ABSTRACT Software agents and multi‐agent systems are critical components in the development of distributed autonomous systems, as they exhibit a wide range of “intelligence,” from reactive behaviors to advanced reasoning and planning. Integrating these capabilities into multi‐agent systems enables distributed artificial intelligence, a programming paradigm in which autonomous entities work together efficiently across a network to achieve shared goals. Another important aspect we consider in this work is represented by the disruptive impact of 5G technology, which is moving computation to the edge of the network. As a result of this shift, embedded devices with microcontrollers—which are renowned for their low power consumption and limited computing power—have proliferated. This paradigm fits well an environment made of small cooperating entities scattered across the network, but, at the same time, it is difficult to provide agent platforms for embedded devices. Indeed, the most recent state‐of‐the‐art multi‐agent systems platforms, which are primarily Java‐based, raise issues with microcontroller speed and memory overhead. To achieve this goal, we designed and implemented an ecosystem, entirely written in C++, to create multi‐agent systems in an actual Internet of Things setting. Our ecosystem is composed by Democle , a logic/declarative agent programming framework, and Hermes , a unified wireless network interface, both specifically created for embedded systems. In this paper, we describe our ecosystem's functionality, architecture, and benefits of use. In addition, we provide a case study and a few experiments to prove how our platform works and its efficiency in terms of overhead.

This paper introduces an intelligent monitoring and anti-theft system designed for marine environments. Using Internet of Things technologies, the system utilises onboard devices that function as a finite-state machine, equipped with multiple sensors to gather speed, orientation and movement data. The onboard device is configured by the user with a personal device, which is used to arm and disarm the system via Bluetooth Low Energy. In case of theft, live position data is transmitted across a LoRa Mesh network, enabling long-range communication provided that enough boat nodes are available. The packets are transmitted using an adequate cryptography system based on AES128 keys. The data transmitted is recovered by a dock node, and it can be seen by the dock keeper and simultaneously provided to the users’ application.

This work introduces an advanced system designed to ensure operator safety in dead zones, common in underground and remote areas with limited internet connectivity. Using Internet of Things technologies, the system employs wearable devices with sensors to provide real-time data for precise location tracking, movement analysis, and monitoring of high-risk areas within these zones. The system enables real-time vital sign monitoring through wearable technology and integrates machine learning for fall detection, health monitoring, and distance estimation between operators and anchors in tunnels. These wearable devices operate independently in connectivity-challenged environments, enhancing system coverage and troubleshooting efficiency. Data collected is securely stored internally and accessible via an external IP network for remote monitoring, configuration management, and oversight of operator location and health status.

As the demand for computational power continues to grow, traditional data processing approaches face limitations, primarily related to physical constraints in materials and processing units. In order to deal with such limitations, the computing industry has turned to multi-core and Symmetric multiprocessing technology, harnessing the combined processing capabilities of multiple processors to spread workloads to improve performance. One of the key strategies in providing computational power for parallel processing is represented by the "multi-tenant approach" of Cloud Computing, where multiple tenants can share a huge amount of computational power for the execution of services. Private Cloud is a slightly different approach which ensures more security and control. At the same time, it can provide service continuity and reliability at the system level rather than relying on individual components. In this paper, we discuss the key aspects of cloud computing w.r.t. the existing different service models, particularly focusing on the private Cloud model. To this end, we discuss established technologies for Cloud Computing, particularly the well-known OpenStack. We discuss the key aspects related to the deployment of OpenStack using PuppetStack, which is a software solution we employed for our research. PuppetStack can support the deployment of high-availability private cloud infrastructure.

The Internet of Things is rapidly evolving, thanks also to technological innovations, in particular to new technologies of radio communication with low energy consumption. Nevertheless, testing a possible complex infrastructure to verify the management of several devices and their communication could be difficult to implement and especially wasteful. LWN Simulator is a LoRaWAN network simulator, able to simulate device nodes and gateway and it is equipped with a web interface with which to interact with the simulation in real-time. The simulator lets the user create and interact with Virtual Gateways, changing their status, geographical position and other network information or adding a Real Gateway already present in a real network. Also, the simulator allows the simulation of all device classes, supporting the Adaptive Data Rate, MAC commands and the personalisation of the payloads. Finally, the entire simulation environment, devices and gateways, runs on a real LoRaWAN infrastructure, such as Chirpstack or The Things Stack. This feature allows a real network design that can be implemented using real devices with the same settings and configurations.

This paper presents a software architecture to let users interact with their smart home devices, through the commonly used social network channels. The software, called FABULOS, is the result of the combination of components used to interact with devices and social networks, and a rule-based artificial intelligence, which implements the base logic for the automation. The core of the software architecture is the translation service, which has the objective of extracting the meaning of the sentences provided by text and voice messages by users in natural language, transforming them into proper device commands. In order to achieve this, the proposed solution relies on an approach which has the capability to interpret and extract the meaning of the intentions plus the entities involved, associated to the sentences sent by the users. A description of a realistic case-study, which shows an example of how the proposed software behaves in a real interaction with an user, is also included in the paper.