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Nimmagadda Srilakshmi,Arun Kumar Sangaiah 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.4
In real time applications, due to their effective cost and small size, wireless networks play an important role inreceiving 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 localisationof nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, faultdetection, and quality of service, among others. Conventional methods were programmed, for static networkswhich made it difficult for networks to respond dynamically. Here, machine learning strategies can be appliedfor dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with lesshuman intervention and reprogramming. In this paper, we present a wireless networks survey based ondifferent machine learning algorithms and network lifetime parameters, and include the advantages anddrawbacks 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 evaluationof the survey, the motive for choosing specific techniques to deal with wireless network problems, and a briefdiscussion on the challenges inherent in this area of research.
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Srilakshmi, Nimmagadda,Sangaiah, Arun Kumar Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.4
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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