Most Internet-of-Things (IoT) devices and smart sensors are connected via the
Internet using IP communication driectly accessed by a server that collect
sensor information periodically or event-based. Although, Internet access is
widely available, there are places that are not covered and WLAN and mobile
cell communication requires a descent amount of power not always available.
Finally, the spatial context (the environment in which the sensor or devices is
situated) is not considered (or lost) by Internet connectivity. In this work,
smart devices communicate connectionless and ad-hoc by using low-energy
Bluetooth broadcasting available in any smartphone and in most embedded
computers, e.g., the Raspberry PI devices. Bi-directional connectionless
communication is established via the advertisements and scanning modes. The
communication nodes can exchange data via functional tuples using a tuple space
service on each node. Tuple space access is performed by simple evenat-based
agents. Mobile devices act as tuple carriers that can carry tuples between
different locations. Additionally, UDP-based Intranet communication can be used
to access tuple spaces on a wider spatial range. The Bluetooth Low Energy Tuple
Space (BeeTS) service enables opportunistic, ad-hoc and loosely coupled device
communication with a spatial context.

通过 Bluetooth 广播技术与 Tuple Space 服务建立的 BeeTS 服务，实现了物联网智能设备的短距离、低能耗、面向时空的无连接与自组织通信。

BeeTS: 基于蓝牙和 IP 广播的智能分布式传感器元组空间与代理相结合

BeeTS: Smart Distributed Sensor Tuple Spaces combined with Agents using Bluetooth and IP Broadcasting

We propose an interactive multimodal framework for language learning. Instead
of being passively exposed to large amounts of natural text, our learners
(implemented as feed-forward neural networks) engage in cooperative referential
games starting from a tabula rasa setup, and thus develop their own language
from the need to communicate in order to succeed at the game. Preliminary
experiments provide promising results, but also suggest that it is important to
ensure that agents trained in this way do not develop an adhoc communication
code only effective for the game they are playing

提出了一种交互式多模态框架，通过协作推理游戏，实现神经网络学习语言，初步实验结果可喜，但需注意避免智能体发展出仅针对游戏有效的特定通信编码。