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Performance Analysis of LEACH with Deep Learning in Wireless Sensor Networks

Prajapati Hardik K., Joshi Rutvij

International Journal of Electronics and Telecommunications

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2022

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Vol. 68. No. 4

799--805

EN

Thousands of low-power micro sensors make up Wireless Sensor Networks, and its principal role is to detect and report specified events to a base station. Due to bounded battery power these nodes are having very limited memory and processing capacity. Since battery replacement or recharge in sensor nodes is nearly impossible, power consumption becomes one of the most important design considerations in WSN. So one of the most important requirements in WSN is to increase battery life and network life time. Seeing as data transmission and reception consume the most energy, it’s critical to develop a routing protocol that addresses the WSN’s major problem. When it comes to sending aggregated data to the sink, hierarchical routing is critical. This research concentrates on a cluster head election system that rotates the cluster head role among nodes with greater energy levels than the others.We used a combination of LEACH and deep learning to extend the network life of the WSN in this study. In this proposed method, cluster head selection has been performed by Convolutional Neural Network (CNN). The comparison has been done between the proposed solution and LEACH, which shows the proposed solution increases the network lifetime and throughput.

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Comparative Analysis of Procedures and Solutions to Improve Energy Efficiency of Massive MIMO

Phogat Ritu Singh, Joshi Rutvij

International Journal of Electronics and Telecommunications

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2022

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Vol. 68. No. 4

677--686

EN

The blustery growth of high data rate applications leads to more energy consumption in wireless networks to satisfy service quality. Therefore, energy-efficient communications have been paid more attention to limited energy resources and environmentally friendly transmission functioning. Countless publications are present in this domain which focuses on intensifying network energy efficiency for uplink-downlink transmission. It is done either by using linear precoding schemes, by amending the number of antennas per BS, by power control problem formulation, antenna selection schemes, level of hardware impairments, and by considering cell-free (CF) Massive-MIMO. After reviewing these techniques, still there are many barriers to implement them practically. The strategies mentioned in this review show the performance of EE under the schemes as raised above. The chief contribution of this work is the comparative study of how Massive MIMO EE performs under the background of different methods and architectures and the solutions to few problem formulations that affect the EE of network systems. This study will help choose the best criteria to improve EE of Massive MIMO while formulating a newer edition of testing standards. This survey provides the base for interested readers in energy efficient Massive MIMO.

3

Single Image Super-Resolution through Sparse Representation via Coupled Dictionary learning

Patel Rutul, Thakar Vishvjit, Joshi Rutvij

International Journal of Electronics and Telecommunications

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2020

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Vol. 66, No. 2

347--353

EN

Single Image Super-Resolution (SISR) through sparse representation has received much attention in the past decade due to significant development in sparse coding algorithms. However, recovering high-frequency textures is a major bottleneck of existing SISR algorithms. Considering this, dictionary learning approaches are to be utilized to extract high-frequency textures which improve SISR performance significantly. In this paper, we have proposed the SISR algorithm through sparse representation which involves learning of Low Resolution (LR) and High Resolution (HR) dictionaries simultaneously from the training set. The idea of training coupled dictionaries preserves correlation between HR and LR patches to enhance the Super-resolved image. To demonstrate the effectiveness of the proposed algorithm, a visual comparison is made with popular SISR algorithms and also quantified through quality metrics. The proposed algorithm outperforms compared to existing SISR algorithms qualitatively and quantitatively as shown in experimental results. Furthermore, the performance of our algorithm is remarkable for a smaller training set which involves lesser computational complexity. Therefore, the proposed approach is proven to be superior based upon visual comparisons and quality metrics and have noticeable results at reduced computational complexity.