In this article, a compact triple‐band frequency‐tunable (FT) hexagonal‐shaped graphene antenna through a machine learning (ML) approach for terahertz (THz) application is presented. The proposed THz antenna is designed on a polyamide () substrate with a thickness of 10 μm, and graphene is used as an antenna radiator. The size of the substrate is 38 × 46 μm2. The FT is achieved by changing the chemical potential of graphene material. The performance of the proposed THz antenna has been investigated, and the impacts of several conducting materials like gold, aluminum, copper, and graphene and dielectric materials like Rogers RT/duroid 5880, polyamide, quartz, and SiO2 are explored. The proposed THz antenna provides three operating bands. The frequency of operation in Band‐1 is 2.51–5.05 THz, Band‐2 is 5.99–7.43 THz, and Band‐3 is 7.94–9.63 THz. The bandwidth in Band‐1, Band‐2, and Band‐3 are 2.54, 1.44, and 1.69 THz, respectively. The % of impedance bandwidth in Band‐1, Band‐2, and Band‐3 are 67.19%, 24.02%, and 21.28% respectively. The proposed antenna has a maximum peak gain of 5 dBi. The proposed antenna is optimized through various ML algorithms like random forest (RF), extreme gradient boosting (XGB), K‐nearest neighbor (KNN), decision tree (DT), and artificial neural network (ANN). The RF algorithm gives more than 99% accuracy compared to other ML algorithms and accurately predicts the S11 of the proposed antenna. The proposed THz antenna would be suitable for applications related to imaging, medical, sensing, and ultra‐speed short‐distance communication applications in the THz region.
{"title":"Machine Learning Enabled Compact Frequency‐Tunable Triple‐Band Hexagonal‐Shaped Graphene Antenna for THz Communication","authors":"Jayant Kumar Rai, Uditansh Patel, Poonam Tiwari, Pinku Ranjan, Rakesh Chowdhury","doi":"10.1002/dac.6044","DOIUrl":"https://doi.org/10.1002/dac.6044","url":null,"abstract":"In this article, a compact triple‐band frequency‐tunable (FT) hexagonal‐shaped graphene antenna through a machine learning (ML) approach for terahertz (THz) application is presented. The proposed THz antenna is designed on a polyamide () substrate with a thickness of 10 μm, and graphene is used as an antenna radiator. The size of the substrate is 38 × 46 μm<jats:sup>2</jats:sup>. The FT is achieved by changing the chemical potential of graphene material. The performance of the proposed THz antenna has been investigated, and the impacts of several conducting materials like gold, aluminum, copper, and graphene and dielectric materials like Rogers RT/duroid 5880, polyamide, quartz, and <jats:italic>SiO</jats:italic><jats:sub><jats:italic>2</jats:italic></jats:sub> are explored. The proposed THz antenna provides three operating bands. The frequency of operation in Band‐1 is 2.51–5.05 THz, Band‐2 is 5.99–7.43 THz, and Band‐3 is 7.94–9.63 THz. The bandwidth in Band‐1, Band‐2, and Band‐3 are 2.54, 1.44, and 1.69 THz, respectively. The % of impedance bandwidth in Band‐1, Band‐2, and Band‐3 are 67.19%, 24.02%, and 21.28% respectively. The proposed antenna has a maximum peak gain of 5 dBi. The proposed antenna is optimized through various ML algorithms like random forest (RF), extreme gradient boosting (XGB), K‐nearest neighbor (KNN), decision tree (DT), and artificial neural network (ANN). The RF algorithm gives more than 99% accuracy compared to other ML algorithms and accurately predicts the <jats:italic>S</jats:italic><jats:sub>11</jats:sub> of the proposed antenna. The proposed THz antenna would be suitable for applications related to imaging, medical, sensing, and ultra‐speed short‐distance communication applications in the THz region.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"112 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665591","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 majority of wireless sensor network (WSN) systems include multiple data traffic with different service requirements. Small batteries are used to supply energy to the sensor nodes. This research work explores a new optimal hybrid MAC protocol for heterogeneous WSN to carry out efficient routing. The major intention of the designed protocol is the incorporation of features of both IEEE 802.15.4 and Low‐Energy Adaptive Clustering Hierarchy (LEACH) to solve the challenges. The energy‐saving circuit is adopted by predetermining the cluster heads (CHs), whereas the usual nodes are powered by a battery. Thus, it is suggested to extend the lifespan of network operation, where the designed hybrid protocol is intended to transfer the essential activities to the elected cluster heads when reducing the activity of the nodes. Here, a new optimizer known as the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is suggested for selecting the cluster heads and also for promoting the IEEE 802.15.4 protocol. This protocol is assisted to ensure performance regarding self‐healing, scalability, self‐reconfigurability, and energy efficiency. Thus, the performance evaluation is conducted in terms of various performance measures like throughput and energy consumption over the Adaptive Leach Protocol and multi‐channel MAC protocol with IEEE 802.15.4.
摘要大多数无线传感器网络（WSN）系统都包括具有不同服务要求的多种数据流量。小型电池用于为传感器节点提供能量。这项研究工作为异构 WSN 探索了一种新的最佳混合 MAC 协议，以实现高效路由。设计该协议的主要意图是结合 IEEE 802.15.4 和低能耗自适应聚类层次结构（LEACH）的特点来解决所面临的挑战。节能电路是通过预先确定簇头（CHs）来实现的，而普通节点则由电池供电。因此，建议延长网络运行寿命，所设计的混合协议旨在减少节点活动时，将基本活动转移到选出的簇头。在此，建议使用一种称为增强亨利气体溶解度优化（EHGSO）算法的新优化器来选择簇头，同时推广 IEEE 802.15.4 协议。该协议有助于确保自愈、可扩展性、自重新配置和能效方面的性能。因此，对 IEEE 802.15.4 自适应浸出协议和多通道 MAC 协议的吞吐量和能耗等各种性能指标进行了性能评估。
{"title":"Implementation of optimal routing in heterogeneous wireless sensor network with multi‐channel Media Access Control protocol using Enhanced Henry Gas Solubility Optimizer","authors":"D. Pravin Kumar, P. Ganesh Kumar","doi":"10.1002/dac.5980","DOIUrl":"https://doi.org/10.1002/dac.5980","url":null,"abstract":"SummaryThe majority of wireless sensor network (WSN) systems include multiple data traffic with different service requirements. Small batteries are used to supply energy to the sensor nodes. This research work explores a new optimal hybrid MAC protocol for heterogeneous WSN to carry out efficient routing. The major intention of the designed protocol is the incorporation of features of both IEEE 802.15.4 and Low‐Energy Adaptive Clustering Hierarchy (LEACH) to solve the challenges. The energy‐saving circuit is adopted by predetermining the cluster heads (CHs), whereas the usual nodes are powered by a battery. Thus, it is suggested to extend the lifespan of network operation, where the designed hybrid protocol is intended to transfer the essential activities to the elected cluster heads when reducing the activity of the nodes. Here, a new optimizer known as the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is suggested for selecting the cluster heads and also for promoting the IEEE 802.15.4 protocol. This protocol is assisted to ensure performance regarding self‐healing, scalability, self‐reconfigurability, and energy efficiency. Thus, the performance evaluation is conducted in terms of various performance measures like throughput and energy consumption over the Adaptive Leach Protocol and multi‐channel MAC protocol with IEEE 802.15.4.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"29 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257204","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}
SummaryNowadays, wireless sensor networks (WSNs) have paid huge attention among researchers due to their wide applications. WSNs possess multiple sensor nodes that transmit data to each other by using constrained energy resources. The sensor nodes are highly affected by collision due to the transmission of packets over the network by one or two nodes at the same time. Collision detection is necessary to increase network security and enhance the lifetime of sensor nodes. In most of the previous research, efficiently implementing collision detection algorithms while minimizing resource usage remains a significant challenge. Thus, a hybrid deep learning model deep Kronecker recurrent neural network (DKRNN) is developed in this research. Here, the cluster head is selected using the chronological skill optimization algorithm (CSOA) algorithmic approach by considering multi‐objective parameters like energy, distance, delay, and trust. The network‐based parameters are then extracted from the network. Later, the collision is detected using the DKRNN approach and the collision is mitigated finally using a packet pre‐scheduling model named Dolphin Ant Lion Optimization (Dolphin ALO). Moreover, the detection performance of CSOA+ DKRNN is validated, and it achieved superior performance with a collision detection rate (CDR) of 0.940, packet delivery ratio (PDR) of 0.660, throughput of 0.850Mbps, and energy consumption of 0.110 J.
{"title":"Collision detection and mitigation based on optimization and Kronecker recurrent neural network in WSN","authors":"Akhil Khare, Kannapiran Selvakumar, Raman Dugyala","doi":"10.1002/dac.5977","DOIUrl":"https://doi.org/10.1002/dac.5977","url":null,"abstract":"SummaryNowadays, wireless sensor networks (WSNs) have paid huge attention among researchers due to their wide applications. WSNs possess multiple sensor nodes that transmit data to each other by using constrained energy resources. The sensor nodes are highly affected by collision due to the transmission of packets over the network by one or two nodes at the same time. Collision detection is necessary to increase network security and enhance the lifetime of sensor nodes. In most of the previous research, efficiently implementing collision detection algorithms while minimizing resource usage remains a significant challenge. Thus, a hybrid deep learning model deep Kronecker recurrent neural network (DKRNN) is developed in this research. Here, the cluster head is selected using the chronological skill optimization algorithm (CSOA) algorithmic approach by considering multi‐objective parameters like energy, distance, delay, and trust. The network‐based parameters are then extracted from the network. Later, the collision is detected using the DKRNN approach and the collision is mitigated finally using a packet pre‐scheduling model named Dolphin Ant Lion Optimization (Dolphin ALO). Moreover, the detection performance of CSOA+ DKRNN is validated, and it achieved superior performance with a collision detection rate (CDR) of 0.940, packet delivery ratio (PDR) of 0.660, throughput of 0.850Mbps, and energy consumption of 0.110 J.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"67 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196548","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}
SummaryThis paper presents the design and performance evaluation of a single‐layer, high‐gain, millimeter‐wave (mm‐wave), corporate–series‐fed, 16‐element circular patch array antenna tailored for the 28 GHz frequency band, pertinent to fifth‐generation (5G) wireless communication systems. The proposed antenna configuration employs a dual‐port feeding technique, where consecutive junction patches are interconnected with two separate feed networks. By simultaneously exciting the two ports with identical amplitude but opposite phases, the antenna achieves high gain directed towards the broadside. The proposed structure is fabricated on a grounded substrate, enabling accurate performance measurement of the prototype. Close agreement between simulated and measured results validates the precision of the designed structure. The measured performance of the proposed antenna configuration demonstrates an impedance bandwidth of 3.79% within the desired frequency band of 27.6‐28.7 GHz for S11 ≤ −10 dB. Experimental measurements demonstrated that the mutual coupling between the two distinct ports is <−30 dB, with a diversity gain exceeding 9.99 dB. Simulated radiation efficiency exceeds 90% at the 28 GHz center frequency, while the measured peak gain approaches 17.7 dBi. Measured stable radiation plots specify that the proposed array exhibits broadside patterns with half‐power beamwidths (HPBWs) of 30.4° and 11.3°, sidelobe levels (SLLs) below −25 and −10 dB, and cross‐polarization levels <−25 dB in both the E and H planes, respectively. The superior performance characteristics of the proposed array antenna make it well‐suited for 28 GHz mm‐wave 5G applications, facilitating efficient and reliable long‐range communication in the mm‐wave spectrum.
{"title":"Dual‐port circular patch antenna array: Enhancing gain and minimizing cross‐polarization for mm‐wave 5G networks","authors":"Sourav Ghosh, Gaurav Singh Baghel, M. V. Swati","doi":"10.1002/dac.5990","DOIUrl":"https://doi.org/10.1002/dac.5990","url":null,"abstract":"SummaryThis paper presents the design and performance evaluation of a single‐layer, high‐gain, millimeter‐wave (mm‐wave), corporate–series‐fed, 16‐element circular patch array antenna tailored for the 28 GHz frequency band, pertinent to fifth‐generation (5G) wireless communication systems. The proposed antenna configuration employs a dual‐port feeding technique, where consecutive junction patches are interconnected with two separate feed networks. By simultaneously exciting the two ports with identical amplitude but opposite phases, the antenna achieves high gain directed towards the broadside. The proposed structure is fabricated on a grounded substrate, enabling accurate performance measurement of the prototype. Close agreement between simulated and measured results validates the precision of the designed structure. The measured performance of the proposed antenna configuration demonstrates an impedance bandwidth of 3.79% within the desired frequency band of 27.6‐28.7 GHz for <jats:italic>S</jats:italic><jats:sub>11</jats:sub> ≤ −10 dB. Experimental measurements demonstrated that the mutual coupling between the two distinct ports is <−30 dB, with a diversity gain exceeding 9.99 dB. Simulated radiation efficiency exceeds 90% at the 28 GHz center frequency, while the measured peak gain approaches 17.7 dBi. Measured stable radiation plots specify that the proposed array exhibits broadside patterns with half‐power beamwidths (HPBWs) of 30.4° and 11.3°, sidelobe levels (SLLs) below −25 and −10 dB, and cross‐polarization levels <−25 dB in both the E and H planes, respectively. The superior performance characteristics of the proposed array antenna make it well‐suited for 28 GHz mm‐wave 5G applications, facilitating efficient and reliable long‐range communication in the mm‐wave spectrum.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"16 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196751","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 growth of software‐defined networking (SDN) enhances network strength and provides flexible routing, especially in heterogeneous environments. Hence, an efficient framework is required for recent networks. Recently, hybrid SDN with the restricted deployment of SDN switches has been integrated with a conventional network that provides improved communication performance compared to traditional SDN systems. However, the recent hybrid SDNs lack effective link protection and optimal routing when used with complex topologies. Hence, this study presents a novel deep learning–based hybridized multi‐stacked autoencoder with the duo‐directed gated recurrent unit (MSAE‐DDGRU) for automatic link failure prediction in hybrid SDN. Moreover, a multi‐objective zebra optimizer (MO‐ZeO) is introduced to perform optimal routing by solving multiple routing constraints. The developed study is processed with the Python platform, and publicly available GEANT topology is utilized for the whole experimental process. Various assessment measures like accuracy, precision, sensitivity, packet loss, cost, maximum link utilization (MLU), policy violation rates (PVRs), packet delivery ratio (PDR), and delay are analyzed and compared with existing studies. The developed technique achieved an accuracy of 96%, precision of 92%, sensitivity of 93%, PDR of 99.4%, PVR of 0.0005, and delay of 1.2 s are obtained.
摘要软件定义网络（SDN）的发展增强了网络强度，提供了灵活的路由选择，尤其是在异构环境中。因此，最近的网络需要一个高效的框架。最近，通过限制 SDN 交换机的部署，混合 SDN 与传统网络实现了整合，与传统 SDN 系统相比，混合 SDN 提高了通信性能。然而，最近的混合 SDN 在使用复杂拓扑时缺乏有效的链路保护和最佳路由选择。因此，本研究提出了一种基于深度学习的新型混合多堆栈自动编码器与双向门控递归单元（MSAE-DDGRU），用于混合 SDN 中的自动链路故障预测。此外，还引入了多目标斑马优化器（MO-ZeO），通过解决多个路由约束条件来执行最优路由。所开发的研究使用 Python 平台进行处理，整个实验过程使用了公开可用的 GEANT 拓扑。分析了各种评估指标，如准确度、精确度、灵敏度、丢包率、成本、最大链路利用率（MLU）、策略违反率（PVR）、数据包交付率（PDR）和延迟，并与现有研究进行了比较。所开发的技术获得了 96% 的准确度、92% 的精确度、93% 的灵敏度、99.4% 的 PDR、0.0005 的 PVR 和 1.2 秒的延迟。
{"title":"Performance enhancement in hybrid SDN using advanced deep learning with multi‐objective optimization frameworks under heterogeneous environments","authors":"Deepak Bishla, Brijesh Kumar","doi":"10.1002/dac.5989","DOIUrl":"https://doi.org/10.1002/dac.5989","url":null,"abstract":"SummaryThe growth of software‐defined networking (SDN) enhances network strength and provides flexible routing, especially in heterogeneous environments. Hence, an efficient framework is required for recent networks. Recently, hybrid SDN with the restricted deployment of SDN switches has been integrated with a conventional network that provides improved communication performance compared to traditional SDN systems. However, the recent hybrid SDNs lack effective link protection and optimal routing when used with complex topologies. Hence, this study presents a novel deep learning–based hybridized multi‐stacked autoencoder with the duo‐directed gated recurrent unit (MSAE‐DDGRU) for automatic link failure prediction in hybrid SDN. Moreover, a multi‐objective zebra optimizer (MO‐ZeO) is introduced to perform optimal routing by solving multiple routing constraints. The developed study is processed with the Python platform, and publicly available GEANT topology is utilized for the whole experimental process. Various assessment measures like accuracy, precision, sensitivity, packet loss, cost, maximum link utilization (MLU), policy violation rates (PVRs), packet delivery ratio (PDR), and delay are analyzed and compared with existing studies. The developed technique achieved an accuracy of 96%, precision of 92%, sensitivity of 93%, PDR of 99.4%, PVR of 0.0005, and delay of 1.2 s are obtained.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196752","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}
Chandra Mohan Dharmapuri, B. V. Ramana Reddy, Ashish Payal
SummaryDue to the rise in information rate prerequisite and the heterogeneity level, the modification in network traffic in the upcoming wireless communication (WC) encompasses innovative challenges in the case of energy efficiency (EE) and spectrum management. To tackle this issue, several existing techniques have been imposed but none of the frameworks provided effective solutions to compatible with recent WC applications. This framework introduces an innovative deep learning (DL)–based distributed cognitive radio network (DCRN). The proposed scheme emphasizes single base station (BS) management, where resource effectiveness is obtained by solving active resource allocation (RA) problems using a bipartite matching (BM) technique. A DL scheme is emphasized to predict the traffic load (TL) for effective EE using a residual inception‐enriched recurrent convolutional neural network (R‐InceptionRCNN). The proposed method is implemented in Python, and the performance metrics including uplink (UL) achievable capacity per secondary user (SU), UL achievable capacity per SU, cost of energy consumption, EE, and mean energy saving (MES) are scrutinized and compared with conventional techniques. The proposed scheme achieved the overall costs, EE, MES, and UL capacity of 14.33 C/J, 149.99 J/MB, 13.49%, and 22.33 Mbps, respectively, on performing RA and TL prediction in the CRN platform.
摘要由于信息速率前提条件和异构水平的提高，在即将到来的无线通信（WC）中，网络流量的变化给能源效率（EE）和频谱管理带来了新的挑战。为解决这一问题，人们采用了多种现有技术，但没有一种框架能提供与最新无线通信应用兼容的有效解决方案。本框架引入了一种创新的基于深度学习（DL）的分布式认知无线电网络（DCRN）。所提出的方案强调单基站（BS）管理，通过使用双匹配（BM）技术解决主动资源分配（RA）问题来提高资源效率。该方案强调使用残差初始富集递归卷积神经网络（R-InceptionRCNN）预测流量负载（TL）以实现有效的 EE 的 DL 方案。提出的方法在 Python 中实现，其性能指标包括上行链路 (UL) 每个二级用户 (SU) 的可实现容量、UL 每个 SU 的可实现容量、能耗成本、EE 和平均节能 (MES)，并与传统技术进行了仔细研究和比较。在 CRN 平台上执行 RA 和 TL 预测时，拟议方案的总体成本、EE、MES 和 UL 容量分别为 14.33 C/J、149.99 J/MB、13.49% 和 22.33 Mbps。
{"title":"Empowering cognitive radio networks: residual inception–enriched recurrent convolutional neural network–driven QOS enhancement and energy efficiency strategy","authors":"Chandra Mohan Dharmapuri, B. V. Ramana Reddy, Ashish Payal","doi":"10.1002/dac.5986","DOIUrl":"https://doi.org/10.1002/dac.5986","url":null,"abstract":"SummaryDue to the rise in information rate prerequisite and the heterogeneity level, the modification in network traffic in the upcoming wireless communication (WC) encompasses innovative challenges in the case of energy efficiency (EE) and spectrum management. To tackle this issue, several existing techniques have been imposed but none of the frameworks provided effective solutions to compatible with recent WC applications. This framework introduces an innovative deep learning (DL)–based distributed cognitive radio network (DCRN). The proposed scheme emphasizes single base station (BS) management, where resource effectiveness is obtained by solving active resource allocation (RA) problems using a bipartite matching (BM) technique. A DL scheme is emphasized to predict the traffic load (TL) for effective EE using a residual inception‐enriched recurrent convolutional neural network (R‐InceptionRCNN). The proposed method is implemented in Python, and the performance metrics including uplink (UL) achievable capacity per secondary user (SU), UL achievable capacity per SU, cost of energy consumption, EE, and mean energy saving (MES) are scrutinized and compared with conventional techniques. The proposed scheme achieved the overall costs, EE, MES, and UL capacity of 14.33 C/J, 149.99 J/MB, 13.49%, and 22.33 Mbps, respectively, on performing RA and TL prediction in the CRN platform.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"48 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196754","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}
Aravind Papasani, G. P. Saradhi Varma, P. V. G. D. Prasad Reddy, V. Ramanjaneyulu Yannam
SummarySoftware‐defined networking (SDN) is an emerging networking architecture paradigm that decouples the control and data planes. The problem of figuring out the number and positions of controllers and mapping of switches to them is known as the controller placement problem. To provide the resilience against the failure of a controller, each switch is mapped to a primary controller (first reference controller or FRC) and a backup controller (second reference controller or SRC). An existing work aims to minimize the worst‐case latency (WCL) from switch to controller when a controller fails. But this work misses the constraint specifying the definition of a switch's SRC, which might cause an increase in the latency between some switches and their controllers in the event of a controller failure. In order to address this issue, a model is proposed in this paper by incorporating the missing constraint. But the addition of this constraint can potentially cause an increase in the minimum number of required controllers. In order to address this issue, a second model is proposed in this paper by modifying the capacity constraint based on the observation that the capacity of a controller need not be reserved for all the switches for which it acts as SRC. The two proposed models aim at minimizing the WCL from switch to controller when a controller fails. Three network topologies are used to test the proposed models and compare their performance with the existing model in terms of principal and subsidiary metrics. The results demonstrate that the proposed models perform on equal level with the existing model in terms of WCL from switch to SRC while outperforming it in terms of average latency (AL). For example, the first proposed model achieves an average AL reduction of 21.63%, 8.55%, and 25.13% compared with the existing model on three networks. Similarly, the second proposed model achieves an average AL reduction of 21.3%, 8.55%, and 24.19% in each network on three networks. Moreover, the second proposed model achieves a fair trade‐off between the minimum number of controllers required and AL while outperforming both the existing and the first proposed models in terms of the average percentage of reserved controller capacity.
{"title":"Enhanced capacitated next controller placement in software‐defined network with modified capacity constraint","authors":"Aravind Papasani, G. P. Saradhi Varma, P. V. G. D. Prasad Reddy, V. Ramanjaneyulu Yannam","doi":"10.1002/dac.5979","DOIUrl":"https://doi.org/10.1002/dac.5979","url":null,"abstract":"SummarySoftware‐defined networking (SDN) is an emerging networking architecture paradigm that decouples the control and data planes. The problem of figuring out the number and positions of controllers and mapping of switches to them is known as the controller placement problem. To provide the resilience against the failure of a controller, each switch is mapped to a primary controller (first reference controller or FRC) and a backup controller (second reference controller or SRC). An existing work aims to minimize the worst‐case latency (WCL) from switch to controller when a controller fails. But this work misses the constraint specifying the definition of a switch's SRC, which might cause an increase in the latency between some switches and their controllers in the event of a controller failure. In order to address this issue, a model is proposed in this paper by incorporating the missing constraint. But the addition of this constraint can potentially cause an increase in the minimum number of required controllers. In order to address this issue, a second model is proposed in this paper by modifying the capacity constraint based on the observation that the capacity of a controller need not be reserved for all the switches for which it acts as SRC. The two proposed models aim at minimizing the WCL from switch to controller when a controller fails. Three network topologies are used to test the proposed models and compare their performance with the existing model in terms of principal and subsidiary metrics. The results demonstrate that the proposed models perform on equal level with the existing model in terms of WCL from switch to SRC while outperforming it in terms of average latency (AL). For example, the first proposed model achieves an average AL reduction of 21.63%, 8.55%, and 25.13% compared with the existing model on three networks. Similarly, the second proposed model achieves an average AL reduction of 21.3%, 8.55%, and 24.19% in each network on three networks. Moreover, the second proposed model achieves a fair trade‐off between the minimum number of controllers required and AL while outperforming both the existing and the first proposed models in terms of the average percentage of reserved controller capacity.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"42 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196753","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}
SummaryInternet of things–enabled wireless sensor networks face challenges like inflexibility, poor scalability, suboptimal cluster head selection, and energy inefficiencies. This is due to the faster data transmission rates between cluster nodes during data packet routing. This creates unnecessary energy consumption burdens for those actively transmitting nodes. Conceptually, an effective cluster formation phase supports better data routing mechanisms, while sustaining the energy efficiency of individual nodes. This paper proposes a Neuro‐Fuzzy based Cluster Formation (NFCF) scheme to facilitate adaptive and energy‐efficient cluster topologies. NFCF utilizes fuzzy logic and neural networks to identify optimal super nodes for flexible cluster formations. This approach enables configurable cluster sizes along with inclusion/exclusion criteria for member nodes based on energy thresholds. Parameters evaluated for node selection include the degree of super node, expected energy per cluster, energy variance, and residual energy. Nodes not meeting the thresholds are excluded. The neural network updates fuzzy rules to guide optimal clustering decisions based on anticipated energy dynamics under different conditions. The performance of the proposed NFCF scheme is evaluated based on objective function changes related to data transmission, individual node energy variation, energy variance before and after transmissions, and averaged end‐to‐end delay across transmission cycles. Results are compared against genetic fuzzy clustering, fuzzy energy‐aware clustering, fuzzy‐based distributed clustering, fuzzy logic‐based multi‐hop clustering, and fuzzy weighted k‐means clustering.
摘要支持物联网的无线传感器网络面临着缺乏灵活性、可扩展性差、簇头选择不理想和能源效率低等挑战。这是因为在数据包路由过程中，簇节点之间的数据传输速率较快。这给那些积极传输数据的节点带来了不必要的能耗负担。从概念上讲，一个有效的集群形成阶段可以支持更好的数据路由机制，同时维持单个节点的能效。本文提出了一种基于神经模糊的簇形成（NFCF）方案，以促进自适应和高能效的簇拓扑。NFCF 利用模糊逻辑和神经网络为灵活的簇形成识别最佳超级节点。这种方法可根据能量阈值配置簇大小以及成员节点的包含/排除标准。节点选择的评估参数包括超级节点的度数、每个簇的预期能量、能量方差和剩余能量。不符合阈值的节点将被排除在外。神经网络根据不同条件下的预期能量动态更新模糊规则，以指导最佳聚类决策。根据与数据传输相关的目标函数变化、单个节点的能量变化、传输前后的能量变化以及跨传输周期的平均端到端延迟，对所提出的 NFCF 方案的性能进行了评估。结果与遗传模糊聚类、模糊能量感知聚类、基于模糊的分布式聚类、基于模糊逻辑的多跳聚类和模糊加权 K 均值聚类进行了比较。
{"title":"Neuro‐fuzzy‐based cluster formation scheme for energy‐efficient data routing in IOT‐enabled WSN","authors":"Sakthi Shunmuga Sundaram Paulraj, Vijayan Kannabiran","doi":"10.1002/dac.5984","DOIUrl":"https://doi.org/10.1002/dac.5984","url":null,"abstract":"SummaryInternet of things–enabled wireless sensor networks face challenges like inflexibility, poor scalability, suboptimal cluster head selection, and energy inefficiencies. This is due to the faster data transmission rates between cluster nodes during data packet routing. This creates unnecessary energy consumption burdens for those actively transmitting nodes. Conceptually, an effective cluster formation phase supports better data routing mechanisms, while sustaining the energy efficiency of individual nodes. This paper proposes a Neuro‐Fuzzy based Cluster Formation (NFCF) scheme to facilitate adaptive and energy‐efficient cluster topologies. NFCF utilizes fuzzy logic and neural networks to identify optimal super nodes for flexible cluster formations. This approach enables configurable cluster sizes along with inclusion/exclusion criteria for member nodes based on energy thresholds. Parameters evaluated for node selection include the degree of super node, expected energy per cluster, energy variance, and residual energy. Nodes not meeting the thresholds are excluded. The neural network updates fuzzy rules to guide optimal clustering decisions based on anticipated energy dynamics under different conditions. The performance of the proposed NFCF scheme is evaluated based on objective function changes related to data transmission, individual node energy variation, energy variance before and after transmissions, and averaged end‐to‐end delay across transmission cycles. Results are compared against genetic fuzzy clustering, fuzzy energy‐aware clustering, fuzzy‐based distributed clustering, fuzzy logic‐based multi‐hop clustering, and fuzzy weighted k‐means clustering.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"192 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196755","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 manuscript, a nature‐inspired optimization method, named transient search optimization (TSO), is proposed. Energy‐based monetary custom is a serious issue on the wireless sensor network (WSN). Here, the network clustering is an effectual technique to reduce node energy depletion and increased network lifetime. The proposed method aims to improve the efficiency of sensor nodes (SNs) by reducing their detachment, minimizing energy transmission, and protecting excessive energy stored in the nodes. This approach helps decrease delays, reduce traffic flow, and optimize network performance. The execution is implemented on the NS2 software. The experimental outcomes exhibit that the proposed system performs better based on two wireless sensor architectures, such as 50 nodes and 100 nodes. The parameter produces 52.24%, 54.38%, and 56.37% better network lifetime; 44.71%, 46.24%, and 49.45% higher alive node; and 39.26%, 36.26%, and 28.65% lesser dead SNs compared with existing techniques like multi‐objective cluster head (CH)–based energy‐aware optimized routing approach in wireless sensor network (MOCH‐ORR‐WSN), energy effective CH selection with improved sparrow search algorithm in WSN (ECH‐ISS‐WSN), and energy effectual cluster basis routing protocol under butterfly optimization along ant colony optimization algorithms for WSN (EEC‐BOA‐ACO‐WSN).
{"title":"An efficient cluster head selection in WSNs using transient search optimization (TSO) algorithm","authors":"Sumithra Subramanian, Dhurgadevi Muthusamy, Gunasekaran Kulandaivelu, Karpaga Selvi Subramanian","doi":"10.1002/dac.5970","DOIUrl":"https://doi.org/10.1002/dac.5970","url":null,"abstract":"SummaryIn this manuscript, a nature‐inspired optimization method, named transient search optimization (TSO), is proposed. Energy‐based monetary custom is a serious issue on the wireless sensor network (WSN). Here, the network clustering is an effectual technique to reduce node energy depletion and increased network lifetime. The proposed method aims to improve the efficiency of sensor nodes (SNs) by reducing their detachment, minimizing energy transmission, and protecting excessive energy stored in the nodes. This approach helps decrease delays, reduce traffic flow, and optimize network performance. The execution is implemented on the NS2 software. The experimental outcomes exhibit that the proposed system performs better based on two wireless sensor architectures, such as 50 nodes and 100 nodes. The parameter produces 52.24%, 54.38%, and 56.37% better network lifetime; 44.71%, 46.24%, and 49.45% higher alive node; and 39.26%, 36.26%, and 28.65% lesser dead SNs compared with existing techniques like multi‐objective cluster head (CH)–based energy‐aware optimized routing approach in wireless sensor network (MOCH‐ORR‐WSN), energy effective CH selection with improved sparrow search algorithm in WSN (ECH‐ISS‐WSN), and energy effectual cluster basis routing protocol under butterfly optimization along ant colony optimization algorithms for WSN (EEC‐BOA‐ACO‐WSN).","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"25 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196761","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}
Md. Zikrul Bari Chowdhury, Mohammad Tariqul Islam, Ismail Hossain, Md Samsuzzaman
SummaryThis study presents the design, fabrication, and measurement of a novel MIMO antenna for 28 GHz 5G applications. The design includes two compact antennas with dimensions of 12.8 × 12.8 × 1.6 mm3, placed side by side in a symmetrical arrangement. The antenna being considered operates within the 28 GHz frequency range and has excellent characteristics in terms of reflection coefficient, isolation, and impedance matching. The measurements conducted encompassed both single and MIMO setups and focused on important parameters such the reflection coefficient, transmission coefficient, gain, and efficiency. The MIMO antenna exhibited a reflection coefficient of −62.52 dB at a frequency of 27.93 GHz, while the transmission coefficient was found to be −37.23 dB. The antenna attained a gain of 6.02 dB relative to an isotropic radiator (dBi) and exhibited a maximum efficiency of 90.73%, encompassing a bandwidth of 4.45 MHz (MHz). The simulated envelope correlation coefficient (ECC) was found to be less than 0.003, indicating a very low error rate. Additionally, the antenna attained a diversity gain of 9.998 dB. The suggested MIMO antenna is very suitable for 5G applications operating at 28 GHz.
{"title":"A compact 6‐shaped high isolation MIMO antenna for 28 GHz 5G applications","authors":"Md. Zikrul Bari Chowdhury, Mohammad Tariqul Islam, Ismail Hossain, Md Samsuzzaman","doi":"10.1002/dac.5991","DOIUrl":"https://doi.org/10.1002/dac.5991","url":null,"abstract":"SummaryThis study presents the design, fabrication, and measurement of a novel MIMO antenna for 28 GHz 5G applications. The design includes two compact antennas with dimensions of 12.8 × 12.8 × 1.6 mm<jats:sup>3</jats:sup>, placed side by side in a symmetrical arrangement. The antenna being considered operates within the 28 GHz frequency range and has excellent characteristics in terms of reflection coefficient, isolation, and impedance matching. The measurements conducted encompassed both single and MIMO setups and focused on important parameters such the reflection coefficient, transmission coefficient, gain, and efficiency. The MIMO antenna exhibited a reflection coefficient of −62.52 dB at a frequency of 27.93 GHz, while the transmission coefficient was found to be −37.23 dB. The antenna attained a gain of 6.02 dB relative to an isotropic radiator (dBi) and exhibited a maximum efficiency of 90.73%, encompassing a bandwidth of 4.45 MHz (MHz). The simulated envelope correlation coefficient (ECC) was found to be less than 0.003, indicating a very low error rate. Additionally, the antenna attained a diversity gain of 9.998 dB. The suggested MIMO antenna is very suitable for 5G applications operating at 28 GHz.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196757","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}
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