Internet of Things: Introduction, Issues and Challenges

In recent years, internet of Things (IoT) and its relevant technologies have been gaining more interest of researchers from academic world, industry, and government. As the concept of IoT are quite different from what the Internet today can offer, several pioneering techniques have been gradually urbanized and incorporated into IoT. This term is referred to as the Future Internet of Things (FIoT). The most crucial issue of it is how to extract “data” and transfer them into “knowledge” from sensing layer to application layer. This paper includes an overview of IoT and FIoT. Further there is a discussion on key issues in the area of IoT and the technical challenges of this field

Internet of Things: Introduction, Issues and Challenges

In recent years, internet of Things (IoT) and its relevant technologies have been gaining more interest of researchers from academic world, industry, and government. As the concept of IoT are quite different from what the Internet today can offer, several pioneering techniques have been gradually urbanized and incorporated into IoT. This term is referred to as the Future Internet of Things (FIoT). The most crucial issue of it is how to extract “data” and transfer them into “knowledge” from sensing layer to application layer. This paper includes an overview of IoT and FIoT. Further there is a discussion on key issues in the area of IoT and the technical challenges of this field

Unsupervised Machine Learning based Energy Efficient Routing for Mobile Ad-Hoc Networks

Mobile Ad-hoc Networks (MANETs) are temporary networks formed by a group of mobile hosts without the need for centralized administration or specific support services. Energy consumption is a critical issue in MANETs due to their reliance on limited battery resources. Reducing energy consumption is crucial for increasing network lifespan and throughput. Existing energy-saving techniques often fall short in their effectiveness. This research proposes a novel approach that combines a proactive MANET routing protocol with an energy-efficient strategy to address these limitations. The proposed solution considers both node mobility and energy levels in the routing process. Traditional AODV routing relies on flooding, which broadcasts RREQ packets to all nodes within the sender's range. This often leads to unnecessary retransmissions of RREQ and RREP packets, resulting in collisions and network congestion. To overcome this issue, we propose an optimized route discovery mechanism for AODV. The key idea is to leverage the K-means clustering algorithm to select the optimal cluster of nodes to forward RREQ packets instead of relying on broadcasting. This approach aims to alleviate network congestion and reduce end-to-end delay by minimizing unnecessary control packet transmissions

Big Data and Social Media Analytics: A Key to Understanding Human Nature

Big Data and Social Media have transformed knowledge and comprehension in this age of technological advancement. Corporate leaders and professionals in several industries have focused on Big Data, a large collection of data from multiple sources. Meanwhile, social media networks' fast data growth has been lauded as a way to comprehend human behaviours. This study paper examines the critical need to extract intelligent information from the large volume, wide variety, and quick pace of data to meet modern corporate needs. Using specialized tools and procedures for large-scale dataset analysis and effective data management structures are crucial in this context. Big Data and Social Media Analytics offer new insights into human behaviour. This study analyzes how these two fields may work together to create new management strategies. We show that Big Data and Social Media Analytics may provide unmatched opportunities for understanding human behaviour through practical examples and case studies. This integration helps organizations navigate a rapidly changing global market by assessing client preferences, anticipating industry trends, and understanding societal shifts. This study emphasizes the need of using modern technical driving elements to better understand human nature. Integration of several data sources provides insights that give a competitive edge and aid decision-making across sectors. This article examines Big Data and Social Media Analytics, which improves management tactics and deepens understanding of the complex network of human activities and attitudes

COMPREHENSIVE ANALYSIS OF SOME RECENT COMPETITIVE CBIR TECHNIQUES

In today’s real life applications complexity of multimedia contents is significantly increased. This is highly demanding the development of effective retrieval systems to satisfy human desires. Recently, extensive research efforts have been carried out in the field of content-based image retrieval (CBIR). These research efforts are based on various parameters; feature extraction (to find content of image), similarity matching (compare the content of a query image with content of other images), indexing (index images based on their content), and relevance feedback (consider users view to get better output). The efforts result many promising solutions in designing effective and interactive CBIR systems. This paper mainly includes study of some recent CBIR techniques with the goal to design efficient system. Additionally, this study presents a detailed framework of CBIR system. Further it includes improvements achieved in the major areas like feature extraction, indexing, similarity matching, relevance feedback