Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528534
B. S. Rikan, DaeYoung Choi, Reza E. Rad, Arash Hejazi, Younggun Pu, Kangyoon Lee
This paper presents a 12-bit Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) designed for a Bluetooth Low Energy (BLE) application. The objective of this work is to reduce the number of capacitors in the Capacitor Digital to Analog Converter (CDAC). To achieve this, a hybrid type DAC has been applied where 8 Most Significant Bits (MSB)s are decided through capacitive DAC and 4 Least Significant Bits (LSB)s are decided in a Resistor DAC (RDAC). The conversion speed for this design reaches up to 6 MS/s. The prototype ADC is designed in a 90 nm complementary metal-oxide semiconductor (CMOS) process. The analog and digital supply voltage range for this design are 2.7-5.5 V and 1.1-1.3 V respectively. For 6 MS/s conversion rate, this ADC achieves up to 11.8 and 11.2 effective number of bits (ENOBs), for maximum and minimum supply voltages respectively. The current consumption from a 5 V supply voltage is 980 µA and the Figure of Merit (FOM) is 229 fJ/Conv.step.
{"title":"12-Bit 5 MS/s SAR ADC with Hybrid Type DAC for BLE Applications","authors":"B. S. Rikan, DaeYoung Choi, Reza E. Rad, Arash Hejazi, Younggun Pu, Kangyoon Lee","doi":"10.1109/ICUFN49451.2021.9528534","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528534","url":null,"abstract":"This paper presents a 12-bit Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) designed for a Bluetooth Low Energy (BLE) application. The objective of this work is to reduce the number of capacitors in the Capacitor Digital to Analog Converter (CDAC). To achieve this, a hybrid type DAC has been applied where 8 Most Significant Bits (MSB)s are decided through capacitive DAC and 4 Least Significant Bits (LSB)s are decided in a Resistor DAC (RDAC). The conversion speed for this design reaches up to 6 MS/s. The prototype ADC is designed in a 90 nm complementary metal-oxide semiconductor (CMOS) process. The analog and digital supply voltage range for this design are 2.7-5.5 V and 1.1-1.3 V respectively. For 6 MS/s conversion rate, this ADC achieves up to 11.8 and 11.2 effective number of bits (ENOBs), for maximum and minimum supply voltages respectively. The current consumption from a 5 V supply voltage is 980 µA and the Figure of Merit (FOM) is 229 fJ/Conv.step.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124464478","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528550
Taegun An, H. Ryu, Changhee Joo
With recognition of quantum computer's enormous computational ability, it is of paramount importance to develop fault-tolerant quantum computing systems for their practical use. Recently, it has been shown that fault-tolerant systems can be achieved using a small set of basic quantum operations. This, however, incurs technical difficulties in finding an optimal sequence of basic operations toward a specific target computation and may limit possible quantum computations. In this work, we aim to achieve arbitrary target quantum computations under the restriction of four universal quantum gates of Pauli-X, -Y, -Z and SWAP. We develop two gate-sequence search methods based on the fidelity measure and deep neural networks. We verify the performance of our proposed methods through numerical results comparing total search space and the number of searched nodes.
{"title":"Sequencing Universal Quantum Gates for Arbitrary 2-Qubit Computations","authors":"Taegun An, H. Ryu, Changhee Joo","doi":"10.1109/ICUFN49451.2021.9528550","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528550","url":null,"abstract":"With recognition of quantum computer's enormous computational ability, it is of paramount importance to develop fault-tolerant quantum computing systems for their practical use. Recently, it has been shown that fault-tolerant systems can be achieved using a small set of basic quantum operations. This, however, incurs technical difficulties in finding an optimal sequence of basic operations toward a specific target computation and may limit possible quantum computations. In this work, we aim to achieve arbitrary target quantum computations under the restriction of four universal quantum gates of Pauli-X, -Y, -Z and SWAP. We develop two gate-sequence search methods based on the fidelity measure and deep neural networks. We verify the performance of our proposed methods through numerical results comparing total search space and the number of searched nodes.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121063825","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528768
A. V. Ong, Marnel S. Peradilla
The Internet of Things (IoT) connects a complex set of devices that perform data collection, processing, and environmental control in various applications. Due to the extent of potential monitoring and control capabilities, its infrastructure and data security is an essential design consideration. This presents unique challenges due to the heterogeneity of devices and dynamism involved. Context should thus be considered when applying suitable security measures without unnecessarily taxing the network. To do so, a four-layer framework that incorporates Software-defined Networking (SDN) and Network Function Virtualization (NFV) is proposed due to their flexibility in rapidly adjusting to network conditions to support context-aware security in IoT applications.
{"title":"An IoT Framework Based on SDN and NFV for Context-Aware Security","authors":"A. V. Ong, Marnel S. Peradilla","doi":"10.1109/ICUFN49451.2021.9528768","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528768","url":null,"abstract":"The Internet of Things (IoT) connects a complex set of devices that perform data collection, processing, and environmental control in various applications. Due to the extent of potential monitoring and control capabilities, its infrastructure and data security is an essential design consideration. This presents unique challenges due to the heterogeneity of devices and dynamism involved. Context should thus be considered when applying suitable security measures without unnecessarily taxing the network. To do so, a four-layer framework that incorporates Software-defined Networking (SDN) and Network Function Virtualization (NFV) is proposed due to their flexibility in rapidly adjusting to network conditions to support context-aware security in IoT applications.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125764856","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528627
Tariq Rahim, Arslan Musaddiq, Dong-Seong Kim
Recently, a lot of concentration is on how early diagnosis for critical diseases can be accommodated with deep learning (DL). e-health is an emerging area in the junction of medical informatics, public health, and business, indicating health assistance and data delivered or improved by the Internet and associated technologies. Resource management as bandwidth allocation problem is a key problem while transmitting processed medical data where both data integrity and quality are of utmost importance. To address the early intelligent detection and diagnosis of the diseases, an end-to-end DL model i.e., You Only Look Once (YOLOv3-tiny) is selected for the detection of the tumor within with wireless capsule endoscopy videos. The DL mode is an improved version of the YOLOv3-tiny wherein each convolutional layer, different convolutional filters, is employed to extract both local and global features. The motivation is early detection of the critical disease followed by remote physician diagnosis where resource management as a bandwidth allocation is investigated using encoders like H.265/HEVC and VP9. The proposed scheme controls the frame rate, video resolution, and compression ratio as quantization based on the intelligent decision from the DL model. The performance of the improved YOLOv3-tiny model is benchmarked with YOLOv3-tiny and our previous work in terms of precision, sensitivity, F1-score, and F2-score. Furthermore, the resource management results are shown in terms of bandwidth and storage for both encoders.
最近，很多人都在关注如何利用深度学习(DL)来进行危重疾病的早期诊断。电子卫生是医学信息学、公共卫生和商业结合的新兴领域，表明通过互联网和相关技术提供或改进的卫生援助和数据。在医疗数据传输过程中，资源管理作为带宽分配问题是一个关键问题，数据的完整性和质量至关重要。为了解决疾病的早期智能检测和诊断，选择端到端深度学习模型，即You Only Look Once (YOLOv3-tiny)，通过无线胶囊内窥镜视频检测内部肿瘤。DL模式是YOLOv3-tiny的改进版本，其中每个卷积层，不同的卷积滤波器，用于提取局部和全局特征。其动机是早期发现危重疾病，然后进行远程医生诊断，其中使用H.265/HEVC和VP9等编码器调查资源管理作为带宽分配。该方案基于深度学习模型的智能决策，对帧率、视频分辨率和压缩比进行量化控制。改进的YOLOv3-tiny模型在精度、灵敏度、F1-score和F2-score方面与YOLOv3-tiny模型和我们之前的工作进行了基准测试。此外，两种编码器的资源管理结果显示在带宽和存储方面。
{"title":"e-Health and Resource Management Scheme for a Deep Learning-based Detection of Tumor in Wireless Capsule Endoscopy Videos","authors":"Tariq Rahim, Arslan Musaddiq, Dong-Seong Kim","doi":"10.1109/ICUFN49451.2021.9528627","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528627","url":null,"abstract":"Recently, a lot of concentration is on how early diagnosis for critical diseases can be accommodated with deep learning (DL). e-health is an emerging area in the junction of medical informatics, public health, and business, indicating health assistance and data delivered or improved by the Internet and associated technologies. Resource management as bandwidth allocation problem is a key problem while transmitting processed medical data where both data integrity and quality are of utmost importance. To address the early intelligent detection and diagnosis of the diseases, an end-to-end DL model i.e., You Only Look Once (YOLOv3-tiny) is selected for the detection of the tumor within with wireless capsule endoscopy videos. The DL mode is an improved version of the YOLOv3-tiny wherein each convolutional layer, different convolutional filters, is employed to extract both local and global features. The motivation is early detection of the critical disease followed by remote physician diagnosis where resource management as a bandwidth allocation is investigated using encoders like H.265/HEVC and VP9. The proposed scheme controls the frame rate, video resolution, and compression ratio as quantization based on the intelligent decision from the DL model. The performance of the improved YOLOv3-tiny model is benchmarked with YOLOv3-tiny and our previous work in terms of precision, sensitivity, F1-score, and F2-score. Furthermore, the resource management results are shown in terms of bandwidth and storage for both encoders.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122371777","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528812
Dongkyu Lee, Seungmin Lee, Daejin Park
Recently, studies to analyze heart disease using ECG signals are emerging. The proposed platform generates multiple reference signals trained for individuals in real time by reducing the learning time. The data in the cluster is compressed by linear approximation to speed up diagnosis and reduce memory usage, allowing more diagnosis to be performed with limited resources. Platforms using FPGA can accelerate ECG signal diagnosis by adding hardware. As a result of diagnosing ECG signals of 10 people using the processor and accelerator, the execution time when using the accelerator was 71% lower than that when using the processor.
{"title":"FPGA-based Cloudification of ECG Signal Diagnosis Acceleration","authors":"Dongkyu Lee, Seungmin Lee, Daejin Park","doi":"10.1109/ICUFN49451.2021.9528812","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528812","url":null,"abstract":"Recently, studies to analyze heart disease using ECG signals are emerging. The proposed platform generates multiple reference signals trained for individuals in real time by reducing the learning time. The data in the cluster is compressed by linear approximation to speed up diagnosis and reduce memory usage, allowing more diagnosis to be performed with limited resources. Platforms using FPGA can accelerate ECG signal diagnosis by adding hardware. As a result of diagnosing ECG signals of 10 people using the processor and accelerator, the execution time when using the accelerator was 71% lower than that when using the processor.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129182266","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528399
Jinho Kang, Kangyoon Lee
In this paper, we design a DC-DC Boost Converter for driving transducers. To maximize power, we study the circuit techniques required for HV Pulser using digital feedback techniques. Boost DC-DC converters are expected to benefit in area and cost by implementing low-power, low-area circuits using digital feedback loop. This paper designed the required circuit using the 0.13um process. The designed Boost DC-DC converter converts 3.7V input to 12V output voltage. Provides up to 89% efficiency when supplying 100mA.
{"title":"Design of DC-DC Boost Converter With Digital Pulse Width Modulation for Transducer","authors":"Jinho Kang, Kangyoon Lee","doi":"10.1109/ICUFN49451.2021.9528399","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528399","url":null,"abstract":"In this paper, we design a DC-DC Boost Converter for driving transducers. To maximize power, we study the circuit techniques required for HV Pulser using digital feedback techniques. Boost DC-DC converters are expected to benefit in area and cost by implementing low-power, low-area circuits using digital feedback loop. This paper designed the required circuit using the 0.13um process. The designed Boost DC-DC converter converts 3.7V input to 12V output voltage. Provides up to 89% efficiency when supplying 100mA.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127317968","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528632
Yeun Jeong Park, Kangyoon Lee
GaN (Gallium nitride) semiconductors have more than 10 times the power density of Si-based Latterly Diffused Metal Oxide Semiconductor (LDMOS) transistors used in conventional Power Amplifiers, enabling more than 30% power savings and higher power density and efficiency. In this paper, we design a high-efficiency, high-power Class-D Power Amplifier with output higher than 40W by controlling the full-bridge structure composed of GaN elements using GaN drivers. The proposed Power Amplifier uses the Samsung 180nm process and designs 5 V as supply power.
{"title":"High-efficiency, High-power Class-D Power Amplifier with 50W Output Using GaN Devices","authors":"Yeun Jeong Park, Kangyoon Lee","doi":"10.1109/ICUFN49451.2021.9528632","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528632","url":null,"abstract":"GaN (Gallium nitride) semiconductors have more than 10 times the power density of Si-based Latterly Diffused Metal Oxide Semiconductor (LDMOS) transistors used in conventional Power Amplifiers, enabling more than 30% power savings and higher power density and efficiency. In this paper, we design a high-efficiency, high-power Class-D Power Amplifier with output higher than 40W by controlling the full-bridge structure composed of GaN elements using GaN drivers. The proposed Power Amplifier uses the Samsung 180nm process and designs 5 V as supply power.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132566623","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528786
Junbeom Kim, Hoon Lee, Seung‐Eun Hong, Seok-Hwan Park
This paper studies deep learning-based beamforming design schemes for multi-user downlink systems. Two distinct objectives are considered: sum-rate maximization and min-rate maximization. Each of formulations is first tackled by classical majorization-minimization (MM) algorithms that find a locally optimum point iteratively. To reduce computational overheads of the MM algorithms, deep neural networks (DNNs) are introduced which yield optimized beamforming solutions from channel vector inputs. Performance of trained DNNs is evaluated in terms of bit-error rate (BER) measure. Numerical results show that deep learning approaches achieve the BER performance very close to MM algorithms with much reduced complexity. Also, it is desirable to adopt the minimum-rate criterion to achieve low BER performance rather than sum-rate.
{"title":"Deep Learning-Assisted Beamforming Design and BER Evaluation in Multi-User Downlink Systems","authors":"Junbeom Kim, Hoon Lee, Seung‐Eun Hong, Seok-Hwan Park","doi":"10.1109/ICUFN49451.2021.9528786","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528786","url":null,"abstract":"This paper studies deep learning-based beamforming design schemes for multi-user downlink systems. Two distinct objectives are considered: sum-rate maximization and min-rate maximization. Each of formulations is first tackled by classical majorization-minimization (MM) algorithms that find a locally optimum point iteratively. To reduce computational overheads of the MM algorithms, deep neural networks (DNNs) are introduced which yield optimized beamforming solutions from channel vector inputs. Performance of trained DNNs is evaluated in terms of bit-error rate (BER) measure. Numerical results show that deep learning approaches achieve the BER performance very close to MM algorithms with much reduced complexity. Also, it is desirable to adopt the minimum-rate criterion to achieve low BER performance rather than sum-rate.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131673978","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528610
H. Kim, Kangyoon Lee
As the wireless network market has been grown, high-performance and efficient communication technology are demanded for devices. Specifically, reference clock signal forms an essential part of designing devices such as wearable one or the Internet of Things. The conventional structure of XOR is used to multiply the reference frequency. The structure of DLL illustrates that how frequency is extracted from application based on various values of desired supply voltage.
{"title":"Design of Frequency Multiplier with Delay Locked Loop that is insensitive to PVT Variation and prescreen Harmonic Lock","authors":"H. Kim, Kangyoon Lee","doi":"10.1109/ICUFN49451.2021.9528610","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528610","url":null,"abstract":"As the wireless network market has been grown, high-performance and efficient communication technology are demanded for devices. Specifically, reference clock signal forms an essential part of designing devices such as wearable one or the Internet of Things. The conventional structure of XOR is used to multiply the reference frequency. The structure of DLL illustrates that how frequency is extracted from application based on various values of desired supply voltage.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127908173","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528673
M. Hossain, Subina Khanal, E. Huh
Vehicular Edge Computing (VEC) is a new leading technology to enhance the vehicular performance through task offloading where resource-confined vehicles offload their computing task to the vehicular multi-access edge computing (MEC) networks in proximity. However, the environment of vehicular task offloading is extremely dynamic and faces some challenges to determine the location of processing the offloaded task. As a result, to achieve optimal performance by using traditional VEC system is difficult because in advance we don't know the demand of vehicles. Therefore, a non-cooperative game theory-based efficient task offloading (NGTO) scheme is proposed in this study where the offloading decisions are taken either the MEC server or remote cloud server through the game-theoretic approach. To reduce the processing latency of the vehicles' computation tasks and assure the maximum utility of each vehicle, we used a distributed best response offloading strategy. Our proposed strategy accommodates its offloading probability to achieve a unique equilibrium under certain conditions. Detailed performance evaluation affirms that our proposed NGTO scheme can outperform in all scenarios. It can minimize the response time at almost 41.2 % and average task failure rate at approximately 56.3% when compared with a local roadside unit computing (LRC) scheme. The reduced response time and task failure rates are approximately 25.2% and 20.4%, respectively, when compared with a collaborative (LRC with cloud via roadside unit) offloading scheme.
{"title":"Efficient Task Offloading for MEC-Enabled Vehicular Networks: A Non-Cooperative Game Theoretic Approach","authors":"M. Hossain, Subina Khanal, E. Huh","doi":"10.1109/ICUFN49451.2021.9528673","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528673","url":null,"abstract":"Vehicular Edge Computing (VEC) is a new leading technology to enhance the vehicular performance through task offloading where resource-confined vehicles offload their computing task to the vehicular multi-access edge computing (MEC) networks in proximity. However, the environment of vehicular task offloading is extremely dynamic and faces some challenges to determine the location of processing the offloaded task. As a result, to achieve optimal performance by using traditional VEC system is difficult because in advance we don't know the demand of vehicles. Therefore, a non-cooperative game theory-based efficient task offloading (NGTO) scheme is proposed in this study where the offloading decisions are taken either the MEC server or remote cloud server through the game-theoretic approach. To reduce the processing latency of the vehicles' computation tasks and assure the maximum utility of each vehicle, we used a distributed best response offloading strategy. Our proposed strategy accommodates its offloading probability to achieve a unique equilibrium under certain conditions. Detailed performance evaluation affirms that our proposed NGTO scheme can outperform in all scenarios. It can minimize the response time at almost 41.2 % and average task failure rate at approximately 56.3% when compared with a local roadside unit computing (LRC) scheme. The reduced response time and task failure rates are approximately 25.2% and 20.4%, respectively, when compared with a collaborative (LRC with cloud via roadside unit) offloading scheme.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114239687","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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