An Energy-Efficient and Blockchain-Integrated Software Defined Network for the Industrial Internet of Things.

The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works.

A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts.

Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem. In this research, a unique Convolutional Neural Network (CNN) technique is utilized to train the characteristics of the palm leaf characters. By this training, CNN can classify the palm leaf characters significantly on training phase. Initially, a preprocessing technique to remove noise in the input image is done through morphological operations. Text Line Slicing segmentation scheme is used to segment the palm leaf characters. In feature processing, there are some major steps used in this study, which include text line spacing, spacing without obstacle, and spacing with an obstacle. Finally, the extracted cursive characters are given as input to the CNN technique for final classification. The experiments are carried out with collected cursive Tamil palm leaf manuscripts to validate the performance of the proposed CNN with existing deep learning techniques in terms of accuracy, precision, recall, etc. The results proved that the proposed network achieved 94% of accuracy, where existing ResNet achieved 88% of accuracy.

Artificial Bee Colony Reinforced Extended Kalman Filter Localization Algorithm in Internet of Things with Big Data Blending Technique for Finding the Accurate Position of Reference Nodes.

In recent years, the growth of internet of things (IoT) is immense, and the observations of their evolution need to be carried out effectively. The development of the IoT has been broadly adopted in the construction of intelligent environments. There are various challenging IoT issues such as routing messages, addressing, Localizing nodes, data blending, etc. Formerly learning eloquent information from big data systems to construct a data-gathering setup in an IoT environment is challenging. Among many viable data sources, the IoT is a rich big data source: Various IoT nodes produce a massive quantity of data. Localization is one of the crucial problems that make a significant impact inside the IoT system. It needs to be engaged with proper and effective procedures to collect all sorts of data without noise. Numerous localization procedures and schemes have been initiated by deploying the IoT sensor with wireless sensor networks for both interior and outside environments. To accomplish higher localization accuracy, with less cost for the large volume of data, it is considered a hectic task in the IoT sensor environment. By viewing the nature of the IoT, the merging of different technologies such as the internet, WiFi, etc., can aid diverse ways to acquire information about various objects' locations. Location-based service is an exceptional service of the IoT, whereas localization accuracy is a significant issue. The data generated from the sensor are available in both static and dynamic forms. In this article, a sophisticated accuracy localization scheme for big data is proposed with an optimization approach that can effectively produce proper and effective outcomes for IoT environments. The theme of the article is to develop an enriched Swarm Intelligence algorithm based on Artificial Bee Colony by using the EKF (Extended Kalman Filter) data blend technique for improving Localization in IoT for the unsuspecting environment. The performance of the proposed algorithm is evaluated by using communication consumption and Localization accuracy and its comparative advantage.

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users.

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.