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Cloud Computing (CC) is a promising technology due to its pervasive features, such as online storage, high scalability, and seamless accessibility, in that it plays an important role in reduction of the capital cost and workforce, which attracts organizations to conduct their businesses and financial activities over the cloud. Even though CC is a great innovation in the aspect of computing with ease of access, it also has some drawbacks. With the increase of cloud usage, security issues are proportional to the increase. To address these, there has been much work done in this domain, whereas research work considering the growing constrained applications provided by the Internet of Things (IoT) and smart city networks are still lacking. In this survey, we provide a comprehensive security analysis of CC-enabled IoT and present state-of-the-art in the research area. Finally, future research work and possible areas of implementation and consideration are given to discuss open issues.
Abeer Tahirkheli; Muhammad Shiraz; Bashir Hayat; Muhammad Idrees; Ahthasham Sajid; Rahat Ullah; Nasir Ayub; Ki-Il Kim. A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges. Electronics 2021, 10, 1811 .
AMA StyleAbeer Tahirkheli, Muhammad Shiraz, Bashir Hayat, Muhammad Idrees, Ahthasham Sajid, Rahat Ullah, Nasir Ayub, Ki-Il Kim. A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges. Electronics. 2021; 10 (15):1811.
Chicago/Turabian StyleAbeer Tahirkheli; Muhammad Shiraz; Bashir Hayat; Muhammad Idrees; Ahthasham Sajid; Rahat Ullah; Nasir Ayub; Ki-Il Kim. 2021. "A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges." Electronics 10, no. 15: 1811.
Today, simulator technology has been widely used as an important part of quadrotor development such as validation and testing. A good quadrotor simulator can simulate the quadrotor system as closely as possible to the real one. Therefore, in case of multi-quadrotor simulator, the simulator should not only can simulate a multi-quadrotor system, but also every quadrotor should be able to leverage their own resources. To solve this issues, in this paper, we present a hypervisor-based multi-quadrotor simulator. We used RT-Xen as hypervisor, a real-time Xen hypervisor. To ensure every quadrotor runs in real-time manner, we implemented quadrotor simulator in Litmus-RT which is a real-time extension of Linux. In this paper, we conducted some testing and performance evaluation for particular cases on our multi-quadrotor simulator: step-input responses, computation time, and response times. Based on the performance evaluation, our hypervisor-based multi-quadrotor simulator environment is proven to meet the real-time requirements. The results show that three important tasks in quadrotor system: Stability Controllability Augmented System (SCAS), Equation of Motion (EOM), and waypoint following task, are finished before their deadlines; in fact, 20 ms, 10 ms, and 40 ms before the deadlines for SCAS, EOM, and waypoint following, respectively.
Muhammad Fathoni; Seonah Lee; Yoonsoo Kim; Ki-Il Kim; Kyong Kim. Development of Multi-Quadrotor Simulator Based on Real-Time Hypervisor Systems. Drones 2021, 5, 59 .
AMA StyleMuhammad Fathoni, Seonah Lee, Yoonsoo Kim, Ki-Il Kim, Kyong Kim. Development of Multi-Quadrotor Simulator Based on Real-Time Hypervisor Systems. Drones. 2021; 5 (3):59.
Chicago/Turabian StyleMuhammad Fathoni; Seonah Lee; Yoonsoo Kim; Ki-Il Kim; Kyong Kim. 2021. "Development of Multi-Quadrotor Simulator Based on Real-Time Hypervisor Systems." Drones 5, no. 3: 59.
Neural relation extraction (NRE) models are the backbone of various machine learning tasks, including knowledge base enrichment, information extraction, and document summarization. Despite the vast popularity of these models, their vulnerabilities remain unknown; this is of high concern given their growing use in security-sensitive applications such as question answering and machine translation in the aspects of sustainability. In this study, we demonstrate that NRE models are inherently vulnerable to adversarially crafted text that contains imperceptible modifications of the original but can mislead the target NRE model. Specifically, we propose a novel sustainable term frequency-inverse document frequency (TFIDF) based black-box adversarial attack to evaluate the robustness of state-of-the-art CNN, CGN, LSTM, and BERT-based models on two benchmark RE datasets. Compared with white-box adversarial attacks, black-box attacks impose further constraints on the query budget; thus, efficient black-box attacks remain an open problem. By applying TFIDF to the correctly classified sentences of each class label in the test set, the proposed query-efficient method achieves a reduction of up to 70% in the number of queries to the target model for identifying important text items. Based on these items, we design both character- and word-level perturbations to generate adversarial examples. The proposed attack successfully reduces the accuracy of six representative models from an average F1 score of 80% to below 20%. The generated adversarial examples were evaluated by humans and are considered semantically similar. Moreover, we discuss defense strategies that mitigate such attacks, and the potential countermeasures that could be deployed in order to improve sustainability of the proposed scheme.
Ijaz Haq; Zahid Khan; Arshad Ahmad; Bashir Hayat; Asif Khan; Ye-Eun Lee; Ki-Il Kim. Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks. Sustainability 2021, 13, 5892 .
AMA StyleIjaz Haq, Zahid Khan, Arshad Ahmad, Bashir Hayat, Asif Khan, Ye-Eun Lee, Ki-Il Kim. Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks. Sustainability. 2021; 13 (11):5892.
Chicago/Turabian StyleIjaz Haq; Zahid Khan; Arshad Ahmad; Bashir Hayat; Asif Khan; Ye-Eun Lee; Ki-Il Kim. 2021. "Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks." Sustainability 13, no. 11: 5892.
Partial discharge (PD) detection studies aiming at the fault diagnosis for facilities and power cables in transmission networks have been conducted over the years. Recently, the deep learning models for PD detection have been used to diagnose the PD fault of facilities and cables. Most PD studies have been conducted in the field, such as gas-insulated switchgear (GIS) and power cables for high voltage transmission networks. There are few studies of PD fault detection for on-site low-voltage distribution networks. Additionally, there are few studies of PD detection algorithms for improving the accuracy of the deep learning models using small real PD data only. In this study, a PD online detection system and a model for long-term operational sustainability of on-site low voltage distribution networks are proposed using convolutional neural network (CNN) transfer-learning. The proposed PD online system makes it possible to acquire as many real PD data as possible through continuous monitoring of PD occurrence. The PD detection accuracy results showed that the proposed CNN transfer-learning models are more effective models for obtaining improved accuracy (97.4%) than benchmark models, such as CNN and support vector machine (SVM) using only small real PD data acquired from PD online detection system.
Jinseok Kim; Ki-Il Kim. Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning. Sustainability 2021, 13, 4692 .
AMA StyleJinseok Kim, Ki-Il Kim. Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning. Sustainability. 2021; 13 (9):4692.
Chicago/Turabian StyleJinseok Kim; Ki-Il Kim. 2021. "Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning." Sustainability 13, no. 9: 4692.
Face-routing is one of the reliable recovery schemes when geographic routing fails to transmit data packets. Although studies on face-routing can overcome the failure of the data transmission, they lead to much energy consumption due to frequent data transmissions between adjacent nodes for carrying out the rule of face-routing. To avoid the frequent data transmissions, several face-routing schemes have been recently proposed to transmit data packets to the farthest-neighbor node. However, they happen with many data retransmissions because the farthest-neighbor node has a relatively low transmission success ratio. To solve this problem, we propose a new face-routing scheme that determines the most appropriate neighbor node to balance the trade-off between energy efficiency and transmission reliability with two viewpoints. The first viewpoint focuses on how to increase the distance progress of the data delivery in one-hop range to enhance energy efficiency. After that, the second viewpoint focuses on how to increase the success ratio of the data delivery to enhance the transmission reliability. As a result of the simulation, it was confirmed that the proposed method achieves better performance in terms of energy efficiency than existing face-routing research, and it is better than recent face-routing research in terms of reliability and retransmission.
Hyunchong Cho; Sangdae Kim; Seungmin Oh; Euisin Lee; Sang-Ha Kim. Energy-Efficient and Reliable Face-Routing Scheme in Wireless Networks. Sensors 2021, 21, 2746 .
AMA StyleHyunchong Cho, Sangdae Kim, Seungmin Oh, Euisin Lee, Sang-Ha Kim. Energy-Efficient and Reliable Face-Routing Scheme in Wireless Networks. Sensors. 2021; 21 (8):2746.
Chicago/Turabian StyleHyunchong Cho; Sangdae Kim; Seungmin Oh; Euisin Lee; Sang-Ha Kim. 2021. "Energy-Efficient and Reliable Face-Routing Scheme in Wireless Networks." Sensors 21, no. 8: 2746.
In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset.
Muhammad Atif Butt; Asad Masood Khattak; Sarmad Shafique; Bashir Hayat; Saima Abid; Ki-Il Kim; Muhammad Waqas Ayub; Ahthasham Sajid; Awais Adnan. Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems. Complexity 2021, 2021, 1 -11.
AMA StyleMuhammad Atif Butt, Asad Masood Khattak, Sarmad Shafique, Bashir Hayat, Saima Abid, Ki-Il Kim, Muhammad Waqas Ayub, Ahthasham Sajid, Awais Adnan. Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems. Complexity. 2021; 2021 ():1-11.
Chicago/Turabian StyleMuhammad Atif Butt; Asad Masood Khattak; Sarmad Shafique; Bashir Hayat; Saima Abid; Ki-Il Kim; Muhammad Waqas Ayub; Ahthasham Sajid; Awais Adnan. 2021. "Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems." Complexity 2021, no. : 1-11.
Jinseok Kim; Babar Shah; Ki-Il Kim. Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans. Computers, Materials & Continua 2021, 68, 283 -301.
AMA StyleJinseok Kim, Babar Shah, Ki-Il Kim. Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans. Computers, Materials & Continua. 2021; 68 (1):283-301.
Chicago/Turabian StyleJinseok Kim; Babar Shah; Ki-Il Kim. 2021. "Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans." Computers, Materials & Continua 68, no. 1: 283-301.
Daniel Godfrey; Beom-Su Kim; Haoran Miao; Babar Shah; Bashir Hayat; Imran Khan; Tae-Eung Sung; Ki-Il Kim. Q-Learning Based Routing Protocol for Congestion Avoidance. Computers, Materials & Continua 2021, 68, 3671 -3692.
AMA StyleDaniel Godfrey, Beom-Su Kim, Haoran Miao, Babar Shah, Bashir Hayat, Imran Khan, Tae-Eung Sung, Ki-Il Kim. Q-Learning Based Routing Protocol for Congestion Avoidance. Computers, Materials & Continua. 2021; 68 (3):3671-3692.
Chicago/Turabian StyleDaniel Godfrey; Beom-Su Kim; Haoran Miao; Babar Shah; Bashir Hayat; Imran Khan; Tae-Eung Sung; Ki-Il Kim. 2021. "Q-Learning Based Routing Protocol for Congestion Avoidance." Computers, Materials & Continua 68, no. 3: 3671-3692.
In wireless sensor networks (WSNs), detection and report of continuous object, such as forest fire and toxic gas leakage, is one of the major applications. In large-scale continuous object tracking in WSNs, there might be many source nodes simultaneously, detecting the continuous object. Each nodes reports its data to both a base station and mobile workers in the industry field. For communication between the source nodes and a mobile worker, sink location service is needed to continuously notify the location of the mobile worker. But, as the application has a large number of sources, it causes a waste of energy consumption. To address this issue, in this paper, we propose a two-phase sink location service scheme. In the first phase, the proposed scheme constructs a virtual grid structure for merging the source nodes. Then, the proposed scheme aggregates the merging points from an originated merging point as the second phase. Simulation results show that the proposed scheme is superior to other schemes in terms of energy consumption.
Cheonyong Kim; Sangdae Kim; Hyunchong Cho; Sang-Ha Kim; Seungmin Oh. An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks. Sensors 2020, 20, 7282 .
AMA StyleCheonyong Kim, Sangdae Kim, Hyunchong Cho, Sang-Ha Kim, Seungmin Oh. An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks. Sensors. 2020; 20 (24):7282.
Chicago/Turabian StyleCheonyong Kim; Sangdae Kim; Hyunchong Cho; Sang-Ha Kim; Seungmin Oh. 2020. "An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks." Sensors 20, no. 24: 7282.
Although various unmanned aerial vehicle (UAV)-assisted routing protocols have been proposed for vehicular ad hoc networks, few studies have investigated load balancing algorithms to accommodate future traffic growth and deal with complex dynamic network environments simultaneously. In particular, owing to the extended coverage and clear line-of-sight relay link on a UAV relay node (URN), the possibility of a bottleneck link is high. To prevent problems caused by traffic congestion, we propose Q-learning based load balancing routing (Q-LBR) through a combination of three key techniques, namely, a low-overhead technique for estimating the network load through the queue status obtained from each ground vehicular node by the URN, a load balancing scheme based on Q-learning and a reward control function for rapid convergence of Q-learning. Through diverse simulations, we demonstrate that Q-LBR improves the packet delivery ratio, network utilization and latency by more than 8, 28 and 30%, respectively, compared to the existing protocol.
Bong-Soo Roh; Myoung-Hun Han; Jae-Hyun Ham; Ki-Il Kim. Q-LBR: Q-learning Based Load Balancing Routing for UAV-assisted VANET. Sensors 2020, 20, 5685 .
AMA StyleBong-Soo Roh, Myoung-Hun Han, Jae-Hyun Ham, Ki-Il Kim. Q-LBR: Q-learning Based Load Balancing Routing for UAV-assisted VANET. Sensors. 2020; 20 (19):5685.
Chicago/Turabian StyleBong-Soo Roh; Myoung-Hun Han; Jae-Hyun Ham; Ki-Il Kim. 2020. "Q-LBR: Q-learning Based Load Balancing Routing for UAV-assisted VANET." Sensors 20, no. 19: 5685.
Forecasting technologies aiming to reduce building electricity consumption and costs have recently been developed. However, to design a more accurate forecasting model using a physical-based approach, complicated mathematical modeling and multi-variable parameters and constraints must be considered. Moreover, single data-driven models usually have a high generalization error regarding previously unseen data. Most hybrid forecasting models usually do not consider shaving the peak demand costs based on time of use (TOU) to save building electricity costs. To overcome this limitation, we propose a data-driven hybrid forecasting model and an operating algorithm to shave the peak demand costs based on the TOU. The proposed model and algorithm are expected to reduce peak demand costs by 22%. In addition, the experimental results indicate that the hybrid model can reduce generalization errors because it improves the forecasting performance, achieving the highest recall (91.01%) among other models using a previously unseen test dataset.
Jinseok Kim; Ki-Il Kim. Data-driven hybrid model and operating algorithm to shave peak demand costs of building electricity. Energy and Buildings 2020, 229, 110493 .
AMA StyleJinseok Kim, Ki-Il Kim. Data-driven hybrid model and operating algorithm to shave peak demand costs of building electricity. Energy and Buildings. 2020; 229 ():110493.
Chicago/Turabian StyleJinseok Kim; Ki-Il Kim. 2020. "Data-driven hybrid model and operating algorithm to shave peak demand costs of building electricity." Energy and Buildings 229, no. : 110493.
Various simulation studies for wireless body area networks (WBANs) based on the IEEE 802.15.6 standard have recently been carried out. However, most of these studies have applied a simplified model without using any major components specific to IEEE 802.15.6, such as connection-oriented link allocations, inter-WBAN interference mitigation, or a two-hop star topology extension. Thus, such deficiencies can lead to an inaccurate performance analysis. To solve these problems, in this study, we conducted a comprehensive review of the major components of the IEEE 802.15.6 standard and herein present modeling strategies for implementing IEEE 802.15.6 MAC on an NS-3 simulator. In addition, we configured realistic network scenarios for a performance evaluation in terms of throughput, average delay, and power consumption. The simulation results prove that our simulation system provides acceptable levels of performance for various types of medical applications, and can support the latest research topics regarding the dynamic resource allocation, inter-WBAN interference mitigation, and intra-WBAN routing.
Beom-Su Kim; Tae-Eung Sung; Ki-Il Kim. An NS-3 Implementation and Experimental Performance Analysis of IEEE 802.15.6 Standard under Different Deployment Scenarios. International Journal of Environmental Research and Public Health 2020, 17, 4007 .
AMA StyleBeom-Su Kim, Tae-Eung Sung, Ki-Il Kim. An NS-3 Implementation and Experimental Performance Analysis of IEEE 802.15.6 Standard under Different Deployment Scenarios. International Journal of Environmental Research and Public Health. 2020; 17 (11):4007.
Chicago/Turabian StyleBeom-Su Kim; Tae-Eung Sung; Ki-Il Kim. 2020. "An NS-3 Implementation and Experimental Performance Analysis of IEEE 802.15.6 Standard under Different Deployment Scenarios." International Journal of Environmental Research and Public Health 17, no. 11: 4007.
Over the past two decades, the subject of extension of the lifetime of Wireless Sensor Networks (WSN) based on the Internet of Things (IoT) has been thoroughly investigated by researcher. As WSN, and its new form based on IoT, are increasingly being deployed in time-critical applications, users require certain network lifetime guarantees to satisfy application requirements. Few research efforts in the past have focused on guaranteeing the IoT-based network lifetime. Most such efforts pay little or no attention to other network performance indicators such as sensing coverage and network connectivity. To address this challenge, this work proposes a new centralized approach that analyzes a network’s energy consumption to optimize node duty cycles. In the proposed scheme, the sink node periodically assigns an active/sleep role to each node for the next network cycle by coupling the residual energy, total active time, and possible coverage area to guarantee their lifetimes. We show through extensive simulation that the proposed guaranteed lifetime protocol achieves less average end-to-end delay and better packet delivery ratio when compared to its best rival protocols formulated in past research, i.e., the CERACC, A-Mac, and Coverage Preserving protocols.
Babar Shah; Ali Abbas; Gohar Ali; Farkhund Iqbal; Asad Masood Khattak; Omar Alfandi; Ki-Il Kim. Guaranteed lifetime protocol for IoT based wireless sensor networks with multiple constraints. Ad Hoc Networks 2020, 104, 102158 .
AMA StyleBabar Shah, Ali Abbas, Gohar Ali, Farkhund Iqbal, Asad Masood Khattak, Omar Alfandi, Ki-Il Kim. Guaranteed lifetime protocol for IoT based wireless sensor networks with multiple constraints. Ad Hoc Networks. 2020; 104 ():102158.
Chicago/Turabian StyleBabar Shah; Ali Abbas; Gohar Ali; Farkhund Iqbal; Asad Masood Khattak; Omar Alfandi; Ki-Il Kim. 2020. "Guaranteed lifetime protocol for IoT based wireless sensor networks with multiple constraints." Ad Hoc Networks 104, no. : 102158.
Since the traditional geographical routing protocol and its variants use only single metric in routing process, they cannot handle with several problems such as congestion, network holes and link loss. To solve these problems, multi-metric geographical routing protocol have been proposed, however, most of them cannot adaptively change their forwarding policy according to network conditions because they fail to define the relationship or priorities between multiple metrics. To address this limitations, in this paper, we apply multi-criteria decision making (MCDM) method to the geographical routing protocol to accommodate multiple metrics in a logical way. In addition, we propose a new dynamic priority scheme to select the most suitable next hop by differentiating the priorities based on the network environment. Simulation results prove that our approach can achieve better performance than existing geographical routing protocols in the aspects of packet delivery ratio, end-to-end delay and routing overhead.
Beom-Su Kim; Sana Ullah; Kyong Hoon Kim; Bongsoo Roh; Jae-Hyun Ham; Ki-Il Kim. An enhanced geographical routing protocol based on multi-criteria decision making method in mobile ad-hoc networks. Ad Hoc Networks 2020, 103, 102157 .
AMA StyleBeom-Su Kim, Sana Ullah, Kyong Hoon Kim, Bongsoo Roh, Jae-Hyun Ham, Ki-Il Kim. An enhanced geographical routing protocol based on multi-criteria decision making method in mobile ad-hoc networks. Ad Hoc Networks. 2020; 103 ():102157.
Chicago/Turabian StyleBeom-Su Kim; Sana Ullah; Kyong Hoon Kim; Bongsoo Roh; Jae-Hyun Ham; Ki-Il Kim. 2020. "An enhanced geographical routing protocol based on multi-criteria decision making method in mobile ad-hoc networks." Ad Hoc Networks 103, no. : 102157.
Many applications are able to obtain enriched information by employing a wireless multimedia sensor network (WMSN) in industrial environments, which consists of nodes that are capable of processing multimedia data. However, as many aspects of WMSNs still need to be refined, this remains a potential research area. An efficient application needs the ability to capture and store the latest information about an object or event, which requires real-time multimedia data to be delivered to the sink timely. Motivated to achieve this goal, we developed a new adaptive QoS routing protocol based on the (m,k)-firm model. The proposed model processes captured information by employing a multimedia stream in the (m,k)-firm format. In addition, the model includes a new adaptive real-time protocol and traffic handling scheme to transmit event information by selecting the next hop according to the flow status as well as the requirement of the (m,k)-firm model. Different from the previous approach, two level adjustment in routing protocol and traffic management are able to increase the number of successful packets within the deadline as well as path setup schemes along the previous route is able to reduce the packet loss until a new path is established. Our simulation results demonstrate that the proposed schemes are able to improve the stream dynamic success ratio and network lifetime compared to previous work by meeting the requirement of the (m,k)-firm model regardless of the amount of traffic.
Beom-Su Kim; Sangdae Kim; Kyong Hoon Kim; Tae-Eung Sung; Babar Shah; Ki-Il Kim. Adaptive Real-Time Routing Protocol for (m,k)-Firm in Industrial Wireless Multimedia Sensor Networks. Sensors 2020, 20, 1633 .
AMA StyleBeom-Su Kim, Sangdae Kim, Kyong Hoon Kim, Tae-Eung Sung, Babar Shah, Ki-Il Kim. Adaptive Real-Time Routing Protocol for (m,k)-Firm in Industrial Wireless Multimedia Sensor Networks. Sensors. 2020; 20 (6):1633.
Chicago/Turabian StyleBeom-Su Kim; Sangdae Kim; Kyong Hoon Kim; Tae-Eung Sung; Babar Shah; Ki-Il Kim. 2020. "Adaptive Real-Time Routing Protocol for (m,k)-Firm in Industrial Wireless Multimedia Sensor Networks." Sensors 20, no. 6: 1633.
To improve the packet delivery ratio in wireless sensor networks, many approaches such as multipath, opportunistic, and learning-based routing protocols have been proposed. However, the performance of the existing protocols are degraded under long-hop wireless sensor networks because the additional overhead is proportional to the number of hops. To deal with the overhead, we propose an opportunistic multipath routing that forecasts the required number of paths, as well as bifurcation based on opportunistic routing according to the reliability requirement. In the proposed scheme, an intermediate node is able to select a different node for each transmission and to handle path failure adaptively. Through a performance evaluation, we demonstrate that the proposed scheme achieves a higher packet delivery ratio and reduces the energy consumption by at least approximately 33% and up to approximately 65% compared with existing routing protocols, under the condition of an 80% link success ratio in the long-hop sensor network.
Sangdae Kim; Beom-Su Kim; Kyong Hoon Kim; Ki-Il Kim. Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks. Sensors 2019, 19, 4072 .
AMA StyleSangdae Kim, Beom-Su Kim, Kyong Hoon Kim, Ki-Il Kim. Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks. Sensors. 2019; 19 (19):4072.
Chicago/Turabian StyleSangdae Kim; Beom-Su Kim; Kyong Hoon Kim; Ki-Il Kim. 2019. "Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks." Sensors 19, no. 19: 4072.
In Mobile-Assisted Sensing, a number of mobile devices may be gateways. Thus, the gateway selection procedure should be performed whenever multiple gateways attempt to acquire data from a sensor. Owing to different capacity and status of each mobile gateway, a selected gateway has great impact on the data collection. In this paper, we present possible criteria for gateway selection and compare their performance.
Cheonyong Kim; Ki-Il Kim. A Comparative Study on Gateway Selection in Mobile-Assisted Sensor Data Collection. 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) 2019, 1 -2.
AMA StyleCheonyong Kim, Ki-Il Kim. A Comparative Study on Gateway Selection in Mobile-Assisted Sensor Data Collection. 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall). 2019; ():1-2.
Chicago/Turabian StyleCheonyong Kim; Ki-Il Kim. 2019. "A Comparative Study on Gateway Selection in Mobile-Assisted Sensor Data Collection." 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) , no. : 1-2.
Recently, multi-metric routing protocols have been proposed to enhance the performance of the typical single metric routing protocols in mobile ad-hoc networks. Generally, most of them employ simple value computation to derive a cost value as a way by combining multiple metrics. However, this simple approach fails to define the relationship or priorities between multiple metrics. To overcome this limitation, in this study, we apply the multi-criteria decision making (MCDM) method to determine the weight factors between the metrics. We define criteria to accommodate multiple metric in logical way and decide a better path. For the case studies, we extend the existing proactive and reactive routing protocols, that is, ad-hoc on-demand distance vector (AODV) and optimized link state routing protocol (OLSR). In AODV, we present a strategy for modifying the route request and route reply mechanism to generate a stable path using the MCDM. On the other hand, in OLSR, we propose a modification strategy of MPR selection algorithm to maintain a stable topology using the MCDM. The simulation results show that proposed routing scheme reduces the routing overhead by 15% and 13%, packet loss rate by 12% and 14%, and end-to-end delay by 21% and 19% approximately, compared with other routing schemes such as fixed weighted AODV, OLSR and MCDM based geographical routing protocol.
Beom-Su Kim; Bongsoo Roh; Jae-Hyun Ham; Ki-Il Kim. Extended OLSR and AODV based on multi-criteria decision making method. Telecommunication Systems 2019, 73, 241 -257.
AMA StyleBeom-Su Kim, Bongsoo Roh, Jae-Hyun Ham, Ki-Il Kim. Extended OLSR and AODV based on multi-criteria decision making method. Telecommunication Systems. 2019; 73 (2):241-257.
Chicago/Turabian StyleBeom-Su Kim; Bongsoo Roh; Jae-Hyun Ham; Ki-Il Kim. 2019. "Extended OLSR and AODV based on multi-criteria decision making method." Telecommunication Systems 73, no. 2: 241-257.
Sana Ullah; Ki-Il Kim; Kyong Hoon Kim; Muhammad Imran; Pervez Khan; Eduardo Tovar; Farman Ali. UAV-enabled healthcare architecture: Issues and challenges. Future Generation Computer Systems 2019, 97, 425 -432.
AMA StyleSana Ullah, Ki-Il Kim, Kyong Hoon Kim, Muhammad Imran, Pervez Khan, Eduardo Tovar, Farman Ali. UAV-enabled healthcare architecture: Issues and challenges. Future Generation Computer Systems. 2019; 97 ():425-432.
Chicago/Turabian StyleSana Ullah; Ki-Il Kim; Kyong Hoon Kim; Muhammad Imran; Pervez Khan; Eduardo Tovar; Farman Ali. 2019. "UAV-enabled healthcare architecture: Issues and challenges." Future Generation Computer Systems 97, no. : 425-432.
Ki-Il Kim; Sana Ullah; Christos Verikoukis; Han-Chieh Chao. Editorial on “Special issue on fog computing for healthcare”. Peer-to-Peer Networking and Applications 2019, 12, 1214 -1215.
AMA StyleKi-Il Kim, Sana Ullah, Christos Verikoukis, Han-Chieh Chao. Editorial on “Special issue on fog computing for healthcare”. Peer-to-Peer Networking and Applications. 2019; 12 (5):1214-1215.
Chicago/Turabian StyleKi-Il Kim; Sana Ullah; Christos Verikoukis; Han-Chieh Chao. 2019. "Editorial on “Special issue on fog computing for healthcare”." Peer-to-Peer Networking and Applications 12, no. 5: 1214-1215.