Rutiranje paketa podataka u dinamičkim bežičnim ad hoc mrežama veoma je zahtevan proces zbog brzih promena u topologiji mreže, što može prouzrokovati česte prekide veza između čvorova, a samim tim i veliki stepen izgubljenih paketa. Jedan od načina da se ovaj problem prevaziđe jeste primena veštačke inteligencije u protokolima rutiranja, kako bi se proces rutiranja prilagodio dinamičkoj prirodi ovih mreža. Veoma značajna oblast veštačke inteligencije koja se, u poslednje vreme, sve više primenjuje u dinamičkim bežičnim ad hoc mrežama je mašinsko učenje (machine learning), a posebno se ističe tip mašinskog učenja pod nazivom učenje potkrepljivanjem (reinforcement learning). U ovom radu predstavljen je pregled aktuelnih rezultata u primeni učenja potkrepljivanjem u protokolima rutiranja za različite dinamičke bežične ad hoc mreže. Protokoli rutiranja najpre su klasifikovani prema tipu mreže na protokole za VANET (Vehicular Ad-hoc Networks) i FANET (Flying Ad-hoc Networks) mreže, a zatim i prema nekim drugim karakteristikama i specifičnostima.
– This paper presents a forecast of a number of call arrivals in the call center per hour using supervised machine learning. For the forecast, the WEKA machine learning software tool was used. The results of the forecast are verified using several methods, which shows very good results. Finally, the results of the forecast are presented graphically using Excel diagrams. Keywords – Machine learning, Forecasting, WEKA
Vehicular and flying ad hoc networks (VANETs and FANETs) are becoming increasingly important with the development of smart cities and intelligent transportation systems (ITSs). The high mobility of nodes in these networks leads to frequent link breaks, which complicates the discovery of optimal route from source to destination and degrades network performance. One way to overcome this problem is to use machine learning (ML) in the routing process, and the most promising among different ML types is reinforcement learning (RL). Although there are several surveys on RL-based routing protocols for VANETs and FANETs, an important issue of integrating RL with well-established modern technologies, such as software-defined networking (SDN) or blockchain, has not been adequately addressed, especially when used in complex ITSs. In this paper, we focus on performing a comprehensive categorisation of RL-based routing protocols for both network types, having in mind their simultaneous use and the inclusion with other technologies. A detailed comparative analysis of protocols is carried out based on different factors that influence the reward function in RL and the consequences they have on network performance. Also, the key advantages and limitations of RL-based routing are discussed in detail.
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