सेंसर नेटवर्क और डेटा संचार के अंतर्राष्ट्रीय जर्नल

This research focuses on optimizing data transmission in Internet of Things (IoT) sensor networks by implementing a smart approach to dynamic node selection. Leveraging predictive analytics, the proposed system facilitates efficient and accurate targeting of data transmission nodes. By dynamically selecting nodes based on predictive models, the network enhances resource utilization, reduces latency, and conserves energy, contributing to overall system efficiency. The study explores the integration of predictive analytics into IoT sensor networks, evaluates the performance of the dynamic node selection strategy, and highlights its effectiveness in achieving reliable and timely data transmission.

This research introduces a resilient mesh-grid fiber optic sensor network with a self-reconfigurable topology designed for efficient multiplexing of discrete and distributed sensors. The proposed system leverages advanced fiber optic technology to create a flexible and robust infrastructure capable of adapting to dynamic environmental conditions. The self-reconfigurable topology enables automatic adjustments in response to changes in sensor configurations, ensuring optimal performance and resource utilization. Through the integration of discrete and distributed sensors, the network achieves enhanced sensing capabilities, providing a versatile solution for diverse monitoring applications. The study explores the implementation, performance evaluation, and practical applications of the proposed sensor network, showcasing its effectiveness in real-world scenarios.

Spectrum sensing is a challenging issue in cognitive radio network. In particular, wideband spectrum sensing gains more attention due to emerging 5G wireless networks characterized by high data rates in the order of hundreds of Gbps. Conventional sensing techniques uses samples for its observations based on Nyquist rate. Due to hardware cost and sampling rate limitations those techniques can sense only one band at a time. Enhanced cognitive radio networks (E-CRNs) based on Spectrum Sharing (SS) and Spectrum Aggregation (SA) are proposed for the fifth Generation (5G) wireless networks. The E-CRNs jointly exploit the licensed spectrum shared with the Primary User (PU) networks and the unlicensed spectrum aggregated from the Industrial, Scientific, and Medical (ISM) bands. The PU networks include TV systems in TV White Space (TVWS) and different incumbent systems in the Long Term Evolution (LTE) Time Division Duplexing (TDD) bands. The harmful interference from the E-CRNs to the PU networks are delicately controlled. Furthermore, the coexistence between the E-CRNs and other unlicensed systems, such as WiFi is studied. In spite of this issue secondary users have to sense multiple frequency bands using frontend technologies which lead into increased cost, time and complexity. Considering the facts and issues, compressive sensing was introduced to minimize the computation time by improving the sensing process even for high dimensional resources. Holding the essential information and reduces the sample size which is related to high dimensional data acquisition is performed in compressive sensing. In the last decade various researchers paid more attention to improve the performance of compressive sensing in cognitive radio networks based on sensing matrix, sparse representation and recovery process. This survey paper provides an in-depth analysis of conventional models and its sensing strategies in cognitive radio networks along with its merits and demerits to obtain a detailed insight about compressive sensing. The ECRNs framework provides a spectrum usage prototype for 5G wireless communication networks.