Kyong Hoon Kim

Cloud computing has received a lot of attention from both researcher and developer in last decade due to its unique structure of providing services to the user. As the digitalization of world, heterogeneous devices, and with the emergence of Internet of Things (IoT), these IoT devices produce different type of data with distinct frequency, which require real‐time and latency sensitive services. This provides great challenge to cloud computing framework. Fog computing is a new framework to accompaniment cloud platform and is proposed to extend services to the edge of the network. In fog computing, the entire user's tasks are offloaded to distributed fog nodes to the edge of network to avoid delay sensitivity. We select fog computing network dwell different set of fog nodes to provide required services to the users. Allocation of defined resource to the users in order to achieve optimal result is a big challenge. Therefore, we propose dynamic resource allocation strategy for cloud, fog node, and users. In the framework, we first formulate the ranks of fog node using TOPSIS to identify most suitable fog node for the incoming request. Simultaneously logistic regression calculates the load of individual fog node and updates the result to send back to the broker for next decision. Simulation results demonstrate that the proposed scheme undoubtedly improves the performance and give accuracy of 98.25%.

In this article, we propose a creation order reconstruction method of deleted files for the FAT32 file system with Windows operating systems. Creation order of files is established using a correlation between storage locations of the files and their directory entry locations. This method can be utilized to derive the creation-time bound of files recovered without the creation-time information. In this article, we first examine the file allocation behavior of Windows FAT32 file system. Next, based on the examined behavior, we propose a novel method that finds the creation order of deleted files after being recovered without the creation-time information. Due to complex behaviors of Windows FAT32 file system, the method may find multiple creation orders although the actual creation order is unique. In experiments with a commercial device, we confirm that the actual creation order of each recovered file belongs to one of the creation orders found by the method.

Object tracking is one of the most promising applications in wireless sensor networks (WSN). However, current scalar based WSN cannot provide diverse object information for type and shape of object. To defeat this problem, multimedia WSN is employed to support various information for the object. When using multimedia, service quality problems naturally arise, which makes it necessary for network technology as to support service quality stably. In this paper, we present real-time network technology for applying (m,k) -firm stream to object tracking in multimedia WSN. The proposed method satisfies the (m,k) -firm requirement of the data transmitted from each sensor node to the sink, and at the same time, aggregates the multimedia data to compensate the technology at intermediate node. In addition, according to current status, adaptive QoS routing protocol chooses the path to satisfy the new (m,k)-firm for users. Finally, simulation result is given to prove the suitability of the proposed scheme by presenting stream dynamic failure ratio value for the (m,k)-firm stream.

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.

Activities on Unmanned Aerial Vehicle (UAV) have increased over the last years and there are many fields in which UAVs can be used. One of the basic applications is reconnaissance of a given area using multiple UAVs. To perform reconnaissance mission, there are two methods: (i) path planning to navigate the pre-determined route; and (ii) random mobility method to explore without prior knowledge. In this paper, we indicate the imbalance problem of existing random mobility models for reconnaissance and propose a new model considering reconnaissance balance based on the number of visits. We divide the scanning area into N zones and then select a zone stochastically in which the search is insufficient. We evaluated the performance of the model by focusing on the coverage rate and average inter-visiting time. The proposed model shows that the 90%-coverage reaching time is improved by about 25% and the average inter-visiting time is improved by up to 15% compared to the previous approach.

Before discovering meaningful knowledge from big data systems, it is first necessary to build a data-gathering infrastructure. Among many feasible data sources, wireless sensor networks (WSNs) are rich big data sources: a large amount of data is generated by various sensor nodes in large-scale networks. However, unlike typical wireless networks, WSNs have serious deficiencies in terms of data reliability and communication owing to the limited capabilities of the nodes. Moreover, a considerable amount of sensed data are of no interest, meaningless, and redundant when a large number of sensor nodes is densely deployed. Many studies address the existing problems and propose methods to overcome the limitations when constructing big data systems with WSN. However, a published paper that provides deep insight into this research area remains lacking. To address this gap in the literature, we present a comprehensive survey that investigates state-of-the-art research work on introducing WSN in big data systems. Potential applications and technical challenges of networks and infrastructure are presented and explained in accordance with the research areas and objectives. Finally, open issues are presented to discuss promising directions for further research.

In wireless body area networks, temperature-aware routing plays an important role in preventing damage of surrounding body tissues caused by the temperature rise of the nodes. However, existing temperature-aware routing protocols tend to choose the next hop according to the temperature metric without considering transmission delay and data loss caused by human posture. To address this problem, multiple research efforts exploit different metrics such as temperature, hop count and link quality. Because their approaches are fundamentally based on simple computation through weighted factor for each metric, it is rarely feasible to obtain reasonable weight value through experiments. To solve this problem, we propose an enhanced mobility and temperature-aware routing protocol based on the multi-criteria decision making method. The proposed protocol adopts the analytical hierarchy process and simple additive weighting method to assign suitable weight factors and choose the next hop while considering multiple routing criteria. Simulation results are presented to demonstrate that the proposed protocol can efficiently improve transmission delay and data loss better than existing protocols by preventing the temperature rise on the node.

A significant amount of research has been conducted in the past on compositional real-time scheduling as it has become a useful foundational theory for real-time operating systems and hypervisors. However, compositional frameworks suffer from abstraction overhead in composing components. In this paper, we decompose the abstraction overhead into: 1) supply abstraction overhead associated with the supply from a resource provider and 2) demand abstraction overhead associated with the component workload. Then, we provide sufficient conditions for each abstraction overhead to be eliminated. In addition, this paper provides a heuristic technique that transforms a component to satisfy the sufficient conditions so that the abstraction overhead can be minimized. In experiments, we show that our technique outperforms two prior overhead-reducing techniques. The reduction in overhead is about 10% on average when compared to a technique that uses a single global period and about 8% on average when compared to a technique based on harmonicity.