Mobile Adhoc Network is fully autonomous in which mobile node plays dual role as node itself and as a router to forward packets. Fully distributed operation in which there is no any centralized authority. Network structure are fully dynamic in which every nodes is free to move anywhere with different speed.As distributive and dynamic approach of MANET, security often needs to be considered as an important one. There are also some more critical issues like packet loss due to mobility and also cause link breakage. In this paper we focus on malicious behavior of AODV under attacks which are mainly WormHole, BlackHole and GrayHole in network layer. In our proposed work, we only analyzed behavior of BlackHole and GrayHole attacks under AODV protocol and show the effects of both on network layer. We have also tested malicious behavior of AODV under above attacks using various performance parameters like throughput, packet delivery ratio, normalized network load and end to end delay using different simulation parameters.
In recent years, the utilization of Internet has turned out to be one of the everyday activities in our life. Social networks constitute a noteworthy segment of the Web and made an upheaval. It incorporates social media, forum conversations, blogs and micro-blogs like twitter. Due to this, large numbers of comments are produced on daily basis. So, nowadays most of the researchers or analyzers are concentrating on extracting significant data from social networks in order to understand the public viewpoint. This research has been reached out outside the computer science to cover other areas like business, political and social science. Hence, Sentiment analysis and Opinion mining are popular field of research in Data mining. This paper delineates various aspects of sentiment analysis in detail inclusive of important concepts, classification, process, importance, challenges and applications. The following paper presents experiment on sentiment analysis of public opinion on demonetization in India. Sentiment analysis is performed on tweets related to demonetization in India extracted from twitter. Polarity of the opinion is observed through the experimental analysis. Through the outcome of this analysis, the sentiments of the citizens that are determined help the government in improving their decisions and work for the welfare of the citizens.
The tremendous proliferation of Multi-Modal data and the flexible need of users has drawn attention to the field of Cross-Modal Retrieval (CMR), which can perform image-sketch matching, text-image matching, audio-video matching and near infrared-visual image matching. Such retrieval is useful in many applications like criminal investigation, recommendation systems and person reidentification. The real challenge in CMR is to preserve semantic similarities between various modalities of data. To preserve semantic similarities, existing deep learning-based approaches use pairwise labels and generate binary-valued representation. The generated binary-valued representation provides fast retrieval with low storage requirement. However, the relative similarity between heterogeneous data is ignored. So, the objective of this work is to reduce the modality-gap by preserving relative semantic similarities among various modalities. So, a model named "Deep Cross-Modal Retrieval (DCMR)" is proposed, which takes triplet labels as the input and generates binary-valued representation. The triplet labels locate semantic similar data points nearer and dissimilar points far in the vector space. Extensive experiments are performed and the result is compared with deep learning-based approaches, which shows that the performance of DCMR increases by 2% to 3% for Image→Text retrieval and by 2% to 5% for Text→Image retrieval in mean average precision (mAP) on MSCOCO, XMedia, and NUS-WIDE datasets. So, the binary-valued representation generated from triplet labels preserve better relative semantic similarities than pairwise labels.
The industrial internet of things (IoT) plays a major role in the growth of automation and increasing digital connectivity for machine-to-machine communication. The research community has extensively investigated the possibility of IoT and blockchain integration for the last couple of years. The major research is focused on the benefits of integrating blockchain with IoT. In this work, we first focus on the issue of integrating IoT nodes with blockchain networks, especially for non-real-time IoT nodes that do not have an in-built clock mechanism. As a result, they cannot establish communication with real-time blockchain networks. Another critical security issue is protecting data coming from IoT devices to blockchain networks. Blockchain is enough mature to protect the data in its ecosystem. However, information coming from outside of the world does not have any guarantee of data integrity and security. This paper first addresses the clock synchronization issue of IoT nodes with blockchain using a network time protocol and then proposes an IoT-blockchain light-weight cryptographic (IBLWC) approach to secure the entire IoT-blockchain ecosystem. This paper also presents the performance analysis of IBLWC as a suitable and cost-effective solution that incurs less processing overhead for IoT-blockchain-based applications.
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