A Neural-Q-Learning Based Approach for Delay Tolerant Underwater Acoustic Wireless Sensor Networks
نویسندگان
چکیده
Delay tolerance is a major design concern for supporting underwater acoustic wireless sensor networks (UA-WSNs) to carry out tasks in harsh subsea environments. Due to the great difference between the underwater acoustic channel and the radio frequency channel, most of the existing routing protocols developed for terrestrial wireless sensor networks perform poorly in underwater acoustic communications. In this work, we present a Neural-Q-Learning algorithm based delay tolerant protocol for UA-WSNs. Due to the advantages of the artificial neural network along with the Q-Learning algorithm, the ferry node is capable of determining an optimal route in a two-dimensional continuous space to relay packets effectively and efficiently between sensors. Simulation results show that the delivery delay and delivery cost of the network significantly decrease by maximizing the meeting probability between the ferry node and sensors.
منابع مشابه
A Q-Learning Based Approach for Delay Tolerant Underwater Acoustic Wireless Sensor Networks
Underwater acoustic wireless sensor networks (UAWSNs) are capable of supporting underwater missions. Due to the unique characteristics of underwater environments e.g. large attenuation and long propagation delay, the transmission range of sensor nodes in UA-WSNs is usually much shorter than that of the terrestrial counterparts, which limits communications between sensors and makes the system in...
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