Showing papers by "Mayank Dave published in 2014"

Hybrid Technique for Robust and Imperceptible Image Watermarking in DWT–DCT–SVD Domain

Abstract: In this paper an algorithm for digital watermarking based on discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and singular value decomposition (SVD) has been proposed. In the embedding process, the host image is decomposed into first level DWTs. Low frequency band (LL) is transformed by DCT and SVD. The watermark image is also transformed by DCT and SVD. The S vector of watermark information is embedded in the S component of the host image. Watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DCT and inverse DWT. Watermark is extracted using an extraction algorithm. The proposed method has been extensively tested against numerous known attacks and has been found to be giving superior performance for robustness and imperceptibility compared to existing methods suggested by other authors.

Wavelet Based Image Watermarking: Futuristic Concepts in Information Security

Abstract: In this paper, we have reviewed the state-of-art of the various wavelet based image watermarking techniques in spatial as well as in transform domain based on the robustness, imperceptibility, capacity and security. The embedding and extraction methods suggested by various researchers/scientists have been studied along with their advantages and disadvantages. We have also discussed some important research challenges and requirements for making the watermarking system more efficient. It will be more important for researchers to implement effective watermarking method.

Fuzzy based clustering and aggregation technique for Under Water Wireless Sensor Networks

Abstract: The Under Water Sensor Network (UWSN) contains a set of underwater local area networks (UW-LAN) which is also called as clusters or cells. Inside the cluster, each sensor node can be linked with the sink via direct paths at multiple hops. Data gathering in UWSN is really a challenging task since energy is constrained and usually batteries cannot be recharged as solar energy cannot be exploited. Moreover, sensors are more vulnerable for failures due to pollution and corrosion. Retrieving information using the sensors manually is subject to very long delays. In this paper, we propose to design a fuzzy based clustering and aggregation technique for UWSN. In this technique the parameters residual energy, distance to sink, node density, load and link quality are considered as input to the fuzzy logic and based on the output of fuzzy logic module, appropriate cluster heads will be elected and will act as aggregator nodes. Simulation results show that the proposed technique reduces the average energy consumption and delay thereby improving the packet delivery ratio.

A Hybrid Timer Based Single Node Failure Recovery Approach for WSANs

Abstract: The inter-actor connectivity is a very crucial issue to maintain network operation in the wireless sensor and actor networks. Most of the applications have been proposed for harsh environments where the backbone actor nodes are prone to failure or get damaged due to their battery power exhaustion or get physically damaged. Such failures can partition the network due to failure of the cut-vertex node and eventually decrease the network performance or even sometimes make the network useless. Currently, a few approaches have been proposed to restore the partitioned network due to failure of the cut-vertex node but without considering the recovery node capabilities. This paper proposes a localized hybrid timer based cut-vertex node failure recovery approach called distributed prioritized connectivity restoration algorithm (DPCRA) to handle such partitions and restore connectivity with the help of a small number of nodes. The main idea is to proactively identify whether the failure of an actor node causes partition or not in the network. If partition occurs the designated failure handlers (FHs) detect that partition and repair it locally using minimum information stored in each actor node. In case first designated node is unable to start the recovery process within a permissible reaction time the next designated FH could start the recovery process. The main strength of our paper is the use of multiple backup nodes for the guaranteed partitioned recovery. The experimental simulation shows that DPCRA outperforms other existing state-of-the-art approaches in terms of the number of participated repairing nodes and their total moving distance for the recovery to restore the disconnected partitions.

Hybrid technique for robust and imperceptible dual watermarking using error correcting codes for application in telemedicine

Abstract: In this paper, the effects of different error correction codes on the robustness and the image quality are investigated. Three different error correcting codes such as Hamming, the Bose, Ray-Chaudhuri, Hocquenghem (BCH) and the Reed-Solomon code are considered to encode the watermark. The embedding watermarks method based on the two most popular transform techniques which are discrete wavelet transforms (DWTs) and singular value decomposition (SVD). The proposed algorithm is robust against a number of signal processing attacks without significant degradation of the image quality. The experimental results demonstrate that this algorithm combines the advantages and remove the disadvantages of these two transform. Out of three error correcting codes tested, it has been found that Reed-Solomon shows the best performance. A detailed analysis of the results of implementation is given.

Semantic Approach for Classification of Web Services Using Unsupervised Normalized Similarity Measure

Abstract: Automatic or semiautomatic categorization of web services facilitates the relevant service retrieval as well as it helps the administrators in attaining globally consistent classification decisions that are independent of the administrator’s knowledge of application domain, organization of taxonomies and service characteristics. Lack of automatic mechanisms to help service publishers in the classification task, irrelevant and huge number of services returned by the UDDI, and lack of standard mechanisms that helps in the discovery of desired web services are some of the issues that need focus. In this paper, an automatic approach for service categorization is proposed that uses a lexical semantic network constructed from the web snippets as a knowledge base for semantic similarity calculation between the service profiles and the categories. Our approach involves mapping of service profiles to a category based dimension vector by using the notion of semantic similarity and aims at alleviating the administrator’s job by automatically providing them with a set of categories ranked based on the degree of semantic similarity. Empirical evaluation on a set of OWL-S services shows that the proposed approach helps in better decisions for relevant classification of services by giving an ordered set of categories based on the similarity scores.

Secure data storage operations with verifiable outsourced decryption for mobile cloud computing

Abstract: Due to recent advances in hardware and communication technology, the paradigm of computing has been shifted from fixed to mobile. But the mobile devices possess limited data storage capacity besides inadequate battery power. We can augment mobile device with cloud services but which in turn imposes serious security based implication. One of the major challenges is to check the integrity of outsourced data besides providing privacy and secrecy. To tackle the aforementioned issues we can augment secure data storage operations to provide remote integrity check. It needs to calculate the hash of data just once and does not need to posses outsourced data by the mobile user. So the complete framework provides encryption, outsourced decryption and data integrity as whole thus consuming a very little battery power.

ACO Based QoS Aware Routing for Wireless Sensor Networks with Heterogeneous Nodes

Abstract: Most of the existing routing protocols for Wireless Sensor Networks (WSN) consider homogeneous nodes wherein all sensor nodes have the same capabilities in terms of sensing, communication and computation capabilities. However, a homogeneous sensor network may suffer from poor performance and scalability. This paper presents an ant-based QoS routing protocol for Heterogeneous Wireless Sensor Networks (HWSN). The key feature of the protocol is its ability to meet diverse QoS requirements posed by different kinds of traffic generated due to heterogeneous nature of nodes thus maximizing network performance and its utilization. We have evaluated and compared the proposed novel solution with EEABR and AODV for environments of dynamic topology.

Mobility Models and their Affect on Data Aggregation and Dissemination in Vehicular Networks

Abstract: Vehicular networks have been continuously attracting the attention from both academia and research industries One of the most important challenges faced by the vehicular network is the definition of a generic mobility model providing accurate and realistic mobility description However, due to the availability of large number of mobility models, it is hard to realize incomparable features, real capabilities and understanding the true degree of realism with respect to vehicular mobility As all the vehicles in vehicular networks are sharing a limited network bandwidth, data aggregation (combining correlated data items from different vehicles) and data dissemination (distributing the data to other vehicles or roadside units) save the network bandwidth and, allows more vehicular applications to co-exist Many papers exist in the literature that either discuss mobility models, data aggregation, data dissemination or propose a new mobility model, data aggregation scheme, data dissemination scheme However, the performance of data aggregation and dissemination schemes varies with the change in mobility model Therefore in performance studies of vehicular networks, investigating the influence of mobility models on the data aggregation and dissemination plays a major role In this paper, we first describe the different levels of mobility and various factors affecting the mobility patterns Then, we discuss the performance metrics for analyzing the performance of various algorithms in vehicular networks Subsequently, we illustrate the different categories of mobility models with the description of each Finally, we propose an overview and taxonomy of several mobility models available for vehicular networks with simulation The objective is to provide readers with a guideline to easily understand the impact of mobility models on data aggregation and dissemination and, allows them to choose the best mobility model required for the application

Normalized similarity based semantic approach for discovery of web services

Abstract: The practical challenges on the web are irrelevant and huge number of services returned by the UDDI and lack of standard mechanisms that helps in the discovery of desired web services. The utilization of the implicit semantic information from the service profiles can help the service consumers in selecting the most relevant services from a set of offered services. In this paper, an approach for web service discovery is proposed which uses a lexical semantic network constructed from the web snippets as a knowledge base for the calculation of semantic similarity between the service profiles. Our approach takes into account the text descriptions and involves mapping of service profiles to a category based dimension vector by using the notion of semantic similarity which is further merged with the IR based techniques of weight generation and is used for calculating the semantic degree of similarity between the services. We present results that we obtained by applying the approach on set of 106 OWL-S service profiles. Empirical evaluation shows that the proposed approach helps in better discovery of semantically similar and relevant services which are otherwise shown to be unrelated by the keyword based approaches.

Data security protocol for cloudlet based architecture

Abstract: Mobile cloud computing is a combination of mobile computing and cloud computing that provides a platform for mobile users to offload heavy tasks and data on the cloud, thus, helping them to overcome the limitations of their mobile devices. However, while utilizing the mobile cloud computing technology users lose physical control of their data; this ultimately calls for the need of a data security protocol. Although, numerous such protocols have been proposed,none of them consider a cloudlet based architecture. A cloudlet is a reliable, resource-rich computer/cluster which is well-connected to the internet and is available to nearby mobile devices. In this paper, we propose a data security protocol for a distributed cloud architecture having cloudlet integrated with the base station, using the property of perfect forward secrecy. Our protocol not only protects data from any unauthorized user, but also prevents exposure of data to the cloud owner.

A distributed approach for selection of optimal actor nodes in wireless sensor and actor networks

Abstract: In this research paper we study the problem of optimal actor nodes selection based on mutual exclusion in the context of wireless sensor and actor network (WSAN) and propose a novel distributed approach to solve it. The major requirements for any proposed approach in such scenario are: (1) the proposed approach should select the minimum number of actor nodes to act on the given incident event region, (2) the overlaps between acting ranges should be minimum to avoid redundant action by actor nodes i.e. wastage of resources should be minimal, and finally, whole event region must be covered by one or more than one actors as per their availability in the network i.e. full coverage should be achieved. We have proposed one novel approach called Distributed Optimal Actor nodes Selection based on Mutual Exclusion approach (DOASME) in this research paper. The simulation results show the performance in terms of size of actor cover set, overlapped region, non-overlapped region and maximum actor coverage degree. We have also compared our simulated results with previously proposed benchmark algorithms.

Hybrid cognitive model for semantic discovery and selection of services

Abstract: Lack of (semi)automatic mechanisms for service classification in the Universal Description Discovery and Integration repositories and non utilization of explicit or implicit semantic information of a service during its publishing are the two major challenges in the area of web service discovery and selection. We propose a semantic model of human-machine collaboration for the classification, discovery and selection of web services that integrates the semantic as well as syntactic data of the web services to achieve the hybrid cognition. This proposed cognitive approach uses the principals from the machine learning, measures of semantic relatedness and information retrieval where the cognitive information from the WordNet based Omiotis measure of semantic relatedness is merged with the syntactic service profiles and further these semantically enriched service vectors are passed to the supervised learning algorithms to achieve the decision support for the discovery and selection of relevant services. Empirical evaluation of the proposed approach implemented on OWL-X data set has been presented and a comparison of two different supervised classifiers has been made.

Semantic discovery of web services using statistical methods and measures of semantic relatedness

Abstract: Discovering a service over the web that meets the desired functionalities is still one of the most challenging tasks in the area of Service Oriented Computing. Lack of semantic information in the web services profiles poses a restriction in the automated discovery of services. Irrelevant and huge number of services returned by the UDDI and lack of standard mechanisms are the main problems faced by the users today during service discovery. In this paper, we propose a Web service discovery approach independent of the description model that tries to manage with the heterogeneity found in semantic service description frameworks. This proposed approach uses the principals from text mining, measures of semantic relatedness and information retrieval where the semantic information of the services is integrated with the syntactic service profiles to give hybrid service vectors. Empirical evaluation of the proposed approach implemented on OWL-X services has been presented to show the feasibility of the approach. Experimental results have shown that the proposed approach is able to discover better semantic relationship between services, therefore, more relevant results are ensured during discovery.