• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.1-10
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• 2021

The Internet of things (IoT) is the main advancement in data processing and communication technologies. In IoT, intelligent devices play an exciting role in wireless communication. Although, sensor nodes are low-cost devices for communication and data gathering. However, sensor nodes are more vulnerable to different security threats because these nodes have continuous access to the internet. Therefore, the multiparty security credential-based key generation mechanism provides effective security against several attacks. The key generation-based methods are implemented at sensor nodes, edge nodes, and also at server nodes for secure communication. The main challenging issue in a collaborative key generation scheme is the extensive multiplication. When the number of parties increased the multiplications are more complex. Thus, the computational cost of batch key and multiparty key-based schemes is high. This paper presents a Secure Multipart Key Distribution scheme (SMKD) that provides secure communication among the nodes by generating a multiparty secure key for communication. In this paper, we provide node authentication and session key generation mechanism among mobile nodes, head nodes, and trusted servers. We analyzed the achievements of the SMKD scheme against SPPDA, PPDAS, and PFDA schemes. Thus, the simulation environment is established by employing an NS 2. Simulation results prove that the performance of SMKD is better in terms of communication cost, computational cost, and energy consumption.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1117-1127
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• 2017

With the advent of Internet of Things (IoT), the number of devices connected to Internet has rapidly increased, but the security for IoT is still vulnerable. It is difficult to integrate existing security technologies due to generating a large amount of traffic by using different protocols to use various IoT devices according to purposes and to operate in a low power environment. Therefore, in this paper, we propose a normal network behavior profiling method based on big data analysis techniques. The proposed method utilizes a Hadoop/Hive for Big Data analytics and an R for statistical computing. Also we verify the effectiveness of the proposed method through a simulation.

• Annual Conference of KIPS
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• 2020.05a
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• pp.231-234
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• 2020

In the past few years, the industrial internet of things (IIoT) has received great attention in various industrial sectors which have potentially increased a high level of integrity, availability, and scalability. The increasing of IIoT is expected to create new smart industrial enterprises and build the next generation smart system. However existing IIoT systems rely on centralized servers that are vulnerable to a single point of failure and malicious attack, which exposes the data to security risks and storage. To address the above issues, blockchain is widely considered as a promising solution, which can build a secure and efficient environment for data storing, processing and sharing in IIoT. In this paper, we propose a decentralized, peer-to-peer platform for secure data storing in industrial IoT base on the ethereum blockchain. We exploit ethereum to ensure data security and reliability when smart devices store the data.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.215-222
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• 2015

In this paper, the Digital DoorLock by Internet of Things (DDiT) is introduced. In order to implement DDiT, an integrated micro-controller platform, Arduino is used to control an existing digital doorlock and an android type smart phone is adopted as a mobile platform. One of the advantages of DDiT is that it can be added to an existing digital doorlock and a smart phone application is used as a digital key. Owing to the smart phone application, several other types of applications could also be made. Therefore, DDiT could be used effectively and conveniently in ordinary homes as well as in high security applications such as in hotels, institutes, and companies.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.10
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• pp.983-988
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• 2016

Internet of Things Iranian people and things, things and things are connected through the Cloud. It can significantly save energy through a combination of LED lighting as a new ICT technologies and industry-to provide a human-centric, eco-friendly, and the content is embedded multifunction solutions that meet your needs, environment, implementation time according to user requirements, technology It can be systematized as converged next-generation lighting. In this paper, we have developed regarding whether the building in relation to the LED lighting control system using smart devices and Cloud-based user as a human connection through the board to the Arduino Yun lit LED lighting, wireless smart device or to the Cloud or off. After the Arduino Yun is connected to the Internet, taking the current date and time information from the Linux shell command used the way coming across the bridge (BRIDGE) its value.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.65-69
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• 2018

In this paper, we propose an interworking architecture for building indoor air quality monitoring server (BEMS) using IIoT (Industrial Internet of Things). The proposed monitoring server adopts IIoT-based standard protocol so that interaction with BEMS installed in existing buildings can be performed easily. It can effectively communicate with indoor air quality measurement sensor installed in the building based on IIoT, Indoor air quality monitoring is possible. We implemented a proposed monitoring server, and confirmed the availability and monitoring of data from sensors in the building.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of KIISE
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• v.42 no.10
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• pp.1268-1279
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• 2015

In order to manage a large number of devices connected to the Internet of Things (IoT), the Internet Engineering Task Force (IETF) proposed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). The route of the RPL network is generated through the use of an Objective Function (OF) that is suitable for the service that is required for the IoT network. Since the route of the RPL network is conventionally simply chosen only by considering the link quality between the nodes, it is sensible to seek an OF that can also provide better Quality of Service (QoS). In previous studies, the end-to-end delay might possibly be sub-optimal because they only deal with problems related to the reduction of energy consumption and not to the link quality on the path to the sink node. In this study, we propose a scheme that reduces the end-to-end delay but also gives full consideration to both the quality on the entire route to the destination and to the expected lifetime of nodes with bottlenecks from heaped traffic. Weighting factors for the proposed scheme are chosen by experiments and the proposed scheme can reduce the end-to-end delay and the energy consumption of previous studies by 20.8% and 10.5%, respectively.

• Information Systems Review
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• v.18 no.4
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• pp.173-187
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• 2016

This study on the social effects of Internet of Things (IoTs) provides an overview of future job prospects through the scenario planning approach, highlighting the challenges and opportunities that IoTs will bring in the future. IoTs and the related field of technological innovations have become increasingly important in both academic and business communities in the past few years because of computing power breakthrough and its price drop. IoTs enables people to deal with routine works efficiently and challenges them even in non-routine and/or cognitive tasks, which are considered a unique area for individuals. The scenario planning analysis helps us to define the uncertain boundary and to estimate the potential opportunities and inherent threats to provide decision makers with a mind map on how the development of IoTs can influence employment. To assess the potential effects on jobs described in our scenarios, we briefly examine the local structure of employment and discuss which careers are expected to decline or grow in particular among the 52 standard occupational classifications in Korea.

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