In the era of Internet of Things, how to develop a smart sensor system with
sustainable power supply, easy deployment and flexible use has become a
difficult problem to be solved. The traditional power supply has problems such
as frequent replacement or charging when in use, which limits the development
of wearable devices. The contact-to-separate friction nanogenerator (TENG) was
prepared by using polychotomy thy lene (PTFE) and aluminum (AI) foils. Human
motion energy was collected by human body arrangement, and human motion posture
was monitored according to the changes of output electrical signals. In 2012,
Academician Wang Zhong lin and his team invented the triboelectric
nanogenerator (TENG), which uses Maxwell displacement current as a driving
force to directly convert mechanical stimuli into electrical signals, so it can
be used as a self-driven sensor. Teng-based sensors have the advantages of
simple structure and high instantaneous power density, which provides an
important means for building intelligent sensor systems. At the same time,
machine learning, as a technology with low cost, short development cycle,
strong data processing ability and prediction ability, has a significant effect
on the processing of a large number of electrical signals generated by TENG,
and the combination with TENG sensors will promote the rapid development of
intelligent sensor networks in the future. Therefore, this paper is based on
the intelligent sound monitoring and recognition system of TENG, which has good
sound recognition capability, and aims to evaluate the feasibility of the sound
perception module architecture in ubiquitous sensor networks.

在物联网时代，如何开发具有可持续电源供应、易部署和灵活使用的智能传感器系统已成为一个难题。本文基于 TENG 的智能声音监测和识别系统，旨在评估万能传感器网络中声音感知模块架构的可行性。

基于自驱动传感器和深度学习的人工智能应用进展

Progress in artificial intelligence applications based on the  combination of self-driven sensors and deep learning

Developing efficient solutions for inference problems in intelligent sensor
networks is crucial for the next generation of location, tracking, and mapping
services. This paper develops a scalable distributed probabilistic inference
algorithm that applies to continuous variables, intractable posteriors and
large-scale real-time data in sensor networks. In a centralized setting,
variational inference is a fundamental technique for performing approximate
Bayesian estimation, in which an intractable posterior density is approximated
with a parametric density. Our key contribution lies in the derivation of a
separable lower bound on the centralized estimation objective, which enables
distributed variational inference with one-hop communication in a sensor
network. Our distributed evidence lower bound (DELBO) consists of a weighted
sum of observation likelihood and divergence to prior densities, and its gap to
the measurement evidence is due to consensus and modeling errors. To solve
binary classification and regression problems while handling streaming data, we
design an online distributed algorithm that maximizes DELBO, and specialize it
to Gaussian variational densities with non-linear likelihoods. The resulting
distributed Gaussian variational inference (DGVI) efficiently inverts a
$1$-rank correction to the covariance matrix. Finally, we derive a diagonalized
version for online distributed inference in high-dimensional models, and apply
it to multi-robot probabilistic mapping using indoor LiDAR data.

开发智能传感器网络推理问题的高效解决方案对于下一代位置、跟踪和地图服务至关重要。本文开发了一种适用于连续变量、难以处理的后验概率和大规模实时数据的可扩展分布式概率推理算法。