Pub Date : 2024-06-01DOI: 10.1016/j.icte.2023.12.008
Joonseo Ha, Heejun Roh
Recently, QUIC for the secure and faster connections has standardized but it is unclear that QUIC can cope with website fingerprinting (WF), a technique to infer visited websites from network traffic, since most existing efforts targeted TCP-induced traffic. To this end, we propose a novel QUIC WF technique based on Automated Machine Learning (AutoML). In our approach, we revisit traffic features appeared in literature, but relies on an AutoML framework to achieve best practice without manual intervention. Through experiments, we show that our technique outperforms state-of-the-art WF techniques with an F1-score of 99.79% and a 20-precision of 92.60%.
{"title":"QUIC website fingerprinting based on automated machine learning","authors":"Joonseo Ha, Heejun Roh","doi":"10.1016/j.icte.2023.12.008","DOIUrl":"https://doi.org/10.1016/j.icte.2023.12.008","url":null,"abstract":"<div><p>Recently, QUIC for the secure and faster connections has standardized but it is unclear that QUIC can cope with website fingerprinting (WF), a technique to infer visited websites from network traffic, since most existing efforts targeted TCP-induced traffic. To this end, we propose a novel QUIC WF technique based on Automated Machine Learning (AutoML). In our approach, we revisit traffic features appeared in literature, but relies on an AutoML framework to achieve best practice without manual intervention. Through experiments, we show that our technique outperforms state-of-the-art WF techniques with an F1-score of 99.79% and a 20-precision of 92.60%.</p></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405959523001662/pdfft?md5=167bdfd44dc869b16bc3198356f20e4e&pid=1-s2.0-S2405959523001662-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141439133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.icte.2023.12.001
Manh Cuong Ho , Anh Tien Tran , Donghyun Lee , Jeongyeup Paek , Wonjong Noh , Sungrae Cho
Federated learning (FL) has emerged as a promising distributed machine learning technique. It has the potential to play a key role in future Internet of Things (IoT) networks by ensuring the security and privacy of user data combined with efficient utilization of communication resources. This paper addresses the challenge of maximizing energy efficiency in FL systems. We employed simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) techniques. Also, we jointly optimized power allocation and central processing unit (CPU) resource allocation to minimize latency-constrained energy consumption. We formulated an optimization problem using a Markov decision process (MDP) and utilized a deep deterministic policy gradient (DDPG) reinforcement learning algorithm to solve our MDP problem. We tested the proposed algorithm through extensive simulations and confirmed it converges in a stable manner and provides enhanced energy efficiency compared to conventional schemes.
{"title":"A DDPG-based energy efficient federated learning algorithm with SWIPT and MC-NOMA","authors":"Manh Cuong Ho , Anh Tien Tran , Donghyun Lee , Jeongyeup Paek , Wonjong Noh , Sungrae Cho","doi":"10.1016/j.icte.2023.12.001","DOIUrl":"10.1016/j.icte.2023.12.001","url":null,"abstract":"<div><p>Federated learning (FL) has emerged as a promising distributed machine learning technique. It has the potential to play a key role in future Internet of Things (IoT) networks by ensuring the security and privacy of user data combined with efficient utilization of communication resources. This paper addresses the challenge of maximizing energy efficiency in FL systems. We employed simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) techniques. Also, we jointly optimized power allocation and central processing unit (CPU) resource allocation to minimize latency-constrained energy consumption. We formulated an optimization problem using a Markov decision process (MDP) and utilized a deep deterministic policy gradient (DDPG) reinforcement learning algorithm to solve our MDP problem. We tested the proposed algorithm through extensive simulations and confirmed it converges in a stable manner and provides enhanced energy efficiency compared to conventional schemes.</p></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405959523001534/pdfft?md5=b839734416c8d6f8c91205a34647aba5&pid=1-s2.0-S2405959523001534-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138620639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.icte.2024.01.003
Jongtaek Oh , Sunghoon Kim
Although estimating the azimuth using a geomagnetic sensor is very useful, the estimation error may be very large due to the surrounding geomagnetic disturbance. We proposed a novel method for preprocessing appropriately for geomagnetic and inertial sensor data to be suitable for the proposed Artificial Neural Network model and training method for the model. As a result, the probability of azimuth estimation error within 1 degree is 96.4% with regression estimation. For classification estimation, when the azimuth estimation probability is 90% or more, the probability that the azimuth estimation error is within 1 degree is 100%.
{"title":"Azimuth estimation based on CNN and LSTM for geomagnetic and inertial sensors data","authors":"Jongtaek Oh , Sunghoon Kim","doi":"10.1016/j.icte.2024.01.003","DOIUrl":"10.1016/j.icte.2024.01.003","url":null,"abstract":"<div><p>Although estimating the azimuth using a geomagnetic sensor is very useful, the estimation error may be very large due to the surrounding geomagnetic disturbance. We proposed a novel method for preprocessing appropriately for geomagnetic and inertial sensor data to be suitable for the proposed Artificial Neural Network model and training method for the model. As a result, the probability of azimuth estimation error within 1 degree is 96.4% with regression estimation. For classification estimation, when the azimuth estimation probability is 90% or more, the probability that the azimuth estimation error is within 1 degree is 100%.</p></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405959524000031/pdfft?md5=e1253fa6fcefd9e12cab4c7859badc1b&pid=1-s2.0-S2405959524000031-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139539471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.icte.2024.04.004
Daniar Estu Widiyanti, Krisma Asmoro, Soo Young Shin
Beyond 6G services and applications demand high and efficient processing capacity due to the massive connectivity of users equipment (UEs). However, the high computational capability and energy consumption of UEs are limited, which becomes a main challenge to overcome. Multi-access edge computing (MEC) has recently been studied widely as it can potentially assist complex tasks executed at UEs. Furthermore, several techniques have been proposed to optimize task offloading among users. Thus, another challenge in MEC is emerging due to the fact that mobile users do not always have a line-of-sight (LoS) to the base station (BS) due to the blocking object. Therefore, it can affect users data rate and result in incremental energy consumption. This research introduces the concept of reconfigurable intelligence surfaces (RIS) to support multiple-input-single-output (MISO) base stations (BS) in both uplink (UL) and downlink (DL) using BCD algorithms. While previous studies concentrate on enhancing task offloading and neglecting inter-user interference, this study suggests an optimization approach for UL and DL data rates, as well as minimizing task offloading delays. The results indicate that optimizing task placement, phase shift, and precoding can reduce the duration of task offloading.
{"title":"Joint optimization of phase shift and task offloading for RIS-assisted multi-access edge computing in beyond 6G communication","authors":"Daniar Estu Widiyanti, Krisma Asmoro, Soo Young Shin","doi":"10.1016/j.icte.2024.04.004","DOIUrl":"10.1016/j.icte.2024.04.004","url":null,"abstract":"<div><p>Beyond 6G services and applications demand high and efficient processing capacity due to the massive connectivity of users equipment (UEs). However, the high computational capability and energy consumption of UEs are limited, which becomes a main challenge to overcome. Multi-access edge computing (MEC) has recently been studied widely as it can potentially assist complex tasks executed at UEs. Furthermore, several techniques have been proposed to optimize task offloading among users. Thus, another challenge in MEC is emerging due to the fact that mobile users do not always have a line-of-sight (LoS) to the base station (BS) due to the blocking object. Therefore, it can affect users data rate and result in incremental energy consumption. This research introduces the concept of reconfigurable intelligence surfaces (RIS) to support multiple-input-single-output (MISO) base stations (BS) in both uplink (UL) and downlink (DL) using BCD algorithms. While previous studies concentrate on enhancing task offloading and neglecting inter-user interference, this study suggests an optimization approach for UL and DL data rates, as well as minimizing task offloading delays. The results indicate that optimizing task placement, phase shift, and precoding can reduce the duration of task offloading.</p></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405959524000419/pdfft?md5=3ad3f653a2f1f8aa864d3acf87e8b4c4&pid=1-s2.0-S2405959524000419-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140770447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The network of resource constraint devices, also known as the Low power and Lossy Networks (LLNs), constitutes the edge tire of the Internet of Things applications like smart homes, smart cities, and connected vehicles. The IPv6 Routing Protocol over Low power and lossy networks (RPL) ensures efficient routing in the edge tire of the IoT environment. However, RPL has inherent vulnerabilities that allow malicious insider entities to instigate several security attacks in the IoT network. As a result, the IoT networks suffer from resource depletion, performance degradation, and traffic disruption. Recent literature discusses several machine learning algorithms to detect one or more routing attacks. However, IoT infrastructures are expanding, and so are the attack surfaces. Therefore, it is essential to have a solution that can adapt to this change. This paper introduces a comprehensive framework to detect routing attacks within Low Power and Lossy Networks (LLNs). The proposed solution leverages deep learning by combining Restricted Boltzmann Machine (RBM) and Long Short-Term Memory (LSTM). The framework is trained on 11 network parameters to understand and predict normal network behavior. Anomalies, identified as deviations from the forecast trends, serve as indicators of potential routing attacks and thus address vulnerabilities in the RPL.
{"title":"Routing attack induced anomaly detection in IoT network using RBM-LSTM","authors":"Rashmi Sahay , Anand Nayyar , Rajesh Kumar Shrivastava , Muhammad Bilal , Simar Preet Singh , Sangheon Pack","doi":"10.1016/j.icte.2024.04.012","DOIUrl":"10.1016/j.icte.2024.04.012","url":null,"abstract":"<div><p>The network of resource constraint devices, also known as the Low power and Lossy Networks (LLNs), constitutes the edge tire of the Internet of Things applications like smart homes, smart cities, and connected vehicles. The IPv6 Routing Protocol over Low power and lossy networks (RPL) ensures efficient routing in the edge tire of the IoT environment. However, RPL has inherent vulnerabilities that allow malicious insider entities to instigate several security attacks in the IoT network. As a result, the IoT networks suffer from resource depletion, performance degradation, and traffic disruption. Recent literature discusses several machine learning algorithms to detect one or more routing attacks. However, IoT infrastructures are expanding, and so are the attack surfaces. Therefore, it is essential to have a solution that can adapt to this change. This paper introduces a comprehensive framework to detect routing attacks within Low Power and Lossy Networks (LLNs). The proposed solution leverages deep learning by combining Restricted Boltzmann Machine (RBM) and Long Short-Term Memory (LSTM). The framework is trained on 11 network parameters to understand and predict normal network behavior. Anomalies, identified as deviations from the forecast trends, serve as indicators of potential routing attacks and thus address vulnerabilities in the RPL.</p></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405959524000493/pdfft?md5=49d65ad955ce303fd98e4af529009f98&pid=1-s2.0-S2405959524000493-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141029786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.icte.2024.02.011
Najam Us Saqib , Shilun Song , Huiyang Xie , Zhenyu Cao , Gyeong-June Hahm , Kyung-Yul Cheon , Hyenyeon Kwon , Seungkeun Park , Sang-Woon Jeon , Hu Jin
Digital twin (DT) technologies have been increasingly important and useful for wireless communications. In particular, to support soaring wireless traffic with limited frequency spectrum assigned to legacy cellular systems, efficient operation and management of wireless resources as well as preemptively prediction of future spectrum usage are crucially important. For such purpose, DT networks for current fourth-generation (4G) and fifth-generation (5G) networks are constructed in this paper, by simultaneously utilizing measurement data from user equipments (UEs) and geographical information and characteristics of 4G and 5G base stations (BSs) within a specific observation area. Representative case studies are provided to demonstrate the usefulness of DT enabled cellular network management and prediction. As a real or near real time DT application, the impact and benefit of dual connectivity and dynamic spectrum sharing between 4G and 5G networks are analyzed. As a non-real time DT application, long-term improvement of 5G networks such as densification of BSs, implementation of advanced multiple input and multiple output technologies, and assignment of additional spectrum are analyzed and compared.
{"title":"Digital twin enabled cellular network management and prediction","authors":"Najam Us Saqib , Shilun Song , Huiyang Xie , Zhenyu Cao , Gyeong-June Hahm , Kyung-Yul Cheon , Hyenyeon Kwon , Seungkeun Park , Sang-Woon Jeon , Hu Jin","doi":"10.1016/j.icte.2024.02.011","DOIUrl":"https://doi.org/10.1016/j.icte.2024.02.011","url":null,"abstract":"<div><p>Digital twin (DT) technologies have been increasingly important and useful for wireless communications. In particular, to support soaring wireless traffic with limited frequency spectrum assigned to legacy cellular systems, efficient operation and management of wireless resources as well as preemptively prediction of future spectrum usage are crucially important. For such purpose, DT networks for current fourth-generation (4G) and fifth-generation (5G) networks are constructed in this paper, by simultaneously utilizing measurement data from user equipments (UEs) and geographical information and characteristics of 4G and 5G base stations (BSs) within a specific observation area. Representative case studies are provided to demonstrate the usefulness of DT enabled cellular network management and prediction. As a real or near real time DT application, the impact and benefit of dual connectivity and dynamic spectrum sharing between 4G and 5G networks are analyzed. As a non-real time DT application, long-term improvement of 5G networks such as densification of BSs, implementation of advanced multiple input and multiple output technologies, and assignment of additional spectrum are analyzed and compared.</p></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405959524000249/pdfft?md5=7dcbb6b72beaeccf0c7b92156f6e9327&pid=1-s2.0-S2405959524000249-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.icte.2024.03.002
Hyunwoo Cho , Jae Min Ahn , Jae Hee Noh , Hong-Yeop Song
In this paper, we design some new LDPC-coded orthogonal modulation (OM) schemes for high data rate transmissions (HDRT) in the navigation satellite systems. We analyze their error-performance utilizing soft-decision bit metrics and compare them with those of L61 and L62 signals in the quasi-zenith satellite system (QZSS) for centimeter-level augmentation services (CLAS). Compare to the L62 signals of QZSS, both schemes have higher data rates (14.6% increase) and essentially the better error performance at high SNR region. At the region where frame error rate (FER) , one of the proposed schemes has better error performance of 1.4 dB in terms of carrier-to-noise ratio .
{"title":"Some new LDPC-coded orthogonal modulation schemes for high data rate transmissions in navigation satellite systems","authors":"Hyunwoo Cho , Jae Min Ahn , Jae Hee Noh , Hong-Yeop Song","doi":"10.1016/j.icte.2024.03.002","DOIUrl":"https://doi.org/10.1016/j.icte.2024.03.002","url":null,"abstract":"<div><p>In this paper, we design some new LDPC-coded orthogonal modulation (OM) schemes for high data rate transmissions (HDRT) in the navigation satellite systems. We analyze their error-performance utilizing soft-decision bit metrics and compare them with those of L61 and L62 signals in the quasi-zenith satellite system (QZSS) for centimeter-level augmentation services (CLAS). Compare to the L62 signals of QZSS, both schemes have higher data rates (14.6% increase) and essentially the better error performance at high SNR region. At the region where frame error rate (FER) <span><math><mrow><mo>=</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span>, one of the proposed schemes has better error performance of 1.4 dB in terms of carrier-to-noise ratio <span><math><mrow><mo>(</mo><mi>C</mi><mo>/</mo><msub><mrow><mi>N</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>)</mo></mrow></math></span>.</p></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405959524000262/pdfft?md5=11c32e7a05dd74090a11d21c01013434&pid=1-s2.0-S2405959524000262-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141439132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we have investigated the performance of energy efficiency (EE) for Intelligent Transportation Systems (ITS), which recently emerged and advanced to preserve speed as well as safe transportation expansion via a cooperative IRS-relay network. To improve the EE, the relay model has been integrated with an IRS block consisting of a number of passive reflective elements. We analyze the ITS in terms of EE, and achievable rate, with different signal-to-noise ratio (SNR) values under Nakagami-m fading channel conditions that help the system to implement in a practical scenario. From the numerical results it is noticed that the EE for the only relay, IRS, and proposed cooperative relay-IRS-aided network at SNR value of 100 dBm is 30, 17, and 48 bits/joule respectively. In addition, we compare the impact of multi-IRS with the proposed cooperative IRS-relay and conventional relay-supported ITS. Simulation results show that both the proposed cooperative IRS-relay-aided ITS network and multi-IRS-aided network outperform the relay-assisted ITS with the increase in SNR.
在本文中,我们研究了智能交通系统(ITS)的能源效率(EE)性能,ITS 是最近出现和发展起来的,通过合作 IRS- 中继网络来保持速度和安全的交通扩展。为了提高能效,中继模型与由多个无源反射元件组成的 IRS 模块进行了整合。我们分析了在 Nakagami-m 消隐信道条件下,不同信噪比(SNR)值下 ITS 的 EE 和可实现速率,这有助于系统在实际场景中的实施。从数值结果可以看出,在信噪比为 100 dBm 时,唯一中继、IRS 和拟议的合作中继-IRS 辅助网络的 EE 分别为 30、17 和 48 比特/焦耳。此外,我们还比较了多中继系统与拟议的合作中继-IRS 和传统中继辅助 ITS 的影响。仿真结果表明,随着信噪比的增加,拟议的合作 IRS-中继辅助 ITS 网络和多IRS 辅助网络都优于中继辅助 ITS。
{"title":"A novel energy efficient IRS-relay network for ITS with Nakagami-m fading channels","authors":"Shaik Rajak , Inbarasan Muniraj , Poongundran Selvaprabhu , Vinoth Babu Kumaravelu , Md. Abdul Latif Sarker , Sunil Chinnadurai , Dong Seog Han","doi":"10.1016/j.icte.2023.11.005","DOIUrl":"10.1016/j.icte.2023.11.005","url":null,"abstract":"<div><p>In this paper, we have investigated the performance of energy efficiency (EE) for Intelligent Transportation Systems (ITS), which recently emerged and advanced to preserve speed as well as safe transportation expansion via a cooperative IRS-relay network. To improve the EE, the relay model has been integrated with an IRS block consisting of a number of passive reflective elements. We analyze the ITS in terms of EE, and achievable rate, with different signal-to-noise ratio (SNR) values under Nakagami-m fading channel conditions that help the system to implement in a practical scenario. From the numerical results it is noticed that the EE for the only relay, IRS, and proposed cooperative relay-IRS-aided network at SNR value of 100 dBm is 30, 17, and 48 bits/joule respectively. In addition, we compare the impact of multi-IRS with the proposed cooperative IRS-relay and conventional relay-supported ITS. Simulation results show that both the proposed cooperative IRS-relay-aided ITS network and multi-IRS-aided network outperform the relay-assisted ITS with the increase in SNR.</p></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405959523001480/pdfft?md5=0dab6f9a82eaa1ac09e78e02eb3a68ed&pid=1-s2.0-S2405959523001480-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139304121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.icte.2024.04.002
Sooyoung Jang, Hyung-Il Kim
Despite the growing interest in using deep reinforcement learning (DRL) for drone control, several challenges remain to be addressed, including issues with generalization across task variations and agent training (which requires significant computational power and time). When the agent’s input changes owing to the drone’s sensors or mission variations, significant retraining overhead is required to handle the changes in the input data pattern and the neural network architecture to accommodate the input data. These difficulties severely limit their applicability in dynamic real-world environments. In this paper, we propose an efficient DRL method that leverages the knowledge of the source agent to accelerate the training of the target agent under task variations. The proposed method consists of three phases: collecting training data for the target agent using the source agent, supervised pre-training of the target agent, and DRL-based fine-tuning. Experimental validation demonstrated a remarkable reduction in the training time (up to 94.29%), suggesting a potential avenue for the successful and efficient application of DRL in drone control.
{"title":"Efficient deep reinforcement learning under task variations via knowledge transfer for drone control","authors":"Sooyoung Jang, Hyung-Il Kim","doi":"10.1016/j.icte.2024.04.002","DOIUrl":"10.1016/j.icte.2024.04.002","url":null,"abstract":"<div><p>Despite the growing interest in using deep reinforcement learning (DRL) for drone control, several challenges remain to be addressed, including issues with generalization across task variations and agent training (which requires significant computational power and time). When the agent’s input changes owing to the drone’s sensors or mission variations, significant retraining overhead is required to handle the changes in the input data pattern and the neural network architecture to accommodate the input data. These difficulties severely limit their applicability in dynamic real-world environments. In this paper, we propose an efficient DRL method that leverages the knowledge of the source agent to accelerate the training of the target agent under task variations. The proposed method consists of three phases: collecting training data for the target agent using the source agent, supervised pre-training of the target agent, and DRL-based fine-tuning. Experimental validation demonstrated a remarkable reduction in the training time (up to 94.29%), suggesting a potential avenue for the successful and efficient application of DRL in drone control.</p></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S240595952400033X/pdfft?md5=7d370e1bd566b1fe70dbc9a76bf4c077&pid=1-s2.0-S240595952400033X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140765468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Computing-aware networking (CAN) is introduced to unite the computing resources distributed in different platforms. This paper proposes a joint routing and resource allocation model to minimize the total operational cost in CAN. We formulate the problem as an integer linear programming problem. We introduce a polynomial-time algorithm for larger-size problems. The numerical results reveal that the introduced algorithm reduces the computation time 9.18 times, with increasing the objective no more than 4% compared to the optimal solution; the proposed model can reduce 70% of the total cost compared to a baseline adopting a two-stage strategy in our examined cases.
计算感知网络(CAN)的引入是为了联合分布在不同平台上的计算资源。本文提出了一种联合路由和资源分配模型,以最小化 CAN 中的总运行成本。我们将该问题表述为一个整数线性规划问题。我们为较大的问题引入了一种多项式时间算法。数值结果表明,与最优解相比,引入的算法减少了 9.18 倍的计算时间,目标增加不超过 4%;在我们研究的案例中,与采用两阶段策略的基线相比,所提出的模型可减少 70% 的总成本。
{"title":"Optimal routing and heterogeneous resource allocation for computing-aware networks","authors":"Hongqing Ding , Fujun He , Pengfei Zhang , Liang Zhang , Xiaoxiao Zhang , Meiyu Qi","doi":"10.1016/j.icte.2024.01.004","DOIUrl":"10.1016/j.icte.2024.01.004","url":null,"abstract":"<div><p>Computing-aware networking (CAN) is introduced to unite the computing resources distributed in different platforms. This paper proposes a joint routing and resource allocation model to minimize the total operational cost in CAN. We formulate the problem as an integer linear programming problem. We introduce a polynomial-time algorithm for larger-size problems. The numerical results reveal that the introduced algorithm reduces the computation time 9.18 times, with increasing the objective no more than 4% compared to the optimal solution; the proposed model can reduce 70% of the total cost compared to a baseline adopting a two-stage strategy in our examined cases.</p></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405959524000043/pdfft?md5=7f67010b74f61693f4d4107c7f0f7b8b&pid=1-s2.0-S2405959524000043-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139537059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}