• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.228-236
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• 2014

One of major purposes of these standard technologies is to ensure interoperability between entities from different vendors and enable interworking between various technologies. As interoperability and interworking are essential for machine-to-machine communications (M2M) and Internet of Things (IoT) for them to achieve their ultimate goal, i.e., things to be connected each other, multiple standards organizations are now working on development M2M/IoT related specifications. This paper reviews the current activities of some of the most relevant standardization bodies in the area of M2M and IoT: third-generation partnership project (3GPP) core and radio network aspects, broadband forum, and oneM2M. The major features and issues being focused upon in these standards bodies are summarized. Finally, some key common trends among the different bodies are identified: a common service layer platform, new technologies mitigating an explosive growth of network traffic, and considerations and efforts related to the development of device management technologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.480-497
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• 2012

As a new business concept, machine-to-machine (M2M) communications are born from original telemetry technology with the intrinsic features of automatic data transmissions and measurement from remote sources typically by cable or radio. M2M includes a number of technologies that need to be combined in a compatible manner to enable its deployment over a broad market of consumer electronics. In order to provide better understanding for this emerging concept, the correlations among M2M, wireless sensor networks, cyber-physical systems (CPS), and internet of things are first analyzed in this paper. Then, the basic M2M architecture is introduced and the key elements of the architecture are presented. Furthermore, the progress of global M2M standardization is reviewed, and some representative applications (i.e., smart home, smart grid and health care) are given to show that the M2M technologies are gradually utilized to benefit people's life. Finally, a novel M2M system integrating intelligent road with unmanned vehicle is proposed in the form of CPS, and an example of cyber-transportation systems for improving road safety and efficiency are introduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.687-708
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• 2017

The machine-to-machine (M2M) communication is featured by tremendous number of devices, small data transmission, and large uplink to downlink traffic ratio. The massive access requests generated by M2M devices would result in the current medium access control (MAC) protocol in LTE/LTE-A networks suffering from physical random access channel (PRACH) overload, high signaling overhead, and resource underutilization. As such, fairness should be carefully considered when M2M traffic coexists with human-to-human (H2H) traffic. To tackle these problems, we propose an adaptive Slotted ALOHA (S-ALOHA) and time division multiple access (TDMA) hybrid protocol. In particular, the proposed hybrid protocol divides the reserved uplink resource blocks (RBs) in a transmission cycle into the S-ALOHA part for M2M traffic with small-size packets and the TDMA part for H2H traffic with large-size packets. Adaptive resource allocation and access class barring (ACB) are exploited and optimized to maximize the channel utility with fairness constraint. Moreover, an upper performance bound for the proposed hybrid protocol is provided by performing the system equilibrium analysis. Simulation results demonstrate that, compared with pure S-ALOHA and pure TDMA protocol under a target fairness constraint of 0.9, our proposed hybrid protocol can improve the capacity by at least 9.44% when ${\lambda}_1:{\lambda}_2=1:1$and by at least 20.53% when ${\lambda}_1:{\lambda}_2=10:1$, where ${\lambda}_1,{\lambda}_2$ are traffic arrival rates of M2M and H2H traffic, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1700-1706
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• 2016

To improve the number of simultaneous transmissions of machine-to-machine traffic in a spread spectrum ALOHA channel, we propose a new spreading technique called doubly truncated cyclic code shift keying (DTCCSK). By truncating the codeset of cyclic code shift keying, DTCCSK freely adjusts the spreading factor and the symbol length. As a result, DTCCSK exhibits both a high spectral efficiency of M-ary signaling and low implementation complexity of a direct sequence.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.288-293
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• 2011

To resolve ambiguities in speech act classification, various machine learning models have been proposed over the past 10 years. In this paper, we review these machine learning models and present the results of experimental comparison of three representative models, namely the decision tree, the support vector machine (SVM), and the maximum entropy model (MEM). In experiments with a goal-oriented dialogue corpus in the schedule management domain, we found that the MEM has lighter hardware requirements, whereas the SVM has better performance characteristics.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.181-186
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• 2014

In this paper, we propose an overload control scheme to resolve an overload problem in a random access channel of cellular machine-to-machine (M2M) communication networks. The M2M applications are characterized by small-sized data intermittently transmitted by a massive number of machines. Due to this characteristics, an overload situation in random access channel (RACH) can happen when a large number of devices try to send a signal via the RACH. To address this overload problem, we propose a scheme in which a base station estimates the total load in the network and controls the load by using a p-persistent method based on the estimated load.

• Information and Communications Magazine
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• v.20 no.11
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• pp.1550-1559
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• 2003

The convergence of fixed and wireless networks in data communication is providing the necessary driver for M2M commerce to take-off. The opportunities provided by M2M Commerce areonly limited by imagination. Automotive Fleet and Freight, Tolling, Water and Power Metering, Supply Chain Management including Asset Management, Remote Monitoring and Diagnostics, Energy Management and Access Control and Security are among the many M2M applications that are currently getting rolled out. ARC Group expects the worldwide solutions market to be worth in excess of US$ 100 billion by 2007. In addition, operator revenues worldwide from the transport of Telematics data alone will rise from US$ 3.5 billion in 2002 to US$ 78 billion by 2007. This paper discusses some of the lifestyle and business opportunities provided by M2M Commerce in the new ear of network convergence. It also provides some case studies to demonstrate the benefits of M2M Commerce across the supply chain. The key focus of the paper is on achieving enhanced lifestyle, cost reduction, improved profitability and enhanced customer relationship management through M2M Commerce.

• ETRI Journal
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• v.43 no.5
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• pp.787-798
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• 2021

This paper applies learning automata to improve the performance of a Q-learning based random access channel (QL-RACH) scheme in a cellular machine-to-machine (M2M) communication system. A prioritized learning automata QL-RACH (PLA-QL-RACH) access scheme is proposed. The scheme employs a prioritized learning automata technique to improve the throughput performance by minimizing the level of interaction and collision of M2M devices with human-to-human devices sharing the RACH of a cellular system. In addition, this scheme eliminates the excessive punishment suffered by the M2M devices by controlling the administration of a penalty. Simulation results show that the proposed PLA-QL-RACH scheme improves the RACH throughput by approximately 82% and reduces access delay by 79% with faster learning convergence when compared with QL-RACH.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1233-1235
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• 2016

It is desirable to place virtual machines for minimizing the number of operational servers in order to save energy in high-density cloud computing environment. However, the compacted servers can incur heat islands. This paper firstly finds out the relationship between the server utilization by the virtual machine placement and the energy consumption of servers and heat from servers. Then, this paper proposes a virtual machine placement algorithm to save energy consumed and avoid heat islands.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.215-226
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• 2023

Deep learning has made great strides in the field of unstructured data such as text, images, and audio. However, in the case of tabular data analysis, machine learning algorithms such as ensemble methods are still better than deep learning. To keep up with the performance of machine learning algorithms with good predictive power, several deep learning methods for tabular data have been proposed recently. In this paper, we review the latest deep learning models for tabular data and compare the performances of these models using several datasets. In addition, we also compare the latest boosting methods to these deep learning methods and suggest the guidelines to the users, who analyze tabular datasets. In regression, machine learning methods are better than deep learning methods. But for the classification problems, deep learning methods perform better than the machine learning methods in some cases.