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Journal articles on the topic 'Communications de type machine'

Author: Grafiati

Published: 15 March 2025

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1

Dutkiewicz, Eryk, Xavier Costa-Perez, Istvan Z. Kovacs, and Markus Mueck. "Massive Machine-Type Communications." IEEE Network 31, no. 6 (November 2017): 6–7. http://dx.doi.org/10.1109/mnet.2017.8120237.

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2

Osseiran, Afif, JaeSeung Song, Jose F. Monserrat, and Roland Hechwartner. "IoT and Machine Type Communications." IEEE Communications Standards Magazine 4, no. 2 (June 2020): 40. http://dx.doi.org/10.1109/mcomstd.2020.9139044.

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3

Zheng, Tongyi, Lei Ning, Qingsong Ye, and Fan Jin. "An XGB-Based Reliable Transmission Method in the mMTC Scenarios." Security and Communication Networks 2021 (December 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/9929051.

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Massive machine-type communications (mMTCs) for Internet of things are being developed thanks to the fifth-generation (5G) wireless systems. Narrowband Internet of things (NB-IoT) is an important communication technology for machine-type communications. It supports many different protocols for communication. The reliability and performance of application layer communication protocols are greatly affected by the retransmission time-out (RTO) algorithm. In order to improve the reliability and performance of machine-type communications, this study proposes a novel RTO algorithm UDP-XGB based on the user datagram protocol (UDP) and NB-IoT. It combines traditional algorithms with machine learning. The simulation results show that real round-trip time (RTT) is close to the RTO, which is obtained by this algorithm, and the reliability and performance of machine-type communications have improved.

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4

Dawy, Zaher, Walid Saad, Arunabha Ghosh, Jeffrey G. Andrews, and Elias Yaacoub. "Toward Massive Machine Type Cellular Communications." IEEE Wireless Communications 24, no. 1 (February 2017): 120–28. http://dx.doi.org/10.1109/mwc.2016.1500284wc.

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5

Jain, Puneet, Peter Hedman, and Haris Zisimopoulos. "Machine type communications in 3GPP systems." IEEE Communications Magazine 50, no. 11 (November 2012): 28–35. http://dx.doi.org/10.1109/mcom.2012.6353679.

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6

Choi, Dae-Sung, and Hyoung-Kee Choi. "An Group-based Security Protocol for Machine Type Communications in LTE-Advanced." Journal of the Korea Institute of Information Security and Cryptology 23, no. 5 (October 31, 2013): 885–96. http://dx.doi.org/10.13089/jkiisc.2013.23.5.885.

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7

Lai, Chengzhe, Rongxing Lu, Hui Li, Dong Zheng, and Xuemin Sherman Shen. "Secure machine-type communications in LTE networks." Wireless Communications and Mobile Computing 16, no. 12 (July 17, 2015): 1495–509. http://dx.doi.org/10.1002/wcm.2612.

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8

Yahiya, Tara I. "Towards Society Revolution." UKH Journal of Science and Engineering 2, no. 2 (December 26, 2018): 37–38. http://dx.doi.org/10.25079/ukhjse.v2n2y2018.pp37-38.

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It is expected that the 5G will change the landscape of the communication paradigm as it will offer huge number of device connections, high data rate, evolutionary channel modulation, etc. The 5G predicts to have billions of devices connected through its new scenarios involving Internet of Things (IoT), Machine Type Communications (MTC), Machine- to- Machine Communications (M2M) via the use of different types of devices including but not restricted to smartphones based IP packet.

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9

Iivari, Antti, Teemu Väisänen, Mahdi B. Alaya, Tero Riipinen, and Thierry Monteil. "Harnessing XMPP for Machine-to-Machine Communications & Pervasive Applications." Journal of Communications Software and Systems 10, no. 3 (March 16, 2017): 163. http://dx.doi.org/10.24138/jcomss.v10i3.121.

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An ever increasing number of interconnected embedded devices, or Machine-to-Machine (M2M) systems, are changing the way we live, work and play. M2M systems as a whole are typically characterized by the diversity in both the type of device and type of network access technology employed, and such systems are often still today task-specific and built for just one specific application. Smart lighting, remote monitoring and control of all kinds of consumer devices and industrial equipment, safety and security monitoring devices and smart health and fitness products, exemplify this revolution of intercommunicating machines. However, the differences in communication technologies and data formats among such devices and systems are leading to a huge complexity explosion problem and a strongly fragmented market, with no true interoperability. Due to these problems, the full potential of M2M technology has yet to be fulfilled. In this paper, we examine the suitability of the Extensible Messaging and Presence Protocol (XMPP) and experiment with its potential to rise to the challenge of machine-to-machine communications and meet the needs of modern pervasive applications. Experimental implementations and some proof-of-concept solutions are also presented.

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10

Liu, Liang, Erik G. Larsson, Petar Popovski, Giuseppe Caire, Xiaoming Chen, and Saeed R. Khosravirad. "Guest Editorial: Massive Machine-Type Communications for IoT." IEEE Wireless Communications 28, no. 4 (August 2021): 56. http://dx.doi.org/10.1109/mwc.2021.9535445.

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11

Bagaa, Miloud, Tarik Taleb, Ali Chelli, and Hamed Hellaoui. "Constraint Hubs Deployment for Efficient Machine-Type Communications." IEEE Transactions on Wireless Communications 17, no. 12 (December 2018): 7936–51. http://dx.doi.org/10.1109/twc.2018.2873293.

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12

Zhao, Caidan, Lianfen Huang, Yifeng Zhao, and Xiaojiang Du. "Secure Machine-Type Communications toward LTE Heterogeneous Networks." IEEE Wireless Communications 24, no. 1 (February 2017): 82–87. http://dx.doi.org/10.1109/mwc.2017.1600141wc.

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13

Atat, Rachad, Lingjia Liu, Nicholas Mastronarde, and Yang Yi. "Energy Harvesting-Based D2D-Assisted Machine-Type Communications." IEEE Transactions on Communications 65, no. 3 (March 2017): 1289–302. http://dx.doi.org/10.1109/tcomm.2016.2639507.

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14

Zhang, Jingjing, Michael Mao Wang, and Tingting Xia. "Practical Synchronization Waveform for Massive Machine-Type Communications." IEEE Transactions on Communications 67, no. 2 (February 2019): 1467–79. http://dx.doi.org/10.1109/tcomm.2018.2875009.

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15

Taleb, Tarik, Adlen Ksentini, and Abdellatif Kobbane. "Lightweight Mobile Core Networks for Machine Type Communications." IEEE Access 2 (2014): 1128–37. http://dx.doi.org/10.1109/access.2014.2359649.

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16

Li, Ran, Peinan Hao, Fengyuan Sun, Yanling Li, and Lei You. "Machine-Type Video Communication Using Pretrained Network for Internet of Things." Security and Communication Networks 2021 (December 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/6184797.

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With the increasing demand for internet of things (IoT) applications, machine-type video communications have become an indispensable means of communication. It is changing the way we live and work. In machine-type video communications, the quality and delay of the video transmission should be guaranteed to satisfy the requirements of communication devices at the condition of limited resources. It is necessary to reduce the burden of transmitting video by losing frames at the video sender and then to increase the frame rate of transmitting video at the receiver. In this paper, based on the pretrained network, we proposed a frame rate up-conversion (FRUC) algorithm to guarantee low-latency video transmitting in machine-type video communications. At the IoT node, by periodically discarding the video frames, the video sequences are significantly compressed. At the IoT cloud, a pretrained network is used to extract the feature layers of the transmitted video frames, which is fused into the bidirectional matching to produce the motion vectors (MVs) of the losing frames, and according to the output MVs, the motion-compensated interpolation is implemented to recover the original frame rate of the video sequence. Experimental results show that the proposed FRUC algorithm effectively improve both objective and subjective qualities of the transmitted video sequences.

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17

Ouaissa, Mariya, and Abdallah Rhattoy. "A secure group based authentication protocol for machine to machine communications in LTE-WLAN interworking architecture." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (November 1, 2019): 848. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp848-859.

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Machine to Machine (M2M) communication has been used in applications such as telemetry, industry, automation and health. Support for a large number of devices has been considered an essential requirement in M2M communications. During this time, security is the most important challenge; M2M cannot access secure networks through effective authentication, all relevant M2M applications cannot be accepted. The challenge of M2M research is authentication by the group when a large number of M2M devices simultaneously accessing the network will cause severe authentication signaling congestion. The group based model under an M2M architecture, especially when the Machine Type Communication (MTC) devices belong to the non 3rd Generation Partnership Project (3GPP) network, will face a new challenge of access authentication. In this paper, we propose a group based authentication and key agreement protocol for machine type communications combining Elliptic Curve based Diffie-Hellman (ECDH) on the Extensible Authentication Protocol (EAP). Compared to EAP-AKA and other existing authentication protocols, our solution provides increased security against various malicious activities and better performance in terms of signaling overhead, bandwidth consumption and transmission cost.

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18

Su, Jingrui, Guangliang Ren, and Bo Zhao. "NOMA-Based Coded Slotted ALOHA for Machine-Type Communications." IEEE Communications Letters 25, no. 7 (July 2021): 2435–39. http://dx.doi.org/10.1109/lcomm.2021.3067932.

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19

Li, Yuanjie, Jincheng Dai, Zhongwei Si, Kai Niu, Chao Dong, Jiaru Lin, Sen Wang, and Yifei Yuan. "Unsourced multiple access for 6G massive machine type communications." China Communications 19, no. 3 (March 2022): 70–87. http://dx.doi.org/10.23919/jcc.2022.03.005.

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20

Choi, Jinho. "Secret key transmission for OFDM based machine type communications." Journal of Communications and Networks 19, no. 4 (August 2017): 363–70. http://dx.doi.org/10.1109/jcn.2017.000060.

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21

Ijaz, Ayesha, Lei Zhang, Atta ul Quddus, and Rahim Tafazolli. "HARQ in Relay-Assisted Transmission for Machine Type Communications." IEEE Wireless Communications Letters 5, no. 2 (April 2016): 172–75. http://dx.doi.org/10.1109/lwc.2016.2514423.

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22

Husain, Syed, Andreas Kunz, Athul Prasad, JaeSeung Song, and Takeshi Koshimizu. "Optimisations in machine type communications for sensor data networking." IET Wireless Sensor Systems 6, no. 4 (August 1, 2016): 102–8. http://dx.doi.org/10.1049/iet-wss.2015.0052.

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23

de Figueiredo, Felipe A. P., Fabbryccio A. C. M. Cardoso, João Paulo Miranda, Ingrid Moerman, Claudio F. Dias, and Gustavo Fraidenraich. "Large-Scale Antenna Systems and Massive Machine Type Communications." International Journal of Wireless Information Networks 27, no. 3 (June 4, 2020): 317–39. http://dx.doi.org/10.1007/s10776-020-00487-3.

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24

Chen, Li-Sheng, Chih-Hsiang Ho, Cheng-Chang Chen, Yu-Shan Liang, and Sy-Yen Kuo. "Repetition with Learning Approaches in Massive Machine Type Communications." Electronics 11, no. 22 (November 8, 2022): 3649. http://dx.doi.org/10.3390/electronics11223649.

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In the 5G massive machine type communication (mMTC) scenario, user equipment with poor signal quality requires numerous repetitions to compensate for the additional signal attenuation. However, an excessive number of repetitions consumes additional wireless resources, decreasing the transmission rate, and increasing the energy consumption. An insufficient number of repetitions prevents the successful deciphering of the data by the receivers, leading to a high bit error rate. The present study developed adaptive repetition approaches with the k-nearest neighbor (KNN) and support vector machine (SVM) to substantially increase network transmission efficacy for the enhanced machine type communication (eMTC) system in the 5G mMTC scenario. The simulation results showed that the proposed repetition with the learning approach effectively improved the probability of successful transmission, the resource utilization, the average number of repetitions, and the average energy consumption. It is therefore more suitable for the eMTC system in the mMTC scenario than the common lookup table.

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25

Narender Reddy Kampelli and Chandrasekhar Reddy Puttha. "Energy-Efficient Dynamic MAC Protocol for M2M Communication." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 02 (February 24, 2024): 105–12. http://dx.doi.org/10.47392/irjaeh.2024.0020.

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In the Internet of Things, which is vital for smart city applications, a vast network of battery-operated Machine Type Communication Devices (MTCDs) requires Machine Type Communication. These MTCDs connect directly to a network with a base station, facilitating a wide range of smart city functionalities. The development of energy-efficient communication protocols is essential to support the deployment of energy-constrained battery-operated devices. These protocols aim to minimize energy consumption, thereby enhancing network lifetime. Additionally, they must address scalability, reduce collisions and delays, and ensure a high Quality of Service (QoS) to effectively manage the vast number of devices involved. Clustering is a viable strategy to enhance scalability and energy efficiency in network communications. However, within this framework, particularly in the proposed Power Efficient Dynamic MAC protocol for M2M Communication (PMAC), there's still a need for energy-efficient communication between the Machine Type Communication Devices (MTCDs). This requirement is crucial to optimize the overall performance and sustainability of the network. In this paper, we analyses the Energy Efficient CSMA/CA (E-CSMA/CA) protocol, specifically developed for facilitating communication between Machine Type Communication Devices (MTCDs) in short-range communications. This analysis is set within the context of smart city applications, with a particular focus on smart metering. This approach underscores the importance of efficient, reliable communication protocols in modern urban infrastructure, especially for applications like smart metering that are integral to the smart city ecosystem.

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26

Mehmood, Yasir, Lei Zhang, and Anna Förster. "Power Consumption Modeling of Discontinuous Reception for Cellular Machine Type Communications." Sensors 19, no. 3 (February 1, 2019): 617. http://dx.doi.org/10.3390/s19030617.

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Machine-type communication (MTC) is an emerging communication trend where intelligent machines are capable of communicating with each other without human intervention. Mobile cellular networks, with their wide range, high data rates, and continuously decreasing costs, offer a good infrastructure for implementing them. However, power consumption is a great issue, which has recently been addressed by 3GPP (3rd Generation Partnership Project) by defining power-saving mechanisms. In this paper, we address the problem of modeling these power-saving mechanisms. Currently existing modeling schemes do not consider the full range of states in the discontinuous reception (DRX) mechanism in LTE-A networks. We propose a semi-Markov based analytical model, which closes this gap and shows very good results in terms of predicting performance evaluation metrics, such as the power-saving factor and wake-up latency of MTC devices compared to simulation experiments. Furthermore, we offer an evaluation of the DRX parameters and their impact on power consumption of MTC devices.

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27

Urošević, Uglješa, and Uroš Bibić. "Technologies for enabling MTC in vehicles domain." Tehnika 79, no. 5 (2024): 587–93. http://dx.doi.org/10.5937/tehnika2405587u.

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MTC (Machine Type Communications) and the technologies that enable them are at the peak of their development and are gaining increasing significance in the management and control of M2M devices. Since these objects possess intelligence, communication capabilities, and the ability to interact with their environment, they are considered a key component of future smart cities, buildings, vehicles, etc. This paper discusses the technologies that enable MTCs in the domain of vehicles - V2X, the architectures of existing communication systems, as well as the goals and challenges of further development of machine communications in this field.

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28

Xia, Nian, Hsiao-Hwa Chen, and Chu-Sing Yang. "Emerging Technologies for Machine-Type Communication Networks." IEEE Network 34, no. 1 (January 2020): 214–22. http://dx.doi.org/10.1109/mnet.001.1900132.

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29

Ma, Guoyu, Bo Ai, Fanggang Wang, Xia Chen, Zhangdui Zhong, Zhuyan Zhao, and Hao Guan. "Coded Tandem Spreading Multiple Access for Massive Machine-Type Communications." IEEE Wireless Communications 25, no. 2 (April 2018): 75–81. http://dx.doi.org/10.1109/mwc.2018.1700107.

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30

Di Renna, Roberto B., Carsten Bockelmann, Rodrigo C. de Lamare, and Armin Dekorsy. "Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions." IEEE Access 8 (2020): 180928–54. http://dx.doi.org/10.1109/access.2020.3027523.

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31

Andres-Maldonado, Pilar, Pablo Ameigeiras, Jonathan Prados-Garzon, Jorge Navarro-Ortiz, and Juan M. Lopez-Soler. "Narrowband IoT Data Transmission Procedures for Massive Machine-Type Communications." IEEE Network 31, no. 6 (November 2017): 8–15. http://dx.doi.org/10.1109/mnet.2017.1700081.

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32

Taleb, Tarik, and Andreas Kunz. "Machine type communications in 3GPP networks: potential, challenges, and solutions." IEEE Communications Magazine 50, no. 3 (March 2012): 178–84. http://dx.doi.org/10.1109/mcom.2012.6163599.

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33

Cheng, Ming-Yuan, Guan-Yu Lin, Hung-Yu Wei, and Alex Hsu. "Overload control for Machine-Type-Communications in LTE-Advanced system." IEEE Communications Magazine 50, no. 6 (June 2012): 38–45. http://dx.doi.org/10.1109/mcom.2012.6211484.

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34

Islam, Mohammad, Abd-elhamid Taha, and Selim Akl. "A survey of access management techniques in machine type communications." IEEE Communications Magazine 52, no. 4 (April 2014): 74–81. http://dx.doi.org/10.1109/mcom.2014.6807949.

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35

Di Renna, Roberto B., and Rodrigo C. de Lamare. "Iterative List Detection and Decoding for Massive Machine-Type Communications." IEEE Transactions on Communications 68, no. 10 (October 2020): 6276–88. http://dx.doi.org/10.1109/tcomm.2020.3007525.

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36

Ali, Samad, Nandana Rajatheva, and Walid Saad. "Fast Uplink Grant for Machine Type Communications: Challenges and Opportunities." IEEE Communications Magazine 57, no. 3 (March 2019): 97–103. http://dx.doi.org/10.1109/mcom.2019.1800475.

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37

Di Renna, Roberto B., and Rodrigo C. de Lamare. "Adaptive Activity-Aware Iterative Detection for Massive Machine-Type Communications." IEEE Wireless Communications Letters 8, no. 6 (December 2019): 1631–34. http://dx.doi.org/10.1109/lwc.2019.2932674.

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38

Shahab, Muhammad Basit, Sarah J. Johnson, Mahyar Shirvanimoghaddam, Marwa Chafii, Ertugrul Basar, and Mischa Dohler. "Index Modulation Aided Uplink NOMA for Massive Machine Type Communications." IEEE Wireless Communications Letters 9, no. 12 (December 2020): 2159–62. http://dx.doi.org/10.1109/lwc.2020.3015920.

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39

Gu, Yu, Qimei Cui, Qiang Ye, and Weihua Zhuang. "Game-Theoretic Optimization for Machine-Type Communications Under QoS Guarantee." IEEE Internet of Things Journal 6, no. 1 (February 2019): 790–800. http://dx.doi.org/10.1109/jiot.2018.2856898.

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40

Zhou, Jianhong, Gang Feng, Tak-Shing Peter Yum, Mu Yan, and Shuang Qin. "Online Learning-Based Discontinuous Reception (DRX) for Machine-Type Communications." IEEE Internet of Things Journal 6, no. 3 (June 2019): 5550–61. http://dx.doi.org/10.1109/jiot.2019.2903347.

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41

Yeo, Woon-Young, Yong-Hee Jo, and Dong-Jun Lee. "Overload control of massive random access for machine-type communications." Expert Systems with Applications 88 (December 2017): 217–29. http://dx.doi.org/10.1016/j.eswa.2017.06.018.

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42

Chang, Hui-Ling, and Meng-Hsun Tsai. "Optimistic DRX for Machine-Type Communications in LTE-A Network." IEEE Access 6 (2018): 9887–97. http://dx.doi.org/10.1109/access.2018.2791466.

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43

Song, Jiho, Byungju Lee, Song Noh, and Jong-Ho Lee. "Limited Feedback Designs for Machine-Type Communications Exploiting User Cooperation." IEEE Access 7 (2019): 95154–69. http://dx.doi.org/10.1109/access.2019.2928633.

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44

Moon, Jihun, and Yujin Lim. "Collision Reduction for Machine Type Communications in LTE-A Networks." International Journal of Future Generation Communication and Networking 9, no. 9 (September 30, 2016): 143–52. http://dx.doi.org/10.14257/ijfgcn.2016.9.9.13.

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45

Zhou, Kaijie, Navid Nikaein, and Thrasyvoulos Spyropoulos. "LTE/LTE-A Discontinuous Reception Modeling for Machine Type Communications." IEEE Wireless Communications Letters 2, no. 1 (February 2013): 102–5. http://dx.doi.org/10.1109/wcl.2012.120312.120615.

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46

Formaggio, Francesco, Andrea Munari, and Federico Clazzer. "On receiver diversity for grant-free based machine type communications." Ad Hoc Networks 107 (October 2020): 102245. http://dx.doi.org/10.1016/j.adhoc.2020.102245.

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47

Gavrilovska, Liljana, Valentin Rakovic, and Aleksandar Ichkov. "Virtualization Approach for Machine-Type Communications in Multi-RAT Environment." Wireless Personal Communications 100, no. 1 (March 14, 2018): 67–79. http://dx.doi.org/10.1007/s11277-018-5611-y.

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48

Osseiran, Afif, Omar Elloumi, JaeSeung Song, and Jose F. Monserrat. "IoT and Machine-Type Communication." IEEE Communications Standards Magazine 3, no. 2 (June 2019): 44. http://dx.doi.org/10.1109/mcomstd.2019.8823844.

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49

Hechwartner, Roland, Jose F. Monserrat, Afif Osseiran, and JaeSeung Song. "Series Editorial: IoT and Machine-Type Communication." IEEE Communications Standards Magazine 6, no. 2 (June 2022): 50. http://dx.doi.org/10.1109/mcomstd.2022.9855455.

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50

Ramírez-Durán, Víctor Julio, Idoia Berges, and Arantza Illarramendi. "ExtruOnt: An ontology for describing a type of manufacturing machine for Industry 4.0 systems." Semantic Web 11, no. 6 (October 29, 2020): 887–909. http://dx.doi.org/10.3233/sw-200376.

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Semantically rich descriptions of manufacturing machines, offered in a machine-interpretable code, can provide interesting benefits in Industry 4.0 scenarios. However, the lack of that type of descriptions is evident. In this paper we present the development effort made to build an ontology, called ExtruOnt, for describing a type of manufacturing machine, more precisely, a type that performs an extrusion process (extruder). Although the scope of the ontology is restricted to a concrete domain, it could be used as a model for the development of other ontologies for describing manufacturing machines in Industry 4.0 scenarios. The terms of the ExtruOnt ontology provide different types of information related with an extruder, which are reflected in distinct modules that constitute the ontology. Thus, it contains classes and properties for expressing descriptions about components of an extruder, spatial connections, features, and 3D representations of those components, and finally the sensors used to capture indicators about the performance of this type of machine. The ontology development process has been carried out in close collaboration with domain experts.

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