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"exec" writes:

Arthur T Knackerbracket has found the following story:

The wireless Internet of Things (IoT) is a network of devices in which each device can directly send information to another over wireless channels of communication, without human intervention. With the number of IoT devices increasing every day, the amount of information on the wireless channels is also increasing. This is causing congestion over the network, leading to loss of information due to interference and the failure of information delivery. Research to solve this problem of congestion is ongoing, and the most widely accepted and applied solution is the "multi-channel" technology. In this technology, information transmission is distributed among various parallel channels based on the traffic in a particular channel at a given time.

But, at present, optimal information transmission channels are selected using algorithms that cannot be supported by most existing IoT devices because these are resource-constrained; i.e., they have low storage capacity and low processing power, and must be power-saving while remaining in operation for long periods of time. In a recent study published in Applied Sciences, a group of scientists from the Tokyo University of Science and Keio University, Japan, propose the use of a machine-learning algorithm, based on the tug-of-war model (which is a fundamental model, earlier proposed by Professor Song-Ju Kim from Keio University, that is used to solve such problems as that of how to distribute information across channels), to select channels. "We realized that this algorithm could be applied to IoT devices, and we decided to implement it and experiment with it," Professor Mikio Hasegawa, the lead scientist from the Tokyo University of Science, says.

-- submitted from IRC

Jing Ma, So Hasegawa, Song-Ju Kim, Mikio Hasegawa. A Reinforcement-Learning-Based Distributed Resource Selection Algorithm for Massive IoT. Applied Sciences, 2019; 9 (18): 3730 DOI: 10.3390/app9183730

Original Submission