium (IEEE-GIIS), pp. 1-6, 2016.
[7]. Iftikhar, N., Liu, X., Nordbjerg, F.E., Danalachi, S, A prediction-based smart meter data generator", In 19th IEEE International Conference on Network-Based Information Systems (NBiS), pp. 173-180., 2016.
[8]. Noda, T., Nagashima, T., Sekisue, T., Kabasawa, Y., Kato, S., Sekiba, Y., Tokuda, H., Kounoto, M, Standard models for smart grid simulations, In IEEE International on Power Electronics (IPEC-Hiroshima 2014-ECCE-ASIA), pp. 2175-2182 , 2014
[9]. Dong, Q., Yu, L., Song, W., Yang, J., Wu, Y., Qi, J, Fast distributed demand response algorithm in smart grid", IEEE/CAA Journal of Automatica Sinica, 4(2) , pp. 280-296, 2017.
[10]. Arrigoni, C., Bigoloni, M., Rochira, I., Bovo, C., Merlo, M., Ilea, V., Bonera, R, Smart distribution management system: Evolution of mv grids supervision & control systems", In IEEE AEIT International Annual Conference (AEIT), pp. 1-6 , 2016.
[11]. Chen, Q., Wang, F., Hodge, B.M., Zhang, J., Li, Z., Shahe-khah, M., Catalao, J.P, Dynamic price vector formation model based automatic demand response strategy for pv-assisted ev charging station" IEEE Transactions on Smart Grid, pp. , 2017.
[12]. Kulkarni, S.A., Mishra, B., Analysis of peak to average power reduction technique in presence of nonlinear distortion", International Conference & Workshop on Electronics & Telecommunication Engineering (ICWET 2016), pp. 1-5, 2016.
[13]. Agnetis, A., de Pascale, G., Detti, P., Vicino, A., Load scheduling for household energy consumption optimization", IEEE Transactions on Smart Grid, 4(4), pp. 2364-2373, 2013.
[14]. Raiei, S., Bakhshai, A., A review on energy efficiency optimization in smart grid", In: IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pp. 5916-5919, 2012.
[15]. Dlamini, F., Nicolae, D., An approach to quantify the technical impact of power quality in medium voltage distribution systems", In IEEE International Conference on Power Electronics and Motion Control Conference (PEMC), pp. 315-321, 2016.
[16]. Goudarzi, H., Hatami, S., Pedram, M, Demand-side load scheduling incentivized by dynamic energy prices", In IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 351-356, 2011.
[17]. Yan, B., Fan, H., Luh, P.B., Moslehi, K., Feng, X., Yu, C.N., Bragin, M.A., Yu, Y, Grid integration of wind generation considering remote wind farms: hybrid markovian and interval unit commitment" IEEE/CAA Journal of Automatica Sinica, 4(2) , pp. 205-215, 2017.
[18]. Cardosa, M., Wang, C., Nangia, A., Chandra, A., Weissman, J, Exploring mapreduce efficiency with highly-distributed data", In Proceedings of the second international workshop of ACM on MapReduce and its applications, pp. 27-34, 2011.

Sweety Jain, Sudeep Tanwar, Pradeep Kumar Singh, "Energy Optimization in Smart Grid using Tensor Decomposition," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.177-181, 2017.

In this current era, Information technology opens the door through which human enters into the smart , developing society with modern services in all aspects of living as well as several areas of business, medical and scientific studies, engineering and resulting a massive and exponential growth of data. Handling this voluminous data with traditional information technology frame work becomes a challenging and time demanding task in terms of data collection, storage, retrieval, analysis and application. At the same time cloud computing becomes a powerful model for data processing, storing massive amount of data and perform complex computation. We need to influence cloud computing techniques and solutions to deal with big data problems. In this review paper we focus on integration of big data in cloud environment, a comprehensive description of big data and its features, cloud computing and its characteristics and the relationship between both technologies and challenges it’s facing.

Big Data, Big Data Management Tools, Cloud Computing, Cloud Services, Cloud Issues

[1] Zikopoulos PC, Eaton C, DeRoos D, Deutsch T, Lapis G. Understanding big data.New York, NY: McGraw-Hill; 2012.
[2] Bell G, Hey T, Szalay “A. Beyond the data deluge”. Science 2009;323(5919):12978.
[3] Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, et al. Big data: the next frontier for innovation, competition, and productivity. MacKinsey Global Institute;2011.
[4] Big Data Research and Development Initiative, http://www.whitehouse.gov/sites/ default/files/microsites/ostp/big_data_press_release_final_2.pdf..
[5] Gupta R, Gupta H, Mohania M. “Cloud computing and big data analytics: what is new from databases perspective? Big data analytics. “Berlin, Heidelberg: Springer; 2012. p. 4261.
[6] Ibrahim Abaker Targio Hashem , Ibrar Yaqoob , Nor Badrul Anuar , Salimah Mokhtar , Abdullah Gani , Samee Ullah Khan “The rise of “big data” on cloud computing: Review and open research issues” ,
[7] “Big Data Processing in Cloud Computing Environments” Changqing Ji, Yu Li,Wenming Qiu, Uchechukwu Awada, Keqiu Li
[8] Charlotte Castelino1, Dhaval Gandhi, Harish G. Narula, Nirav H. Chokshi ”Integration of Big Data and Cloud Computing”
[9] Alberto Fernández, Sara del Río,Victoria López,Abdullah Bawakid,María J. del Jesus José M. Benítezand Francisco Herrera ”Big Data with Cloud Computing: an insight on the computing environment, MapReduce,and programming frameworks”.
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T. Saha, K. Das , "Inregration and Interelation of Bigdata With Cloud Computing: A Review," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.182-186, 2017.

Feature selection is a data pre-processing technique specially used for classification problems. It aims at identifying the minimal reduct with less number of features without affecting the classification accuracy of the data set. Its goal is to choose a negligible subset of features as indicated by some sensible criteria with the goal that the first undertaking can be accomplished similarly well, if worse. By picking an insignificant subset of features, unimportant and repetitive features are evacuated by the paradigm. Rough set theory is a technique that has been used for feature selection. It is utilizing to find the basic relationship from the uproarious data, which is utilizing the discritization strategy on discrete-esteemed properties and proceeds with values quality. It depends on making the equalance classes with in the given data, every one of the data tupels are making an equalance classes are indiscernalbe with the regard of the properties depicting data. Though there is many rough set based approaches like quick reduct, relative reduct entropy based reduct, these approaches are able to identify a reduct set. This paper presents a survey on various methods and techniques of feature selection and its advantages and disadvantages.

Feature selection, PSO, ACO, GA, Data mining

[1]. Bin Hu, Yongqiang Dai, Yun Su, Philip Moore, Xiaowei Zhang, Chengsheng Mao, Jing Chen,Lixin Xu “Feature Selection for Optimized High-dimensional Biomedical Data using an Improved Shuffled Frog Leaping Algorithm” IEEE 1545-5963 ©2016.
[2]. Jiye Liang, Feng Wang, Chuangyin Dang, Yuhua Qian “ A Group Incremental Approach to Feature Selection Applying Rough Set Technique” IEEE Transactions on knowledge and data engineering, vol. 26, No. 2 , February ©2014.
[3]. Xiaohui Lin, Huanhuan Song, Meng Fan, Weijie Ren, Lishuang Li, Weihong Yao “The Feature Selection Algorithm Based on Feature Overlapping and Group” IEEE International Conference on Bioinformatics and Biomedicine(BIBM) ©2016.
[4]. Guoqing Cui, Jie Yang, Masoumeh Zareapoor, Jiechen Wang “Unsupervised Feature Selection Algorithm Based on Sparse Representation” The 2016 3rd International Conference on System and Informatics (ICSAI 2016).
[5]. Hong Wang, Xingjian Jing, Ben Niu “Bacterial - Inspired Feature Selection Algorithm and Its Application in Fault Diagnosis of Complex Structures” IEEE © 2016
[6]. Hossam M. Zawbaa, E. Emary, B. PARV , Marwa Sharawi “Feature Selection Approach based on Moth-Flame Optimization Algorithm” IEEE Congress on Evolutionary Computation (CEC) ©2016
[7]. Qian Guo, Yanpeng Qu, Ansheng Deng, Longzhi Yang “A New Fuzzy-rough Feature Selection Algorithm for Mammographic Risk Analysis” 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) 2016
[8]. Sun jiongjiong,liu jun, wei xuguang “Feature Selection algorithm based on SVM” 35th Chinese Control Conference July 27-29, 2016
[9]. Chunyong Yin, Luyu Ma, Lu Feng, Jin Wang, Zhichao Yin “A Hybrid Feature Selection Algorithm” 4th International Conference on Advanced Information Technology and Sensor Application 2015
[10]. Kilho Shin, Tetsuji Kuboyama, Takako Hashimoto “Super-CWC and Super-LCC: Super Fast Feature Selection Algorithms” IEEE International Conference on Big Data(Big Data) 2015
[11]. H. Hannah Inbarania, Ahmad Taher Azarb, G. Jothic “Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis” computer methods and programs in biomedicine 113(2014) 175 – 185.

S. Ramadass, M.Gunasekaran, "Review On Feature Selection Techniques in Data Mining," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.187-191, 2017.

Public transport is a fast and convenient way of travel, but there are many issues related to it. Challenges in current public transport system are: how to estimate the exact arrival time of vehicle and real tracking of vehicle. Solution of these two problems directly save the user time and provide better management for scheduling of vehicles. Many proposal exist in the literature to address above mentioned issues. Keeping the need of intelligent transportation system, this paper provides comparative analysis of all the state-of-art existing proposals. Tracking the vehicles generally takes two types of data: historical, and real time data. For real time tracking of vehicles, Global Positioning System (GPS), sensors, Internet of Things (IoT) devices, etc are used. Due to generation of huge amount of data from IoT enabled devices present in transport system, kalman filtering, artificial neural network, data analytics and machine learning are also used for better scheduling of vehicles. In last section we provide the open issues and challenges that needs to be taken care while designing the Intelligent Transport System (ITS).

Vehicle Tracking, ITS, GPS, Smart City, Historical data, Real time data, Sensor, IoT

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[2]. Y. Zhou, L. Yao, Y. Chen, Y. Gong, and J. Lai, “Bus arrival time calculation model based on smart card data,” Transportation Research Part C: Emerging Technologies, vol. 74, pp. 81–96, 2017.
[3]. C. Bai, Z.-R. Peng, Q.-C. Lu, and J. Sun, “Dynamic bus travel time prediction models on road with multiple bus routes,” Computational intelligence and neuroscience, vol. 2015, p. 63, 2015.
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Jitendra Oza, Zunnun Narmawala, Sudeep Tanwar, Pradeep Kr Singh, "Public Transport Tracking and its Issues," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.192-197, 2017.

Decision Support System (DSS) query is an important type of distributed query. It plays an imperious role in decision making practise. However, it ingest loads of Input Output (I/O), processing and communication assets. Here, a 3-Join DSS query has been optimized using entropy and restricted chromosome based DSS query optimizer (ERC_QO). A study is carried out to inspect the consequences of varying the ratio of I/O and communication costs over Total Costs (total system resources). It is perceived that by plummeting the I/O to communication costs ratio, the communication costs can be more commendably optimized. For a 3-Join DSS query, the communication costs have been reduced by 90% approximately. Moreover, the Total Costs of 3-Join DSS query is abridged by 2%.

DSS query, Query Optimization, I/O costs, Communication Costs

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[8] M. Sharma, G. Singh, R. Singh. “Design and Analysis of Stochastic DSS Query Optimizer in a Distributed Database System”. Egyptian Informatics Journal. doi:10.1016/j.eij.2015.10.003.
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[10] M. Sharma, G. Singh, R. Singh and G Singh. 2015. “Analysis of DSS Queries using Entropy based Restricted Genetic Algorithm”. Applied Mathematics and Information Science. Vol. 9, Issue 5.
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M. Sharma, G. Singh, R. Singh, "DSS Query Optimization and Effect of Input Output / Communication Cost Metrics," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.198-203, 2017.

Packet Scheduling in LTE network is the fundamental task on which the performance of LTE network depends. Scheduling is a process to allocate the resources to users. Radio resource management is used for resource allocation. Many scheduling algorithms have been designed and proposed for scheduling. Scheduling is performed in both direction uplink and downlink. The main aim of this paper is to analyze the performance of downlink scheduling algorithms for real time flow. Three schedulers,i.e, Modified Largest Weight Delay First , Exponential Proportional Fairness, and Proportional Fairness are used for scheduling for real time flow VIDEO and non real time flow VOIP. The performance of schedulers is analyzed in terms of packet loss ratio, throughput and delay parameters. The result shows that performances of PF scheduler are not appropriate for real time application.

LTE architecture, RRM, PF, M-LWDF, EXP-PF

[1] Monika and Deepak Nandal, "Downlink Packet Scheduling Over LTE Network: A Review," International Journal for Research in Applied Science & Engineering Technology, Vol. 5, Issue VI, pp 2293-2297, June 2017.
[2] A. Dagar, Archana, and D. Nandal, “High performance Computing Algorithm Applied in Floyd Steinberg Dithering,” International Journal of Computer Applications., vol. 43, pp. 11–13, Apr. 2012.
[3] Ambreen Ahmad M. T. Beg and S.N. Ahmad, "Resource Allocation Algorithm in LTE: A Comparative Analysis," IEEE. 2015.
[4] Vallari Sharma, P.K. Sharma, “A survey On LTE Downlink Packet Scheduling,” International Journal Of Advanced Research in Computer and Communication Engineering. Vol. Issue 9, pp.7896-7899, Sep. 2014.
[5] AymanHajjawi, Mahamod Ismail, and Tito Yuwono, "Implementation of Three Scheduling Algorithm in Smart Grid Communication Over 4G Networks," International Conference on Space Science and Communication (Icon space), Langkawi, Malaysia, 10-12 Aug. 2015.
[6] F. Capozzi, G. Piro, L. A. Grieco, G. Boggia, and p. Boggia, “Downlink Packet Scheduling in LTE Cellular Networks: Key Design Issues and a Survey,” IEEE. Communication Surveys & Tutorials, Vol. 15, no. 2, Second Quarter 2013.
[7] Sindura Sara Palli, "A Thesis on LTE Downlink Scheduling Algorithm," published by Proquest.
[8] Pardeep Kumar, Sanjeev Kumar and ChetnaDabas, "Comparative Analysis of Downlink Scheduling Algorithm for a Cell Affected by Interference in LTE Network,” Springer, DOI.No. 10 1007/ s4075-016-076-x, 13- April 2013.
[9] Pardeep Kumar and Sanjeev Kumar, "Performance analysis of Downlink Packet scheduling algorithm in LTE networks,” Springer Science + Business Media Singapore 2016.
[10] P. Charanya, A. Pavithra, "Content Caching and Multicasting of 5G Hetrogeneous Cellular Wireless Networks", International Journal of Computer Sciences and Engineering, Vol.5, Issue.7, pp.61-66, 2017.
[11] Le Thanh Tuan, DaesungYoo, Hyungjoo Kim, GwangjuJin, Byungtae Jang, and Soong Hwan Ro, "The Modified Proportional Fair Packet Scheduling Algorithm for Multimedia Traffic in LTE System,” Springer-Verlag Berlin Heidelberg 2012.
[12] Davinder Singh and Preeti Singh, “Radio Resource Scheduling in 3GPP LTE: A Review,” International Journal of Engineering Trends and Technology (IJETT) – Volume 4, Issue 6, pp-2405-2411, June 2013
[13] P. Sengar, N. Bhardwaj, "A Survey on Security and Various Attacks in Wireless Sensor Network", International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.78-84, 2017.
[14] SamiaDardouri, RidhaBouallegue, “Comparative Study of Downlink Packet Scheduling for LTE Networks,” Springer Science + Business Media, New York, 21 Jan. 2015.
[15] BiswasParatapsinghSahoo, "Performance Comparison of Packet Scheduling Algorithm for Video Traffic in LTE Cellular Networks," International Journal of Mobile Networks Communication & Telematics," Vol. 3, No. 3, pp.9-17,June 2013
[16] V. Nandal and D. Nandal, “Maximizing Lifetime of Cluster-based WSN through Energy-Efficient Clustering Method,” International Journal of Computer Science & Management Studies, vol. 12, no. 3,pp 101-105, Sep. 2012.
[17] P. Yadav and D. Nandal, “Proposing new Equalizer of better performance than previous ones for MIMOOFDM Systems,” IJLTET, vol. 7, no. 3, pp. 524–530,September 2016
[18] V. Nandal and D. Nandal, “Energy Efficient, Multi-hop Routing scheme, within Network Aggregation for WSN,” International Journal of Computer Science & Management Studies, vol. 12, pp. 201–207, Jun. 2012.
[19] Giuseppe Piro, Luigi Alfredo Grieco, GennaroBoggia, Francesco Capozzi and PietroCamarda, “Simulating LTE Cellular System: An Open Source Framework,” IEEE Trns. Vehicular Technology, Oct. 2010.

Monika, Deepak Nandal, "Performance Analysis for Real Time Application Over LTE Network," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.204-211, 2017.

A big benefit of using data mining and knowledge management techniques is to create a dynamic knowledge rich health care environment. The application of Knowledge Discovery in Databases (KDD) can be done by skilled employees with good knowledge of health care industry. Thus, meaningful patterns and strategic solutions can be developed while working with massive quantities of data which can help to improve the quality of healthcare services offered to patients. This function is particularly useful for Insurance companies, Physicians, Pharmaceutical companies and by the Government health planners and management personals for the formulation of effective policies. However, there are a many issues that arise while dealing with such massive data, especially how this data can be analyzed in a reliable manner. The basic aim of Health Informatics is to take medical data from the real world and from all levels of human existence to help advance our understanding of health care facilities, medicine and medical practices. In this paper, we explored the Health care data of one of the Northern State of India, Punjab, available with HMIS database, using Big Data tools and approaches, which help in answering several critical questions with respect to healthcare facilities, for effective utilization and policy formulation of resources available. Data of Indoor Patient Department (IPD) and Outdoor Patient Department (OPD) from 2010 to 2017 has been used to forecast the number of patients in advance for coming years, taking into consideration most efficient model based on the accuracy of the forecasts, so that the planning is done well in advance for providing better health care facilities for the forthcoming patients.

Big Data, HMIS, Data mining, KDD, OPD, IPD, Time Series

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pp.60-65, Feb. 2015.
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in INDIACom2017, New Delhi, 2017, pp. 425- 29.

Parveen Singh, Vibhakar Mansotra, "Using Data mining for Forecasting Public Healthcare Services in India: a case study of Punjab," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.212-216, 2017.

The analysis of algorithms is a subject that has always arouses enormous inquisitiveness. It helps us to determine the efficient algorithm in terms of time and space consumed. There are valid methods of calculating the complexity of an algorithm. In general, a suitable solution is to calculate the run time analysis of the algorithm. The present study documents the comparative analysis of seven different sorting algorithms of data structures viz. Bubble sort, Selection sort, Insertion sort, Shell sort, Heap sort, Quick sort and Merge sort. The implementation is carried out in Visual Studio C# by creating a Graphical User Interface to calculate the running time of these seven algorithms.

Algorithm, Complexity, Runing Time, Sorting, Data Structures

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Sourabh Shastri, Vibhakar Mansotra, Arun Singh Bhadwal, Monika Kumari, Avish Khajuria, Dalbir Singh , "A GUI Based Run-Time Analysis of Sorting Algorithms and their Comparative Study," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.217-221, 2017.

In this paper, we describe the implementation of Hybrid Intelligence system for Indian legal domain by using neural network and fuzzy technique. The objective of this research is to develop a legal expert system for auto-insurance, a domain within the Indian legal system. We have proposed legal reasoning system which basically integrates rule based and case based reasoning in a structured manner for critical task units in auto-insurance domain. The end user of the system can be the insurer as well as lawyer in order to take any legal actions. The system mainly handles three main functional blocks of auto-insurance claim processing: i) validation of rules and regulations of motor vehicle act, ii) verification of the ‘extent of damage’ attribute, and iii) analysing history legal cases for reference. The scope of this hybrid system is limited to validation and verification of auto-insurance claim processing pertaining to Indian legal system. All these functional blocks play important role in providing logical solution for claim compensation.

Fuzzy Legal Expert System, Fuzzy Case Based Reasoning System, Hybrid Expert System, Hybrid Legal Intelligent System, Legal Intelligent System, Neural Network

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S. Sridevi, P. Venkata Subba Reddy , "Hybrid Legal Intelligent System Using Fuzzy and Neural Networks," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.222-231, 2017.

Without the dependence of any infrastructure or central administration to create a temporary network that includes a group of nodes and wireless hosts called as the Adhoc Mobile Network (MANET). Where multipath routing in MANET has been used primarily to transmit data without interruption. Everywhere, the routing technique is primarily considered to ensure the transmission of the message while being used in some protocols. But transmission security is one of the main disadvantages of this network. Therefore, use Secure Multipath Optimized Link State Routing Protocol (SMOLSR), which is a proactive approach to routing protocol-controlled tables, to improve efficiency, which primarily depends on multipoint relay selection (MPR). A new routing protocol for MANET is the progress of the OLSR, which integrates the multi-route strategy and the source routing control scheme. In this protocol, a Dijkstra adaptive algorithm (ADA) has been developed to estimate short-time path based multipath routing and create a control node using the hop in hop authentication model through the bidirectional authentication process (TWA). Initially, find a more limited path using the Dijkstra calculation to create a duplicate topology. In this way, delete the inner and third centers, look for a second more limited path using the Dijkstra calculation. This methodology is called a multi-Dijkstra algorithm. The main objective of the research work defining the quality of the link and the selection of the paths. The TWA process is performed for security to effectively transmit the message. If an interrupt occurs in the source-destination path, it automatically confirms the route to deliver the end-user message without loss of data. The transmitted message is well protected by a TWA process. The performance of the proposed SMOLSR protocol is compared with the many traditional protocols such as OLSR, AOMDV and CA-AOMDV. As a result, the results of the proposed SMOLSR protocol have surpassed that of other protocols.

Multi-Point Relay (MPR), Two Way Authentication (TWA), Adaptive Dijkstra Algorithm (ADA)

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Madgula Vijaya Bhaskar, G.A. Ramachandra, Y. Deepika, "Multipath Optimised Link State Protocol (OLSR) with Security for Mobile Ad-Hoc Network," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.232-241, 2017.