Predictive traffic control and differentiation on smart grid neighborhood area networks
| Autor: | Luis J. de la Cruz Llopis, Francisco Rico-Novella, Juan Pablo Astudillo León |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Enginyeria Telemàtica, Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica, Universitat Politècnica de Catalunya. SISCOM - Smart Services for Information Systems and Communication Networks |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Service (systems architecture)
Internet of things Neighborhood area networks General Computer Science Internet de les coses Computer science 020209 energy Congestion control Context (language use) Throughput 02 engineering and technology Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::Serveis telemàtics i de comunicació multimèdia [Àrees temàtiques de la UPC] Smart grid Traffic differentiation Smart power grids neighborhood area networks Machine learning Aprenentatge automàtic 0202 electrical engineering electronic engineering information engineering Wireless General Materials Science Interconnection business.industry Deep learning Quality of service General Engineering deep learning 020206 networking & telecommunications congestion control Network congestion machine learning The Internet Xarxes intel·ligents (Electricitat) Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 Computer network |
| Zdroj: | IEEE Access, Vol 8, Pp 216805-216821 (2020) UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Popis: | Smart Grid (SG) networks include an associated data network for the transmission and reception of control data related to the electric power supply service. A subset of this data network is the SG Neighborhood Area Network (SG NAN), whose objective is to interconnect the subscribers’ homes with the supplier control center. The data flows transmitted through these SG NANs belong to different applications, giving rise to the need for different quality of service requirements. Additionally, other subscriber appliances could use this network to communicate over the Internet. To avoid network congestion, as well as to differentiate the quality of service (QoS) received by the different data flows, a congestion control mechanism with traffic differentiation capabilities is required. The main contribution of this work is the proposal of a new congestion control mechanism based on machine learning techniques to try to guarantee the different QoS requirements to the different data flows. A main problem when applying machine learning techniques is the need for datasets to be used in the training steps. In this sense, a second contribution of this article is the proposal of a method to generate such datasets by means of simulation techniques. The proposed mechanism is then evaluated in the context of a wireless SG NAN. The nodes of this network are the subscriber’s smart meters, which in turn perform the function of concentrating the data traffic sent and received by the rest of the home appliances. Besides, different machine learning classification methods are taken into account. The evaluation carried out shows significant improvements in terms of network throughput, transit time, and quality of service differentiation. Finally, the computational cost of the algorithms used in this proposal has also been evaluated, using real low-cost IoT hardware platforms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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