Current sensor-gateway-cloud solutions for machine learning (ML) life cycle development require extensive coding, hardware, and software expertise. We present ST AIoT Craft, the first no-code/low-code online platform for training, deploying, and managing distributed artificial intelligence of things (AIoT) and big data. The framework provides user-interface dashboards and web applications for sensor data logging and management, dataset preprocessing and visualization, automatic tinyML for in-sensor processors and microcontrollers, and end-to-end Internet of things (IoT) system setup and monitoring. The tools allow developers to manage intelligent IoT devices, gateways, and cloud infrastructure directly from a web browser. ST AIoT Craft is accessible at https://staiotcraft.st.com/.

The Internet of Things (IoT) is radically changing the way people live and interact with society: ranging from wearables to smart cities, the number of IoT devices has grown exponentially. The Message Queuing Telemetry Transport (MQTT) protocol is one of the most widely used IoT communication protocols. However, our investigation over publicly available MQTT endpoints confirms an alarming trend, i.e. many do not provide adequate security measures and often rely on the insecure default configuration. To improve the security awareness on the use of MQTT the paper presents MQTT Security Assistant (MQTTSA), a tool that automatically detects misconfigurations in MQTT-based IoT deployments. To assist IoT system developers, MQTTSA produces a report outlining detected vulnerabilities, together with (high level) hints and code snippets to implement adequate mitigations. The effectiveness of the tool is assessed by a thorough experimental evaluation.

Wireless networks are becoming increasingly popular in professional environments as well as in the home. The success of IEEE 802.11 has cut down equipment costs and people are now building their own residential wireless networks, connecting PCs, MP3 players, broadband gateways and Hi-Fi systems.However, such dramatic growth is creating problems in crowded areas, as the Wi-Fi bandwidth is limited and interference effects start to limit the technology usability.This paper describes a management system for handling the quality of the overall home wireless network, so that interference or bulk noise effects can be alleviated allowing users to enjoy digital content with the reliability they expect. By means of Universal Plug 'n Play, events that may deteriorate the quality of existing services can be propagated to all interested devices so that a smart allocation of network resources can be applied. A simple Admission Control scheme is also used to regulate the admission of real-time streams.