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The sensor network security provide confidentiality for the content of messages, contextual information usually remains exposed. Such contextual information can be exploited by an adversary to derive sensitive information such as the locations of monitored objects and data sinks in the field. Attacks on these components can significantly undermine any network application. Existing techniques defend the leakage of location information from a limited adversary who can only observe network traffic in a small region. However, a stronger adversary, the global eavesdropper, is realistic and can defeat these existing techniques. This paper first formalizes the location privacy issues in sensor networks under this strong adversary model and computes a lower bound on the communication overhead needed for achieving a given level of location privacy. This system then proposes two techniques to provide location privacy to monitored objects (source-location privacy) periodic collection and source simulation and two techniques to provide location privacy to data sinks (sink-location privacy) sink simulation and backbone flooding. These techniques provide trade-offs between privacy, communication cost, and latency. Through analysis and simulation, this project demonstrates that the proposed techniques are efficient and effective for source and sink-location privacy in sensor networks.

Wireless Sensor Network, Sink Simulation, Location Privacy, Eaves Dropper

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[2] B. Bamba, L. Liu, P. Pesti, and T. Wang, “Supporting Anonymous Location Queries in Mobile Environments with Privacygrid,” Proc. Int’l Conf. World Wide Web (WWW ’08), 2008.
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[5] H. Chan, A. Perrig, and D. Song, “Random Key Predistribution Schemes for Sensor Networks,” Proc. IEEE Symp. Security and Privacy (S&P ’03), pp. 197-213, May 2003.
[6] J. Deng, R. Han, and S. Mishra, “Enhancing Base Station Security in Wireless Sensor Networks,” Technical Report CU-CS-951-03, Univ. of Colorado, Dept. of Computer Science, 2003.
[7] J. Deng, R. Han, and S. Mishra, “Intrusion Tolerance and Anti- Traffic Analysis Strategies for Wireless Sensor Networks,” Proc. Int’l Conf. Dependable Systems and Networks (DSN ’04), June 2004.
[8] J. Deng, R. Han, and S. Mishra, “Decorrelating Wireless Sensor Network Traffic to Inhibit Traffic Analysis Attacks,” Pervasive and Mobile Computing J., Special Issue on Security in Wireless Mobile Computing Systems, vol. 2, pp. 159-186, Apr. 2006.
[9] L. Eschenauer and V.D. Gligor, “A Key-Management Scheme for Distributed Sensor Networks,” Proc. ACM Conf. Computer and Comm. Security (CCS ’02), Nov. 2002.
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[13] Y. Jian, S. Chen, Z. Zhang, and L. Zhang, “Protecting Receiver- Location Privacy in Wireless Sensor Networks,” Proc. IEEE INFOCOM, pp. 1955-1963, May 2007.
[14] D.B. Johnson, D.A. Maltz, Y. Hu, and J.G. Jetcheva, “The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks (DSR),” IETF Internet draft, Feb. 2002.
[15] P. Kamat, Y. Zhang, W. Trappe, and C. Ozturk, “Enhancing Source-Location Privacy in Sensor Network Routing,” Proc. Int’l Conf. Distributed Computing Systems (ICDCS ’05), June 2005.
[16] D. Liu and P. Ning, “Establishing Pairwise Keys in Distributed Sensor Networks,” Proc. ACM Conf. Computer and Comm. Security (CCS ’03), Oct. 2003.
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[20] C. Ozturk, Y. Zhang, and W. Trappe, “Source-Location Privacy in Energy-Constrained Sensor Network Routing,” Proc. Workshop Security of Ad Hoc and Sensor Networks (SASN ’04), Oct. 2004.
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[29] Y. Yang, M. Shao, S. Zhu, B. Urgaonkar, and G. Cao, “Towards Event Source Unobservability with Minimum Network Traffic in Sensor Networks,” Proc. ACM Conf. Wireless Network Security (WiSec ’08), 2008

C. Ragavi, R. Meera, "Protecting Location Privacy in Sensor Networks against a Global Eaves Dropper", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.235-238, 2019.

Cloud computing is a specialized form of distributed computing. It involves introduction of utilisation models for effective utilisation, remote provisioning, and scalability of measured resources available, ending up in creating useful working environments. Virtualization separates functions from hardware and allows creation of multiple simulated environments or dedicated resources from one physical hardware system to other. It gives specific users, access to packaged resources in on-demand basis, for specific purposes. Virtualisation allows all types of resources to be added to a system, which is managed by a single operating system or Administrator. The concept of virtualization has two serious battles ahead, one is that of data security and the other one is to meet the demand from corporate to store their client data safely. The threat of data breach is generating newer techniques in virtualisation leading to multiple virtual environments being linked,so that it reduces possibility of hardware failure and data security breach. Virtualisation management is also one key element of the virtualisation process and it provides the ability to create, modify, transform and link the virtualised environments in order to effectively manage the environment. With the advent of linking virtual environments, new concept of Multi cloud has come up, wherein multiple cloud services are interconnected or multiple cloud services are accessed by the user for one particular job. This paper proposes to look upon the techniques that shall transform the future of cloud and the security or insecurity that shall come with it.

Virtualization, Cloud computing, Multi cloud, Data security, Virtual environments

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[25]Sura Khalil Abd, Rawia TahirSalih, S.A. RAl Haddad, FazirulhisyamHashim.“Cloud Computing Security Risks with Authorization Access for Secure Multi-Tenancy Based on AAAS Protocol” IEEE/978–1-4799–8641- 5/15, 2015.
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E. Suganthi, F. Kurus Malai Selvi, "Security Aspects of Cloud Based Environments", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.239-242, 2019.

Underwater sensor network consists of number of various sensors and autonomous underwater vehicles deployed underwater to coordinate, interact and share information among them to carry out sensing and monitoring functions. Underwater environment differs from terrestrial radio environment both in terms of energy costs and channel propagation phenomena. The goal of the project is to reduce the occurrence of delays and attacks, during data transmission among sensor nodes in under water acoustic sensor network by introducing a protocol called delay sensitive energy management cluster head routing protocol in order to enhance the energy efficiency of the sensor network.

Underwater , Delays And Attacks , Sensor Network

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[3]. Liu, L., Zhou, S., and Cui, J. H., (2008)
[4]. “Prospects and Problems of Wireless Communication for Underwater Sensor Networks”, WILEY WCMC, Vol. 8, Pages977-994.
[5]. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, no. 4, pp. 393–422,Mar. 2002.
[6]. A. Liu, J. Ren, X. Li, Z. Chen, and X. Shen, “Design principles and improvement of cost function based energy aware routing algorithms for wireless sensor networks,” Computer Networks, vol. 56, no. 7, pp. 1951–1967, May.2012.
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S. Sahana, V. Vinothini, "Delay Sensitive Energy Management Clusterhead Routing Protocol for Securing Underwater Acoustic Sensor Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.243-245, 2019.

Distributed computing is another registering standard, in which the information handling and information stockpiling are offered to clients like administrations got to over the Internet. The increment in the interest for Cloud Computing Services has realized the rise of new business specialist co-ops, and additionally the development of those that have just taken part in this market. The consistent interest by the purchaser for administrations of more prominent quality occupies the consideration of the market and scholarly foundations towards the issue concerning high vitality utilization of server farms. This work characterizes a choice arrangement of VMs that has as a goal, the decrease of vitality utilization of server farms by methods for the MLR application procedure. It is sectioned into two principle stages The primary stage picks and stores the information alluding to the CPU utilization of the VMs and the vitality utilization of the servers, while the second stage includes presenting these accumulations to the MLR show, and in concurrence with the arrangement acquired, chooses whether or no VM ought to be moved, in addition to here a encouraging folder, which individual will be the finest candidate. The data gathering stage is discontinuous and intermittent. The second stage dependably builds up that the server is as of now qualified as over-burden, and is closed with a decision of regardless of whether to move a VM. Along these lines this determination, is utilized to dissect the vitality utilization dimension of the VMs of an over-burden server, and in this way, pick the suitable movement of the one that spoke to the best level of vitality commitment.

Virtual machines, Multiple linear regression (MLR), Load balancing, energy consumption, migration

[1] C. Gu, H. Huang, and X. Jia, “Green scheduling for cloud datacenters using ESDs to store renewable energy,”in Proc. IEEE ICC,Apr. 2016 pp. 1–6.
[2] Longxiang Fan∗, Chonglin Gu∗, Lining Qiao∗, Wenbin Wu∗, Hejiao Huang∗†∗Harbin Institute of Technology, Shenzhen, China Greensleep: A multi-sleep modes based scheduling of servers for cloud data center 2017.
[3] S. Wang, Z. Qian, J. Yuan, and I. You, “A DVFS based energy efficient tasks scheduling in a data center,” IEEE Access, vol. 5, pp. 13 090–13 102, 2017.
[4] Jie Li, Student Member, IEEE ZuyiLi, Senior Member, IEEE, Towards Optimal Electric Demand Management for Internet Data Centers VOL. 3, NO. 1, MARCH 2012.
[5] Sambit Kumar Mishra, Deepak Puthal, Bibhudatta Sahoo, Prem Prakash Jayaraman, Song Jun, Albert Y. Zomaya, Rajiv Ranjan, Energy-Efficient VM-Placement in Cloud Data Center, (2018).
[6] R. Xie, X. Jia, K. Yang, and B. Zhang, “Energy saving virtual machine allocation in cloud computing,” in Proc. IEEE ICDCSW, Jul. 2013, pp. 132–137.
[7] Dzmitry Kliazovich, Pascal Bouvry, Fabrizio Granelli, Nelson L. S. da Fonseca” Energy Consumption Optimization in Cloud Data Centers”, Cloud Services, Networking and Management.
[8] Y. Liu, H. Zhu, K. Lu, and X. Wang, “Self-adaptive management of the sleep depths of idle nodes in large scale systems to balance between energy consumption and response times,” in Proc. IEEE CloudCom, 2012, pp. 633–639.
[9] V. M. Raj and R. Shriram, “A study on server sleep state transition to reduce power consumption in a virtualized server cluster environment,” in Proc. IEEE COMSNETS, Jan. 2012, pp. 1–6.
[10] G.B.Hima Bindu, Dr. J. Janet,” a statistical survey on vm scheduling in cloud workstation for reducing energy consumption by balancing load in cloud”, 2017 international conference on networks & advances in computational technologies (netact) |20-22 july 2017|.
[11] Harmanpreet Kaur [1], Jasmeet Singh Gurm [2] Department of Computer Science and Engineering [1] & [2] PTU/RIMT Institute of Engineering and Technology” A Survey on the Power and Energy Consumption of Cloud Computing”, International Journal of Computer Science Trends and Technology (IJCST) – Volume 3 Issue 3, May-June 2015.
[12] Abusfian Elgelany, Nader Nada Sudan University, Khartoum, Sudan, Fatih University, Istanbul,Turkey” Energy Efficiency for Data Center and Cloud Computing”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 4, October 2013.

R. Priya , "A Survey on Virtualization for Cloud Data Centers", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.246-248, 2019.

This paper proposes a lossless, a reversible, and a combined data hiding schemes for ciphertext images encrypted by public key cryptosystems with probabilistic and homomorphic properties. In the lossless scheme, the ciphertext pixels are replaced with new values to embed the additional data into several LSB-planes of ciphertext pixels by multi-layer wet paper coding. Then, the embedded data can be directly extracted from the encrypted domain, and the data embedding operation does not affect the decryption of original plaintext image. In the reversible scheme, a preprocessing is employed to shrink the image histogram before image encryption, so that the modification on encrypted images for data embedding will not cause any pixel oversaturation in plaintext domain. Although a slight distortion is introduced, the embedded data can be extracted and the original image can be recovered from the directly decrypted image. Due to the compatibility between the lossless and reversible schemes, the data embedding operations in the two manners can be simultaneously performed in an encrypted image. With the combined technique, a receiver may extract a part of embedded data before decryption, and extract another part of embedded data and recover the original plaintext image after decryption.

Reversible data hiding, Lossless data hiding, Image encryption

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[2] Z. Ni, Y-Q. Shi, N. Ansali, and W. Su, "Reversible data hiding," IEEE Trans. Circuits Syst. Video Technol., vol.` 16, no. 3 , pp. 354-362, Mar.2006.
[3] X. Zhang, "Reversible data hiding in encrypted image," IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255-258, Apr. 2011.
[4] RiniJ ,4th Semester M.Tech ,Dept. of Computer Science and Information Systems FISAT Angamaly ,Kerala, India "Study on Separable Reversible Data Hiding in Encrypted Images" International Journal of Advancements in Research &Teclmology, Volume 2, Issue 12, December-2013 Copyright © 2013 SciResPub. IJOART
[5] VinitAghamDepaltment of Computer Engineeling R C Patel Institute of Technology, Shirpur.Dist. Dhule, Maharashtra, India.TareekPattewar Department oflnfonnation Technology R C Patel Institute of Technology, Shirpur. Dist. Dhule, Maharashtra, India" A Novel Approach Towards Separable Reversible Data Hiding Technique" 2014 Intemational Conference on Issues and Challenges in Intelligent Computing Techniques (IClCT).
[6] Rengarajaswamy I Assistant Professor, Department of Electronics and Communication Engineeling, M.A.M School of Engineering, Trichy, Tamil Nadu "OFT Based Individual Extraction Of Steganographic Compression Oflmages", : IJRET Feb-14.
[7] VinitAghamDepaltment of Computer Engineering R C Patel Institute of Technology, Shirpur.Dist. Dhule, Maharashtra, India.TareekPattewar Department of Infonnation Technology R C Patel Institute of Technology, Shirpur. Dis!.Dhule, Maharashtra, India " A Novel Approach Towards Separable Reversible Data Hiding Technique" 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (lClCT).
[8] Ming Li, Di Xiao, ZhongxianPeng, and Hai Nan, “A modified reversible data hiding in encrypted images using random diffusion and accurate prediction,” ETRIJournal, vol. 36, no. 2, pp. 325–328, 2014.
[9] J. Zhou, W. Sun, L. Dong, X. Liu, O. C. Au, and Y. Y. Tang, “Secure reversible image data hiding over encrypted domain via key modulation,” IEEE Transactionson Circuits and Systems for Video Technology, vol. PP, no. 99, pp. 1–1, 2015.
[10] Xinpeng Zhang, “Separable reversible data hiding in encrypted image,” IEEE Transactions on InformationForensics and Security, vol. 7, no. 2, pp. 826–832, 2012.
[11] Xinpeng Zhang, ZhenxingQian, GuoruiFeng, and YanliRen, “Efficient reversible data hiding in encrypted images,” Journal of Visual Communication and ImageRepresentation, vol. 25, no. 2, pp. 322–328, 2014.
[12] ShuliZheng, Dandan Li, Donghui Hu, Dengpan Ye, Lina Wang, and Jinwei Wang, “Lossless data hiding algorithm for encrypted images with high capacity,” MultimediaTools and Applications, pp. 1–14, 2015.
[13] Xinpeng Zhang, Chuan Qin, and Guangling Sun, “Reversible data hiding in encrypted images using pseudorandom
sequence modulation,” Digital Forensics and Watermaking, vol. 7809, pp. 358–367, 2013.
[14] Zhaoxia Yin, Bin Luo, and Wien Hong, “Separable and error-free reversible data hiding in encrypted image with high payload,” The Scientific World Journal, vol. 2014, pp. 8, 2014.
[15] Zhaoxia Yin, Huabin Wang, Haifeng Zhao, Bin Luo, and Xinpeng Zhang, “Complete separable reversible data hiding in encrypted image,” Cloud Computing andSecurity: First International Conference, ICCCS 2015, pp. 101–110, 2015.
[16] Xiaotian Wu and Wei Sun, “High-capacity reversible data hiding in encrypted images by prediction error,” Signal Processing, vol. 104, pp. 387–400, 2014.
[17] Xinpeng Zhang, Chuan Qin, and Guangling, “Reversible data hiding in encrypted images using pseudorandom
sequence modulation,” Digital Forensics and Watermaking, vol. 7809, pp. 358–367, 2013.
[18] Chong Fu, Jun-jie Chen, HaoZou, Wei-hongMeng, Yong-feng Zhan, and Ya-wen Yu, “A chaos-based digital image encryption scheme with an improved diffusion strategy,” Optics Express, vol. 20, no. 3, pp. 2363–2378, 2012.
[19] Zhicheng Ni, Yun-Qing Shi, N. Ansari, and Wei Su, “Reversible data hiding,” IEEE Transactions on Circuitsand Systems for Video Technology, vol. 16, no. 3, pp. 354–362, 2006.
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[21] Xinpeng Zhang, “Reversible data hiding in encrypted image,” IEEE Signal Processing Letters, vol. 18, no. 4, pp. 255–258, 2011.
[22] K. Ma, Weiming Zhang, Xianfeng Zhao, Nenghai Yu, and Fenghua Li, “Reversible data hiding in encrypted images by reserving room before encryption,” IEEE.
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[25] Z. H. Guan, F. Huang, and W. Guan, _Chaos-based image encryption algorithm,_ Physics Letters A, vol. 346, 2005, pp. 153-157.
[26] N. Y. Pareek, V. Patidar, and K. K. Sud, _Image encryption using chaotic logistic map,_ Image and Vision Computing, vol. 24, 2006, pp. 926-934.

S. Suvetha, B. Vinodha, "Lossless and Reversible Data Hiding in Encrypted Images with Public Key Cryptography", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.249-253, 2019.

Eye gaze is staring at the face of others to examine and see what they`re staring at and to signal interest in interacting. it`s a nonverbal behaviour wont to convey or exchange info or specific emotions while not the utilization of words. The important aspect of this paper is to give the basic idea of Human Computer Interaction (HCI). The outline of this paper includes the introduction of what exactly are these HCI Systems, current existing systems and recent developments in the field, most common frameworks or architectures used in the design of HCI systems that may include unimodal and multimodal modes, and the applications of HCI.

Human, Computer, Interaction, eye, gaze

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V. Geetha, R. Akshaya, "Human Computer Interfacing Using Eye Gazing and Movements", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.254-260, 2019.

The computer system poses a serious threat to the organisational security due to the leak of sensitive data. According to the report of risk based security (RBS), the leaked sensitive data records has increased dramatically during last few years, (i.e.) from 412 million in 2012 to 822 million in 2013. These are caused only by the lack of proper encryption on files and documents and by human errors these causes data loss. Organisation has the responsibility of screening the content which is stored in the system as sensitive data. In this paper, we utilize two techniques, which are levenshtein-distance technique and luecene search framework. These two helps to detect the leakage of data and this technique is used for screening the data which are outsourced and it also keep an track of, who is transferring the data.

Sensitive data, Data leak, Data detection, levenshtein-distance, luecene search

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[2] K. Borders and A. Prakash, “Quantifying information leaks in outbound Web traffic,” in Proc. 30th IEEESymp. Secur. Privacy (SP), May 2009, pp. 129–140.
[3] S. Jha, L. Kruger, and V. Shmatikov, “Towards practical privacy for genomic computation,” in Proc. IEEE Symp.Secur.Privacy, May 2008, pp. 216–230.
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S. Sindhuja, M. Valliammai, "An Efficient Method to Discover Transformed Data Leak", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.261-263, 2019.

Ontology Extraction is an important role in the Semantic Web as well as in knowledge management. The emergence of Semantic Web and the associated technologies promise to make the Web a meaningful experience. On the contrary, success of Semantic Web and its applications depends largely on utilization and interoperability of well-formulated ontology bases in an automated heterogeneous environment. Ontology is what exists in a domain also how they relate with each other. The advantage of ontology is that it represents real world information in a manner that is machine understandable. This leads to a diversity of interesting applications for the benefit of the target user groups. Ontology defines the terms used to describe and represent an area of knowledge. Ontologies are significant for applications that need to search across or merge information from diverse communities. In this paper, we present our move toward to extract relevant ontology concepts and their relationships from a knowledge base of heterogeneous text documents.

heterogeneous, knowledge, machine understandable, Ontology Extraction, Semantic Web

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T. Deepaalakshmi, "Semantic Ontology Extraction in Heterogeneous Text Documents", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.264-268, 2019.

In each query session, the algorithm maintains weights on the data in the database which reflect the assumed relevance of the data. Relevance feedback is used to modify these weights. As a second ingredient, the algorithm uses a minimax principle to select data for presentation to the user: any response of the user will provide significant information about his query, such that relatively few feedback rounds are sufficient to find a satisfactory data. We have implemented this algorithm and have conducted experiments on both simulated data and real data which show promising results. The objective behind developing IMPRO (Industrial Manpower Resource Organizer) is to maintain the hierarchy of the employees within an organization. It provides the manger and administrative department an overall hierarchical view of the complete enterprise and helps them in managing employee’s allocation between the manufacturing plants in large scale industry. Every Organization has many managers, who are responsible for all the activities in the organization. These managers manage different aspects of the organizational management issues, such as manufacturing, production, Marketing, etc; one such essential management issue is IMPRO. As years progressed, the approach of the management changed towards the human capital. Now Hierarchical Organization is part of every organization, and has its own identity and importance.

Content Based, Data Retrieval, Manpower, IMPRO, Feature Selection

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S. Mahalakshmi, A. Elakiya, "Effective Content Based Data Retrieval Algorithm for Industrial Manpower Resource Organizer", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.269-272, 2019.

In recent years, time-significant processing or real-time processing andanalytics of big data have received a huge amount of attentions. There are many areas/domains where real-time processing of data and making timely decision can saves thousands of human lives, minimizing the risks of human lives and resources, enhance the quality of human lives, enhance the chance of effectiveness, capableresources management etc. This paper has describedreal-time big data analytics applications and the tools used and the technical challenges faced by the applications. In addition it presents a general overview of big data to describe a background knowledge on this extent. Some examples of these domains includesecurity, healthcare, transportation, military, and natural disaster. Several big data applications in these domains rely on fast and timely analytics based on available data to make excellence decisions.

bigdata,applications,tools,challenges

[1] Nader Mohamed, Jameela Al-Jaroodi, “Real-Time Big Data Analytics: Applications and Challenges” International Conference on High-performance Computing & Simulation (HPCS), 2014
[2] Big Data Real Time Analytics: Applicationsand ToolsJayanth Kumar K1, Anisha B.S
[3] Real-Time Big Data Processing Framework: Challenges and Solutions Zhigao Zheng1,2, Ping Wang1,3,4,∗ and Jing Liu3 and Shengli Sun 1
[4] Big Data Real Time Analytics: Applications and Tools Jayanth Kumar K1, Anisha B.S.2
[5] B. Balis, T. Bartynski, M. Bubak, G. Dyk, T. Gubala, and M. Kasztelnik, “A Development and Execution Environment for Early Warning Systems for Natural Disasters,” In 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 575-582. IEEE, 2013.
[6] Real-timebig data analytics-applications and challenges-Nader Mohamed Jameela Al-Jaroodi
[7] A Thommandram, JE Pugh, JM Eklund, C McGregor, andAG James. Classifying neonatal spells using real-time temporalanalysis of physiological data streams: Algorithm development.
[8] In IEEE Point-of-Care Healthcare Technologies(PHT 2013), pages 240–243, New York, USA,Bangalore, India,2013. IEEE.

R. Uthra, A. Mahalakshmi, "Real-Time Big Data Analytics: Applications and Challenges", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.273-276, 2019.