Energy Efficiency has been a main challenge in Wireless sensor networks (WSNs). Battlefield surveillance, target monitoring, disaster management etc. are very important application of target tracking. This is consuming more energy. We understand the problem of conserving energy of sensor node in WSNs. In this thesis, target tracking is done using novel algorithm, named “Hope Counting Tracking Algorithm (HCTA)”. HCTA reduces energy consumption for tracking targets in WSNs during sensing and communication. HCTA based single hop routing technique and LEACH (Low Energy Adaptive Clustering Hierarchy) based clustering routing technique is simulated on Castalia simulator with same parameters and the results analyzed. We find the clustering routing technique reduces the energy consumption by  at least 50% as compared to the single hop routing technique.

Keywords— Data Aggregation, Energy Efficiency, Target Tracking, Wireless Sensor Network

Data Mining is a powerful tool for academic intervention. Mining in education environment is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational database and can used for decision making in educational system. In our work, we collected the student’s data from engineering institute that have different information about their previous and current academics records like students S.No., Name, Branch, 10th, 12th, B.Tech passing percentage and final grade & then apply different classification algorithm using Data Mining tools (WEKA) for analysis the students academics performance for Training & placement department or company executives. This paper deals with a comparative study of various classification data mining algorithms for the performance analysis of the student’s academic records and check which algorithm is optimal for classifying students’ based on their final grade. This analysis also classifies the performance of Students into Excellent, Good and Average categories.

Keywords– Data Mining, Discover knowledge, Technical Education, Educational Data, Mining, Classification, WEKA, Classifiers. 

— Using C# as a parser, we process about 3.4 million pieces of data over 680 thousand torrents from thepiratebay.org, and create a graphical representation of the data by info-graphic.  Info-graphic presents the information in an easily readable format, and also can be distributed across many web-mediums. Based on the representation/analysis of the data, we are able to determine some interesting characteristics and properties of the torrents hosted there, such as operating system (OS) specific share ratios, average file size, average torrent seeder ratios, and much more. The discovered characteristics can help torrent users to make informative decision about their torrent usage, and seeding/leeching habits.

Keywords— Torrents, Pirate Bay, infographic, data analysis, C#, visualization, BitTorrent.

The inherent differences between secret-based authentication (such as passwords and PINs) and biometric authentication have left gaps in the credibility of biometrics. These gaps are due, in large part, to the inability to adequately cross-compare the two types of authentication. This paper provides a comparison between the two types of authentication by equating biometric entropy in the same way entropy of secrets are represented. Similar to the method used by Ratha, Connell, and Bolle [1], the x and y dimensions of the fingerprints were examined to determine all possible locations of minutiae. These locations were then examined based on the observed probability of minutiae occurring in each of the designated locations. The results of this work show statistically significant differences in the frequencies and probabilities of occurrence for minutiae location, type, and angle, across all possible minutiae locations. These components were applied to Shannon’s Information Theory [2] to determine the entropy of fingerprint biometrics, which was estimated to be equivalent to an 8.3-character, randomly chosen password. 

Keywords — Fingerprint recognition; authentication methods; entropy

In this paper presents a novel data encryption and storage scheme to address this challenge. Treating the data as a binary bit stream, our self-encryption (SE) scheme generates a key stream by randomly extracting bits from the stream. The length of the key stream depends on the user's security requirements. The bit stream is encrypted and the cipher text is stored on the mobile device, whereas the key stream is stored separately. This makes it computationally not feasible to recover the original data stream from the cipher text alone. Our scheme achieves the integration of storage correctness insurance and data error localization, i.e., the identification of misbehaving server(s). Unlike most prior works, the new scheme further supports secure and efficient dynamic operations on data blocks, including: data update, delete and append. Extensive security and performance analysis shows that the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks. An algorithm is described which guarantees reliable storage of data in a distributed system, even when different portions of the data base, stored on separate machines, are updated as part of a single transaction. The algorithm is implemented by a hierarchy of rather simple abstractions, and it works properly regardless of crashes of the client or servers. Some care is taken to state precisely the assumptions about the physical components of the system (storage, processors and communication).In which crash recovery can be performed through self encryption scheme.

Keywords: Data security, stream cipher, wireless network

As a large-scale, high-density multi-hop network becomes desirable in many applications, there exists a greater demand for scalable mobile ad hoc network (MANET) architecture. This paper proposes a new routing protocol for mobile adhoc networks. The idea is to significantly reduce the control overheads such as route query packets as well as the flooding time for collecting the network topology information at a destination. Clustering algorithms select master nodes and maintain the cluster structure dynamically as nodes move. Routing protocols utilize the underlying cluster structure to maintain routing and location information in an efficient manner. This paper discusses the various issues in scalable clustered network architectures for MANETs. This includes a classification of link-clustered architectures, an overview of clustering algorithms focusing on master selection, and a survey of cluster-based routing protocols. A disconnected route can be replaced by backup route, if available. Our simulation results show that terminode routing performs well in networks of various sizes. In smaller networks, the performance is comparable to MANET routing protocols. In larger networks that are not uniformly populated with nodes, terminode routing outperforms existing location-based or MANET routing protocols. No additional computational overheads are increased for computing the backup route.. It exhibits all these desirable characteristics without compromising on other important performance measures.

Keywords: Cluster, Ad-Hoc Networks, Routing Protocol, Location monitoring,

Huge volume of discovered association rules from the database, limits the usefulness of it. Generally based on statistical information, all the extracted rules are not interesting to the user and it is difficult to analyze manually. To overcome this drawback, efficient post-processing task is used to integrate the user knowledge. Thus, it is crucial to help the decision-maker with an efficient reducing rule number. Hence, to prune and filter discovered rules a new interactive approach is used. In post processing step, Ontology’s and Rule Schemas supervise association rule mining. At first, Terminological Ontology’s are used to extract conceptual hierarchies. Second, the Rule Schema formalism is used to express the user expectations. Weighted rule mining and filtering process can be integrated with the ARIPSO scheme. The rule-mining scheme is enhanced to handle quantitative attributes. To assist the user throughout the analyzing task, interactive framework is designed. Thus by integrating domain expert knowledge over voluminous sets of rules, the number of rules are reduced to several dozens or less.

Keywords: Clustering, Classification, and Association rules, Ontology, Rule Schema, Knowledge management applications.