Volume 07 Issue 04, July 2019

Error Optimized in Mobile Ad Hoc Network using Non-Learning Mobile Nodes Based on Particle Swarm Optimization and OPNA

Mohd. Naveen Maansoori, Dharmendra Kumar Singh

Page no:01-05


There are varied applications that need actual position of the occurring event in a very Wireless device Network (WSN) with low location computation value. A study within the field of mobile wireless device networks. The evolution of mobile wireless device networks, with advancements in hardware style, communication protocols, resource potency, and different aspects. Additionally if some nodes have quality (either anchor or target), correct localization method becomes quite difficult. This paper proposes a technique supported swarm intelligence for locating nodes in moving anchors WSN surroundings that is computationally efficient. The simulation primarily based localization is finished for fourteen counts at that the anchor nodes have totally different positions because of quality. The advantage of the rule employed in this paper is that there's just one anchor is needed for the localization of a target node (no would like for 3 anchors). the only anchor used for the localization of a target node can create its own 2 virtual anchor nodes for localization. The nodes that are with efficiency localized with its localization error and proportion localization error are ascertained during this paper. All simulations victimization totally different eventualities are done on MATLAB software package.

Improving Image Visual Presentation Based on Image Histogram Equalization Technique and PIELNNT

Hirdesh Kumar Sahu, Dharmendra kumar singh

Page no:06-10


Image improvement is one in every of the tough issues in low level image method. Image improvement completely different methods like bar graph equalization, multipoint bar graph equalizations and movie part dependent distinction protecting, but of this system are not up to marks. projected technique a picture reciprocity linear perception network technique for image improvement that contains a lot of sturdy result for distinction improvement with brightness preservation. Image part reciprocity linear perception network supported curve let transform and perceptron network. Curve let transform image remodel into multi-resolution mode. it's a realize part distinction of component for the dependency of characteristic and matrix work as a weight vector for perception network and thus the perceptron network is in work to vary the load of input image or values. Image mutuality linear perceptron network for distinction improvement has applied on several photos and compared the results of our projected methodology with various image improvement methods like bar graph equalization. Absolute mean brightness error is used to measure the degree of brightness preservation. Smaller AMBE is best and Peak signal to noise quantitative relation (PSNR) is employed to measure the degree of distinction improvement, larger PSNR is best. By examination image secure encryption improvement technique exploitation bar graph equalization with supported the AMBE and PSNR. Image secure cryptography improvement have found that projected technique (PIELNNT) is best than existing technique HET and CLAHET.


Eun Young Choi

Page no:11-16


The Internet of Things (IoT) is high world-wide, and devel-opments of various technologies to establish the IoT environ-ment have been ongoing. In particular, massive IoT devices will be in our midst because the 5G technology that the world is paying attention to is about to be realized. Hence, security technologies should be introduced to secure safety for the IoT environment since it c an be the target of hacking or cyber intrusion. The encryp-tion technology can be applied to security, and encryption key management is crucial since the core of the encryption tech-nology is the safety of the encryption key. In this paper, we review the security vulnerability of IoT and recommenda-tion/standard related to the encryption key. Then, we propose the factors to be considered in relation to the design of the technology to analyze the safety of efficient IoT EKM(En-cryption Key Management) needed to construct a safe IoT en-vironment.

Improved Encryption Images Data Using Reversible Data Hiding Block Histogram Algorithm and PBSA

Ambika Sewaniya, Surendra Chadokar

Page no:17-20


Reversible information hiding may be a wide used technique on the idea of watermarking. The host image will be recovered specifically. Reversible information activity technique is applied at medical and military applications. the information embedding method can sometimes introduce permanent loss to the cover medium. In many fields like medical, military, and law forensics degradation of cover isn't allowed. Reversible information activity algorithms in encrypted pictures (RDHAEI) with bar graph, since it maintains the excellent property that the first image cover will be losslessly recovered once information embedded is extracted whereas protective the image content’s as confidential. Reversible information activity techniques recover the first carrier specifically once the extraction of the key encrypted information. Reversible information concealment Techniques are classified supported the strategy of implementation. During this paper a survey on the various techniques applicable supported distinction expansion, bar graph shifting. during this survey paper totally different reversible information hiding strategies are analyzed. All previous strategies insert information by reversibly encrypted pictures and information extraction and/or image restoration. Reversible information concealment that permits pictures to information in hidden type and improved to their origin by removing digital hidden information.

Optimal Data Analysis Based on Unsupervised Learning New Projection KMeans Initialization Clustering Algorithm and EMCA

Jyoti Kurmi, Amit Thakur

Page no:21-25


Data mining could be a method of extracting desired and helpful data from the pool of information. Clusterin data processing is that the grouping of information points with some common similarity. Cluster is a vital aspect of information mining. It simply clusters the information sets into given no. of clusters. Various no. of ways are used for the information cluster among that Ksuggests that is that the most generally used cluster formula. During this paper, we've briefed within the kind of a review work done by completely different researcher’s victimization K-means cluster formula. As a partition primarily based cluster algorithmic program, K-Means is wide employed in several areas for the options of its efficiency and simply understood. However, it's documented that the K-Means algorithmic program could get suboptimal solutions, looking at the selection of the initial cluster centers. During this paper, they propose a projection-based K-Means low-level formatting formula. The planned formula initial uses standard mathematician kernel density estimation techniques to search out the extremely density information areas in one dimension. Then the projection step is to iteratively use density estimation from the lower variance dimensions to the upper variance ones till all the scale square measure computed. Experiments on actual datasets show that our technique will get similar results compared with different standard ways with fewer computation tasks.