International Journal of Communication Systems最新文献

{"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}

{"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}

{"title":"Collision detection and mitigation based on optimization and Kronecker recurrent neural network in WSN","authors":"Akhil Khare,&nbsp;Kannapiran Selvakumar,&nbsp;Raman Dugyala","doi":"10.1002/dac.5977","DOIUrl":"10.1002/dac.5977","url":null,"abstract":"<div>\u0000 \u0000 <p>Nowadays, 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.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-11","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}

{"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}

{"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}

{"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}

{"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}

{"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":"63 1","pages":""},"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}

{"title":"Comparative assessment and performance analysis of interference mitigation techniques for co-existent non-geostationary and geostationary satellites","authors":"Faik Öztürk,&nbsp;Elif Aydın,&nbsp;Ali Kara","doi":"10.1002/dac.5956","DOIUrl":"10.1002/dac.5956","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, technological developments with user demands, reduced production, and launch costs have rapidly increased the number of Low Earth Orbit (LEO) satellites in space. Since LEO satellites use the same frequency band as existing Geostationary Earth Orbit (GEO) satellites, the interference coordination between the two satellite networks is vital. In order to minimize the co-existent interference between these satellite networks, studies perform on different interference mitigation strategies. In this paper, analysis and comparative assessment of these interference mitigation techniques are presented for the co-existent Non-Geostationary Earth (NGEO) and GEO systems. More specifically, power control (PC) and spatial isolation-based link adaptation (SILA) techniques are studied comparatively for the performance evaluation. It is shown that the communication link bandwidth is more efficiently utilized in the SILA technique when compared with the PC technique. Moreover, the multi-objective optimization problem (MOP) approach in the SILA technique is demonstrated to be more effective when compared with the single-objective optimization problem (SOP) approach used in the PC technique as the simultaneous prioritizing objective functions outperforms single prioritization. Finally, it is shown that when the PC technique is applied together with the SILA technique, the exclusive angle (EA) can be reduced up to 8% for 100 Mbps, and 8.5% for 200 Mbps transmission bit rates in different operational scenarios. The presented performance evaluation in this paper may help the satellite operator or decision-maker gain insights on which mitigation technique can be used in the case of a co-existent interference.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"37 18","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196756","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}

{"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":"48 1","pages":""},"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}