Kijun Han’s research while affiliated with Kyungpook National University and other places

Emergence of smart grid notion has revolutionized energy consumption patterns and energy conservation strategies of end users by addressing the broadening gap between electricity demand and supply. Accordingly, domestic appliance scheduling came into the play, gaining phenomenal popularity owing to its consideration on sustainable energy, user behaviors, and varying electricity tariff. To maximize the desired benefits of appliance scheduling, herein, we propose a least slack time (LST)–based scheduling algorithm embedded with consumption thresholds, which minimizes cost on grid electricity, while maximizing user comfort and sustainable energy usage. A modified LST (M‐LST) algorithm was experimented for a multiple‐house scenario with 100 houses. Simulated results were compared with an instance without scheduling and an instance of an existing scheduling scheme based on value on lost load. Renewable energy sources aggregated to M‐LST further improves performance through aggregated M‐LST (M‐LST(A)). Results confirmed the remarkable superiority of the proposed M‐LST(A) in reducing electricity bill, waiting time, peak load, and peak‐to‐average ratio. Proposed M‐LST scheduling mechanism reduced peak load energy consumption to achieve 14.24% cumulative cost saving, while preserving user convenience.

The requisition for electrical energy, smart grid, and renewable energy paradigm extend a new space for Electrical Energy Data Management and Processing Systems (EEDMS), in such a way that can mitigate the consumption of electrical energy. Similarly, the implementation and maintenance of the EEDMS is a challenging task. Moreover, the heterogeneous energy data generated from residential and commercial sector are the leading challenges for standard Internet of Things (IoT) architecture. This contributes enormous energy data preprocessing and analyzing solutions to IoT landscape. To overcome these challenges, we present a scalable multitasking Internet of Things Gateway (IoTGW) for the modern era of IoT by placing reliance on a new entity called Data Loading and Storing Module (DLSM). The provided DLSM module combine with the Gateway module services like orchestrator, flexibility of bridging front end grid, back end grid and fast formatted data trade between sensing domain and application domain enables a high dynamic distributed framework. Specifically, we add Adaboost‐Multilayer Perceptron hybrid data classifier module to the proposed work to enhance service provision of IoT gateway toward various IoT application services and protocols to facilitate IoT demands such as multitasking, interoperability, classification, and fast data delivery between different modules. IoTGW is implemented and tested using a real‐time IoT data streaming network. The experimental results confirms the superiority of proposed work in terms of scalability to serve novel applications and facilitate broad scope of IoT. Overview of smart Internt of Things Gateway.

Adoption of the Internet of Things for the realization of smart cities in various domains has been pushed by the advancements in Information Communication and Technology. Transportation, power delivery, environmental monitoring, and medical applications are among the front runners when it comes to leveraging the benefits of IoT for improving services through modern decision support systems. Though with the enormous usage of the Internet of Medical Things, security and privacy become intrinsic issues, thus adversaries can exploit these devices or information on these devices for malicious intents. These devices generate and log large and complex raw data which are used by decision support systems to provide better care to patients. Investigation of these enormous and complicated data from a victim's device is a daunting and time-consuming task for an investigator. Different feature-based frameworks have been proposed to resolve this problem to detect early and effectively the access logs to better assess the event. But the problem with the existing approaches is that it forces the investigator to manually comb through collected data which can contain a huge amount of irrelevant data. These data are provided normally in textual form to the investigators which are too time-consuming for the investigations even if they can utilize machine learning or natural language processing techniques. In this paper, we proposed a visualization-based approach to tackle the problem of investigating large and complex raw data sets from the Internet of Medical Things. Our contribution in this work is twofold. Firstly, we create a data set through a dynamic behavioral analysis of 400 malware samples. Secondly, the resultant and reduced data set were then visualized most feasibly. This is to investigate an incident easily. The experimental results show that an investigator can investigate large amounts of data in an easy and time-efficient manner through the effective use of visualization techniques.

In an automated steering system of the self-driving vehicles, the steering wheel angle is measured by the absolute angular displacement sensors or relative angle sensors. However, these sensors either encompass global navigation satellite systems (GNSS)/gyroscope – Micro Electromechanical-Sensor (MEMS) based solutions or comprise of the complex gear-based mechanical structure which results in latency and additive bias in the accumulative steering angle assessment. To address these issues, we propose a novel steering angle assessment system based on enhanced gear mechanism along with the adapted rotation paradigm for the customized self-driving vehicles. Additionally, a digital signal processing system has been introduced to resolve the issues in the identification of absolute central and max-bounding steering wheels position in self-driving vehicles. In assistance with the proposed mechanism, an algorithm has also been proposed to optimize the computed steering angle to minimalize the effect of additive bias in the accuracy. The proposed mechanism has been installed in the customized self-driving testbed vehicle and rigor validation has been performed in the straight and curvy road scenarios. Finally, the comparison study has been carried out between the conventional relative sensor and the proposed mechanism to show the accuracy and effectiveness of the proposed mechanism in terms of error rate, stability, and deviation.

In the last couple of years, academia-industry collaboration has demonstrated rapid advancements in the development of self-driving vehicles. Since it is evident that self-driving vehicles are going to reshape the traditional transportation systems in near future through enhancement in safe and smart mobility, motion control in self-driving vehicles while performing driving tasks in a dynamic road environment is still a challenging task. In this regard, we present a comprehensive study considering the evolution of steering control methods for self-driving vehicles. Initially, we discussed an insight into the traditional steering systems of the vehicles. To the best of our knowledge, currently, there is no taxonomy available, which elaborates steering control methods for self-driving vehicles. In this paper, we present a novel taxonomy including different steering control methods which are categorized into deterministic and heuristic steering control methods. Concurrently, the abovementioned techniques are critically reviewed elaborating their strengths and limitations. Based on the analysis, key challenges/research gaps in existing steering control methods along with the possible solutions have been briefly discussed to improve the effectiveness of the steering system of self-driving vehicles.

Supporting deep learning is a challenge for Internet of Things hardware with limited computing capacity. Edge computing is a promising solution that supports such hardware as it solves transferring and processing bottlenecks. To allocate appropriate loads to utilize edge computing efficiently, we propose an edge computing solution for smart city environments that assigns some of the deep learning layers to edge nodes in order to support deep learning tasks on Internet of Things devices. The proposed deep learning layer assignment in edge computing algorithm determines the ideal number of deep learning layers to be assigned to each edge considering computing capacity and bandwidth of each edge separately. Simulation results of the proposed algorithm were compared with other existing methods such as Li's offline and online method, fixed assignment, and cloud-only method. The comparison showed that the proposed algorithm handles the most deep learning tasks while maximizing resource utilization of edges.

A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activities comprise expected and unexpected behavior events; therefore, detecting these events consisting of mutual dependent activities poses a key challenge in the activities detection paradigm. Besides, the battery-powered sensor ubiquitously and extensively monitors activities, disputes, and sensor energy depletion. Therefore, to address these challenges, we propose an Energy and Event Aware-Sensor Duty Cycling scheme. The proposed model predicts the future expected event using the Bi-Directional Long-Short Term Memory model and allocates Predictive Sensors to the predicted event. To detect the unexpected events, the proposed model localizes a Monitor Sensor within a cluster of Hibernate Sensors using the Jaccard Similarity Index. Finally, we optimize the performance of our proposed scheme by employing the Q-Learning algorithm to track the missed or undetected events. The simulation is executed against the conventional Machine Learning algorithms for the sensor duty cycle, scheduling to reduce the sensor energy consumption and improve the activity detection accuracy. The experimental evaluation of our proposed scheme shows significant improvement in activity detection accuracy from 94.12% to 96.12%. Besides, the effective rotation of the Monitor Sensor significantly improves the energy consumption of each sensor with the entire network lifetime.

Vehicular ad hoc network (VANET) is a special form of mobile ad hoc network (MANET), which plays a key role in the intelligent transportation system (ITS). Though many outstanding geographic routing protocols are designed for VANETs, a majority of them use parameters that only affect routing performance. In this article, we propose an intersection routing based on fuzzy multi-factor decision (IRFMFD), which utilizes several features. The scheme is divided into two parts, namely vehicular decision management and intersection decision management. In the vehicular component, candidate vehicles between two static nodes (SNs) located at two intersections derive potential routing paths considering distance, neighbor quantity, and relative velocity. In the intersection component, the candidate SN was chosen from the current intersection’s 2-hop neighbors which were connected with the current intersection by a route that was decided on in part one. To get the best scheme, we also introduced other factors to estimate the number of hops in each link and link lifetime. The simulation shows that the IRFMFD outperforms on delivery ratio and end-to-end delay compared with AODV (Ad hoc on-demand distance vector), GPSR (Greedy perimeter stateless routing) and GeOpps (Geographical opportunistic routing).