Pub Date : 2024-03-18DOI: 10.2174/0122103279292479240226111739
Lakshmi Prasad Mudarakola, Vamshi Krishna B, Swati Dhondiram Jadhav, G. S. Sekhar, Swati Sharma, Saptarshi Mukherjee, Pundru Chandra Shaker Reddy
Increased traffic volume is a major challenge for effective network management in the wake of the proliferation of mobile computing and the Internet of Things (IoT). Earlier models surrender efficiency to achieve high-precision classification outcomes, which are no longer fitting for limited assets in edge network circumstances, making traffic classification a difficult task for network administrators everywhere. Given the nature of the problem, the current state of the art in traffic classification is characterized by extremely high computational complexity and large parameters. Increased traffic volume is a major challenge for effective network management in the wake of the proliferation of mobile computing and the Internet-of-Things(IoT). Earlier models surrender efficiency to achieve high-precision classification outcomes, which are no longer fitting for limited assets in an edge network circumstances, making traffic classification a difficult task for network administrators everywhere. Given the nature of the problem, the current state of the art in traffic classification is characterized by extremely high computational complexity and large parameters. To strike a clever balance between performance and size, we present a deep learning (DL)-based traffic classification model. We begin by decreasing the amount of model parameters and calculations by modifying the model's scale, width, and resolution. To further improve the capability of feature extraction at the traffic flow level, we secondly incorporate accurate geographical information on the attention mechanism. Thirdly, we get multiscale flow-level features by employing lightweight multiscale feature fusion. To strike a clever balance between performance and size, we present a deep learning (DL)-based traffic classification model. We begin by decreasing the amount of model parameters and calculations by modifying the model's scale, width, and resolution. To further improve the capability of feature extraction at the traffic flow level, we secondly incorporate accurate geographical information on the attention mechanism. Thirdly, we get multiscale flow-level features by employing lightweight multiscale feature fusion. The results of our experiments demonstrate that our model has high classification accuracy and efficient operation. Our study presents a traffic categorization model with an accuracy of over 99.82%, a parameter reduction of 0.26M, and a computation reduction of 5.26M. Therefore, this work offers a practical design used in a genuine IoT situation, where IoT traffic and tools' profiles are anticipated and classified while easing the data dispensation in the higher levels of an end-to-end communication strategy.
{"title":"A Deep Learning Framework for IoT Lightweight Traffic Multi-Classification: Smart-Cities","authors":"Lakshmi Prasad Mudarakola, Vamshi Krishna B, Swati Dhondiram Jadhav, G. S. Sekhar, Swati Sharma, Saptarshi Mukherjee, Pundru Chandra Shaker Reddy","doi":"10.2174/0122103279292479240226111739","DOIUrl":"https://doi.org/10.2174/0122103279292479240226111739","url":null,"abstract":"\u0000\u0000Increased traffic volume is a major challenge for effective network\u0000management in the wake of the proliferation of mobile computing and the Internet of Things\u0000(IoT). Earlier models surrender efficiency to achieve high-precision classification outcomes, which\u0000are no longer fitting for limited assets in edge network circumstances, making traffic classification\u0000a difficult task for network administrators everywhere. Given the nature of the problem, the current\u0000state of the art in traffic classification is characterized by extremely high computational complexity\u0000and large parameters.\u0000\u0000\u0000\u0000Increased traffic volume is a major challenge for effective network management in the wake of the proliferation of mobile computing and the Internet-of-Things(IoT). Earlier models surrender efficiency to achieve high-precision classification outcomes, which are no longer fitting for limited assets in an edge network circumstances, making traffic classification a difficult task for network administrators everywhere. Given the nature of the problem, the current state of the art in traffic classification is characterized by extremely high computational complexity and large parameters.\u0000\u0000\u0000\u0000To strike a clever balance between performance and size, we present a deep learning\u0000(DL)-based traffic classification model. We begin by decreasing the amount of model parameters\u0000and calculations by modifying the model's scale, width, and resolution. To further improve the capability\u0000of feature extraction at the traffic flow level, we secondly incorporate accurate geographical\u0000information on the attention mechanism. Thirdly, we get multiscale flow-level features by employing\u0000lightweight multiscale feature fusion.\u0000\u0000\u0000\u0000To strike a clever balance between performance and size, we present a deep learning (DL)-based traffic classification model. We begin by decreasing the amount of model parameters and calculations by modifying the model's scale, width, and resolution. To further improve the capability of feature extraction at the traffic flow level, we secondly incorporate accurate geographical information on the attention mechanism. Thirdly, we get multiscale flow-level features by employing lightweight multiscale feature fusion.\u0000\u0000\u0000\u0000The results of our experiments demonstrate that our model has high classification accuracy\u0000and efficient operation. Our study presents a traffic categorization model with an accuracy of over\u000099.82%, a parameter reduction of 0.26M, and a computation reduction of 5.26M.\u0000\u0000\u0000\u0000Therefore, this work offers a practical design used in a genuine IoT situation, where\u0000IoT traffic and tools' profiles are anticipated and classified while easing the data dispensation in the\u0000higher levels of an end-to-end communication strategy.\u0000","PeriodicalId":508758,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"50 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140234221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.2174/0122103279291431240216061325
Hussein Jdeed, Wissam Altabban, Samer Jamal
Spectrum scarcity, spectrum efficiency, power constraints, and jamming attacks are core challenges that face wireless networks. While cognitive radio networks (CRNs) enable the sharing of licensed bands when they are unoccupied, the spectrum should be used efficiently by the SU to ensure a high data rate transmission. In addition, the mobility of the secondary users (SUs) makes power consumption a matter of concern in wireless networks. Because of the open environment, the jamming attack can easily deteriorate the performance and disrupt the connections. Various anti-jamming schemes have been proposed to mitigate the attacker's impact on Cognitive Radio Networks (CRNs), some of the proposed schemes aim to increase channel capacity or improve spectrum-efficient gain. However, few of them have considered the secondary user's (SU’s) power consumption. We aim to enhance the performance of CRN and establish more reliable connections for the SU in the presence of smart jammer by ensuring efficient spectrum utilization and extending the network lifetime. To achieve our objectives, we propose an anti-jamming approach that adopts frequency hopping. Our approach assumes that SUs observe spectrum availability and channel gain. Then, SU learns the jammer behaviour and goes for the appropriate policy in terms of the number of data and control channels that optimize jointly spectrum efficiency and power consumption. Within, the interaction between the SU and the jammer is modelled as a zero-sum stochastic game, and we employ reinforcement learning to address this game. SUs learn the optimal policy that maximizes the spectrum efficiency and minimizes the power consumption in the presence of a smart jammer. Simulation results show that the low channel gain leads the SU to select a high number of data channels. However, when the channel gain is high, the SU increases the number of control channels to guarantee a more reliable connection. Taking into account the spectrum efficiency, SUs save their energy by decreasing the number of used channels. The proposed strategy achieves better performance in comparison with myopic learning and the random strategy. Under a jamming attack, considering the gain of utilized channels, SUs select the appropriate number of control and data channels to ensure a reliable, efficient, and long-term connection.
频谱稀缺、频谱效率、功率限制和干扰攻击是无线网络面临的核心挑战。虽然认知无线电网络（CRN）可以在空闲时共享许可频段，但二级用户（SU）应有效使用频谱，以确保高数据传输速率。此外，次级用户（SU）的移动性也使功耗成为无线网络中的一个令人担忧的问题。为了减轻攻击者对认知无线电网络（CRN）的影响，人们提出了各种抗干扰方案，其中一些方案旨在增加信道容量或提高频谱效率增益。我们的目标是提高 CRN 的性能，并通过确保有效利用频谱和延长网络寿命，在智能干扰器存在的情况下为 SU 建立更可靠的连接。我们的方法假定 SU 观察到频谱可用性和信道增益。然后，SU 会了解干扰者的行为，并根据数据和控制信道的数量选择适当的策略，从而共同优化频谱效率和功耗。在这一博弈中，SU 与干扰者之间的互动被模拟为零和随机博弈，我们采用强化学习来解决这一博弈。仿真结果表明，低信道增益会导致 SU 选择大量数据信道。然而，当信道增益较高时，SU 会增加控制信道的数量，以保证更可靠的连接。考虑到频谱效率，SU 会通过减少使用信道的数量来节省能量。在干扰攻击下，考虑到已用信道的增益，SU 选择适当数量的控制信道和数据信道，以确保可靠、高效和长期的连接。
{"title":"Spectrum and Power Efficient Anti-Jamming Approach for Cognitive Radio Networks Based on Reinforcement Learning","authors":"Hussein Jdeed, Wissam Altabban, Samer Jamal","doi":"10.2174/0122103279291431240216061325","DOIUrl":"https://doi.org/10.2174/0122103279291431240216061325","url":null,"abstract":"\u0000\u0000Spectrum scarcity, spectrum efficiency, power constraints, and jamming\u0000attacks are core challenges that face wireless networks. While cognitive radio networks (CRNs)\u0000enable the sharing of licensed bands when they are unoccupied, the spectrum should be used efficiently\u0000by the SU to ensure a high data rate transmission. In addition, the mobility of the secondary\u0000users (SUs) makes power consumption a matter of concern in wireless networks. Because of the\u0000open environment, the jamming attack can easily deteriorate the performance and disrupt the connections.\u0000\u0000\u0000\u0000Various anti-jamming schemes have been proposed to mitigate the attacker's impact on Cognitive Radio Networks (CRNs), some of the proposed schemes aim to increase channel capacity or improve spectrum-efficient gain. However, few of them have considered the secondary user's (SU’s) power consumption.\u0000\u0000\u0000\u0000We aim to enhance the performance of CRN and establish more reliable connections\u0000for the SU in the presence of smart jammer by ensuring efficient spectrum utilization and extending\u0000the network lifetime.\u0000\u0000\u0000\u0000To achieve our objectives, we propose an anti-jamming approach that adopts frequency\u0000hopping. Our approach assumes that SUs observe spectrum availability and channel gain. Then, SU\u0000learns the jammer behaviour and goes for the appropriate policy in terms of the number of data and\u0000control channels that optimize jointly spectrum efficiency and power consumption. Within, the interaction\u0000between the SU and the jammer is modelled as a zero-sum stochastic game, and we employ\u0000reinforcement learning to address this game.\u0000\u0000\u0000\u0000SUs learn the optimal policy that maximizes the spectrum efficiency and minimizes the\u0000power consumption in the presence of a smart jammer. Simulation results show that the low channel\u0000gain leads the SU to select a high number of data channels. However, when the channel gain is\u0000high, the SU increases the number of control channels to guarantee a more reliable connection.\u0000Taking into account the spectrum efficiency, SUs save their energy by decreasing the number of\u0000used channels. The proposed strategy achieves better performance in comparison with myopic\u0000learning and the random strategy.\u0000\u0000\u0000\u0000Under a jamming attack, considering the gain of utilized channels, SUs select the appropriate\u0000number of control and data channels to ensure a reliable, efficient, and long-term connection.\u0000","PeriodicalId":508758,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"76 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140282438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-02DOI: 10.2174/0122103279288496240121074942
Seda Kirtay, Kazim Yildiz, Veysel Gokhan Bocekci
Non-Orthogonal Multiple Access (NOMA) is an innovation that has great potential in wireless communication. It permits multiple users to efficiently allot a frequency band by adjusting their power allocations. Nevertheless, attaining fair power allocation in NOMA structures presents complex challenges that require specific models, extensive training data, and addressing issues of generalization. This review aims to explore the applications of Artificial Intelligence (AI) and Deep Learning (DL) methods to tackle the challenges associated with fair power allocation in NOMA systems. The focus is on developing strong AI-DL models and creative optimization methods specifically designed for dynamic environments to improve transparency and interpretability. This study explores a wide range of techniques, including Reinforcement Learning, Convolutional Neural Networks (CNN) for power allocation, Generative Adversarial Networks, Deep Reinforcement Learning, and Transfer Learning. The goal is to enhance various aspects, such as power allocation, user coupling, scheduling strategies, interference cancellation, user mobility, security, and deeplearning-based NOMA. Despite the difficulties, impartial power allocation algorithms based on AI and DL show promise in improving user performance and promoting fair power distribution in NOMA systems. This study emphasizes the significance of continuous research efforts to overcome current obstacles, enhance efficiency, and strengthen the dependability of wireless communication systems. This highlights the significance of NOMA as an advanced innovation for upcoming wireless generations that go beyond 5G. Future areas of study involve investigating federated learning and novel techniques for gathering data and utilizing interpretable AI-DL models to address existing constraints. Overall, this review highlights the potential of AI and DL techniques in achieving fair power distribution in NOMA systems. However, further investigation is crucial to addressing obstacles and fully exploring the capabilities of NOMA technology
{"title":"Artificial Intelligence-Based Fair Allocation in NOMA Technique: A\u0000Review","authors":"Seda Kirtay, Kazim Yildiz, Veysel Gokhan Bocekci","doi":"10.2174/0122103279288496240121074942","DOIUrl":"https://doi.org/10.2174/0122103279288496240121074942","url":null,"abstract":"\u0000\u0000Non-Orthogonal Multiple Access (NOMA) is an innovation that has great potential in\u0000wireless communication. It permits multiple users to efficiently allot a frequency band by adjusting\u0000their power allocations. Nevertheless, attaining fair power allocation in NOMA structures presents\u0000complex challenges that require specific models, extensive training data, and addressing issues of\u0000generalization. This review aims to explore the applications of Artificial Intelligence (AI) and Deep\u0000Learning (DL) methods to tackle the challenges associated with fair power allocation in NOMA\u0000systems. The focus is on developing strong AI-DL models and creative optimization methods specifically designed for dynamic environments to improve transparency and interpretability. This\u0000study explores a wide range of techniques, including Reinforcement Learning, Convolutional Neural Networks (CNN) for power allocation, Generative Adversarial Networks, Deep Reinforcement\u0000Learning, and Transfer Learning. The goal is to enhance various aspects, such as power allocation,\u0000user coupling, scheduling strategies, interference cancellation, user mobility, security, and deeplearning-based NOMA. Despite the difficulties, impartial power allocation algorithms based on AI\u0000and DL show promise in improving user performance and promoting fair power distribution in\u0000NOMA systems. This study emphasizes the significance of continuous research efforts to overcome\u0000current obstacles, enhance efficiency, and strengthen the dependability of wireless communication\u0000systems. This highlights the significance of NOMA as an advanced innovation for upcoming wireless generations that go beyond 5G. Future areas of study involve investigating federated learning\u0000and novel techniques for gathering data and utilizing interpretable AI-DL models to address existing constraints. Overall, this review highlights the potential of AI and DL techniques in achieving\u0000fair power distribution in NOMA systems. However, further investigation is crucial to addressing\u0000obstacles and fully exploring the capabilities of NOMA technology\u0000","PeriodicalId":508758,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"20 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139869237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-02DOI: 10.2174/0122103279288496240121074942
Seda Kirtay, Kazim Yildiz, Veysel Gokhan Bocekci
Non-Orthogonal Multiple Access (NOMA) is an innovation that has great potential in wireless communication. It permits multiple users to efficiently allot a frequency band by adjusting their power allocations. Nevertheless, attaining fair power allocation in NOMA structures presents complex challenges that require specific models, extensive training data, and addressing issues of generalization. This review aims to explore the applications of Artificial Intelligence (AI) and Deep Learning (DL) methods to tackle the challenges associated with fair power allocation in NOMA systems. The focus is on developing strong AI-DL models and creative optimization methods specifically designed for dynamic environments to improve transparency and interpretability. This study explores a wide range of techniques, including Reinforcement Learning, Convolutional Neural Networks (CNN) for power allocation, Generative Adversarial Networks, Deep Reinforcement Learning, and Transfer Learning. The goal is to enhance various aspects, such as power allocation, user coupling, scheduling strategies, interference cancellation, user mobility, security, and deeplearning-based NOMA. Despite the difficulties, impartial power allocation algorithms based on AI and DL show promise in improving user performance and promoting fair power distribution in NOMA systems. This study emphasizes the significance of continuous research efforts to overcome current obstacles, enhance efficiency, and strengthen the dependability of wireless communication systems. This highlights the significance of NOMA as an advanced innovation for upcoming wireless generations that go beyond 5G. Future areas of study involve investigating federated learning and novel techniques for gathering data and utilizing interpretable AI-DL models to address existing constraints. Overall, this review highlights the potential of AI and DL techniques in achieving fair power distribution in NOMA systems. However, further investigation is crucial to addressing obstacles and fully exploring the capabilities of NOMA technology
{"title":"Artificial Intelligence-Based Fair Allocation in NOMA Technique: A\u0000Review","authors":"Seda Kirtay, Kazim Yildiz, Veysel Gokhan Bocekci","doi":"10.2174/0122103279288496240121074942","DOIUrl":"https://doi.org/10.2174/0122103279288496240121074942","url":null,"abstract":"\u0000\u0000Non-Orthogonal Multiple Access (NOMA) is an innovation that has great potential in\u0000wireless communication. It permits multiple users to efficiently allot a frequency band by adjusting\u0000their power allocations. Nevertheless, attaining fair power allocation in NOMA structures presents\u0000complex challenges that require specific models, extensive training data, and addressing issues of\u0000generalization. This review aims to explore the applications of Artificial Intelligence (AI) and Deep\u0000Learning (DL) methods to tackle the challenges associated with fair power allocation in NOMA\u0000systems. The focus is on developing strong AI-DL models and creative optimization methods specifically designed for dynamic environments to improve transparency and interpretability. This\u0000study explores a wide range of techniques, including Reinforcement Learning, Convolutional Neural Networks (CNN) for power allocation, Generative Adversarial Networks, Deep Reinforcement\u0000Learning, and Transfer Learning. The goal is to enhance various aspects, such as power allocation,\u0000user coupling, scheduling strategies, interference cancellation, user mobility, security, and deeplearning-based NOMA. Despite the difficulties, impartial power allocation algorithms based on AI\u0000and DL show promise in improving user performance and promoting fair power distribution in\u0000NOMA systems. This study emphasizes the significance of continuous research efforts to overcome\u0000current obstacles, enhance efficiency, and strengthen the dependability of wireless communication\u0000systems. This highlights the significance of NOMA as an advanced innovation for upcoming wireless generations that go beyond 5G. Future areas of study involve investigating federated learning\u0000and novel techniques for gathering data and utilizing interpretable AI-DL models to address existing constraints. Overall, this review highlights the potential of AI and DL techniques in achieving\u0000fair power distribution in NOMA systems. However, further investigation is crucial to addressing\u0000obstacles and fully exploring the capabilities of NOMA technology\u0000","PeriodicalId":508758,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"275 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139809415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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