Guest Editorial:Special issue on explainable AI empowered for indoor positioning and indoor navigation

The convergence of Internet of Things(IoT),vehicularad hoc network(VANET),and mobile ad hoc network relies on sensor networks to gather data from nodes or objects.These networks involve nodes,gateways,and anchors,operating on limited battery power,mainly used in broadcasting.IoT applications,like healthcare,smart cities,and transportation,often need position data and face challenges in delay sensitivity.Localisation is important in ITS and VANETs,influencing autonomous vehicles,collision warning systems,and road information dissemination.A robust localisation system,often combining GPS with techniques like Dead Reckoning and Image/Video Localisation,is essential for accuracy and security.Artificial intelligence(AI)integration,particularly in machine learning,enhances indoor wireless localisation effectiveness.Advancements in wireless communication(WSN,IoT,and massive MIMO)transform dense environments into programmable entities,but pose challenges in aligning self‐learning AI with sensor tech for accuracy and budget considerations.We seek original research on sensor localisation,fusion,protocols,and positioning algorithms,inviting contributions from industry and academia to address these evolving challenges.

Aerial vehicle guidance based on passive machine learning technique

Purpose-The purpose of this paper is to develop a new guidance scheme for aerial vehicles based on artificial intelligence.The new guidance scheme must be able to intercept maneuvering targets with higher probability and precision compared to existing algorithms.Design/methodology/approach-A simulation setup of the aerial vehicle guidance problem is developed.A model-based machine learning technique known as Q-learning is used to develop a new guidance scheme.Several simulation experiments are conducted to train the new guidance scheme.Orthogonal arrays are used to define the training experiments to achieve faster convergence.A wellknown guidance scheme known as proportional navigation guidance(PNG)is used as a base model for training.The new guidance scheme is compared for performance against standard guidance schemes like PNG and augmented proportional navigation guidance schemes in presence of sensor noise and computational delays.Findings-A new guidance scheme for aerial vehicles is developed using Q-learning technique.This new guidance scheme has better miss distances and probability of intercept compared to standard guidance schemes.Research limitations/implications-The research uses simulation models to develop the new guidance scheme.The new guidance scheme is also evaluated in the simulation environment.The new guidance scheme performs better than standard existing guidance schemes.Practical implications-The new guidance scheme can be used in various aerial guidance applications to reach a dynamically moving target in three-dimensional space.Originality/value-The research paper proposes a completely new guidance scheme based on Q-learning whose performance is better than standard guidance schemes.