• Journal of Internet Technology
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• v.20 no.1
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• pp.293-300
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• 2019

The Internet of Things (IoT) enables embedded devices to connect to the internet either through IP or the web in a physical environment. The increase in performance of wireless access services, adaptive load balancing, and interference routing metric becomes the key challenges in Wireless Mesh Networks (WMN). However, in the case of IoT over WMN, a large number of users generate abundant net flows, which can result in network traffic jam. Therefore, in this paper, we propose a Load Balancing and Interference Delay Aware routing metric algorithm to efficiently address the issues present in the current work. The proposed scheme efficiently utilizes the available mesh station queue information and the number of mesh stations suffering from channel interference in the available path. The simulations results show that the proposed scheme performed superior to the existing routing metrics present in the current literature for similar purposes.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.27-33
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• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.109-112
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• 2020

Many devices are connected on the internet to give functionalities for interconnected services. In 2020', The number of devices connected to the internet will be reached 5.8 billion. Moreover, many connected service provider such as Google and Amazon, suggests edge computing and mesh networks to cope with this situation which the many devices completely connected on their networks. This paper introduces the current state of the introduction of the wireless mesh network and edge cloud in order to efficiently manage a large number of nodes in the exploding Internet of Things (IoT) network and introduces the existing Network Time Protocol (NTP). On the basis of this, we propose a relatively accurate time synchronization method, especially in heterogeneous mesh networks. Using this NTP, multiple time coordinators can be placed in a mesh network to find the delay error using the average delay time and the delay time of the time coordinator. Therefore, accurate time can be synchronized when implementing IoT, remote metering, and real-time media streaming using IoT mesh network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1774-1794
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• 2024

In the field of environmental sensing, it is necessary to develop radio planning techniques for the next generation Internet of Things (IoT) networks over mixed terrains. Such techniques are needed for smart remote monitoring of utility supplies, with links situated close to but out of range of cellular networks. In this paper, a three-dimension (3-D) geometric optimization algorithm is proposed, considering the positions of edge IoT devices and antenna coupling factors. Firstly, a multi-level single linkage (MLSL) iteration method, based on geometric objectives, is derived to evaluate the data rates over ISM 915 MHz channels, utilizing optimized power-distance profiles of continuous waves. Subsequently, a federated learning (FL) data selection algorithm is designed based on the 3-D geometric positions. Finally, a measurement example is taken in a meadow biome of the Mexican Colima district, which is prone to fluvial floods. The empirical path loss model has been enhanced, demonstrating the accuracy of the proposed optimization algorithm as well as the possibility of further prediction work.

• Journal of Korea Multimedia Society
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• v.19 no.7
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• pp.1166-1178
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• 2016

In Internet of Things (IoT) environments, billions of interconnected devices and multimedia sensors generate a huge amount of multimedia traffic. Since the environment are in general deployed as a server-centric architecture wireless sensor networks could be bottlenecks between IoT gateways and IoT devices. The bottleneck causes high power consumption of the device and triggers very heavy network overload by transmission of sensing data. The deterioration could decrease the quality of multimedia streaming service due to delay, loss, and waste of device power. Thus, in this paper, we propose a context-based adaptive multimedia streaming scheme to support enhanced QoS and low power consumption in IoT environments. The goal of the scheme is to increase quality score per voltage of the streaming service, given an adaptation algorithm with context that are classified network and hardware such as throughput, RTT, and CPU usage. From the both context, the quality score per voltage is used in the comparison of a only network context-based adaptive multimedia streaming scheme, a fixed multimedia streaming and our scheme. As a result, we achieves a high improvement that means the quality score per voltage is increased up to about 4, especially in case of resolution change.

• Smart Media Journal
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• v.12 no.4
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• pp.19-26
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• 2023

IoT edge services utilizing neuromorphic hardware architectures are suitable for autonomous IoT applications as they perform intelligent processing on the device itself. However, spiking neural networks applied to neuromorphic hardware are difficult for IoT developers to comprehend due to their complex structures and various hyper-parameters. In this paper, we propose a method for generating spiking neural network (SNN) models that satisfy user performance requirements while considering the constraints of neuromorphic hardware. Our proposed method utilizes previously trained models from pre-processed data to find optimal SNN model parameters from profiling data. Comparing our method to a naive search method, both methods satisfy user requirements, but our proposed method shows better performance in terms of runtime. Additionally, even if the constraints of new hardware are not clearly known, the proposed method can provide high scalability by utilizing the profiled data of the hardware.

• Journal of KIISE
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• v.43 no.11
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• pp.1233-1244
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• 2016

In the IoT network environments for LLNs(Low power and Lossy networks), IPv6 Routing Protocol for Low Power and Lossy networks(RPL) has been proposed by IETF(Internet Engineering Task Force). The goal of RPL is to create a directed acyclic graph, without loops. As recommended by the IETF standard, RPL route recovery mechanisms in the event of a failure of a node should avoid loop, loop detection, DIO Poisoning. In this process, route recovery time and control message might be increased in the sub-tree because of the repeated route search. In this paper, we suggested RPL route recovery method to solve the routing overhead problem in the sub-tree during a loss of a link in the RPL routing protocol based on IoT wireless networks. The proposed method improved local repair process by utilizing a route that could not be selected as the preferred existing parents. This reduced the traffic control packet, especially in the disconnected node's sub tree. It also resulted in a quick recovery. Our simulation results showed that the proposed RPL local repair reduced the recovery time and the traffic of control packets of RPL. According to our experiment results, the proposed method improved the recovery performance of RPL.

• Journal of IKEEE
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• v.25 no.4
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• pp.598-606
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• 2021

In this paper, we presents a new service binding and resource management model for context based services in mobile edge computing (MEC) networks. The proposed control is composed of two layers: MEC service bindng control layer (MCL) and user context control layer (UCL). The MCL manages service binding construction, resource allocation, and service policy construction from a system point of view; and the UCL manages real-time service adaptation using meta-objects. Through simulations, we confirmed that the proposed control offers enhanced throughput and content transfer time when it is compared to the legacy computing and control models. The proposed control model can be employed as a key component for the context based various internet-of-things (IoT) services in MEC environments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.147-153
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• 2016

Recently, IETF (Internet Engineering Task) working group has adopted CoAP (Constrained Application Protocol) as a standard IoT proctocol. CoAP is a specialized web transfer protocol for use with constrained nodes and constrained environment such as small memory and low power networks. In this paper, we design and implement a registration service with CoAP protocol based on RD(Resource Directory) to connect IoT nodes in mobile Internet environments. The resource directory between the mobile terminal and IoT nodes provides to discover the IoT nodes and get the context data. The mobile terminal has as the CoAP client and embedded IoT nodes includes as the CoAP server so that it can conveniently manage the constrained IoT nodes to get the context data and control devices in a mobile environments.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.1-7
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• 2022

As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.