Massive connectivity with machine learning for the Internet of Things
| Autor: | Abdullah Balci, Radosveta Sokullu |
|---|---|
| Přispěvatelé: | Ege Üniversitesi |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science Internet of Things 02 engineering and technology Machine learning computer.software_genre 0202 electrical engineering electronic engineering information engineering Wireless Aloha Access network Grand-based access business.industry Wireless network NOMA 020206 networking & telecommunications Spectral efficiency Telecommunications network Random access Handshaking 020201 artificial intelligence & image processing Artificial intelligence business Grant-free access computer Communication channel |
| Popis: | Driven by the need to ensure the connectivity of an unprecedentedly huge number of IoT devices with no human intervention the issues of massive connectivity have recently become one of the main research areas in IoT studies. Conventional wireless communication technologies are designed for Human-to-Human (H2H) communication which leads to major problems in primary access, channel utilization and spectrum efficiency when massive numbers of devices require connectivity. Current random access procedures are based on a four-step handshaking with control messages which contradicts the requirements of IoT applications in terms of small data payloads and low complexity. Targeted channel utilization and spectrum efficiency cannot be achieved using traditional orthogonal approaches. Thus the goal of our work is to review the most recent developments and critically evaluate the existing work related to the evolution of network access methods in the new communication era. The paper covers three major aspects: first the primary random access procedures, proposed for IoT communications are discussed. The second aspect focuses on the approaches for integration of existing random multiple access schemes with non-orthogonal multiple access methods (NOMA). This integration of random access procedures with NOMA opens a new research trend in the field of massive connectivity. Operating on space domains additional to the physical domain such as code and power domains, NOMA integration targets increased channel utilization and spectrum efficiency to complement the flexibility of random access. On the other hand, the design of efficient algorithms for massive connectivity in IoT is also challenged by the highly application and environmentally dependent traffic model. A new angle of tackling this problem has emerged thanks to the extensive developments in machine learning and the possibilities of their incorporation in communication networks. Thus, the final aspect this review paper addresses are the newly emerging research directions of incorporating machine learning (ML) methods for providing efficient IoT connectivity. Breakthrough ML techniques allow wireless networking devices to perform transmissions by learning and building knowledge about the communication and networking environment. A critical evaluation of the large body of work accumulated in this area in the most recent years and outlining of some major open research issues concludes the paper. © 2020 Elsevier B.V. Ege Üniversitesi: FDK-2020-21820 This study is supported by Ege University Scientific Research Projects Coordination Unit. Project Number: FDK-2020-21820. Radosveta Sokullu received her PhD in Telecommunications in 1992 from the Technical University Sofia and joined the Dept. of EEE at Ege University as an Assistant Professor in 2000. She became Head of Telecommunications Branch in 2004 and Associate Professor at the same department in 2016. Currently she is also Head of the Wireless Communications Lab. Her current research covers different wireless communication networks and protocols (IEEE 802.15.4, Bluetooth, Wireless Sensor Networks, Cellular Networks) with a focus on MAC layer and PHY layer protocol design, resource sharing, resource allocation, IoT and Machine-to-Machine Communications. Most recently she has focused on cell free MIMO, massive IoT networks and incorporating machine learning methods for resource allocation and random access in 5G and Beyond 5G networks. She participated in FP 7 CRUISE project as a leader of the Turkish team, and has lead over 10 research projects sponsored by the various organizations among which the National Research Council of Turkey (TUBITAK), the Ministry of Industry (SANTEZ) (in cooperation with large electronic companies in Turkey) and the University Research Fund. Radosveta Sokullu is a an IEEE Senior Member, member of the International Federation of University Women (IAUW) and a consultant of the IEEE Student Branch and the WIE Student Branch at the department. |
| Databáze: | OpenAIRE |
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