Indian Journal Of Science And Technology最新文献

{"title":"Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT","authors":"P. K. Shashidhar, T. C. Thanuja, Rajashekar Kunabeva","doi":"10.17485/ijst/v17i5.2627","DOIUrl":"https://doi.org/10.17485/ijst/v17i5.2627","url":null,"abstract":"Objectives: The main objectives of this research endeavor encompass the development of the Adaptive RPL Optimization (ARPLO) model to enhance data transmission efficiency within IoT networks. This includes constructing a grid-based network structure optimized for data transfer, selecting the most suitable nodes as grid head nodes to maximize network lifespan while minimizing energy consumption, implementing an innovative objective function-driven approach to optimize parent node selection, and integrating an Adaptive Deep Neural Network (ADNN) to accurately classify medical data. Methods: The research methodology entails several key steps. A grid-based network structure is established with IoT nodes and root nodes, where a DODAG process incorporating DIO messages is utilized for node ranking. To enhance energy efficiency, the Trickle algorithm is employed for control message optimization. Grid head nodes are chosen based on metrics such as root node fairness, residual energy, and load influence index. The novel Middle Order Optimal Routing (MOOR) objective function is utilized to optimize routing decisions. ADNN is implemented for precise medical data classification. The proposed model's performance is evaluated through simulation in a Python environment. Findings: The research findings demonstrate that the ARPLO model yields notable benefits compared to existing models. It achieves higher energy efficiency, improved throughput, enhanced packet delivery ratio (PDR), and an extended network lifespan. The Trickle algorithm contributes to efficient control message optimization. The MOOR-based routing approach improves multimedia medical data transfer. Moreover, the integration of ADNN enhances the accuracy of data classification, particularly in healthcare applications. The research outcomes align with the broader field's existing values and reports while offering novel insights that contribute to enhancing the existing knowledge base. ARPLO protocol performance reveals that there is increase of throughput of 31.2%, PDR by 7.12%, lifetime of 10.7 % with reduction of energy consumption by 12.72%, control overhead by 31.01% and end-to-end delay by 33.01%. Novelty: The novelty of this research lies in its comprehensive approach that integrates a grid-based network structure, MOOR-based optimization, and ADNN-based classification. The incorporation of the Trickle algorithm for energy-efficient communication is an innovative feature. The introduction of new metrics for grid head node selection, along with the application of the MOOR objective function for multimedia medical data routing, showcases the research's innovative contributions. Keywords: Internet of Things (IoT), RPL (Routing Protocol for Low­Power and Lossy Networks), Optimization, Routing, Multimedia, Healthcare","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"485 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140480106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Burst Assembly Algorithm Based on Transition Count Number for OBS Network","authors":"Shamandeep Singh, Simranjit Singh, Bikrampal Kaur","doi":"10.17485/ijst/v17i5.2701","DOIUrl":"https://doi.org/10.17485/ijst/v17i5.2701","url":null,"abstract":"Background: Optical transport has emerged as a candidate solution to cope with the rising data transmission challenges of enormously evolving data. In Optical Burst Switching (OBS) networks, determining an adaptive burst size is a difficult task that must be performed efficiently during burst assembling. Methods: This research proposes a hybrid burst assembly algorithm that determines the optimal burst size during the burst creation time. The proposed algorithm uses the Transition Count Number (TCN) based method to maintain the optimal burst size when the incoming traffic is unpredictable. The efficiency of the proposed approach is investigated in terms of queuing delay, burst utilization, burst size, and burst size consistency. Findings: Three types of traffic variations (H = 0.5, H = 0.6, and H = 0.7) are imposed to evaluate the performance of the proposed burst assembly approach. As compared to the E-hybrid (time/length) strategy, the research outcomes demonstrate a 13.15% reduction in average queuing latency and a 21.26% improvement in average burst utilization. Novelty: A new burst assembly approach (hybrid burst assembly) has been proposed for OBS networks. Keywords: Burst assembly, Optical Burst Switching (OBS), burstification, burst consistency","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"519 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140472664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-aided Channel Estimation Combined with Advanced Pilot Assignment Algorithm to Mitigate Pilot Contamination for Cell-Free Networks","authors":"Swapnaja Deshpande, Mona Aggarwal, Pooja Sabherwal, Swaran Ahuja","doi":"10.17485/ijst/v17i5.2961","DOIUrl":"https://doi.org/10.17485/ijst/v17i5.2961","url":null,"abstract":"Objectives: The performance of Cell-Free Massive Multiple Input Multiple Output (CFMM) is analyzed in this paper for its two bottlenecks i.e., Pilot Contamination (PC) and Channel Estimation Error (CEE). Methods: The CFMM network is strongly affected by PC which is one of the bottlenecks due to which quality of service and accuracy of channel estimation gets impacted. Therefore, we address this problem by presenting advanced pilot assignment algorithm to mitigate PC and deep learning aided channel estimation for reducing CEE for the CFMM systems to maximize spectral efficiency (SE). We derive achievable uplink and downlink SE expressions for the proposed system, and compare with Minimum Mean Square Error and Maximum Ratio combining techniques. As well, the performance is evaluated for different antenna configurations. The advanced pilot assignment algorithm is compared with greedy pilot assignment and random pilot assignment methods. The performance of cellular massive multiple input multiple output (MIMO) is derived for comparison. The performance of CFMM system is evaluated using MATLAB software. Findings: The UL and DL performance of the proposed system in terms of SE is 3.2 times higher than the conventional CFMM with MMSE and MR combining techniques. Average sum spectral efficiency of the proposed system increases with increase in number of access points (APs). Comparison with different antenna configurations reveals that, with 400 APs equipped with single antenna, only UE with good channel condition shows performance enhancement, but when each AP is equipped with 4 antennas, the UE with unfavourable channel condition also give better performance. Advanced pilot assignment scheme proves to be better than greedy and random pilot assignment techniques. For the same cellular set up, the proposed CFMM system achieves higher SE than the cellular massive MIMO. Novelty: Due to the advanced pilot assignment algorithm used in the proposed CFMM system, at a time, only one AP is selected and the selected AP with its full received power serves the desired UE, which suppresses interference resulting in improved SE performance. The serving AP is selected considering the distance between UE and AP, rather than using large scale fading coefficient which is the unique feature of pilot assignment algorithm. The proposed deep learning-aided channel estimation method, minimizes the mean square error (MSE) between the actual channel and the channel estimates obtained from the MMSE estimation resulting in reduction in channel estimation error. Thus, the use of the proposed advanced pilot assignment algorithm and deep learning-aided channel estimation method increase the SE performance of the CFMM system. Keywords: Cell­Free Massive Multiple Input Multiple Output, Pilot Contamination, Channel Estimation Error, Minimum Mean Square Error, Maximum Ratio","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"488 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140472331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient Short-Term Solar Power Forecasting by Hybrid WOA-Based LSTM Model in Integrated Energy System","authors":"Amit Kumar Mittal, Kirti Mathur","doi":"10.17485/ijst/v17i5.2020","DOIUrl":"https://doi.org/10.17485/ijst/v17i5.2020","url":null,"abstract":"Objectives: Due to the irregular nature of sun irradiation and other meteorological conditions, solar power generation is constantly loaded with risks. When solar radiation data isn't captured and sky imaging equipment isn't available, improving forecasting becomes a more difficult endeavor. So our objective to improve the forecasting accuracy for next year solar power generation data. Methods: Our research used a real numerical solar power dataset of Australia and Germany and a standard approach for preprocessing. The feature selection in this research uses the Whale Optimization Algorithm (WOA). A Long Short-Term Memory (LSTM) method is utilized to determine the accuracy of solar power forecasts. The HHO (Harris Hawks Optimization) technique is also used to improve solar power forecasting accuracy. The performances were analyzed and the proposed method is employed in the python platform. Findings: The findings show that the suggested technique considerably increases the accuracy of short-term solar power forecasts for proposed method is 3.07 in comparison of LSTM and SVM at different data types and 15 min and 60 min interval. Novelty: The key novelties of this research is hybrid strategy for improving the precision of solar power forecasting for short periods of time. Including the Whale Optimization Algorithm (WOA), Long Short-Term Memory (LSTM), and Harris Hawks Optimization (HHO). Keywords: Power generation, Solar power forecasting, Whale optimization algorithm, Long Short­Term Memory, Harris hawk's optimization","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"334 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140474462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Analysis of Dual Rotor Radial Flux Induction Motor Used for Industrial Application","authors":"Chetan M Bobade, Swapnil B Mohod, S. K. Singh","doi":"10.17485/ijst/v17i5.2719","DOIUrl":"https://doi.org/10.17485/ijst/v17i5.2719","url":null,"abstract":"Background: Dual Rotor Radial Flux Induction Motor (DRRFIM) is proposed in this study to improve the operating performance in a more concise way for several applications compared to the conventional Induction motor. Mathematical analyses are obtained from the modeling and analytical study of Double Rotor Induction Motors (DRIM) which are useful to determine the dynamical behavior of the motor in saturated and unsaturated conditions. Methods: In normal DRIM, the driving state flow equation is done by the state equations in q-d axis and Park's transformation. To develop and design such models, it is advised to use better optimization techniques adapted MATLAB2020/Simulink software for validation. Further, transient, and steady state performance of the DRRFIM is analyzed in the various scenarios with and without saturation in DRRFIM. Finding: The proposed research also compares conventional IM with optimization possibilities. In addition, a DRIM is being operated at different stages of load connectivity and the level of speed conditions is being further investigated in this work. The method proposed and implemented in this paper achieved the overall efficiency is 82% by optimizing the mention performance metrics like active and reactive power of machine, speed of motor and its torque performance. Novelty and Applications: Dynamical behavior of solving and optimizing the amplitude of unsaturated and saturated magnetic flux are of utmost importance. The study also presents a machine equipped with two rotors that uplift the performance of the induction motor. This machine has the potential to be utilized in industrially important applications as well as in vehicles that are driven by electricity. Keywords: Dual Rotor Radial­Flux Induction Motor (DRRFIM), Saturation, Unsaturation, Mathematical Model, Inner and Outer Rotor","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"31 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140479081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LTE and MMW 5G Integrated MIMO Antenna System","authors":"A. S. K. Nayani, C. A. Sai","doi":"10.17485/ijst/v17i3.2224","DOIUrl":"https://doi.org/10.17485/ijst/v17i3.2224","url":null,"abstract":"","PeriodicalId":508200,"journal":{"name":"Indian Journal Of Science And Technology","volume":"18 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140497631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}