• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.77-82
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• 2020

IoT devices frequently require input resources to communicate with various sensors or IoT platforms. IoT device wastes a lot of time as idle time or waiting time to check the data of the input resource and use the input resource. In addition, IoT devices use various input resources. We compares and analyzes input idle time and input waiting time generated from hardware serial input resource, software serial input resource, digital port input resource, and analog port input resource using Arduino widely used as IoT device. In order to design the IoT device framework, it is necessary to understand the characteristics of input resources and to design them to minimize unnecessary input idle time and input waiting time. The analog input wait time has a much larger input wait time than the digital input wait time, so it must be designed to receive analog information periodically at the appropriate timing. The characteristics of the input resources analyzed in this way help to design an efficient IoT device.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.8-14
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• 2023

The majority of IoT devices already employ AIoT, however there are still numerous issues that need to be resolved before AI applications can be deployed. In order to more effectively distribute IoT edge resources, this paper propose a machine learning-based approach to managing IoT edge resources. The suggested method constantly improves the allocation of IoT resources by identifying IoT edge resource trends using machine learning. IoT resources that have been optimized make use of machine learning convolution to reliably sustain IoT edge resources that are always changing. By storing each machine learning-based IoT edge resource as a hash value alongside the resource of the previous pattern, the suggested approach effectively verifies the resource as an attack pattern in a distributed AIoT context. Experimental results evaluate energy efficiency in three different test scenarios to verify the integrity of IoT Edge resources to see if they work well in complex environments with heterogeneous computational hardware.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.195-209
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• 2016

IoT (Internet of Things) is a collective term referring to application services that provide information through sensors/devices connected to the internet. The real world application of IoT is expanding fast along with growing number of sensors/devices. However, since IoT application relies on vertical combination of sensors/devices networks, information sharing within IoT services remains unresolved challenge. Consequently, IoT sensors/devices demand high construction and maintenance costs, rendering the creation of new IoT services potentially expensive. One solution is to launch an IoT open market for information sharing similar to that of App Store for smart-phones. Doing so will efficiently allow novel IoT services to emerge across various industries, because developers can purchase licenses to access IoT resources directly via an open market. Sharing IoT resource information through an open market will create an echo-system conducive for easy utilization of resources and communication between IoT service providers, resource owners, and developers. This paper proposes the new business model of IoT open market for information sharing, and the requirements for ensuring security and standardization of open markets.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.77-82
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• 2016

Recently, an IoT(Internet of Things) application communication protocol is standardizing for connectivity between every things on Internet. In this paper, we design and implement an indoor resource control service using IETF (Internet Engineering Task Force) CoAP (Constrained Application Protocol) based on Web. We present an indoor resource control architecture based on Web included functionalities of proxy and RD (Resource Directory) in a web server. Developed indoor resource control service supports to register low-powered and small-scale IoT nodes to web server using CoAP. This service allows users to control the indoor resources through a web browser using Web proxy with functionality of HTTP-CoAP converting.

• 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.7 no.1
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• pp.1-7
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• 2021

The IoT market continues to expand and grow, but the security threat to IoT devices is also increasing. However, it is difficult to apply the security technology applied to the existing system to IoT devices that have a problem of resource limitation. Therefore, in this paper, we present a service that can improve the security of IoT devices by presenting authentication and lightweight cryptographic algorithms that can reduce the overhead of applying security features, taking into account the nature of resource limitations of IoT devices. We want to apply these service to home network IoT equipment to provide security. The authentication and lightweight cryptographic algorithm application protocols presented in this paper have secured the safety of the service through the use of LEA encryption algorithms and secret key generation by users, IoT devices and server in the IoT environment. Although there is no difference in speed from randomly generating secret keys in experiments, we verify that the problem of resource limitation of IoT devices can be solved by additionally not applying logic for secret key sharing to IoT devices.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.6
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• pp.369-375
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• 2017

In this paper, we present a self-adaptive software platform that enables an IoT gateway to perform autonomous operation considering IoT devices connected each other in resource-constrained environments. Based on the oneM2M device software platform publicly available, we have designed an additional part, called SAS (self-adaptive software) consisting of MAM (memory-aware module), NAM (network-aware module), BAM (battery-aware module), DAM (data-aware module), and DH (decision handler). A prototype system is implemented to show the feasibility of the proposed self-adaptive software architecture. Our proposed system demonstrates that it can adaptively adjust the operation of gateway and connected devices to their resource conditions under the desired service scenarios.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.93-100
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• 2022

In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4177-4190
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• 2015

Current research on Internet of Things (IoT) has primarily addressed the means to enhancing smart resource allocation, automatic network operation, and secure service provisioning. In particular, providing satisfactory security service in IoT systems is indispensable to its mission critical applications. However, limited resources prevent full security coverage at all times. Therefore, these limited resources must be deployed intelligently by considering differences in priorities of targets that require security coverage. In this study, we have developed a new application of Cognitive Radio (CR) technology for IoT systems and provide an appropriate security solution that will enable IoT to be more affordable and applicable than it is currently. To resolve the security-related resource allocation problem, game theory is a suitable and effective tool. Based on the Blotto game model, we propose a new strategic power allocation scheme to ensure secure CR communications. A simulation shows that our proposed scheme can effectively respond to current system conditions and perform more effectively than other existing schemes in dynamically changeable IoT environments.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.140-146
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• 2023

Resource allocation is one of the top challenges in Internet of Things (IoT) networks. This is due to the scarcity of computing, energy and communication resources in IoT devices. As a result, IoT devices that are not using efficient algorithms for resource allocation may cause applications to fail and devices to get shut down. Owing to this challenge, this paper proposes a novel algorithm for managing computing resources in IoT network. The fog computing devices are placed near the network edge and IoT devices send their large tasks to them for computing. The goal of the algorithm is to conserve energy of both IoT nodes and the fog nodes such that all tasks are computed within a deadline. A bi-partite graph-based algorithm is proposed for stable matching of tasks and fog node computing units. The output of the algorithm is a stable mapping between the IoT tasks and fog computing units. Simulation results are conducted to evaluate the performance of the proposed algorithm which proves the improvement in terms of energy efficiency and task delay.