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Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks
  • Haitham Afifi
Haitham Afifi
Paderborn University, Paderborn University

Corresponding Author:[email protected]

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In this work we use autonomous vehicles to improve the performance of Wireless Sensor Networks (WSNs).
In contrast to other autonomous vehicle applications, WSNs have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the quality of sensed data (e.g., measurement uncertainties or signal strength).
Second, quality of service (QoS) which is used to measure the network’s performance for data forwarding (e.g., delay and packet losses). As a use case, we consider wireless acoustic sensor networks, where a group of speakers move inside a room and there are autonomous vehicles installed with microphones for streaming the audio data. We formulate the problem as a Markov decision problem (MDP) and solve it using Deep-QNetworks (DQN). Additionally, we compare the performance
of DQN solution to two different real-world implementations: speakers holding/passing microphones and microphones being preinstalled in fixed positions.
We show that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation in some scenarios. Moreover, we study the impact of the vehicles speed on the learning process of the DQN solution and show how low speeds degrade the performance. Finally, we compare the DQN solution to a heuristic one and provide theoretical analysis of the performance with respect to dynamic WSNs.