SummaryA wireless sensor network (WSN) is a network of spatially distributed autonomous sensor nodes that collaborate to monitor physical or environmental conditions, collect data, and transmit it to a sink node. WSNs have a wide range of applications across various domains due to their ability to provide real‐time data collection, remote monitoring, and data analysis. Still, in a WSN with a fixed sink, sensor nodes closer to the sink tend to have higher traffic loads because they forward data to nodes further away. This can lead to hotspots and uneven energy consumption. Introducing a mobile sink can distribute the traffic more evenly across the network, reducing congestion and balancing the energy consumption among nodes. Hence, this research proposes a novel WSN environment with a focus on energy‐efficient routing. The network is deployed using Voronoi‐based criteria to address network coverage issues. The clustering of nodes is employed using the proposed extended pelican optimization (ExPo) algorithm to improve network lifetime and energy efficiency, critical concerns in WSNs due to limited sensor node battery capacity. Cluster heads (CHs) aggregate and process data locally, reducing the energy needed for long‐range communication. Then, an energy‐efficient optimal sink placement (EEOSP) approach is used to optimize the placement of the mobile sink. The proposed system model is evaluated based on various metrics, including average residual energy, delay, network lifetime, packet delivery ratio, and throughput and acquired the values of 0.99 J, 3.68 ms, 99.55%, 99.55%, and 81 Mbps, respectively.
{"title":"Energy‐efficient optimal sink placement using extended pelican optimization‐based clustering with Voronoi‐based node deployment","authors":"Narayanasami Abdur Rahman, Balraj Shankarlal, Sankarapandian Sivarajan, Pandian Sharmila","doi":"10.1002/dac.5975","DOIUrl":"https://doi.org/10.1002/dac.5975","url":null,"abstract":"SummaryA wireless sensor network (WSN) is a network of spatially distributed autonomous sensor nodes that collaborate to monitor physical or environmental conditions, collect data, and transmit it to a sink node. WSNs have a wide range of applications across various domains due to their ability to provide real‐time data collection, remote monitoring, and data analysis. Still, in a WSN with a fixed sink, sensor nodes closer to the sink tend to have higher traffic loads because they forward data to nodes further away. This can lead to hotspots and uneven energy consumption. Introducing a mobile sink can distribute the traffic more evenly across the network, reducing congestion and balancing the energy consumption among nodes. Hence, this research proposes a novel WSN environment with a focus on energy‐efficient routing. The network is deployed using Voronoi‐based criteria to address network coverage issues. The clustering of nodes is employed using the proposed extended pelican optimization (ExPo) algorithm to improve network lifetime and energy efficiency, critical concerns in WSNs due to limited sensor node battery capacity. Cluster heads (CHs) aggregate and process data locally, reducing the energy needed for long‐range communication. Then, an energy‐efficient optimal sink placement (EEOSP) approach is used to optimize the placement of the mobile sink. The proposed system model is evaluated based on various metrics, including average residual energy, delay, network lifetime, packet delivery ratio, and throughput and acquired the values of 0.99 J, 3.68 ms, 99.55%, 99.55%, and 81 Mbps, respectively.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SummaryThe performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems.
{"title":"Enhanced channel estimation with atomic norm minimization and reconfigurable intelligent surfaces in mmWave MIMO systems","authors":"Sundar Ganapathy, Karthikeyan Muthusamy","doi":"10.1002/dac.5973","DOIUrl":"https://doi.org/10.1002/dac.5973","url":null,"abstract":"SummaryThe performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pradnya A. Gajbhiye, Satya P. Singh, Madan Kumar Sharma
SummaryThis paper presents computationally optimized 2‐element and 4‐element Multiple‐Input, Multiple‐Output (MIMO) antennas for WLAN/Wi‐Fi, 5G, and UWB applications. The antenna configuration is constructed with the orthogonal placement of sawtooth‐shaped circular monopole radiating elements. The Particle Swarm Optimization (PSO) and Covariance Matrix Adaptation Evolution Strategy (CMA‐ES) optimization techniques are employed to achieve the best performance and size of the proposed antenna. Among these optimization techniques, CMA‐ES is identified as the better approach. The final optimized geometry of the 2‐element and 4‐element MIMO antennas is 49.40 mm × 24.22 mm and 49.44 mm × 45 mm, respectively. Both optimized antennas are fabricated and experimentally verified. The fractional bandwidth of the antenna is more than 110.3%, and more than −20 dB isolation is attained without employing any decoupling method. The Envelop Correlation Coefficient (ECC), Directivity Gain (DG), Total Active Reflection Coefficient (TARC), and Channel Capacity Limit (CCL) are 0.0001, 9.99, < −15 dB, and 0.1 bits/s/Hz, respectively. The proposed antenna is a good candidate for numerous current wireless applications due to its size and performance.
摘要 本文针对 WLAN/Wi-Fi、5G 和 UWB 应用提出了经过计算优化的 2 元和 4 元多输入多输出（MIMO）天线。天线配置采用正交放置锯齿形圆形单极辐射元件的方式。为了使拟议的天线达到最佳性能和尺寸，采用了粒子群优化（PSO）和协方差矩阵自适应进化策略（CMA-ES）优化技术。在这些优化技术中，CMA-ES 被认为是更好的方法。2 元和 4 元 MIMO 天线的最终优化几何尺寸分别为 49.40 mm × 24.22 mm 和 49.44 mm × 45 mm。这两种优化后的天线均已制作完成并通过实验验证。天线的分数带宽超过 110.3%，在不采用任何去耦方法的情况下，隔离度超过 -20dB。包络相关系数（ECC）、指向性增益（DG）、总有源反射系数（TARC）和信道容量限制（CCL）分别为 0.0001、9.99、< -15 dB 和 0.1 bits/s/Hz。由于体积小、性能好，拟议的天线是当前众多无线应用的理想选择。
{"title":"Computationally optimized multi‐port antenna systems for WLAN/Wi‐Fi (IEEE 802.11a/h/j/n/ac/ax), 5G (mid‐band), and UWB applications","authors":"Pradnya A. Gajbhiye, Satya P. Singh, Madan Kumar Sharma","doi":"10.1002/dac.5985","DOIUrl":"https://doi.org/10.1002/dac.5985","url":null,"abstract":"SummaryThis paper presents computationally optimized 2‐element and 4‐element Multiple‐Input, Multiple‐Output (MIMO) antennas for WLAN/Wi‐Fi, 5G, and UWB applications. The antenna configuration is constructed with the orthogonal placement of sawtooth‐shaped circular monopole radiating elements. The Particle Swarm Optimization (PSO) and Covariance Matrix Adaptation Evolution Strategy (CMA‐ES) optimization techniques are employed to achieve the best performance and size of the proposed antenna. Among these optimization techniques, CMA‐ES is identified as the better approach. The final optimized geometry of the 2‐element and 4‐element MIMO antennas is 49.40 mm × 24.22 mm and 49.44 mm × 45 mm, respectively. Both optimized antennas are fabricated and experimentally verified. The fractional bandwidth of the antenna is more than 110.3%, and more than −20 dB isolation is attained without employing any decoupling method. The Envelop Correlation Coefficient (ECC), Directivity Gain (DG), Total Active Reflection Coefficient (TARC), and Channel Capacity Limit (CCL) are 0.0001, 9.99, < −15 dB, and 0.1 bits/s/Hz, respectively. The proposed antenna is a good candidate for numerous current wireless applications due to its size and performance.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anbhazhagan Purushothaman, Gopalsamy Venkadakrishnan Sriramakrishnan, Ponnusamy Gnanaprakasam Om Prakash, Cristin Rajan
SummaryWireless sensor networks (WSNs) contain different sensors, which collect various data in the monitoring area. In general, one of the significant resources in WSNs is energy, which prolongs the network's lifetime. The energy‐efficient routing algorithms reduce energy consumption and enhance the survival cycle of WSNs. Thus, this work developed the optimization‐based WSN routing and deep learning (DL)–enabled energy prediction scheme for efficient routing in WSNs. Initially, the WSN simulation is carried out, and then, the node with minimum energy consumption is chosen as the cluster head (CH). Here, the proposed rat hawk optimization (RHO) algorithm is established for finding the best CH, and the RHO is the integration of rat swarm optimization (RSO) and fire hawk optimization (FHO). Furthermore, the routing is accomplished by the developed fractional rat hawk optimization (FRHO) using the fitness function includes delay, distance, link lifetime, and predicted energy of a network for predicting the finest route. Here, the fractional calculus (FC) is incorporated with the RHO to form the FRHO. The energy prediction is achieved by deep recurrent neural network (DRNN). The energy, delay, and throughput evaluation metrics are considered for revealing the efficiency of the proposed system, and the proposed system achieves the best results of 0.246 J, 0.190 s, and 67.13 Mbps, respectively.
{"title":"A novel fractional rat hawk optimization–enabled routing with deep learning–based energy prediction in wireless sensor networks","authors":"Anbhazhagan Purushothaman, Gopalsamy Venkadakrishnan Sriramakrishnan, Ponnusamy Gnanaprakasam Om Prakash, Cristin Rajan","doi":"10.1002/dac.5981","DOIUrl":"https://doi.org/10.1002/dac.5981","url":null,"abstract":"SummaryWireless sensor networks (WSNs) contain different sensors, which collect various data in the monitoring area. In general, one of the significant resources in WSNs is energy, which prolongs the network's lifetime. The energy‐efficient routing algorithms reduce energy consumption and enhance the survival cycle of WSNs. Thus, this work developed the optimization‐based WSN routing and deep learning (DL)–enabled energy prediction scheme for efficient routing in WSNs. Initially, the WSN simulation is carried out, and then, the node with minimum energy consumption is chosen as the cluster head (CH). Here, the proposed rat hawk optimization (RHO) algorithm is established for finding the best CH, and the RHO is the integration of rat swarm optimization (RSO) and fire hawk optimization (FHO). Furthermore, the routing is accomplished by the developed fractional rat hawk optimization (FRHO) using the fitness function includes delay, distance, link lifetime, and predicted energy of a network for predicting the finest route. Here, the fractional calculus (FC) is incorporated with the RHO to form the FRHO. The energy prediction is achieved by deep recurrent neural network (DRNN). The energy, delay, and throughput evaluation metrics are considered for revealing the efficiency of the proposed system, and the proposed system achieves the best results of 0.246 J, 0.190 s, and 67.13 Mbps, respectively.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SummaryOn‐demand manufacturing in Industry 4.0 requires flexibility of the networks which can be provided with the fifth generation (5G) of mobile communications wireless connectivity. A key component in the efficient utilization of the radio resources in a manufacturing scenario is network resource management (NRM). We show how NRM can be automated with artificial intelligence (AI). We introduce several futuristic industrial use cases that require AI in various parts of the process. We analyze the AI components' benefits and disadvantages in several deployment scenarios. The findings can be used by business stakeholders interested in deploying the 5G cellular wireless network to choose the best NRM and AI implementation strategy for a particular use case. We show that there are many viable options for the AI component in the process automation, but the cost of AI has to be considered in all cases. Also, we point out that an essential component, the standardized information flow on the status of the productivity key performance indicators (KPIs), is needed for the successful deployment and application of the 5G AI.
{"title":"Deployment options of AI components for network resource management in 5G‐enabled agile industrial production cell","authors":"Géza Szabó, József Pető, Attila Vidács","doi":"10.1002/dac.5983","DOIUrl":"https://doi.org/10.1002/dac.5983","url":null,"abstract":"SummaryOn‐demand manufacturing in Industry 4.0 requires flexibility of the networks which can be provided with the fifth generation (5G) of mobile communications wireless connectivity. A key component in the efficient utilization of the radio resources in a manufacturing scenario is network resource management (NRM). We show how NRM can be automated with artificial intelligence (AI). We introduce several futuristic industrial use cases that require AI in various parts of the process. We analyze the AI components' benefits and disadvantages in several deployment scenarios. The findings can be used by business stakeholders interested in deploying the 5G cellular wireless network to choose the best NRM and AI implementation strategy for a particular use case. We show that there are many viable options for the AI component in the process automation, but the cost of AI has to be considered in all cases. Also, we point out that an essential component, the standardized information flow on the status of the productivity key performance indicators (KPIs), is needed for the successful deployment and application of the 5G AI.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aman Verma, Sanat Thakur, Ankush Kumar, Dharmendra Prasad Mahato
SummaryUnstructured peer‐to‐peer (P2P) networks pose unique challenges for efficient and scalable routing. In this study, we introduce a novel routing algorithm inspired by the foraging behavior of honey bees named Honey Bee Optimization in P2P Networks (HBO_P2P) to address the inherent limitations of routing in unstructured P2P networks, focusing on improving packet delivery, minimizing hop count, reducing message overhead, and optimizing overall throughput. To evaluate the performance of our proposed algorithm, we conducted comprehensive experiments comparing it with existing algorithms commonly used in P2P networks, namely, particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO). After the simulation, we got the results as follows: Our algorithm outperforms ACO, GA, and PSO by exhibiting the highest number of data hops indicating potential efficiency in route optimization. Routing overhead is also minimal as compared to ACO, GA, and PSO. The average data packet delay is also low in our algorithm as compared to ACO, GA, and PSO. HBO_P2P achieves the highest throughput, nearly reaching 100 Mbps. While ACO and GA exhibit similar throughput of around 80 Mbps, and PSO has the lowest throughput, approximately 60 Mbps.
{"title":"Routing algorithm for sparse unstructured P2P networks using honey bee behavior","authors":"Aman Verma, Sanat Thakur, Ankush Kumar, Dharmendra Prasad Mahato","doi":"10.1002/dac.5978","DOIUrl":"https://doi.org/10.1002/dac.5978","url":null,"abstract":"SummaryUnstructured peer‐to‐peer (P2P) networks pose unique challenges for efficient and scalable routing. In this study, we introduce a novel routing algorithm inspired by the foraging behavior of honey bees named Honey Bee Optimization in P2P Networks (HBO_P2P) to address the inherent limitations of routing in unstructured P2P networks, focusing on improving packet delivery, minimizing hop count, reducing message overhead, and optimizing overall throughput. To evaluate the performance of our proposed algorithm, we conducted comprehensive experiments comparing it with existing algorithms commonly used in P2P networks, namely, particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO). After the simulation, we got the results as follows: Our algorithm outperforms ACO, GA, and PSO by exhibiting the highest number of data hops indicating potential efficiency in route optimization. Routing overhead is also minimal as compared to ACO, GA, and PSO. The average data packet delay is also low in our algorithm as compared to ACO, GA, and PSO. HBO_P2P achieves the highest throughput, nearly reaching 100 Mbps. While ACO and GA exhibit similar throughput of around 80 Mbps, and PSO has the lowest throughput, approximately 60 Mbps.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SummaryThe Internet of Things (IoT) acts as a prevalent networking setup that plays a vital role in everyday activities due to the increased services provided through uniform data collection. In this research paper, a hybrid optimization approach for the construction of heterogeneous multi‐hop IoT wireless sensor network (WSN) network topology and data aggregation and reduction is performed using a deep learning model. Initially, the IoT network is stimulated and the network topology is constructed using Namib Beetle Spotted Hyena Optimization (NBSHO) by considering different network parameters and encoding solutions. Moreover, the data aggregation and reduction in the IoT network are performed using a Deep Recurrent Neural Network (DRNN)‐based prediction model. In addition, the performance improvement of the designed NBSHO + DRNN approach is validated. Here, the designed NBSHO + DRNN method achieved a packet delivery ratio (PDR) of 0.469, energy of 0.367 J, prediction error of 0.237, and delay of 0.595 s.
{"title":"Hybrid optimization‐based topology construction and DRNN‐based prediction method for data reduction in IoT","authors":"Bhakti B. Pawar, Devyani S. Jadhav","doi":"10.1002/dac.5969","DOIUrl":"https://doi.org/10.1002/dac.5969","url":null,"abstract":"SummaryThe Internet of Things (IoT) acts as a prevalent networking setup that plays a vital role in everyday activities due to the increased services provided through uniform data collection. In this research paper, a hybrid optimization approach for the construction of heterogeneous multi‐hop IoT wireless sensor network (WSN) network topology and data aggregation and reduction is performed using a deep learning model. Initially, the IoT network is stimulated and the network topology is constructed using Namib Beetle Spotted Hyena Optimization (NBSHO) by considering different network parameters and encoding solutions. Moreover, the data aggregation and reduction in the IoT network are performed using a Deep Recurrent Neural Network (DRNN)‐based prediction model. In addition, the performance improvement of the designed NBSHO + DRNN approach is validated. Here, the designed NBSHO + DRNN method achieved a packet delivery ratio (PDR) of 0.469, energy of 0.367 J, prediction error of 0.237, and delay of 0.595 s.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Rekha, G. Shine Let, S. Radha, P. Lavanya, T. Rajasekhar, S. Ravi Chand
SummaryA compact flag‐shaped slotted multiple input multiple output (MIMO) antenna is deliberated in the article for triple‐band applications. The flag‐shaped slotted antenna is built with the help of four radiating patches having rectangular slots and disconnected ground structures. The suggested flag‐shaped slotted antenna can operate in the key frequencies of 2.4, 3.5, and 5 GHz. The antenna elements are oriented orthogonally with each other, such that no external decoupling structures are required to provide isolation. The complete size of the four‐element MIMO is 30*30*1.6 mm3. The minimum isolation between the four flag‐shaped slotted elements is −61, −55, and −47 dB in the operating frequency band of 2.3–2.8 GHz, 3.4–3.9 GHz, and 4.5–5.6 GHz, respectively. The simulated and measured results agree with each other, and they have stable radiation and gain. The MIMO performance parameters are measured, and it is noticed that the envelope correlation coefficient is lower than 0.04 and the diversity gain is 9.99 dB in the considered working frequency bands. The proposed flag‐shaped slotted MIMO is appropriate for WLAN, 5G sub‐6 GHz, and ISM wireless applications.
{"title":"Investigations on a compact self‐isolated flag‐shaped slotted MIMO antenna for triple‐band applications","authors":"S. Rekha, G. Shine Let, S. Radha, P. Lavanya, T. Rajasekhar, S. Ravi Chand","doi":"10.1002/dac.5967","DOIUrl":"https://doi.org/10.1002/dac.5967","url":null,"abstract":"SummaryA compact flag‐shaped slotted multiple input multiple output (MIMO) antenna is deliberated in the article for triple‐band applications. The flag‐shaped slotted antenna is built with the help of four radiating patches having rectangular slots and disconnected ground structures. The suggested flag‐shaped slotted antenna can operate in the key frequencies of 2.4, 3.5, and 5 GHz. The antenna elements are oriented orthogonally with each other, such that no external decoupling structures are required to provide isolation. The complete size of the four‐element MIMO is 30*30*1.6 mm<jats:sup>3</jats:sup>. The minimum isolation between the four flag‐shaped slotted elements is −61, −55, and −47 dB in the operating frequency band of 2.3–2.8 GHz, 3.4–3.9 GHz, and 4.5–5.6 GHz, respectively. The simulated and measured results agree with each other, and they have stable radiation and gain. The MIMO performance parameters are measured, and it is noticed that the envelope correlation coefficient is lower than 0.04 and the diversity gain is 9.99 dB in the considered working frequency bands. The proposed flag‐shaped slotted MIMO is appropriate for WLAN, 5G sub‐6 GHz, and ISM wireless applications.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SummaryIn this paper, a high gain self‐triplexing antenna based on the substrate integrated waveguide (SIW) leaky wave technology is presented for tri‐frequency operation. Three identical T‐shaped slots are embedded on the upper layer of the proposed SIW antenna, which are energized by two tapered microstrip lines and a simple microstrip line; and the antenna resonates at three different frequencies (7.75 GHz, 9.1 GHz, and 10.15 GHz). The transverse part of the T‐slot achieves the first band (8.8 to 9.3 GHz), while the longitudinal part of the T‐slot achieves the second band (9.6 to 10.9 GHz), and when excited transversely, the T‐slot achieves the third band (7.5 to 8.2 GHz). The isolation level between the ports is more than 25 dB, which contributes to the phenomenon of self‐triplexing. The gain of the presented antenna is high when compared with other self‐triplexing antennas.
{"title":"Design of a high gain self‐triplexing directive antenna using SIW leaky wave technique","authors":"Harsh Kumar, Garima Srivastava, Sachin Kumar","doi":"10.1002/dac.5972","DOIUrl":"https://doi.org/10.1002/dac.5972","url":null,"abstract":"SummaryIn this paper, a high gain self‐triplexing antenna based on the substrate integrated waveguide (SIW) leaky wave technology is presented for tri‐frequency operation. Three identical T‐shaped slots are embedded on the upper layer of the proposed SIW antenna, which are energized by two tapered microstrip lines and a simple microstrip line; and the antenna resonates at three different frequencies (7.75 GHz, 9.1 GHz, and 10.15 GHz). The transverse part of the T‐slot achieves the first band (8.8 to 9.3 GHz), while the longitudinal part of the T‐slot achieves the second band (9.6 to 10.9 GHz), and when excited transversely, the T‐slot achieves the third band (7.5 to 8.2 GHz). The isolation level between the ports is more than 25 dB, which contributes to the phenomenon of self‐triplexing. The gain of the presented antenna is high when compared with other self‐triplexing antennas.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SummarySensor‐enabled systems have been used successfully in agricultural, healthcare, commercial, and military application domains. Recently, there has been significant interest in the intelligent applications of sensor‐enabled technologies, particularly in the domains of smart grid, Internet of Vehicles (IoV), body area networks, and the Internet of Things (IoT). In recent research, various protocols and algorithm are developed for effective energy‐efficient routing and energy balancing. These existing models have some issues like high energy consumption and minimum network life time. In order to overcome these existing issues, a novel cluster head selection and routing mechanism in a wireless sensor network (WSN) environment is proposed. The clustering process has been formed by an enhanced Taylor kernel fuzzy C‐means algorithm (TKFC‐means). The cluster head in the group of sensor nodes has been identified based on energy and distance calculation. Finally, the routing has been performed by a novel energy‐efficient Chebyshev fire hawks optimization‐based routing protocol to route data to the edge server, which helps to balance the energy effectively. This protocol takes into account various factors, including distance, cost, residual energy, load, temperature, latency, and overall energy. The proposed model can obtain a throughput value of 82 Mbps for the sensor nodes at 500 and an end‐to‐end delay of 3.6 at 500 sensor nodes. The packet delivery ratio and loss ratio attain 96.4% and 2.7%, respectively, with 500 sensor nodes in the proposed approach. The proposed method consumes 0.45 mJ of energy with 500 nodes. From this analysis, the proposed model can obtain better results than the existing compared models.
{"title":"An energy‐efficient Chebyshev fire hawks optimization algorithm for energy balancing in sensor‐enabled Internet of Things","authors":"Pravin Yallappa Kumbhar, Apurva Abhijit Naik","doi":"10.1002/dac.5976","DOIUrl":"https://doi.org/10.1002/dac.5976","url":null,"abstract":"SummarySensor‐enabled systems have been used successfully in agricultural, healthcare, commercial, and military application domains. Recently, there has been significant interest in the intelligent applications of sensor‐enabled technologies, particularly in the domains of smart grid, Internet of Vehicles (IoV), body area networks, and the Internet of Things (IoT). In recent research, various protocols and algorithm are developed for effective energy‐efficient routing and energy balancing. These existing models have some issues like high energy consumption and minimum network life time. In order to overcome these existing issues, a novel cluster head selection and routing mechanism in a wireless sensor network (WSN) environment is proposed. The clustering process has been formed by an enhanced Taylor kernel fuzzy C‐means algorithm (TKFC‐means). The cluster head in the group of sensor nodes has been identified based on energy and distance calculation. Finally, the routing has been performed by a novel energy‐efficient Chebyshev fire hawks optimization‐based routing protocol to route data to the edge server, which helps to balance the energy effectively. This protocol takes into account various factors, including distance, cost, residual energy, load, temperature, latency, and overall energy. The proposed model can obtain a throughput value of 82 Mbps for the sensor nodes at 500 and an end‐to‐end delay of 3.6 at 500 sensor nodes. The packet delivery ratio and loss ratio attain 96.4% and 2.7%, respectively, with 500 sensor nodes in the proposed approach. The proposed method consumes 0.45 mJ of energy with 500 nodes. From this analysis, the proposed model can obtain better results than the existing compared models.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：