JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Selection of Machine Learning Techniques for Network Lifetime Parameters and Synchronization Issues in Wireless Networks |
| 영문제목(English Title) |
Selection of Machine Learning Techniques for Network Lifetime Parameters and Synchronization Issues in Wireless Networks |
| 저자(Author) |
Nimmagadda Srilakshmi
Arun Kumar Sangaiah
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| 원문수록처(Citation) |
VOL 15 NO. 04 PP. 0833 ~ 0852 (2019. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.
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| 키워드(Keyword) |
Congestion
Energy Harvesting
Machine Learning Algorithms
Network Lifetime
Wireless Networks
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| 파일첨부 |
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