  Recent advancements in Internet of Things (IoTs) have brought about a surge
of interest in indoor positioning for the purpose of providing reliable,
accurate, and energy-efficient indoor navigation/localization systems. Ultra
Wide Band (UWB) technology has been emerged as a potential candidate to satisfy
the aforementioned requirements. Although UWB technology can enhance the
accuracy of indoor positioning due to the use of a wide-frequency spectrum,
there are key challenges ahead for its efficient implementation. On the one
hand, achieving high precision in positioning relies on the
identification/mitigation Non Line of Sight (NLoS) links, leading to a
significant increase in the complexity of the localization framework. On the
other hand, UWB beacons have a limited battery life, which is especially
problematic in practical circumstances with certain beacons located in
strategic positions. To address these challenges, we introduce an efficient
node selection framework to enhance the location accuracy without using complex
NLoS mitigation methods, while maintaining a balance between the remaining
battery life of UWB beacons. Referred to as the Deep Q-Learning
Energy-optimized LoS/NLoS (DQLEL) UWB node selection framework, the mobile user
is autonomously trained to determine the optimal set of UWB beacons to be
localized based on the 2-D Time Difference of Arrival (TDoA) framework. The
effectiveness of the proposed DQLEL framework is evaluated in terms of the link
condition, the deviation of the remaining battery life of UWB beacons, location
error, and cumulative rewards. Based on the simulation results, the proposed
DQLEL framework significantly outperformed its counterparts across the
aforementioned aspects.
