Implementation of Q-Factor (Reinforcement Learning ) in wireless sensor network using python

We want to build an ad-hoc wireless network (One Sink node and N normal nodes). Each normal node need to find the optimal routing path (in term of less number of hops) up to sink node (Which collect the data from all network nodes). Each node need to get the optimal number of hops through Q-Factor implementation.

Let suppose, we have ‘N’ nodes deployed in network, each node need to get the optimal number of hop to reach upto sink node.

Each node need shortest path in term of less hops.

I will briefly describe before awarding the project to avoid any future confusion.


Real time implementation is needed (For real time, you can take 4 to 5 raspberries or 4 to 5 PCs)

Compétences : Apprentissage Automatique, Python

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Concernant l'employeur :
( 2 commentaires ) TLS, France

Nº du projet : #18113395

3 freelance font une offre moyenne de ₹9092 pour ce travail


-Interested Project. Pro Python & Reinforcement algorithm & Machine Learning expert. Best result in time----- [login to view URL] I read your description very carefully. I am very interesting for your project because I hav Plus

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Hello! I am a python developer. I looked at your project and it seems interesting. I have all necessary skills required for this project. Ping me to discuss in detail.

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Hello, I can do your project 100%. I can do this project. I am working on many field wireless technologies such as Cognitive radio, WSN, MANET, VANET, Ad-Hoc networks, LTE, Standard Protocol, wireless sensor network, Plus

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