• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.212-218
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• 2021

Low-power and Lossy Networks (LLNs) have become the common network infrastructure for a wide scope of Internet of Things (IoT) applications. For efficient routing in LLNs, IETF provides a standard solution, namely the IPv6 Routing Protocol for LLNs (RPL). It enables effective interconnectivity with IP networks and flexibly can meet the different application requirements of IoT deployments. However, it still suffers from different open issues, particularly in large-scale setups. These include the node unreachability problem which leads to increasing routing losses at RPL sink nodes. It is a result of the event of memory overflow at LLNs devices due to their limited hardware capabilities. Although this can be alleviated by the establishment of multi-sink topologies, RPL still lacks the support for effective load balancing among multiple sinks. In this paper, we address the need for an efficient multi-sink load balancing solution to enhance the performance of PRL in large-scale scenarios and alleviate the node unreachability problem. We propose a new RPL objective function, Multi-Sink Load Balancing Objective Function (MSLBOF), and introduce the Memory Utilization metrics. MSLBOF enables each RPL node to perform optimal sink selection in a way that insure better memory utilization and effective load balancing. Evaluation results demonstrate the efficiency of MSLBOF in decreasing packet loss and enhancing network stability, compared to MRHOF in standard RPL.

• Earthquakes and Structures
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• v.24 no.2
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• pp.127-140
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• 2023

The vibration of microtubule in human cells is the source of electrical field around it and inside cell structure. The induction of electrical field is a direct result of the existence of dipoles on the surface of the microtubules. Measuring the electrical fields could be performed using nano-scale sensors and the data could be transformed to other computers using internet of things (IoT) technology. Processing these data is feasible by artificial intelligence-based methods. However, the first step in analyzing the vibrational behavior is to study the mechanics of microtubules. In this regard, the vibrational behavior of the microtubules is investigated in the present study. A shell model is utilized to represent the microtubules' structure. The displacement field is assumed to obey first order shear deformation theory and classical theory of elasticity for anisotropic homogenous materials is utilized. The governing equations obtained by Hamilton's principle are further solved using analytical method engaging Navier's solution procedure. The results of the analytical solution are used to train, validate and test of the deep neural network. The results of the present study are validated by comparing to other results in the literature. The results indicate that several geometrical and material factors affect the vibrational behavior of microtubules.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.828-840
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• 2014

There is a new evolution in technological advancement taking place called the Internet of Things (IoT). The IoT enables physical world objects in our surroundings to be connected to the Internet. For this idea to come to life, two architectures are required: the Sensing Entity in the environment which collects data and connects to the cloud and the Cloud Service that hosts the data. In particular, the combination of wireless sensor network for sensing and cloud computing for managing sensor data is becoming a popular intervention for the IoT era. The pharmaceutical cold chain requires controlled environmental conditions for the sensitive products in order for them to maintain their potency and fit for consumption. The monitoring of distribution process is the only assurance that a process has been successfully validated. The distribution process is so critical that anomaly at any point will result in the process being no longer valid. Taking the cold chain monitoring to IoT and using its benefits and power will result in better management and product handling in the cold chain. In this paper, Arduino based wireless sensor network for storage and logistics (land and sea) is presented and integrated with Xively cloud service to offer a real-time and innovative solution for pharmaceutical cold chain monitoring.

• Electronics and Telecommunications Trends
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• v.33 no.6
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• pp.24-33
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• 2018

In the age of IoT, in which everything is connected to a network, there have been increases in the amount of data traffic, latency, and the risk of personal privacy breaches that conventional cloud computing technology cannot cope with. The idea of edge computing has emerged as a solution to these issues, and furthermore, the concept of ultra-low power edge intelligent semiconductors in which the IoT device itself performs intelligent decisions and processes data has been established. The key elements of this function are an intelligent semiconductor based on artificial intelligence, connectivity for the efficient connection of neurons and synapses, and a large-scale spiking neural network simulation framework for the performance prediction of a neural network. This paper covers the current trends in ultra-low power edge intelligent semiconductors including issues regarding their technology and application.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.255-261
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• 2022

Routing Protocol for Low-Power and Lossy Networks (RPLs) in Internet of Things (IoT) is currently one of the most popular wireless technologies for sensor communication. RPLs are typically designed for specialized applications, such as monitoring or tracking, in either indoor or outdoor conditions, where battery capacity is a major concern. Several routing techniques have been proposed in recent years to address this issue. Nevertheless, the expansion of the network lifetime in consideration of the sensors' capacities remains an outstanding question. In this research, aANN-CUCKOO based optimization technique is applied to obtain a more efficient and dependable energy efficient solution in IOT-RPL. The proposed method uses time constraints to minimise the distance between source and sink with the objective of a low-cost path. By considering the mobility of the nodes, the technique outperformed with an efficiency of 98% compared with other methods. MATLAB software is used to simulate the proposed model.

• Convergence Security Journal
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• v.17 no.3
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• pp.41-49
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• 2017

According to a research by global IT market research institute IDC, CSIM(Converged Security Information Management) market of Korea was estimated to be 1.7 trillion KRW in 2010, and it has grown approximately 32% every year since. IDC forcasts this size to grow to 12.8 trillion KRW by 2018. Moreover, this case study exemplifies growing importance of CSIM market worldwide. Traditional CSIM solution consists of various security solutions(e.g. firewall, network intrusion detection system, etc.) and devices(e.g. CCTV, Access Control System, etc.). With this traditional solution, the the data collected from these is used to create events, which are then used by the on-site agents to determine and handle the situation. Recent development of IoT industry, however, has come with massive growth of IoT devices, and as these can be used for security command and control, it is expected that the overall amount of event created from these devices will increase as well. While massive amount of events could help determine and handle more situations, this also creates burden of having to process excessive amount of events. Therefore, in this paper, we discuss potential events that can happen in CSIM system and classify them into 3 groups, and present a model that can categorize and process these events effectively to increase overall efficieny of CSIM system.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.10-16
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• 2021

As the implementation of smart factories spreads widely, the need for research to improve data efficiency is raised by prioritizing massive amounts of big data using IoT devices in terms of relevance and quality. The purpose of this study is to investigate whether prioritizing big data in management accounting decisions such as cost volatility estimation and recipe optimization can improve smart solution performance and decision-making effectiveness. Based on the survey answers of 84 decision makers at domestic small and medium-sized manufacturers who operate smart solutions such as ERP and MES that link manufacturing data in real time, empirical research was conducted. As a result, it was analyzed that setting prioritization of big data has a positive effect on decision-making in management accounting. became In addition, it was found that big data prioritization has a mediating effect that indirectly affects smart solution performance by using big data in management accounting decision making. Through the research results, it will be possible to contribute as a prior research to develop a scale to evaluate the correlation between big data in the process of business decision making.

• Convergence Security Journal
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• v.15 no.4
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• pp.35-41
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• 2015

Internet of Things has a wide range of vulnerabilities are exposed to information security threats. However, this does not deal with the basic solution, the vaccine does not secure encryption for the data transmission. The encryption and authentication message transmitted from one node to the construction of the secure wireless sensor networks is required. In order to satisfy the constraint, and security requirements of the sensor network, lightweight encryption and authentication technologies, the light key management technology for the sensor environment it is required. Mandatory sensor network security technology, privacy protection technology subchannel attack prevention, and technology. In order to establish a secure wireless sensor networks encrypt messages sent between the nodes and it is important to authenticate. Lightweight it shall apply the intrusion detection mechanism functions to securely detect the presence of the node on the network. From the sensor node is not involved will determine the authenticity of the terminal authentication technologies, there is a need for a system. Network security technology in an Internet environment objects is a technique for enhancing the security of communication channel between the devices and the sensor to be the center.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1957-1963
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• 2017

This paper has proposed a new-type groundwater auto-monitoring system based on Multi-Sensor Device. The system adopted Multi-Sensor Device as host computer of data acquisition, used Windows Mobile which was prevalent operation system of Multi-Sensor Device. It adopted serial port CAN and RS485 as the communication interface between goundwater sensor Device and monitor host machine and utilized serial-linked multi-sensor design to measure effectively according to the depth of groundwater. We present a design for a groundwater monitoring system based on a network of wirelessly linked sensors. The proposed solution will enable groundwater researchers and decision makers to have quick access to the groundwater data with less effort and cost. Though our design is initially meant for groundwater monitoring, it can be easily adapted to other fields of environmental monitoring.