• Journal of Internet Computing and Services
• /
• v.23 no.5
• /
• pp.17-23
• /
• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

• International journal of advanced smart convergence
• /
• v.11 no.1
• /
• pp.42-47
• /
• 2022

As smart factory is the factory which produces the products according to the customer's diverse demand and the changing conditions in it, it can be characterized by flexible production, dynamic reconstruction, and optimized production environment. To implement these characteristics, many kind of configuration elements in the smart factory should be connected to and communicated with each other. So the network is responsible for playing this role in the smart factory. As SDN (Software Defined Network) is the technology that can dynamically cope with the explosive increasing data amount and the hourly changing network condition, it is one of network technologies that can be applied to the smart factory. In this paper, we address SDN function and operation, SDN model suitable for the smart factory, and then performs the simulation for measuring this model.

• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.3
• /
• pp.99-103
• /
• 2021

Recently, convolutional neural networks (CNNs) have been widely utilized due to their outstanding performance in various computer vision fields. However, due to their computational-intensive and high memory requirements, it is difficult to deploy CNNs on hardware platforms that have limited resources, such as mobile devices and IoT devices. To address these limitations, a neural network compression research is underway to reduce the size of neural networks while maintaining their performance. This paper proposes a CNN compression technique that dynamically adjusts the thresholds of pruning, one of the neural network compression techniques. Unlike the conventional pruning that experimentally or heuristically sets the thresholds that determine the weights to be pruned, the proposed technique can dynamically find the optimal thresholds that prevent accuracy degradation and output the light-weight neural network in less time. To validate the performance of the proposed technique, the LeNet was trained using the MNIST dataset and the light-weight LeNet could be automatically obtained 1.3 to 3 times faster without loss of accuracy.

• Smart Media Journal
• /
• v.10 no.2
• /
• pp.92-99
• /
• 2021

Smart space is a technology that supports humans by interacting with the surrounding environment. Smart space has a built-in dynamic intelligent environment. This paper proposes a framework that provides user-customized dynamic intelligent environments in smart spaces. In the existing research that provides user-customized intelligent services, users' interests are only explicitly analyzed, and smart spaces are not considered. Implicit interest analysis can suggest a service that may be of interest to users rather than explicit interest analysis, but it requires higher performance than explicit interest analysis. Smart spaces can obtain useful information by interacting with information in the space. The framework proposed in the study uses a proximity-based social network of things to fit into a smart space. In addition, the implicit interest analysis provides intelligent information for smart spaces using the social media information and spatial information objects. In addition, we propose a method to prevent performance degradation while maintaining accuracy in consideration of the characteristics of the smart space.

• Journal of Internet Computing and Services
• /
• v.23 no.2
• /
• pp.1-7
• /
• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• International Journal of Advanced Culture Technology
• /
• v.10 no.2
• /
• pp.225-232
• /
• 2022

When we drive a vehicle in an IoT environment, there is a problem in that information of car users is collected without permission. The security measures used in the existing wired network environment cannot solve the security problem of cars running in the Internet of Things environment. Information should only be shared with entities that have been given permission to use it. In this paper, we intend to propose a method to prevent the illegal use of vehicle information. The method we propose is to use attribute-based encryption and dynamic threshold encryption. Real-time processing technology and cooperative technology are required to implement our proposed method. That's why we use fog computing's proxy servers to build smart gateways in cars. Proxy servers can collect information in real time and then process large amounts of computation. The performance of our proposed algorithm and system was verified by simulating it using NS2.

• Smart Structures and Systems
• /
• v.26 no.5
• /
• pp.559-573
• /
• 2020

One of the powerful data management tools is Building Information Modeling (BIM) which operates through obtaining, recalling, sharing, sorting and sorting data and supplying a digital environment of them. Employing SHM, a BIM in monitoring systems, would be an efficient method to address their data management problems and consequently optimize the economic aspects of buildings. The recording of SHM data is an effective way for engineers, facility managers and owners which make the BIM dynamic through the provision of updated information regarding the occurring state and health of different sections of the building. On the other hand, digital transformation is a continuous challenge in construction. In a cloud-based BIM platform, environmental and localization data are integrated which shape the Internet-of-Things (IoT) method. In order to improve work productivity, living comfort, and entertainment, the IoT has been growingly utilized in several products (such as wearables, smart homes). However, investigations confronting the integration of these two technologies (BIM and IoT) remain inadequate and solely focus upon the automatic transmission of sensor information to BIM models. Therefore, in this composition, the use of BIM based on SHM and IOT is reviewed and the economic application is considered.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.5
• /
• pp.349-357
• /
• 2016

The era of the Internet of Things (IoT) is upon us. In this era, minimizing power consumption becomes a primary concern for system-on-chip designers. While traditional power minimization and dynamic power management (DPM) techniques have been heavily explored to improve the power efficiency of devices inside very large-scale integration (VLSI) platforms, there is one critical factor that is often overlooked, which is the power conversion efficiency of a power delivery network (PDN). This paper is a tutorial that focuses on the power conversion efficiency of the PDN, and introduces novel methods to improve it. Circuit-, architecture-, and system-level approaches are presented to optimize PDN designs, while case studies for three different VSLI platforms validate the efficacy of the introduced approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.4
• /
• pp.1535-1554
• /
• 2016

This paper proposes a novel channel assignment scheme called multi-cluster based dynamic channel assignment (MC-DCA) to improve system performance for the downlink of dense femtocell networks (DFNs) based on orthogonal frequency division multiple access (OFDMA) and frequency division duplexing (FDD). In order to dynamically assign channels for femtocell access points (FAPs), the MC-DCA scheme uses a heuristic method that consists of two steps: one is a multiple cluster assignment step to group FAPs using graph coloring algorithm with some extensions, while the other is a dynamic subchannel assignment step to allocate subchannels for maximizing the system capacity. Through simulations, we first find optimum parameters of the multiple FAP clustering to maximize the system capacity and then evaluate system performance in terms of the mean FAP capacity, unsatisfied femtocell user equipment (FUE) probability, and mean FAP power consumption for data transmission based on a given FUE traffic load. As a result, the MC-DCA scheme outperforms other schemes in two different DFN environments for commercial and office buildings.

• Journal of Multimedia Information System
• /
• v.4 no.3
• /
• pp.123-144
• /
• 2017

Personalized media includes user-targeted and user-generated content (UGC) exchanged through social media and interactive applications. The increased consumption of UGC presents challenges and opportunities to multimedia information systems. We work towards modeling a deep structure for content networks. To gain insights, a hybrid practice with Media Framework (MF) is presented for network creation of personalized media, which leverages the authoring methodology with user-generated semantics. The system's vertical integration allows users to audition their personalized media networks in the context of a global system network. A navigation scheme with dynamic GUI shifts the interaction paradigm for content query and sharing. MF adopts a multimodal architecture anticipating emerging use cases and genres. To model diversification of platforms, information processing is robust across multiple technology configurations. Physical and virtual networks are integrated with distributed services and transactions, IoT, and semantic networks representing media content. MF applies spatiotemporal and semantic signal processing to differentiate action responsiveness and information responsiveness. The extension of multimedia information processing into authoring enables generating interactive and impermanent media on computationally enabled devices. The outcome of this integrated approach with presented methodologies demonstrates a paradigmatic shift of the concept of UGC as personalized media network, which is dynamical and evolvable.