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UHF RFID Tag Identification Method Based on Physical-layer Features of Backscatter Networks
Yoonseo Kim, Hoorin Park, Minwoo Joo, Wonjun Lee
http://doi.org/10.5626/JOK.2021.48.9.1061
Radio-frequency identification (RFID) systems are becoming essential components in Internet of Things (IoT) networks by virtue of their cost and energy efficiency. Especially, in ultra high frequency (UHF) RFID systems, the process of identifying individual tags is crucial because different tags communicate in a passive manner. However, the tag identifiers used in existing systems are vulnerable to be replicated or predicted due to limited tag operation resources and memory, which leads to severe security threats. In this paper, we propose a technology to extract the unique physical-layer characteristics, which are difficult to be forged, and utilize them for tag identification. The proposed method consists of a fingerprint extraction algorithm to obtain the physical-layer features of time interval error and phase by analyzing the backscatter signals of the tags, and a tag identification algorithm to distinguish tags based on their extracted fingerprints. We provide a model of backscatter signals and analyze the identification accuracy of the proposed method with varying signal-to-noise ratios.
SDN-based Task Allocation for IoT-Fog Network
Dzaky Zakiyal Fawwaz, Sang-Hwa Chung
http://doi.org/10.5626/JOK.2020.47.5.535
The Internet of Things requires that resources are allocated for it to execute tasks. Utilizing fog computing may have many benefits in these circumstances since it offers distributed resources that could give lower latency, lower bandwidth, and various other advantages compared to cloud computing. If we wish to use fog computing, we need to consider how to perform task allocation over multiple fog nodes. The kind of networks used are likely to have not only continuous incoming IoT tasks but also other dynamic network conditions. Hence, we introduce dynamic task allocation that utilizes a Software-Defined Network. Our system handles each incoming task by considering network and fog node statistics. The task allocation method must select the optimal pair of fog nodes and also the path because there are multiple fog nodes and many feasible related paths to deal with. Thus, we define the problem to be one of finding the multi-source, single-target, shortest path on a network graph, to help solve the problem we formulate the joint fog node-link utilization cost. We also propose a Many-to-One Shortest Path algorithm to solve such a problem. The experiments we performed to evaluate our system show that it outperforms the previous state-of-the-art work. Averaging over all the experiment"s topologies, our method achieves higher tasks per second completion rate, a lower response time and lower fog node/link utilization with scores of 37 tasks/sec, 676ms and 65%/24% utilization, respectively.
Recommending Similar Users Through Interaction Analysis in Social IoT Environments
Yeondong Kim, Dojin Choi, Jongtae Lim, Kyoungsoo Bok, Jaesoo Yoo
http://doi.org/10.5626/JOK.2020.47.1.61
Recently, there has been extensive research on the social internet of things(Social IoT) that combines social networks and internet of things. Social IoT is integral for the connection between as well as for establishing relationships between users and objects for sharing information between objects or users. In this paper, we propose a method that recommends similar users by considering interaction between objects and users in the social IoT environments. The similar users can be found by analyzing the behavior of the users around the object. The proposed method improves the accuracy of similarity by calculating similarity in determining interests based on documents written by users in social networks. Finally, it recommends Top-N users as similar users based on the two similarity values. To show the superiority of the proposed method, we conducted various performance evaluations.
CEP Rule Distribution Algorithm for In-network Processing in an IoT Network Environment
http://doi.org/10.5626/JOK.2018.45.7.722
As the number of IoT devices increases, data coming from devices are also increasing exponentially. The data generated from devices are stored and managed through a system structure using the database. However, to manage the surging data, the existing database is limited in terms of maintenance costs and in real time. Too overcome these limitations, Complex Event Processing (CEP), which processes data as much as possible within the network, has emerged, and data processing is being carried out using this strategy. In this paper, we propose a CEP Rule distribution algorithm which can reduce server burden and guarantee network performance through distribution of the CEP Rule in an IoT environment. To prove this, we perform a small experiment using open source, such as the OpenWSN and TelosB node, and verify the mitigation of server load and the performance of data processing according to the algorithm.
Activity Prediction from Sensor Data using Convolutional Neural Networks and an Efficient Compression Method
Woojeong Jin, Dongjin Choi, Youngjin Kim, U Kang
http://doi.org/10.5626/JOK.2018.45.6.564
The identification of the number of occupants and their activities using the IoT system in a building is an important task to improve the power efficiency and reduce the cost of using smart cooling/heating systems. In the actual building management system, it is possible to use equipment such as a camera to understand the current situation in the room, and to directly determine the number of occupants and their types of behavior. However, identifying the number of people and behavior types in this way is inefficient and requires a large amount of storage space for data. In this study, indoor sensor data were collected using an infrared Grid-Eye sensor and noise sensor. Based on this data, we also propose a deep learning model that captures the number of participants and behavior patterns and a deep learning model that considers the temporal characteristics of data. The proposed model identifies the number of people with an accuracy of about 95.3% and human activities with an accuracy of 90.9%. We also propose a method to reduce the storage space while minimizing the loss of accuracy using truncated SVD.
Global Discovery Service for Enhancing Performance of Intra- and Inter-Discovery Services in the Internet of Things
Kiwoong Kwon, Dongsoo Kim, Wondeuk Yoon, Daeyoung Kim
http://doi.org/10.5626/JOK.2018.45.5.502
The smart things on the Internet of Things (IoT) are connected to each other to generate a huge amount of thing data. This data is being stored in globally distributed repositories and used in a variety of IoT applications. Therefore, it is essential to access and retrieve a source of data repositories where the thing data is stored. GS1, an international nonprofit organization, was the first to propose the Discovery Service (DS), which searches for thing data repositories associated with the given thing. However, the existing DS studies focus on improving the performance of Inter-DS, which finds a specific one among distributed Intra-DS, so that the performance degradation of the Intra-DS may cause the degradation of the overall DS performance. To alleviate this problem, we propose the Global-DS (GDS) that considers both Inter- and Intra-DS performance. For performance evaluation, we constructed an experimental testbed, and tested the throughput and delay of GDS. The results showed that data partitioning, load balancing and caching improve the performance of GDS.
Network Topology Discovery with Load Balancing for IoT Environment
Hyunsu Park, Jinsoo Kim, Moosung Park, Youngbae Jeon, Jiwon Yoon
http://doi.org/10.5626/JOK.2017.44.10.1071
With today"s complex networks, asset identification of network devices is becoming an important issue in management and security. Because these assets are connected to the network, it is also important to identify the network structure and to verify the location and connection status of each asset. This can be used to identify vulnerabilities in the network architecture and find solutions to minimize these vulnerabilities. However, in an IoT(Internet of Things) network with a small amount of resources, the Traceroute packets sent by the monitors may overload the IoT devices to determine the network structure. In this paper, we describe how we improved the existing the well-known double-tree algorithm to effectively reduce the load on the network of IoT devices. To balance the load, this paper proposes a new destination-matching algorithm and attempts to search for the path that does not overlap the current search path statistically. This balances the load on the network and additionally balances the monitor"s resource usage.
A Route Repair Scheme for Reducing DIO Poisoning Overhead in RPL-based IoT Networks
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.
Member Organization-based Service Recommendation for User Groups in Internet of Things Environments
Recommender systems can be used to assist users in selecting required services for their tasks in Internet of Things (IoT) environments in which diverse services can be provided by utilizing IoT devices. Traditional research on recommendation mainly focuses on predicting preferences of individual users. However, in IoT environments, not only individual users but also groups of users can access services in the environments. In this study, we analyzed user groups" preferences on services and developed service recommendation approach for new groups that do not have a history of accessing IoT-services in a certain place. Our approach extends the traditional user-based collaborative filtering by considering the similarity between user groups based on their member organization. We conducted experiments with a real-world dataset collected from IoT testbed environments. The results demonstrate that the proposed approach is effective to recommend services to new user groups in IoT environments.
Smart Fog : Advanced Fog Server-centric Things Abstraction Framework for Multi-service IoT System
Gyeonghwan Hong, Eunsoo Park, Sihoon Choi, Dongkun Shin
Recently, several research studies on things abstraction framework have been proposed in order to implement the multi-service Internet of Things (IoT) system, where various IoT services share the thing devices. Distributed things abstraction has an IoT service duplication problem, which aggravates power consumption of mobile devices and network traffic. On the other hand, cloud server-centric things abstraction cannot cover real-time interactions due to long network delay. Fog server-centric things abstraction has limits in insufficient IoT interfaces. In this paper, we propose Smart Fog which is a fog server-centric things abstraction framework to resolve the problems of the existing things abstraction frameworks. Smart Fog consists of software modules to operate the Smart Gateway and three interfaces. Smart Fog is implemented based on IoTivity framework and OIC standard. We construct a smart home prototype on an embedded board Odroid-XU3 using Smart Fog. We evaluate the network performance and energy efficiency of Smart Fog. The experimental results indicate that the Smart Fog shows short network latency, which can perform real-time interaction. The results also show that the proposed framework has reduction in the network traffic of 74% and power consumption of 21% in mobile device, compared to distributed things abstraction.
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