Internet of Things (IoT) is an ever-evolving technological paradigm that is
reshaping industries and societies globally. Real-time data collection,
analysis, and decision-making facilitated by localization solutions form the
foundation for location-based services, enabling them to support critical
functions within diverse IoT ecosystems. However, most existing works on
localization focus on single environment, resulting in the development of
multiple models to support multiple environments. In the context of smart
cities, these raise costs and complexity due to the dynamicity of such
environments. To address these challenges, this paper presents a unified
indoor-outdoor localization solution that leverages transfer learning (TL)
schemes to build a single deep learning model. The model accurately predicts
the localization of IoT devices in diverse environments. The performance
evaluation shows that by adopting an encoder-based TL scheme, we can improve
the baseline model by about 17.18% in indoor environments and 9.79% in outdoor
environments.

本文提出了一个统一的室内外定位解决方案，利用迁移学习在多个环境下构建单一的深度学习模型，准确预测物联网设备在不同环境中的定位。性能评估结果显示，采用基于编码器的迁移学习方案，在室内环境中可以将基线模型的性能提升约 17.18%，在室外环境中可以提升约 9.79%。

一种统一的深度迁移学习模型用于准确的物联网定位在多样化环境中

A Unified Deep Transfer Learning Model for Accurate IoT Localization in  Diverse Environments

Increasing technological sophistication and widespread use of smartphones and
wearable devices provide opportunities for innovative and highly personalized
health interventions. A Just-In-Time Adaptive Intervention (JITAI) uses
real-time data collection and communication capabilities of modern mobile
devices to deliver interventions in real-time that are adapted to the
in-the-moment needs of the user. The lack of methodological guidance in
constructing data-based JITAIs remains a hurdle in advancing JITAI research
despite the increasing popularity of JITAIs among clinical scientists. In this
article, we make a first attempt to bridge this methodological gap by
formulating the task of tailoring interventions in real-time as a contextual
bandit problem. Interpretability requirements in the domain of mobile health
lead us to formulate the problem differently from existing formulations
intended for web applications such as ad or news article placement. Under the
assumption of linear reward function, we choose the reward function (the
"critic") parameterization separately from a lower dimensional parameterization
of stochastic policies (the "actor"). We provide an online actor-critic
algorithm that guides the construction and refinement of a JITAI. Asymptotic
properties of the actor-critic algorithm are developed and backed up by
numerical experiments. Additional numerical experiments are conducted to test
the robustness of the algorithm when idealized assumptions used in the analysis
of contextual bandit algorithm are breached.

通过构建即时自适应干预研究中的任务来填补现有的方法论空白，提供了一个在线演员 - 评论算法的框架来构建和完善基于数据的即时自适应干预，以此解决移动健康领域的可解释性问题。