CCCLA: A cognitive approach for congestion control in Internet of Things using a game of learning automata
| Autor: | Ehsan Tahavori, Soulmaz Gheisari |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Router
Learning automata Computer Networks and Communications business.industry Computer science Quality of service Node (networking) Reliability (computer networking) 020206 networking & telecommunications Throughput 02 engineering and technology Network congestion 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing The Internet Network performance business Computer network |
| Zdroj: | Computer Communications. 147:40-49 |
| ISSN: | 0140-3664 |
| DOI: | 10.1016/j.comcom.2019.08.017 |
| Popis: | Internet of Things (IoT) typically consists of lossy and low powered networks (LLN) of interconnected sensors. Due to low bandwidth and high scale of communication, congestion can occur among the sensor nodes in the LLN, during communicating to a border router, or when some other clients from the Internet access the resources in the LLN. So, having a proper congestion control mechanism is very important for IoT. In this paper we want to cope with congestion in IoT; however the current IoT, which is still based on traditional static architectures, lacks intelligence and cannot comply with the increasing application performance requirements. Adding cognition in IoT empowers it with a brain and high level intelligence. Therefore, firstly a learning automata-based cognitive framework has been proposed for integrating cognition into IoT. Then, based on the framework, we have presented a new cognitive approach for congestion control, named CCCLA (Cognitive Congestion Control in IoT using a game of Learning Automata). In the proposed approach, a team of LA has been assigned to a group of effective controllable parameters; for example parameters, whose values can affect the congestion control. Each automaton has a finite set of possible values of its corresponding parameter, and it tries to learn the best one, which maximize the whole network performance. Each node in the network has its own group of learning automata, which act independently; however, all nodes receive the same feedbacks from the environment. Using simulation, we test the proposed cognitive framework in a congestion control scenario. Based on our findings CCCLA significantly avoids congestion while improves desired QoS parameters such as delay, reliability and throughput, even in highly lossy networks. |
| Databáze: | OpenAIRE |
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