Volume-7 , Issue-3 , Mar - 2019 Go Back

In Wireless Sensor Networks (WSNs), many application scenarios, traditional WSNs with static sink nodes will be replaced by Mobile Sinks (MSs), and also the corresponding application needs secure communication surroundings. Key Management is that the most crucial issue within the security of Wireless detector Networks. Current key management researches pay less concentration to the safety of detector networks with MS. This paper proposes a hybrid key management system supported a Polynomial Pool-based key pre-distribution and Basic Random key pre-distribution (PPBR) to be working in WSNs with Mobile Sink. The system takes full remuneration of those two sorts of ways to boost the excellent problem of the key system. It constructs the oppose have to be compelled to capture a big range of nodes within the network to decrypt the keys since it is to have the polynomial coefficients and random keys at a corresponding time so as to confine the uncompromised nodes. The encryption procedure is performed by utilizing an AES algorithm. Message digest algorithm is used for key generation. Simulation evidently shows that the theme of the research work performs higher in terms of network resilience, property and storage effectiveness compared to alternative wide used schemes.

Wireless sensor networks, Hybrid key, Polynomial pool based key, Basic random key

[1]. Yi Ren, Member, IEEE, Vladimir I. Zadorozhny, Senior Member, IEEE,Vladimir A. Oleshchuk, Senior Member, IEEE, and Frank Y. Li, Senior Member, IEEE, “A Novel Approach to Trust Management in Unattended Wireless Sensor Networks” IEEE transactions on mobile computing, vol. 13, no. 7, July 2014
[2]. Xiaojiang Du, North Dakota State University Hsiao-Hwa Chen, National Cheng Kung University “Security In Wireless Sensor Networks”, IEEE wireless communications August 2008.
[3]. Chen X. MakkiK.Yen K. and Pissinou N. ‘Sensor network security: A survey’, IEEE Commun. Surv. Tuts, vol. 11, no. 2, pp. 52–73Apr./Jun. 2009.
[4]. Zhou Y. Zhang Y. and Fang Y. ‘Access control in wireless sensor networks’, Ad Hoc Netw, vol. 5, no. 1, pp. 3–13 May 2007.
[5]. Kwon T, Hong J. Learning on the job: Secure and Efficient Broadcast Authentication in Wireless Sensor Networks. IEEE Transactions on Computing. 2010 Jun 24; 59(8):1120– 33.
[6]. Khan AS, Fisal N, Bakar ZA, Salawu N, Maqbool W, Ullah R, Safdar H. Secure Authentication and Key Management Protocols for Mobile Multihop WiMAX Networks. Indian Journal of Science and Technology. 2014 Mar; 7(3):282–95.
[7]. Klaoudatou E, Konstantinou E, Kambourakis G, Gritzalis S. A Survey on Cluster-Based Group Key Agreement Protocols for WSNs. IEEE Communications Surveys & Tutorials. 2011; 13(3).
[8]. Zhang J, Varadharajan V. Learning on the job: Wireless sensor network key management survey and taxonomy. Journal of Network and Computer Applications. 2010 Mar; 33(2):63–75.
[9]. Varma AGNS, Reddy AKG, Theja RY, Arunkumar T. Cluster Based Multipath Dynamic Routing (CBDR) Protocol for Wireless Sensor Networks Indian Journal of Science and Technology. 2015 Jan; 8(S2):17–22.
[10]. Simplicio-Jr MA, Barreto PS, Margi CB, Carvalho TC. A survey on key management mechanisms for distributed wireless sensor networks. Computer Networks. 2010 Oct 28; 54(15):2591–612.
[11]. Shang F, Abolhasan M, Wysocki T. An Energy-Efficient adaptive Clustering Algorithm for Wireless Sensor Networks. International Journal of Information Acquisition. 2009; 6(2):117–26.
[12]. Gomathi K, Parvathavarthini B. An Enhanced Distributed Weighted Clustering Routing Protocol for Key Management. Indian Journal of Science and Technology. 2015 Feb; 8(4):342.
[13]. Yuhua L, Yongfeng Z, Jingju GA. New Clustering Mechanism Based on LEACH Protocol. Proceedings of the International Joint Conference on Artificial Intelligence. IEEE Communications Society; 2009 Apr. p. 25–6.
[14]. Liu D, Ning P, Li R. Establishing Pairwise Keys in Distributed Sensor Networks Proceedings 10th ACM Conference Computers and Communication Security; 2003. p. 52–61.
[15]. Diffie, W., Hellman, M. 1976. New direction in cryptography. IEEE Trans. on Info. Theory, Vol. IT-22, Nov. 1976, pp. 644-654 (Invited Paper).
[16]. Eltoweissy, M., Heydari, H., Morales, L., and Sadborough, H. 2004. Combinatorial Optimization of Group Key Management, J. Network and Systems Management 12(1), 33-50.
[17]. Eschenauer, L. and Gligor, V. D. 2002. A key-management scheme for distributed sensor networks. In Proceedings of the 9th ACM Conference on Computer and Communications Security (Washington, DC, USA, November 18 - 22, 2002).
[18]. V. Atluri, Ed. CCS `02. ACM, New York, NY, 41-47. DOI= http://doi.acm.org/10.1145/586110.586117.
[19]. Ghumman, K. 2006. Location-Aware Combinatorial Key Management Scheme for Clustered Sensor Networks. IEEE Trans. Parallel Distrib. Syst. 17, 8 (Aug. 2006), 865- 882. DOI= http://dx.doi.org/10.1109/TPDS.2006.106
[20]. P. LourduGracy, D. Venkatesan, “An Honey Encryption Based Efficient Security Mechanism For Wireless Sensor Networks”. Volume 118 No. 20 2018, 3157-3164, IJPAM, 2018.
[21]. Swapna Naik, “A Novel Authentication approach in Wireless sensor Network Swapna Naik “, International Journal of Scientific & Engineering Research, Volume 5, Issue 3, March-2014
[22]. Manish P. Gangawane,” Implementation Of Zero Knowledge Protocol In Wireless Sensor Network for prevention Of Various Attacks,” International Journal of Emerging Technology and Advanced Engineering , (ISSN 2250-2459, Volume 2, Issue 8, August 2012).
[23]. A Jayanthiladevi, S Suma and T.Lalitha, “Challenges and Authentication in Wireless Sensor Networks,” 2013 IEEE.
[24]. Mahmood Khalel, Ibrahem and Al Nahrain,” Modification of Diffie–Hellman Key Exchange Algorithm for Zero Knowledge Proof,” proceedings of 2012 international conference on future c ommunication networks, IEEE 2012.
[25]. SamantKhajuria and Henrik Tange, “Implementation of Diffie-Hellman Key Exchange on Wireless Sensor Using Elliptic Curve Cryptography,” proceedings of IEEE 2009.
[26]. L. B. Jivanadham ,A.K.M. ,M. Islam2 and Mansoor3,“A Secured Dynamic Cluster-Based Wireless Sensor Network Advanced Informatics School (AIS),” proceedings of 2012 Fourth International Conference on Computational Intelligence,
[27]. Xiaoyong Li, Feng Zhou, and JunpingDu,“LDTS: A Lightweight and Dependable Trust System for Clustered Wireless Sensor Networks”,IEEE transactions on information forensics and security, vol. 8, no. 6, June 2013
[28]. Yi Ren, Member, IEEE, Vladimir I. Zadorozhny, Senior Member, IEEE,Vladimir A. Oleshchuk, Senior Member, IEEE, and Frank Y. Li, Senior Member, IEEE, “A Novel Approach to Trust Management in Unattended Wireless Sensor Networks” IEEE transactions on mobile computing, vol. 13, no. 7, July 2014
[29]. Guoxing Zhan, Weisong Shi, Senior Member, IEEE, and Julia Deng. IEEE “Design and Implementation of TARF: A Trust-Aware Routing Framework for wsns”, transactions on dependable and secure computing, vol. 9, no. 2, March/April 2012.
[30]. Chen X. Makki K.Yen K. and Pissinou N. ‘Sensor network security: A survey’, IEEE Commun . Surv. Tuts, vol. 11, no. 2, pp. 52–73Apr./Jun. 2009.
[31]. Zhou Y. Zhang Y. and Fang Y. ‘Access control in wireless sensor networks’, Ad Hoc Network, vol. 5, no. 1, pp. 3–13 May 2007.
[32]. Sudha.R, Devapriya.M,” Enhanced bio-trusted anonymous authentication routing technique of wireless body area network “,Biomedical Research 2016; Special Issue: S276-S282 , Special Section: Computational Life Science and Smarter Technological Advancement, ISSN 0970-938X.
[33]. Yang Xiao, “A survey of key management schemes in wireless sensor networks”, 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.comcom.2007.04.009
[34]. Syed Muhammad et al, “Key Management Schemes of Wireless Sensor Networks: A Survey” ,http://uclab.khu.ac.kr
[35]. A.Pranusha , “A Hybrid Key Management Scheme For Secure Manet Communications”, eISSN: 2319-1163 | pISSN: 2321-7308
[36]. R.Sudha, P.Nivetha,” A Novel Approach For Secure Data Transmission Using Cp_Abe And Md5 Algorithm In Wban”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 6, Issue 3, May- June 2017 ISSN 2278-6856

M.Infant Angel, R.Sudha, "A Hybrid Key Management Scheme for Data Transmission in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.787-793, 2019.

All Orthogonal Multiple Access techniques including Orthogonal Frequency Division Multiple Access (OFDMA) techniques fail to achieve the system limit due to individuality in resource allocation. To mitigate this issue Non Orthogonal Multiple Access (NOMA) introduce for 5th Generation (5G) wireless communication system. This paper presents the results and detailed survey of (NOMA) techniques that are helpful in improving the 5G system and meeting the demands of users. In this detailed survey, the prime focus is on the different proposed NOMA techniques in the literature and discussion of existing works on performance analysis, resource allocation, Multiple input multiple output NOMA (MIMO- NOMA), Single user NOMA (SU-NOMA), Multi User NOMA (MU-NOMA). Finally, we discuss the features and further research challenges of NOMA.

OFDM,NOMA, IDMA, TDMA, PDMA, MIMO, Mulituser MIMO, SU MIMO, 3G, 4G, 5G, Wi-Fi

[1]. J.G. Andrews, et al. , “ What will 5G Be ?” IEEE Journal on Selected Areas in Communications, Vol. 32, no. 6, June, 2014
[2]. J. Thompson et al, “5G wireless communication systems: prospects and challenges [Gest Editorial],” IEEE Communications Magazine, vol. 52, no. 2, pp. 62-64, Feb. 2014.
[3]. P. Wang, J. Xiao, and L. Ping, “ Comparison of orthogonal and non-orthogonal approaches to future wireless cellualr systems,” IEEE Vehicular Technology Magazine, vol. 1, no. 3, pp. 4-11, Sept. 2006.
[4]. M. Agiwal et al, “ Next Generation 5G Wireless Networks: A Comprehensive Survey,” in IEEE Communications surveys & Tutotials, Vol. 18, No. 3, Third Quarter 2016.
[5]. Andrea Goldsmith,“Wireless Communication,”Stanford University, 2005
[6]. Li Ping et al, “ On Interleave Division Multiple Access,” in IEEE Communication Society 2004, pp. 2869-2873.
[7]. Jie Zeng et al., “ Pattern Division Multiple Access (PDMA) for Cellular Future Radio Access,” in IEEE, 2015.
[8]. Shanzhi Chen et al, “Pattern Division Multiple Access (PDMA) – A Novel Non-orthogonal Multiple Access for 5G Radio Networks,” in IEEE transaction on Vehicular Technology, 2016.
[9]. J. Dai et al, “Polar Coded Non Orthogonal Multiple Access,” in IEEE International Symposium on Information Theory 2016 ,pp. 988-992, August 2016.
[10]. Hoseing Nikopour and Hadi Baligh, “ Sparse Code Multiple Access,” in IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications on 8-11 Sept. 2013, pp. 332-336.
[11]. H. Jin, K.Peng and J. Song, “Bit Division Multiplexing for Broadcasting, “ in IEEE transactions on Broadcasting, 2013.
[12]. Luis A. Vasquez-Toledo, D. L Rodriguez, “ A coordinated Operatoin Access Scheme for 5G cellular systems,” in IEEE ICUFN, pp. 604-606, 2017
[13]. W. Shin et al., “ Coordinated Beamformming for Multi-Cell MIMO-NOMA,” in IEEE Communicatioin letters in 2016.
[14]. Md Shipon Ai, Ekram Hossain and Dong In Kim, “ Non-Orthogonal Multiple Access (NOMA) for Downlink Multiuser MIMO Systems: User Clustering, Beamforming, and Power Allocation.” in IEEE access, 2016.
[15]. X.Chaen et al, “Evaluations of Downlink Non-Orthogonal multiple Acces (NOMA) Combined with SU-MIMO,” in IEEE 25th International Symposium on Personal, Indoor and Mobile Radio Communication 2014,pp. 1887-1891.

Shampal Singh, A.S. Buttar, Dalveer Kaur, "Survey on Non Orthogonal Multiple Access (NOMA) - A key technique for future Radio Network Access.," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.794-799, 2019.

The problems while designing a communication module comes up during hardware implementation in terms of power, area and speed. This paper presents an efficient decimation filter optimized in terms of power and area for wideband Sigma-Delta (ΣΔ) A/D converters. A work flow for a rapid design of this optimized decimation filter in MATLAB, along with its implementation is presented. The design is suited particularly for filters with high decimation factor. The filter offers a decimation factor of 128 having input of 3 bits from over-sampled ΣΔ modulator. The ΣΔ modulator having an input of 0.8MHz and sampling rate of 208MHz provides oversampling by a factor of 128 and resolution of 12 bits. Techniques like transposed direct-form polyphase decomposition, pipelining, retiming, resource sharing and CSD encoding are used for efficient design. The filter offers reduced power consumption and thereby suited for multi-rate filter design in state of art Sigma-Delta Analog to Digital converters.

Sigma Delta, ADC, Decimation filter, CSD, Multi-rate filter

[1] Anjum Mohd Aslam, Mantripatjit kaur, "A Review on Energy Efficient techniques in Green cloud: Open Research Challenges and Issues", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.44-50, 2018.
[2] Candy J. C., Temes G. C., “Oversampling Delta-Sigma Data Converters: Theory, Design and Simulation”, IEEE Press, 1992.
[3] J.M. de la Rosa, “Sigma-Delta Modulators: Tutorial Overview, Design Guide, and State- of-the-Art Survey”, IEEE Trans. on Circuits and Systems I: Regular Papers. Vol. 58, pp. 1-21, 2011.
[4] Schreier R., Temes G. C., 2005. Understanding Delta-Sigma Data Converters New York: IEEE Press.
[5] Norsworthy S. R., Crochiere R. E., 1997. Decimation and interpolation for sigma delta conversion. in Delta Sigma Data Converters. Piscataway, NJ: IEEE Press.
[6] N. N. Hurrah, Z. Jan, A. Bhardwaj, S. A. Parah, A. K. Pandit, 2015. Oversampled Sigma Delta ADC Decimation Filter: Design Techniques, Challenges, Tradeoffs and Optimization. Proc. of RAECS UIET Panjab University Chandigarh.
[7] Proakis J. G., Manolakis D. G., 2007. Digital Signal Processing, Pearson Prentice Hall.
[8] Shuni Chu, Burrus C. S., 1984. Multirate Filter Designs Using Comb Filters. IEEE Trans. on Circuits and Systems. 31(11).
[9] Uwe Meyer-Baese. Digital Signal Processing with Field Programmable Gate Arrays. 3rd Edition, Springer.
[10] Chandan Singh, "Realization of FIR Filter using Distributed Arithmetic Architecture", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.4, pp.7-12, 2015
[11] Ahmed, N.Y., Ashour M. A., Nassar, A.M., 2008. Power Efficient Polyphase Decomposition Comb Decimation Filter in Multi-Rate Telecommunication Receivers. IEEE, Mosharaka International Conference, pp. 1-6.
[12] Noha Younis, Mahmoud Ashour, Amin Nassar, 2009. Power-Efficient Clock/Data Distribution Technique for Polyphase Comb Filter in Digital Receivers. IEEE Trans. on circuits and systems—II: Express Briefs, 56(8).
[13] Abbas, M., Gustafsson, O., Wanhammar, L., 2010. Power Estimation of Recursive and Non-Recursive CIC Filters Implemented in Deep-Submicron Technology. IEEE Int. Conf. Green Circuits Syst., Shanghai, China, June 21-23.
[14] Salgado, G. M., Dolecek, G. J., de la Rosa, J.M., 2013. Power and area efficient comb-based decimator for sigma-delta ADCs with high decimation factors. Proc. of the IEEE International Symposium on Circuits and Systems, pp. 1260-1263.
[15] Yi Xie, Minglei Zhang, Baoyue Wei, Xiaohua Fan, 2014. High-Speed Low-Power Decimation Filter for Wideband Delta-Sigma ADC. Proc. Of IEEE 57th International Midwest Symposium on circuits and systems. pp. 591 – 594.
[16] Xiong Liu, 2009. A High Speed Digital Decimation Filter with Parallel Cascaded Integrator-Comb Pre-filters. IEEE.
[17] Alp Kilic, D. Haghighitalab, H. Mehrez, Hassan Aboushady, 2014. Low-Power Comb Decimation Filter for RF Sigma-Delta ADCs. Proc. of IEEE International Symposium on Circuits and Systems, pp. 1596 – 1599.
[18] Mohan, R., Koppula, R., Balagopal, S., Saxena, V., 2011. Efficient design and synthesis of decimation filters for wideband Delta-Sigma ADCs. SOC Conference (SOCC), IEEE International, Taipei, pp. 380 – 385.
[19] Zhu, X., Wang, Y., Hu, W. and Reiss, J.D., 2016. Practical considerations on optimising multistage decimation and interpolation processes. In Digital Signal Processing (DSP), 2016 IEEE International Conference on (pp. 370-374). IEEE.
[20] B. B. Hogenauer, 1981. An economical class of digital filters for decimation and interpolation. IEEE Trans. on Acoustics, Speech and Signal processing. 29(2). 155-162.
[21] Aboushady, H., Dumonteix, Y., Louërat, M., Mehrez, H., 2001. Efficient Polyphase Decomposition of Comb Decimation Filters in ΣΔ Analogto-Digital Converters. IEEE Transactions on Circuits and Systems II. 48(10), 898-903.
[22] Shahana, T. K., James, R. K., Jose, B. R., Jacob, K. P., Sasi, S., 2007. Polyphase Implementation of Non-recursive Comb Decimators for Sigma-Delta A/D Converters. Electron Devices and Solid-State Circuits, IEEE Conference, Tainan, pp. 825–828.
[23] Yeung K. S. and Chan S. C., 2004. The design and multiplier-less realization of software radio receivers with reduced system delay. IEEE Trans. Circuits Syst. I, Reg. Papers. 51(12), 2444–2459.
[24] J.M. de la Rosa, Rocio del Rio, 2013. Cmos Sigma-Delta Converters Practical Design GUIDE. first edition, John Wiley & Sons.
[25] Fischer, R., Buchenrieder, K. and Nageldinger, U., 2005. Reducing the power consumption of FPGAs through retiming. In Engineering of Computer-Based Systems. ECBS`05. 12th IEEE International Conference and Workshops on the, pp. 89-94.
[26] Bibin John, Fabian Wagner, Wolfgang H. Krautschneider, 2005. Comparison of Decimation Filter Architectures for a Sigma-Delta Analog to Digital Converter. Institute of Nanoelectronics Hamburg University of Technology (TUHH).
[27] Salgado, G.M., Dolecek, G.J. and Jose, M., 2016. Low power two-stage comb decimation structures for high decimation factors. Analog Integrated Circuits and Signal Processing, vol. 88, no. 2, 245-254.
[28] Vaidyanathan, P., Nguyen, T., 1987. A TRICK for the design of FIR halfband filters. IEEE Trans. Circuits Syst., vol. 34, 297-300.
[29] Ian Beavers, 2015. Rethinking Digital Down conversion in Fast, Wideband ADCs. Xcell Journal, spring.
[30] Dolecek, G.J., Baez, J.R.G. and Laddomada, M., 2017. Design of Efficient Multiplierless Modified Cosine-Based Comb Decimation Filters: Analysis and Implementation. IEEE Transactions on Circuits and Systems I: Regular Papers. vol. 64, no. 5, 1051-1063.

N.N. Hurrah, S.A. Parah, N.A. Loan, "Power Efficient Multi-Stage Decimation Filter for Wideband Sigma-Delta ADCs," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.800-806, 2019.

The usage of Mobile Technology is rapidly increasing day by day. With the usage of Android mobiles, the threat of malware is also increasing day by day. All the private and confidential data in Android devices have a high risk of malware. Various Android malware scanning tools are freely available for use. This paper analyses different kinds of Android Malware scanning tools with a proper comparison, pros and cons and their future scope.

Mobile, Android, Mobile Security, Malware, Scanning tools

[1] “AVC UnDroid”, Online Link: https://undroid.av-comparatives.org
[2] “AndroTotal: Scan Android Application”, Online Link: http://andrototal.org
[3] “VirusTotal: Analyse suspicious files”, Online Link: https://www.virustotal.com
[4] “NVISO ApkScan: Scan Android Applications for Malware”, Online Link: https://apkscan.nviso.be/
[5] “VirSCAN.org: Submit and scan your file”, Online Link: http://www.virscan.org
[6] “Hybrid Analysis”, Online Link: https://www.hybrid- analysis.com
[7] “Top 7 Online Android APK Malware analysers”, Online Link: https://www.yeahhub.com/top-7-online-android-apk-malware-analyzers-free/
[8] “VTI for Threat Investigations”, Online Link: https://vt-gtm-wp-media.storage.googleapis.com/vti-threat-investigation-use-case.pdf
[9] Randy Abrams, ”Virus Total Tips, Tricks and Myths” Virus Bulletin Conference, Oct 2017. Online Link: https://www.virusbulletin.com/uploads/pdf/magazine/2017/VB2017-Abrams.pdf
[10] Federico Maggi, Andrea Valdi, Stefano Zanero, “AndroTotal: A Flexible, Scalable Toolbox and Service for testing Mobile Malware Detectors” Conference on Security and privacy in smartphones & mobile devices ACM, Nov 2013.
[11] “Virus Total Getting Started”, Online Link: https://support.virustotal.com/hc/en-us/articles/115003895489-Get-Started
[12] “Virus Total Reports”, Online Link: https://support.virustotal.com/hc/en-us/articles/115002719069-Reports
[13] “Virus Total API”, Online Link: https://support.virustotal.com/hc/en-us/articles/115002100149-API
[14] “VirSCAN About us”, Online Link: http://www.virscan.org/language/en/about
[15] “AVC Undroid About”, Online Link: https://undroid.av-comparatives.org/about.php
[16] “AVC Undroid FAQs”, Online Link: https://undroid.av-comparatives.org/faq.php
[17] Hybrid Analysis FAQs”, Online Link: https://www.hybrid-analysis.com/faq
[18] Prerna Agrawal, Bhushan Trivedi “A Survey on Android Malware and their Detection Techniques”, Third International Conference on Electrical, Computer and Communication Technologies (ICECCT) IEEE, Coimbatore, Feb 2019 (Paper to be Published).
[19] S.Birundha, Dr. V. Vanitha “Survey on Mobile Malware Detection Techniques in Android Operating System” International Journal on Applications in Information and Communication Engineering, vol. 2 issue. 4 Apr 2016.
[20] Saba Arshad, Abid Khan “Android Malware Detection and Protection: A Survey” International Journal of Advanced Computer Science and Applications, vol. 7 no. 2 2016.

Prerna Agrawal, Bhushan Trivedi, "Analysis of Android Malware Scanning Tools," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.807-810, 2019.

e-Government improves the efficiency of public administration, increasing transparency, deducing administrative corruption, improving service delivery, citizen’s empowerment and improving government finance. The private cloud provides the ideal environment for scalable e-Government applications because of its scalable and elastic characteristics. The scalability can be achieved horizontally and vertically within short time in cloud environment. During design and development phase of application, special consideration is needed for scalability and high availability on Application and database layer. Application layer high availability and scalability can be achieved with a server load balancer. Database layer high availability and scalability can be done by database sharding within a database fail-over cluster. In this paper, we propose a scalable highly available architecture for e-Governance applications on private cloud environment.

Availability, Database sharding, Failover Cluster, Load Balancer, Scalability

[1] Ab Rashid Dar and Dr. D. Ravindran, “Survey on Scalability In Cloud Environment”, International Journal of Advanced Research in Computer Engineering & Technology, Vol.5, Issue.7, pp.2124-2128, 2016.
[2] Kamyab Khajehei, “Role of virtualization in cloud computing”, International Journal of Advance Research in Computer Science and Management Studies, Vol.2, Issue.4, pp.15-23, 2014.
[3] Peter Mell and Tim Grance. “The NIST Definition of Cloud Computing”, NIST Special Publication 800-145, USA, pp.2, 2011.
[4] Haroon Shakirat Oluwatosin, “Client-Server Model”, IOSR Journal of Computer Engineering, Vol.16, Issue.1, pp.67-71, 2014.
[5] Anand More and Priyesh Kanungo, “Use of Cloud Computing for Implementation of e-Governance Services”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.115-118, 2017.
[6] Solanke Vikas, Kulkarni Gurudatt, Maske Vishnu and Kumbharkar Prashant, “Private Vs Public Cloud”, International Journal of Computer Science & Communication Networks, Vol.3(2), pp.79-83, 2013.
[7] P. Beaulah Soundarabai, Sandhya Rani A., Ritesh Kumar Sahai, Thriveni J., K.R. Venugopal and L.M. Patnaik, “Comparative Study on Load Balancing Techniques in Distributed Systems”, International Journal of Information Technology and Knowledge Management, Vol.6, No.1, pp.53-60, 2012.
[8] Dubravko Miljković, “Review of Cluster Computing for High Available Business Web Applications”, Proceedings of MIPRO 2008/GVS, Opatija Croatia, pp.261-266, 2008.
[9] Sikha Bagui, “Database Sharding: To Provide Fault Tolerance and Scalability of Big Data on the Cloud”, International Journal of Cloud Applications and Computing, Vol.5(2), pp.36-52, 2015.
[10] Pankaj Deep Kaur and Gitanjali Sharma, “Performance of Scalable Data Stores in Cloud”, International Journal of Engineering and Advanced Technology, Vol.4, Issue.5, pp.212-216, 2015.
[11] Kandi Phani Sai, Sri Rohith and Abhineet Anand, “Analytical Study of different Load balancing algorithms”, International Journal of Advanced Studies in Computer Science & Engineering, Vol.7, Issue.1, pp.21-26, 2018.
[12] Pooja C.S and K.R Prasanna Kumar, “Survey on Load Balancing and Auto Scaling Techniques for Cloud Environment”, International Journal of Engineering and Advanced Technology, Vol.6, Issue.5, pp.28-30, 2017.
[13] C. Venish raja1 and L. Jayasimman, “A Survey on Scalability in Cloud Computing”, International Journal of Computer Sciences and Engineering, Vol.6, Special Issue.2, pp.471-474, 2018.
[14] Kalburgi Tayyaba, Ibrahim and Sajjan R.S, “Cloud Resourse Virtualization”, International Journal of Computer Sciences and Engineering, Vol.4, Special Issue.4, pp.21-27, 2016.

Priyeshkumar T. S., Dharmendra Devaka, "A Scalable and Highly Available Distributed Architecture for e-Governance Applications on Private Cloud Platform," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.811-814, 2019.

Path planning has been a challenging task for researchers working towards automation in various fields. The objective of coverage path planning (CPP) is finding a path that covers all the points in the search space, avoiding obstacles. Coverage path planning is a key component in many robotic applications such as and not limited to automated machinery in agriculture, autonomous underwater vehicles, unmanned aerial vehicles, lawn mowers, floor cleaners, and industrial robots. Major research has been done on optimizing the solution for covering path planning algorithms. However, there is no major survey available on the application of coverage path planning in agriculture. This paper aims to fulfill the void by discussing a detailed survey on the techniques, methodology and the performance of covering path planning algorithms applied in the field of agriculture. Finally, various techniques are compared based on the parameters used for validating the performance of the algorithms. This work is aimed to be a starting point for researchers who are initiating their endeavors in coverage path planning to be applied in the field of agriculture. This work is steered in that direction, to provide a comprehensive review of the various CPP algorithms proposed so far, for application in agriculture.

Coverage Path Planning, 3-Dimensional Coverage, Agriculture, Autonomous Systems

[1] Hu Y, Yang SX., “A knowledge based genetic algorithm for path planning of a mobile robot”’. In: Proceedings of the 2004 IEEE, international conference on robotics & automation, New Orleans USA , pp. 4350–4355, 2004
[2] T. Lozano-Perez, M.A. Wesley, “An algorithm for planning collision-free paths among polyhedral obstacles”, ACM Communications, Vol. 2, pp. 959–962, 1979
[3] S.M. LaValle, “Planning Algorithms”, Cambridge University Press, pp. 130–131, 2006.
[4] J.H. Reif, Z. Sun, “An efficient approximation algorithm for weighed region shortest path problem, Algorithmic and Computational Robotics: New Directions, A.K. Peters, pp. 191–203, 2001
[5] Z.L. Cao, Y. Huang, E.L. Hall, “Region filling operations with random obstacle avoidance for mobile robotics”, Journal of Robotic Systems” Vol 5, Issue 2, pp.87–102, 1988
[6] M. Bosse, N. Nourani-Vatani, J. Roberts, Coverage algorithms for an underactuatedcar-like vehicle in an uncertain environment, in: Proc. IEEE Int.Robotics and Automation Conf., 2007, pp. 698–703.
[7] P. Atkar, A.L. Greenfield, D.C. Conner, H. Choset, A. Rizzi, Uniform coverage of automotive surface patches, The International Journal of Robotics Research Vol 24, Issue 11, pp 883–898, 2005
[8] M. Ollis, A. Stentz, First results in vision-based crop line tracking, in:Proc. Conf. IEEE Int. Robotics and Automation, Vol. 1, pp. 951–956, 1996
[9] M. Ollis, A. Stentz, Vision-based perception for an automated harvester, in: Proc. IEEE/RSJ Int. Intelligent Robots and Systems IROS’97. Vol. 3, pp. 1838–1844, 1997
[10] F. Yasutomi, M. Yamada, K. Tsukamoto, Cleaning robot control, in: Proc. Conf. IEEE Int Robotics and Automation, pp. 1839–1841, 1988.
[11] [E.U. Acar, H. Choset, Y. Zhang, M. Schervish, Path planning for robotic demining: robust sensor-based coverage of unstructured environments and probabilistic methods, International Journal of Robotics Research. Vol. 22, Issue 7, pp. 441–466, 2003.
[12] S. Hert, S. Tiwari, V. Lumelsky, A terrain-covering algorithm for an auv, Autonomous Robots , pp 91–119, 1996.
[13] Z.L. Cao, Y. Huang, E.L. Hall, Region filling operations with random obstacle avoidance for mobile robotics, Journal of Robotic Systems Vol 5, Issue 2, 87–102, 1988.
[14] D. MacKenzie and T. Balch, Making a clean sweep: Behavior based vacuuming, in: AAAI Fall Symposium, Instationating Real-World Agents, 1996.
[15] J. Palacin, T. Palleja, I. Valganon, R. Pernia, J. Roca, Measuring coverage performances of a floor cleaning mobile robot using a vision system, in: Proceedings of the 2005 IEEE International Conference on, Robotics and Automation, 2005.ICRA 2005. pp. 4236–4241.
[16] D. Gage, Randomized search strategies with imperfect sensors, in: Proc. SPIE Mobile Robots VIII, Boston, MA (September 1993) pp. 270–279.
[17] T. Balch, The case for randomized search, in: Workshop on Sensors and Motion, IEEE International Conference on Robotics and Automation, San Francisco, CA (May 2000).
[18] H. Choset,Coverage for robotics—a survey of recent results, Annals of Mathematics and Artificial Intelligence 31 (2001) 113–126.
[19] EnricGalceran, Marc Carreras, A survey on coverage path planning for robotics, Robotics and Autonomous systems 61 (2013) 1258-1276.
[20] H. Moravec and A. Elfes, High resolution maps for wide angles sonar, in: IEEE Int. Conf. on Roboticsand Automation (1985).
[21] A. Elfes, Sonar-based real-world mapping and navigation, IEEE J. Robotics Autom. 3 (1987) 249–265.
[22] J.S. Oh, Y.H. Choi, J.B. Park, Y. Zheng, Complete coverage navigation of cleaning robots using triangular-cell-based map, IEEE Transactions on Industrial Electronics 51 (3) (2004) 718–726.
[23] A. Zelinsky, R.A. Jarvis, J.C. Byrne and S. Yuta, Planning paths of complete coverage of an unstructured environment by a mobile robot, in: Proceedings of International Conference on Advanced Robotics, Tokyo, Japan (November 1993) pp. 533–538.
[24] O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots, Internat. J. Robotics Res. 5 (1986) 90–98.
[25] J. Barraquand and J.-C.Latombe, "Robot Motion Planning: A Distributed Representation Approach", International Journal of Robotics Research Vol. 10 No.6, pp628-649, 1991.
[26] R.A. Jarvis and J.C. Byrne, "Robot Navigation: Touching, Seeing and Knowing", Proceedings of 1st Australian Conference on Artificial Intelligence, November 1986.
[27] V. Shivashankar, R. Jain, U. Kuter, D. Nau, Real-time planning for covering an initially-unknown spatial environment, in: Proceedings of the Twenty- Fourth International Florida Artificial Intelligence Research Society Conference, 2011.
[28] Y. Gabriely, E. Rimon, Spiral-stc: an on-line coverage algorithm of grid environments by a mobile robot, in: Proc. IEEE Int. Conf. Robotics and Automation, ICRA’02, Vol. 1, 2002, pp. 954–960.
[29] E. Gonzalez, O. Alvarez, Y. Diaz, C. Parra, C. Bustacara, Bsa: a complete coverage algorithm, in: Proc. IEEE Int. Conf. Robotics and Automation ICRA 2005, 2005, pp. 2040–2044.
[30] Y.-H. Choi, T.-K.Lee, S.-H.Baek, S.-Y. Oh, Online complete coverage path planning for mobile robots based on linked spiral paths using constrained inverse distance transform, in: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems IROS 2009, 2009, pp. 5788–5793.
[31] T.-K. Lee, S.-H.Baek, Y.-H.Choi, S.-Y. Oh, Smooth coverage path planning and control of mobile robots based on high-resolution grid map representation, Robotics and Autonomous Systems 59 (10) (2011) 801–812.
[32] L. Hodgkin, A.F. Huxley, A quantitative description of membrane current and its application to conduction and exitation in nerve, Journal of Physics London 117 (1952) 500–544.
[33] C. Luo, S. Yang, A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments, IEEE Transactions on Neural Networks 19 (7) (2008) 1279–1298.
[34] X. Qiu, J. Song, X. Zhang, S. Liu, A complete coverage path planning method for mobile robot in uncertain environments, in: Proc. Sixth World Congress Intelligent Control and Automation WCICA 2006, Vol. 2, 2006, pp. 8892–8896.
[35] L. Paull, S. Saeedi, H. Li, V. Myers, An information gain based adaptive path planning method for an autonomous underwater vehicle using sidescan sonar, in: 2010 IEEE Conference on Automation Science and Engineering, CASE, 2010, pp. 835–840.
[36] L. Paull, S. Saeedi Gharahbolagh, M. Seto, H. Li, Sensor driven online coverage planning for autonomous underwater vehicles, in: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2012, pp. 2875–2880.
[37] J.C. Latombe, Robot Motion Planning, Kluwer Academic Publishers, 1991.
[38] H. Choset, K. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. Kavraki, S. Thrun, Principles of Robot Motion: Theory, Algorithms, and Implementation, The MIT Press, 2005.
[39] F.P. Preparata and M.I. Shamos, Computational Geometry: An Introduction (Springer-Verlag, Berlin,1985) pp. 198–257.
[40] J. VanderHeide and N.S.V. Rao, Terrain coverage of an unknown room by an autonomous mobile robot, Technical report ORNL / TM-13117, Oak Ridge National Laboratory, Oak Ridge, TN (1995).
[41] H. Choset, E. Acar, A. Rizzi and J. Luntz, Exact cellular decompositions in terms of critical points of morse functions, in: IEEE International Conference on Robotics and Automation, San Francisco, CA (2000).
[42] H. Choset and P. Pignon, Coverage path planning: The boustrophedon decomposition, in: Proceedings of the International Conference on Field and Service Robotics, Canberra, Australia (December1997).
[43] E.U. Acar, H. Choset, A.A. Rizzi, P.N. Atkar, D. Hull, Morse decompositions for coverage tasks, International Journal of Robotics Research 21 (4) (2002) 331–344.
[44] J. Milnor, Morse Theory, Princeton University Press, 1963.
[45] E.U. Acar, H. Choset, A.A. Rizzi, P.N. Atkar, D. Hull, Morse decompositions for coverage tasks, International Journal of Robotics Research 21 (4) (2002) 331–344.
[46] G. Reeb, Sur les points singuliersd’uneforme de Pfaff complètementintégrableoud’une function numérique, ComptesRendus de l’Académie des Sciences 222 (1946) 847–849.
[47] S.C. Wong, B.A. MacDonald, A topological coverage algorithm for mobile robots, in: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, IROS 2003, Vol. 2, 2003, pp. 1685–1690.
[48] Z.J. Butler, A.A. Rizzi, R.L. Hollis, Contact sensor-based coverage of rectilinear environments, in: Proc. IEEE Int Intelligent Control/Intelligent Systems and Semiotics Symposium, 1999, pp. 266–271.
[49] L. Xu, Graph Planning for Environmental Coverage, Ph.D. Thesis, Carnegie Mellon University, 2011.
[50] V.J. Lumelsky, S. Mukhopadhyay, K. Sun, Dynamic path planning in sensor based terrain acquisition, IEEE Transactions on Robotics and Automation 6 (4) (1990) 462–472.
[51] T.-S. Lee, J.-S.Choi, J.-H.Lee, B.-H. Lee, 3-d terrain covering and map building algorithm for an auv, in: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems IROS 2009, 2009, pp. 4420–4425.
[52] P.N. Atkar, H. Choset, A.A. Rizzi, E.U. Acar, Exact cellular decomposition of closed orientable surfaces embedded in r3, in: Proc. ICRA Robotics and Automation IEEE Int. Conf, Vol. 1, 2001, pp. 699–704.
[53] P. Cheng, J. Keller, V. Kumar, Time-optimal uav trajectory planning for 3D urban structure coverage, in: IEEE/RSJ International Conference on, Intelligent Robots and Systems, 2008, IROS 2008, 2008, pp. 2750–2757.
[54] Kanae Tanigaki, Tateshi Fujiura, Akira Akase, Junichi Imagawa, Cherry-harvesting robot, Computers and Electronics in Agriculture 63 (2008) 65-27
[55] Y.Yanwei, Z.Xiaochao, Z.Huaping, Apple harvesting robot picking path planning and simulation, International conference on Information Engineering and Computer Science, 2009, pp. 1-4
[56] E.J. Van Henten, J.Hemmin, B.A.J. Van Tuijl, J.G. Kornet, J. Bontsema, Collision-free Motion Planning for a Cucumber Picking Robot, Biosystems Engineering (2003) 86 (2), 135-144
[57] A. J. Scarfe, R. C. Flemmer, H. H. Bakker and C. L. Flemmer, Development of An Autonomous Kiwifruit Picking Robot, Proceedings of the 4th International Conference on Autonomous Robots and Agents, Feb 10-12, 2009. pp 380-384.
[58] Yael Edan, Member, IEEE, Dima Rogozin, Tamar Flash, and Gaines E. Miles, IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 16, NO. 6, DECEMBER 2000 pp 831-834
[59] Feng Qingchun, Zheng Wengang, Qiu Quan,Jiang Kai, Guo Rui, Study on Strawberry Robotic Harvesting System, IEEE International Conference on Computer Science and Automation Engineering (CSAE), 2012 pp 320-324
[60] G. Muscato, M. Prestifilippo, Nunzio Abbate and Ivan Rizzuto, A prototype of an orange picking robot: past history, the new robot and experimental results, Industrial Robot: An International Journal 32/2 (2005) pp 128-138
[61] Ibrahim A. Hameed, Dionysis Bochtis, and Claus A. Sørensen, An Optimized Field Coverage Planning Approach for Navigation of Agricultural Robots in Fields Involving Obstacle Areas, Int J Adv Robotic Sy, 2013, Vol. 10, 231:2013, pp 1-9
[62] Whitley D (1994) A Genetic Algorithm Tutorial. Statistics and Computing 4(2): 65‐85.
[63] Jung Won Kang, Si Jong Kim and Myung Jin Chung, Path Planning for Complete and Efficient Coverage Operation of Mobile Robots. In Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation. August 5-8, 2007, Harbin, China.
[64] Simon X. Yang and ChaominLuo.A Neural Network Approach to Complete Coverage Path Planning. IEEE Transactions on Systems, Man,and Cybernetics-Part B:Cybernetics, 2004,34(1):718-725.
[65] Yan Ma, Hua-bo Liu and Shu-huaXu. Path Planning Design for Cleaning Robot. Machinery and Electronics, 2008 (7): 64-67.
[66] A.E.F. Ryerson and Q. Zhang. “Vehicle Path Planning for Complete Field Coverage Using Genetic Algorithms”. Agricultural Engineering International: the CIGR Ejournal. Manuscript ATOE 07 014. Vol. IX. July, 2007. pp 1-11
[67] Lin, H.-S., J. Xiao, and Z. Michalewicz. 1994. Evolutionary algorithm for path planning inmobile robot environment. In Proc. IEEE Conference on Evolutionary Computation, 1:211-216. Piscataway, NJ, USA: IEEE.
[68] Hwang Y.K., Ahuja, N. (1992). Gross Motion Planning.A Survey,.ACMComputing Surveys, vol. 24, no. 3, pp. 219-290, (1992).
[69] D.D. Bochtis, C.G. Sorensen, S.G. Vougioukas, Path planning for in-field navigation-aiding of service units, Computers and Electronics in Agriculture 74 (2010) 80-90
[70] K. Zhou, A. Leck Jensen, C.G. Sørensen, P. Busato, D.D. Bothtis, Agricultural operations planning in fields with multiple obstacle areas, Computers and Electronics in Agriculture 109 (2014) pp 12–22
[71] Toussaint, G.T., 1983. Solving geometric problems with the rotating calipers. In:Proc. 2nd IEEE Mediterranean Electrotechnical Conference (MELECON 1983),pp. 1–4.
[72] Dorigo, M., Gambardella, L.M., 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1 (1),53–66.
[73] Timo Oksanen and Arto Visala, Coverage Path Planning Algorithms for Agricultural Field Machines, Journal of Field Robotics 26(8), 651–668 (2009) pp 651-668
[74] Felkel, P., &Obdrzalek, S. (1998, April). Straight skeleton implementation. In Proceedings of Spring Conferenceon Computer Graphics, Budmerice, Slovakia (pp. 210– 218).
[75] Bochtis, D.D., Oksanen, T., Combined coverage path planning for field operations, 7th European Conference on Precision Agriculture, Wageningen, the Netherlands.
[76] I. Rekleitis, A. New, E. Rankin, H. Choset, Efficient boustrophedon multirobot coverage: an algorithmic approach, Annals of Mathematics and Artificial Intelligence 52 (2009) 109–142. http://dx.doi.org/10.1007/s10472-009-9120-2.
[77] N. Hazon, G. Kaminka, Redundancy, efficiency and robustness in multi-robot coverage, in: Proceedings of the 2005 IEEE International Conference on, Robotics and Automation, 2005, ICRA 2005, 2005, pp. 735–741.
[78] C. Luo, S. Yang, A real-time cooperative sweeping strategy for multiple cleaning robots, in: Proceedings of the 2002 IEEE International Symposium on, Intelligent Control, 2002, 2002, pp. 660–665.
[79] K. Easton, J. Burdick, A coverage algorithm for multi-robot boundary inspection, in: Proceedings of the 2005 IEEE International Conference on, Robotics and Automation, 2005, ICRA 2005, 2005, pp. 727–734.
[80] M.A. Batalin, G.S. Sukhatme, Spreading out: a local approach to multi-robot coverage, in: Proceedings of the 6th International Symposium on Distributed Autonomous Robotics Systems, 2002, pp. 373–382.
[81] Guoyu Zuo, Peng Zhang, and Junfei Qiao, Path Planning Algorithm Based on Sub-Region for Agricultural Robot, 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, pp 197-200
[82] D.D. Bochtis, C.G. Sorensen, S.G. Vougioukas, Path planning for in-field navigation-aiding of service units, Computers and Electronics in Agriculture 74 (2010) 80-90
[83] A. Hameed, Intelligent Coverage Path Planning for Agricultural Robots and Autonomous Machines on Three-Dimensional Terrain, J Intell Robot Syst (2014) 74:965–983
[84] Hameed, I.A., Bochtis, D.D., Sørensen, C.G., Vougioukas, S.: An object oriented model for simulating agricultural in-field machinery activities. Computers and Electronics in Agriculture. 81(1), 24–32 (2012)
[85] Fröba, N., Funk, M.: TeilzeitspezifischeDieselbedarfskalkulationbeiArbeiten in der Außenwirtschaft. KTBL-Arbeitsblatt: Landtechnik und Pflanzenbau Nr. 0255 ‘BenötigteTraktormotornennleistungbeilandwirtschaftlichenArbeiten’ (N. Fröba, 1995)(in Germany) (1995)
[86] Jian Jin and Lie Tang, Coverage Path Planning on Three-Dimensional Terrain for Arable Farming, Journal of Field Robotics 28(3), 424–440 (2011) pp 424-440
[87] Koostra, B. K., Stombaugh, T., Mueller, G. T., & Shearer, A. S.(2006, July). Evaluating the effect of terrain on field area measurements. In 2006 ASABE Annual Meeting. ASABE Paper No. 061045. St. Joseph, MI: ASABE.
[88] Van Doren, A. C., Stauffer, S. R., & Kidder, H. E. Effect of contour farming on soil loss and runoff. Soil Science Society of America Proceedings, 15, 413–417,1950.
[89] Wendt, G. (1998). Guidelines for the use of the revised universal soil loss equation (RUSLE) version 1.06 on mined lands, construction sites, and reclaimed lands. Denver,CO: Peabody Western Coal Company.
[90] Gesch, D. B. (2007). The National Elevation Dataset.In D.Maune (Ed.), Digital elevation model technologies and applications: The DEM users manual, 2nd ed. (pp. 99–118). Bethesda, MD: American Society for Photogrammetry and Remote Sensing.

Kalaivanan Sandamurthy, Kalpana Ramanujam, "A Survey on Coverage Path Planning Algorithms for Autonomous Robots in Agriculture," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.815-827, 2019.

Today’s world is the world of cities and we are bound to make the cities modern and smart in order to accommodate all facilities required for the occupants of modern age. At the same time,it is of utmost importance to deliver the required facilities at the minimum expenditure and maximum reliability of energy which might use Distributed Generation as energy is dependent on our natural resources and also involves cost factor.Climate conditions and air quality are also continuously becoming worse day by day due to pollution and excessive emission of green house gases like CO2, giving rise to use of non-conventional energy sources for power generation. Saving of electrical energy does not only save money but also contributes to a clean atmosphere and conservation of natural resources. In this paper, a building of an institute has been taken to study the energy consumption behavior of a building.Calculations have been made to estimate the energy expenditure of the building using LPD considering each and every room using baseline lighting design i.e. ECBC reference. The actual consumption of all rooms i.e. proposed lighting scheme and hence that of the building is also calculated. The two consumption expenditures have been compared and it is found that in the proposed lighting design energy consumption is showing around 21.16% of saving as compared to that of baseline lighting scheme.

Baseline lighting scheme, Proposed lighting scheme, Light Power Density(LPD), Distributed Generation

[1].MohammadRoyapoor,TonyRoskilly, “Building model calibration using energy and environmental data”, Elsevier, Energy and Building94,2015.
[2]Paul Rocha,AfzalSiddiqui,MichaelStadler”,Improving energy efficiency via smart building management system: a comparison with policy measures”,Energy and Building88(2015)
[3]Sabbella V.V.S.K.Reddy, K. Satyanarayana “Sensing of Ground fault in Bipolar LVDC Grid” IJSRNSC Vol 6 issue 6,Dec 2018, EISSN; 2321-3256,page 19-24.
[4].D.P.Jenkins,S.Patidar,S.A.Simpson,”Synthesising electric demand profiles for UK dwellings”,Energy and Building76(2014)
[5].Ondrej Sikula, Jitka Mohelnikova, Josef Plask, Thermal analysis of light piper for insulated flat roofs, Energy and Building 85(2014)
[6].S.Fotios,Y.Maio,J.Utteley,”Road lighting for pedestrians, effect of luminaire position on the detection of raised and lower trip hazards”, Feb. 2019,Saga lighting research and technology.
[7].Jorgan Rose,Kriton Thomas, Ove c Mark,Mariam Meyoral,” Refurnishing blocks of flats to very low or nearly zero energy level; technical and financial resuts and co-benefits”,Energy and Building,volume 184(1-7)
[8].Tilmann E Kuhn, State of art of advanced solar control device for building,Elsevier, Solar Energy154(2017) 112-133.
[9].Cristoph Maurer, CristophCappiel, Tillmann E. Kuhn,: “Progress in building integrated solar thermal system”, Solar Energy 154(2017) 158-186.
[10]. A. Mellor, D. Alanso Alvarez, I. Gurracino, A.Romos, A.RiverolaLocasta, I. FerreLlin, A.J.Murrel, D.J.Paul, D. Chemisana, C.N.Markides, N.J.Ekans-Daukes : “Roadmap for the next generation of hybrid photovoltaic-thermal solar energy collector.”Solar Energy 174 (2018)
[11].M.B.Keogh,K Elmusharat, P. Barde, K.G.Mcguigan: “Evaluation of the natural coagulant maringa oleifera as a pretreatment for SODIS in contaiminated turbid water” Solar Energy 158(2017).
[12].Sakhir M. Sultan, M.N.ErvinaEfzan : “Review on recent photovoltaic /thermal(PV/T) technology advances and application”,Solar Energy 173(2018).
[13].I.Yarbrough,Q. Sun, D.C.Reeves, K.Hackman,R.Benett, D.S.Henshel, “Visualizing building energydemand for building peak energy analysis”, Energy and Building 91(2015)
[14].T.Kane,S.K.Firth,K.J.Lomas, “How are UK homes heated, A city -wide socio technical survey and implications for energy modeling”, Energy and Building 86(2015)

Vivek Bihari Shrivastava, Anil Kumar , M.A. Khan, "Estimating LPD Based Energy Consumption for an Institute Building: A Comparison with Baseline Lighting Design," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.828-835, 2019.

— According to the International Biometric Group, the term Biometric is defined as “Automated use of physiological or behavioral characteristics to identify and verify identity. Every individual has his/her own characteristics. The face scan, fingerprint, palm print, foot print, iris, hand scan, retinal scan, androgenic hair and DNA comes under the category of physiological characteristics. The behavioral characteristics such as voice scan, keystroke scan, gait and signature scans are better parameters. Face recognition is one of the fastest growing, emerging and interesting areas in the field of biometrics for real time applications such as image processing and film processing. This requires computational models to identify and verify the human face images. Human brain can easily detect the face but it is very difficult for computer to recognize the facial image. A lot of research work has been carried out on various algorithms for recognizing the face from past two decades. This paper provides the fundamentals of face recognition system including major components namely face detection, tracking, alignment and feature extraction. The technical issues and challenges for building a face recognition system are clearly addressed. It also provides the comparative review on existing models of face recognition. In addition to this, the applications of face recognition system are addressed to motivate the researchers for developing the novel face recognition models.

Biometrics, Authentication, Face recognition, Biometric, Physiological, Behavioral, Signature and Keystroke

[1] Anil K Jain, Arun Ross and Salil Prabhakar, “An Introduction to Biometric Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no.1, pp. 1-29, 2004.
[2] Kresimir Delac and Mislav Grgic, “A Survey of Biometric Recognition Methods,” IEEE International Symposium on Electronics in Marine, pp. 184-193, 2004.
[3] Arun Ross and Anil Jain, “Information Fusion in Biometrics,” Pattern Recognition Letters, vol. 24, pp. 2115-2125, 2003.
[4] Manuel R Freire, Julian Fierrez and Javier Ortega-Garcia, “Dynamic Signature Verification with Template Protection using Helper Data,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1713-1716, 2008.
[5] Alonso Fernandez, MC Fairhurst, J Fierrez and J Ortega-Garcia, “Impact of Signature Legibility and Signature Type in Off-line Signature Verification,” IEEE International Biometrics Symposium, pp. 1-6, 2007.
[6] Lucas Ballard, Daniel Lopresti and Fabian Monrose, “Forgery Quality and Its Implications for Behavioral Biometric Security,” IEEE Transactions on System, Man and Cybernetics, vol. 37, no. 5, pp. 1107-1118, 2007.
[7] Shih Yin, Andrew Beng, Jin Teoh and Thian-Song Ong, “Compatibility of Biometric Strengthening with Probabilistic Neural Network,” IEEE International Symposium on Biometrics and Security Technologies, pp. 88-93, 2008.
[8] Hae Min Moon, Chang Ho Seo and Sung Bum Pan, “A Face Recognition System based on Convolution Neural Network using multiple distance face”, Springer Article on Methodologies and Applications, pp. 4995-5002, 2016.
[9] Hameed R Farhan, Mahmuod and ThamirR Saeed, “Discriminative A Novel Face Recognition Method based on One State of Discrete Hidden Markov Model”, IEEE Annual conference on New Trends in Information and Communications Technology Applications, pp. 252-257, 2017.
[10] U K Jaliya and J M Rathod, “An Efficient Illumination Invariant Human Face Recognition using New Preprocessing Approach”, IEEE International Conference on Data mining and Advanced Computing, pp. 185-190, 2016.
[11] Regina Lionnie and Mudrik Alaydrus, “Biometric Identification System based on Principal Component Analysis”, IEEE International Conference on Mathematics, Statistics and their Applications, pp. 59-63, 2016.
[12] DechengLiu, Chunlei, Nannan, Jie and Xinbo Gao, “Composite Face Sketch Recognition based on Components”, IEEE International Conference on Wireless Communications and Signal Processing, pp. 1-5, 2016.
[13] XuanshengWang and Yan Chen, “Differentiated Representation and Applications of Face Recognition”, Elsevier Original research article on optics, pp. 216-222, 2017.
[14] Wenmin Yang, Riqiang Gao, Ying Xu, Xiang Sun and Qingmin Liao, “Discriminative Patch-based Sparse Representation for Face Recognition”, IEEE International Conference on Signal Processing, Communication and Computing, pp. 1-4, 2016.
[15] Yuli Fu, Xiaosi Wu, Yandong Wen and Youjun Xiang, “Efficient Locality-Constrained Occlusion Coding for Face Recognition”, Elsevier Publications on Neurocomputing, pp. 104-111, 2017.
[16] Alessandra Lumini, Loris Nanni, and Sheryl Brahnam, “Ensemble of Texture Descriptors and Classifiers for Face Recognition”, Elsevier Article on Applied Computing and Informatics, pp. 79-91, 2016.
[17] JunYu, Kejia Sun, Fei Gao and Suguo Zhu, “Face Biometric Quality Assessment Via Light CNN”, Elsevier, International Conference on Pattern recognition Letters, pp. 1-8, 2017.
[18] LinaLu Xuelong Hu, Shuhan Chen, Lei Sun and Chunxiao Li, “Face Recognition based on Weighted Wavelet Transform and compressed sensing”, IEEE International Conference on Wireless Communications and Signal Processing, pp. 1-5, 2016.
[19] Xavier Fontaine, Radhakrishna Achanta and Sabine Susstrunk, “Face Recognition in Real-World Images”, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 482-1486, 2017.
[20] Maryam Imani and Gholam Ali Montazer, “Face Recognition using Morphological Profile and Feature Space Discriminant Analysis”, IEEE Iranian International Conference on Electrical Engineering, pp. 1729-1734, 2017.
[21] Cungang Wang, Junqing Li and Bin Wang, “Face Synthesis based on Parts-based Sparse Component Analysis Face Representation”, Elsevier Original research article on Optik, pp. 843-852, 2017.
[22] Jun-Yong Zhu, Wei-Shi-Zheng, Feng Lu and Jian-Huang Lai, “Illumination Invariant Single Face Image Recognition under Heterogeneous Lighting Condition”, Elsevier International Conference on Pattern Recognition, pp. 313-327, 2017.
[23] Nawaf Hazim Barnouti, “Improve Face Recognition Rate using different Image Pre-Processing Techniques”, American Journal of Engineering Research, vol. 5, no. 4, pp. 46-53, 2016.
[24] Brahim Aksasse, Hameed Ouanan and Mohammed Ouanan, “Novel Approach to Pose Invariant Face Recognition”, Elsevier, Procedia of computer science International workshop on Big Data and Network Technologies, pp. 434-439, 2017.
[25] Changxing Ding and Dacheng Tao, “Pose-Invariant Face Recognition with Homography based Normalization”, Elsevier International Conference on Pattern Recognition, pp. 144-152, 2016.
[26] Xin Ai, Yang Wang and Xiaojuan Zheng, “Sub-pattern based Maximum Margin Criterion for Face Recognition”, IEEE International Conference on Image, Vision and Computing, pp. 218-222, 2017.
[27] Gabril Hermosilla Vigneau, Jose Luis, Gonzalo Farias, Francisco and EstebanVera, “Thermal Face Recognition under Temporal Variation Conditions”, IEEE Access Journal, vol. 5, pp. 9663-9672, 2017.
[28] Rangaswamy Y, K B Raja, Venugopal K R and L M Patnaik, “An OLBP Based Transform Domain Face Recognition,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, no. 1, pp. 6851-6868, 2014.
[29] Ganapathi V Sagar, Savitha Y Barker, K B Raja, K Suresh Babu and Venugopal K R, “Convolution based Face Recognition using DWT and Feature Vector Compression,” IEEE International Conference on Image Information Processing, pp. 444-449, 2015.
[30] G Nirmala Priya and R S D Wahida Banu, “Occlusion invariant face recognition using mean based weight matrix and support vector machine,” International Journal of Indian Academy of Science, vol. 39, no. 2, pp. 303-315, 2014.
[31] Arvind Pillai, Rajkumar Soundrapandiyan, Swapnil Satapathy, Suresh Chandra Satapathy, Ki-Hyun Jung and Rajakumar Krishnan , “Local diagonal extrema number pattern: A new feature descriptor for face recognition,” Elseveir, International Journal of Future Generation Computer Systems, vol. 81, pp. 297-306, 2018.
[32] Ramesha K, K B Raja, Venugopal K R and L M Patnaik, “Feature Extraction based Face Recognition, Gender and Age Classification,” International Journal on Computer Science and Engineering, vol. 2, no. 1, pp. 14-23, 2010.
[33] Sateeshkumar H C, C Chowda Reddy and Venugopal K R, “Face Recognition based on STWT and DTCWT using two dimensional Qshift Filters,” International Journal of Engineering and Research, vol. 7, no. 1, pp. 6479, 2017.
[34] Ganapathi V Sagar, Sahitya Reddy M V, K Suresh Babu, K B Raja and Venugopal K R, “Face Recognition based on SWT and Procrustes Analysis,” International Journal of Computer Science, vol. 5, no. 9, pp. 57-74, 2017.
[35] T. Kathirvalavakumar and J. Jebakumari Beulah Vasanthi, “Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks,” International Journal of Intelligent Learning Systems and Applications, vol. 5, pp. 115-122, 2013.
[36] K Raju and Y Srinivasa Rao, “Face Recognition using 2-DPCA, ICA, 2-DWT, Neural Network and SVM,” International Journal of Control Theory and Applications, vol. 10, no. 35, pp. 4964, 2017.
[37] Taqdir and Renu Dhir, “Face Recognition using SIFT Key with Optimal Features Selection Model,” International Journal of Advance Computer Science and Applications, vol. 8, no. 2, pp. 403-409, 2017.
[38] S. Ganesan and Munir Ahamed Rabbani Mohammed, “A Hybrid Face Image Contrast Enhancement Technique for Improved Face Recognition Accuracy,” International Journal of Intelligent Engineering and Systems, vol. 10, no. 6, pp. 106-115, 2017.
[39] Suparna Biswas and Jaya Sil, “An efficient face recognition method using contourlet and curvelet transform,” International Journal of King Saud University – Computer and Information Sciences, pp. 1-12, 2017.
[40] Bensenane Hamdan and Keche Mokhtar, “Face recognition using Angular Radial Transform,” International Journal of King Saud University – Computer and Information Sciences, pp. 1-11, 2016.
[41] Zhaoqiang Xia, Xianlin Peng, Xiaoyi Feng and Abdenour Hadid, “Scarce face recognition via two-layer collaborative representation,” International Journal of IET Biometrics, vol. 7, no. 1, pp. 56-62, 2018.
[42] Anima Majumder, Laxmidhar Behera and Venkatesh K Subramanian, “Automatic Facial Expression Recognition system using Deep Network-based Data Fusion,” IEEE Transactions on Cybernetics, vol. 48, no. 1, pp. 103-114, 2018.
[43] Soumendu Chakraborty, Satish Kumar Singh and Pavan Chakraborty, “Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieva,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 1, pp. 171-180, 2018.
[44] Kapil Juneja, “MPMFFT based DCA-DBT integrated probabilistic model for face expression classification,” International Journal of King Saud University – Computer and Information Sciences, pp. 1-11, 2017.
[45] Archana Harsing Sable, Sanjay N. Talbar and Haricharan Amarsing Dhirbasi, “Recognition of plastic surgery faces and the surgery types: An approach with entropy based scale invariant features,” International Journal of King Saud University – Computer and Information Sciences, vol. 10, no. 6, pp. 1-7, 2017.
[46] J D Woodward, Jr. Nicholas M. Orlans, P. T. Higgins, "Biometrics", McGraw-Hill/Osborne,ISBN-0-07-222227-1, DOI: 10.1036/0072230304, 2003.
[47] R. Hietmeyer, “Biometric identification promises fast and secure processing of airline passengers”, The International Civil Aviation Organization Journal, vol. 55, no. 9, pp. 10-11, 2000.
[48] Kresimir Delac, Mislav Grgic and Marian Stewart Bartlett, Textbook on Recent Advances in Face Recognition, 2008.
[49] Sunil S Harakannanavar, Prashanth C R, K B Raja and Sapna Patil, “Performance Evaluation of Face Recognition based on Multiple Feature Descriptors using Euclidean Distance Classifier”, International Journal of Advanced Networking and Applications, vol. 10, no. 3, pp. 3864-3879, 2018.
[50] Sunil S Harakannanavar, Prashanth C. R and K. B. Raja, “Performance Evaluation of Face Recognition Based on the Fusion of Bit-Plane and Binary Image Compression Techniques Using Euclidean Distance Classifier”, International Journal of Intelligent Engineering and Systems, vol. 11, no. 6, pp. 52-64, 2018.
[51] Suma L and S. 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, no. 5, pp. 6-10, 2018.
[52] B. Akhila and B. Jyothi, “Face Identification through Learned Image High Feature Video Frame Works”, International Journal of Scientific Research in Computer Science and Engineering”, vol. 6, no. 4, pp. 24-29, 2018.
[53] R. Shukla, A. Agarwal and Anil Malviya, “An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images”, International Journal of Scientific Research in Computer Science and Engineering”, vol. 6, no. 3, pp. 39-43, 2018.
[54] E. Shyla and M. Punithavalli, “Hybrid Facial Color Component Feature Identification Using Bayesian Classifier”, International Journal of Scientific Research in Computer Science and Engineering”, vol. 1, no. 3, pp. 14-21, 2013.
[55] A. Gupta, E. Sharma, N. Sachan and N. Tiwari, “Door Lock System through Face Recognition Using MATLAB”, International Journal of Scientific Research in Computer Science and Engineering”, vol. 1, no. 3, pp. 51-55, 2013.

Sunil S. Harakannanavar, Prashanth C R, Vidyashree Kanabur, Veena I. Puranikmath, K. B. Raja, "Technical Challenges, Performance Metrics and Advancements in Face Recognition System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.836-847, 2019.

India is home to one of the most underpaid yet overworking drivers. Transporters expect them to work at least twenty hours straight without any consideration to their health. Due to this the drivers are often tired and often on the verge of fatigue. This leads them to have bursts of micro-sleep: a temporary episode of sleepiness which may last for a smidgen of a second or up to thirty seconds, where the victim fails to react to some stimulus from the environment and becomes unconscious. As a result of this, road accidents have become a common occurrence in India. One solution to this problem is to enhance the vehicles to an extent, so that it’s possible to find out the level of drowsiness of the driver in real time. In this paper, we conduct a survey to find various methods for detecting driver’s drowsiness condition.

Driver Assistant System, Road Accident, Drowsiness, micro-sleep, Image Processing, driver yawning

[1] Srijayathi, K., and M. Vedachary. "Implementation of the Driver Drowsiness Detection System." International Journal of Science, Engineering and Technology Research (IJSETR) 2.9 (2013).
[2] Vural, Esra, et al. "Automated drowsiness detection for improved driving safety." (2008).
[3] De Charette, Raoul, and Fawzi Nashashibi. "Traffic light recognition using image processing compared to learning processes." 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2009.
[4] Al-Rahayfeh, Amer, and Miad Faezipour. "Enhanced frame rate for real-time eye tracking using circular hough transform." 2013 IEEE Long Island Systems, Applications and Technology Conference (LISAT). IEEE, 2013.
[5] Jha, Sumit, and Carlos Busso. "Analyzing the relationship between head pose and gaze to model driver visual attention." 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2016.
[6] Rani, P. Sheela, P. Subhashree, and N. Sankari Devi. "Computer vision based gaze tracking for accident prevention." 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave). IEEE, 2016.
[7] Constantin, Leonte, et al. "Driver Monitoring Using Face Detection and Facial Landmarks." 2018 International Conference and Exposition on Electrical And Power Engineering (EPE). IEEE, 2018.
[8] Garg, Rajat, Vikrant Gupta, and Vineet Agrawal. "A Drowsy Driver Detection and security system." 2009 International Conference on Ultra Modern Telecommunications & Workshops. IEEE, 2009.
[9] Chien, Jong-Chih, Jiann-Der Lee, and Li-Chang Liu. "A fuzzy rules-based driver assistance system." Mathematical Problems in Engineering 2015 (2015).
[10] Mehrubeoglu, Mehrube, et al. "Real-time eye tracking using a smart camera." 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE, 2011.
[11] Venkateswarlu, Ronda. "Eye gaze estimation from a single image of one eye." Proceedings Ninth IEEE International Conference on Computer Vision. IEEE, 2003.
[12] Model, Dmitri, and Moshe Eizenman. "An automatic personal calibration procedure for advanced gaze estimation systems." IEEE Transactions on Biomedical Engineering 57.5 (2010): 1031-1039..

Jothi K R, Varun D Suvarna, Yash Aggarwal, "Intelligent Driver Assistant System: A survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.848-853, 2019.

In present days, weapon detection is an important obstacle for the advancement of the security of people as well as the safety of public assets like airports and buildings. They should be taken into consideration and detection of weapons through videos and images should be made possible. Manually screening of the weapons is common in public places like airports, entrances to sensitive buildings, and public events. It is desirable sometimes to be able to detect weapons from a stand-off distance, especially when it is impossible to arrange the flow of people through a controlled procedure. The goal is to develop an automatic detection and recognition of weapons using sensor technologies and image processing. The focus of this paper is to develop an algorithm using images and a corresponding shapes and structures for weapon detection by the help of image processing.

Image processing, Weapon recognition, Weapon detection, Color image

[1] Image Editing- wikipedia.org/wiki/Image editing
[2] A Beginners guide to Object Detection- www.datacamp.com/community/tutorials/object-detection-guide
[3] Concealed Weapon Detection Using Image Processing 1Bingi Yogi Gopinath, 2Vasa Suresh Krishna, 3G. Srilatha. Dept. of E.C.E, Sir C.R.R College of Engineering, Eluru, AP, India
[4] Xin Jia, Image Recognition Method Based on Deep Learning, in: Control and Decision Conference (CCDC), 2017.
[5] Conference (Ccdc), 2017.
[6] Convolution Neural Network for Automatic Facial Expression Recognition by Xiaoguang Chen, Xuan Yang, Maosen Wang, Jiancheng Zou Proceedings of the
2017 IEEE International Conference on Applied System Innovation IEEE-ICASI 2017 Meen, Prior & Lam (Eds).
[7] Introduction to MPEG-7: Multimedia content description interface.
[8] Sentiment Analysis algorithm and applications: A survey Walaa Medhat, Ahmed Hassan, Hoda Korashy
[9] Objectrecognition.pdf-https://www.scribd.com/document/369120766/ObjectRecognition-pdf
[10] Object recognition: A shape based approach using Artificial Neural Networks Jelmer Philip de Vries.
[11] Image Recognition and Object Detection: Part 1 Learn OpenCV
[12] Object detection-Wikipedia https://en.wikipedia.org/wiki/Object_detection
[13] Image Segmentation using Susan Edge Detector Devidas D. Dighe, J.J.Chopade and N.L.Bhale.

Asadullah Shaikh, Sumeet Bhanage, Shalvi Sawant, Vishal Gawde, "Detection and Recognition of Weapons using Image Processing Fundamentals," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.854-858, 2019.