In this work, we study the data collection problem using a data mule in millimeter Wave (mmWave) sensor net-works. The data mule aims to collect data (from a redundant set of sensors) within a minimum expected time, constrained by a lower bound on the delivery probability. Due to multiple factors of uncertainty, such as duty-cycling of sensors and mmWave link attenuation due to random blockage effects, the data mule faces a non-trivial challenge of how many times to probe a sensor (exploit) and which sensor to probe (explore), in order to meet the aforementioned goal. The aforementioned (exploit/explore) problem is modeled using a partially observable Markov decision process. As the direct method of solving the model characterized by a continuous state space is computationally intensive, we present an alternate method by investigating the structural properties of the model. To this end, a simple threshold-based myopic optimal data collection algorithm is shown to exist, and the closed-form expression for the threshold is derived. The effectiveness of the proposed optimal algorithm is validated against state-of-the-art learning-based protocols through extensive simulations.
On or Move On? Optimal Mobile Data Collection in mmWave Sensor Networks
24.06.2024
1176940 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Optimizing data collection path in sensor networks with mobile elements
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