The results of data analysis depend solely on the techniques that extract essential information from the underlying structure of SIoT with huge uncertainty of data embedded in it. Extracting structural essential information and preserving private sensitive information in SIoT is hindering the knowledge discovery process. The methods proposed in this research paper extracts original and essential structural information from a set of IoT objects and a set of social relations in SIoT. From extracted essential structural information, user sensitive and private information are encrypted using a homomorphic encryption algorithm to produce a graph structure in an encrypted state. The degree of information exchange between graph nodes and the node-optimal module partition method are used to divide the encrypted graph structure into several modules. Further, a k-dimensional essential information algorithm is used to cluster nodes in the module. Normalised essential information, residual uncertainty, and clustering similarity algorithms are used to evaluate the correctness and similarity in clustering results. The simulation experiment results demonstrate results of SIoT graph structure clustering in ciphertext state have better efficiency and scalability. In conclusion, theoretical and security analyses demonstrate that the proposed model produces correct clustering results.

Advances in new Communication and Information innovations has led to a new paradigm known as Internet of Things (IoT). Healthcare environment uses IoT technologies for Patients care which can be used in various medical applications. Patient information is encrypted consistently to maintain the access of therapeutic records by authoritative entities. Healthcare Internet of Things (HIoT) facilitate the access of Patient files immediately in emergency situations. In the proposed system, the Patient directly provides the Key to the Doctor in normal care access. In Emergency care, a Patient shares an Attribute based Key with a set of Emergency Supporting Representatives (ESRs) and access permission to the Doctor for utilizing Emergency key from ESR. The Doctor decrypts the medical records by using Attribute based key and Emergency key to save the Patient's life. The proposed model Secure Information Retrieval using Lightweight Cryptography (SIRLC) reduces the secret key generation time and cipher text size. The performance evaluation indicates that SIRLC is a better option to utilize in Healthcare IoT than Lightweight Break-glass Access Control(LiBAC) with enhanced security and reduced computational complexity.

BATMAN (Better Approach To Mobile Ad-hoc Networking) routing protocol is one of the popular routing protocols for Mobile Ad-hoc Networks. In this paper, we have implemented a testbed containing mobile nodes and compared two flavors of this routing protocol, BATMAN and BATMAN-Adv. These two flavors differ in the layer (ISO/OSI model) in which they are implemented, where BATMAN is implemented in layer 3 of the ISO/OSI model, and BATMAN-Adv in layer 2. Due to this differentiation, there exists significant difference in their performance and stability.In this work, we have looked at Throughput, Jitter and Packet loss of these two flavors of BATMAN routing protocol in different scenarios including single-hop and multi-hop nodes. We find that BATMAN-Adv which is implemented in layer 2 of ISO/OSI model gives significant improvement in throughput as compared to BATMAN. This is especially seen in scenarios where the nodes are mobile. However, with respect to stability in terms of jitter and packet loss, we do notice that BATMAN performs well in many scenarios compared to BATMAN-Adv, but we are not able to conclusively decide on one of them to be very significantly better than the other.

The steganography is used for secure communication. In this paper we propose Dual Tree Complex Wavelet Transform (DTCWT) based high capacity steganography using coefficient replacement and adaptive scaling. The DTCWT is applied on cover image and Lifting Wavelet Transform2 (LWT2) is applied on payload to convert spatial domain into transform domain. The new concept of replacing HH sub band coefficients of DTCWT of cover image by LL sub band coefficients of payload is introduced to generate intermediate stego object. The adaptive scaling factor is used based on entropy of cover image to scale down intermediate stego object coefficient values to generate final stego object. It is observed that the capacity and security are increased in the proposed algorithm compared to existing algorithms.

The face recognition is an efficient biometric system to identify a person. In this paper, we propose Face Recognition using Transform Domain Texture Features (FRTDTF). The face images are preprocessed and two sets of texture features are extracted. In first feature set, the Discrete Wavelet Transform (DWT) is applied on face image and considered only high frequency sub band coefficients to extract edge information efficiently. The Dual Tree Complex Wavelet Transform (DTCWT) is applied on high frequency sub bands of DWT to derive Low and High frequency DTCWT coefficients. The texture features of DTCWT coefficients are computed using Overlapping Local Binary Pattern (OLBP) to generate feature set 1. In second feature set, the DTCWT is applied on preprocessed face image and considered all frequency sub bands coefficients to extract significant information and edge information of face image. The texture features of DTCWT matrix are computed using OLBP to generate feature set 2. The final feature set is the concatenation of feature set 1 and set 2. The Euclidian distance (ED) is used to compare test image features with features of face images in the database. It is observed that, the performance parameter values are better in the case of proposed algorithm compared to existing algorithms.

The optimization of queries is critical in database management systems and the complexity involved in finding optimal solutions has led to the development of heuristic approaches. Answering data mining query involves a random search over large databases. Due to the enormity of the data set involved, model simplification is necessary for quick answering of data mining queries. In this paper, we propose a hybrid model using rough sets and genetic algorithms for fast and efficient query answering. Rough sets are used to classify and summarize the datasets, whereas genetic algorithms are used for answering association related queries and feedback for adaptive classification. Here, we consider three types of queries, i.e., select, aggregate and classification based data mining queries. Summary tables that are built using rough sets and analytical model of attributes are used to speed up select queries. Mining associations, building concept hierarchies and reinforcement of reducts are achieved through genetic algorithms. The experiments are conducted on three real-life data sets, which include KDD 99 Cup data, Forest Cover-type data and Iris data. The performance of the proposed algorithm is analyzed for both execution time and classification accuracy and the results obtained are good

Mammography is a medical imaging technique that combines, low-dose radiation and high-contrast, high-resolution film for examination of the breast and screening for breast cancer. This paper proposes a random forest decision classifier (RFDC) for classifying mammograms. Results of screening the mammograms are organised by classification and finally grouped into three categories i.e., normal, cancerous and benign. Experimental results show that this method performs well with the classification accuracy reaching nearly 90% in comparison with the already existing algorithms

The XML technology, with its self-describing and extensible tags, is significantly contributing to the next generation semantic web. The present search techniques used for HTML and text documents are not efficient when retrieving relevant XML documents. In this paper, Self Adaptive Genetic Algorithms are presented to learn about the tags, which are useful in indexing. The indices and relationship strength metric are used to extract fast and accurate semantically related elements in the XML documents. The Experiments are conducted on the DataBase systems and Logic Programming (DBLP) XML corpus and are evaluated for precision and recall. The proposed SAGAXsearch outperforms XSEarch3 and XRank20 with respect to accuracy and query execution time.

Wavelength routed optical networks have emerged as a technology that can effectively utilize the enormous bandwidth of the optical fiber. Wavelength converters play an important role in enhancing the fiber utilization and reducing the overall call blocking probability of the network. As the distortion of the optical signal increases with the increase in the range of wavelength conversion in optical wavelength converters, limited range wavelength conversion assumes importance. Placement of wavelength converters is a NP complete problem in an arbitrary mesh network. In this paper, we investigate heuristics for placing limited range wavelength converters in arbitrary mesh wavelength routed optical networks. The objective is to achieve near optimal placement of limited range wavelength converters resulting in reduced blocking probabilities and low distortion of the optical signal. The proposed heuristic is to place limited range wavelength converters at the most congested nodes, nodes which lie on the long lightpaths and nodes where conversion of the optical signals is significantly high. We observe that limited range converters at a few nodes can provide almost the entire improvement in the blocking probability as the full range wavelength converters placed at all the nodes. Congestion control in the network is brought about by dynamically adjusting the weights of the channels in the link thereby balancing the load and reducing the average delay of the traffic in the entire network. Simulations have been carried out on a 12-node ring network, 14-node NSFNET, 19-node European Optical Network (EON), 30-node INET network and the results agree with the analysis.

This paper attempts to study the impact of wavelength converters in WDM wavelength routed all-optical networks. A new heuristic approach for placement of wavelength converters to reduce blocking probabilities is explored. Multihop virtual topology is designed to minimize the number and overall cost of the converters. Blocking probabilities for static lightpath establishment (SLE) and dynamic lightpath establishment (DLE) are analyzed. In the case of SLE, arranging lightpaths in ascending order of their path length reduces blocking probability. Wavelength converters placed at nodes with high nodal degree further reduces the blocking probabilities. Simulation studies performed on a 28-node USA long haul network and a 20-node arbitrary mesh network, validate the above observations.