Developing Predictable and Secure IoT for Autonomous Systems

The goal of iSecure is to design and develop secure digital platforms leveraging the edge, fog and cloud computing for enhancing development, production and support. 

On the one hand, current digital platform architectures for IoT lack of timing guarantees for short-latency data communication among the IoT devices. On the other hand, they lack proper mechanism for data privacy and security. iSecure proposed solution consists of designing and developing an architecture towards secure digital platforms ensuring guarantees on short-latency, privacy and security of data. Edge nodes will serve as local controllers for multiple IoT devices, allowing direct communication between devices controlled by the same node and communication between devices on different nodes, via the nodes. This provides for short-latency communication and data security. The global cloud will orchestrate all edge nodes. Each IoT domain consists of multiple IoT devices that communicate with the edge node using time predictable TSCH technology. We propose a dynamic scheduling algorithm for TSCH to accommodate a changing environment, with mobile devices or new additions/removals. SDN will be utilized to dynamically manage communication bandwidth within each IoT domain. To ensure security and confidentiality, the architecture will employ best practices, innovative techniques, and hardware support for confidential computing. iSecure will achieve the following outcomes: 

• A secure edge-cloud architecture with dynamic and time-predictable communication for IoT systems in industrial environments. 
• A confidential data sharing platform for devices, systems and services. 
• Proof-of-concept implementations in two use cases namely autonomous airport and harbour. 
• Commercialization of the secure, data sharing platform in relevant industrial contexts in Sweden and abroad.

[Show all publications]

PyLC+: A Scalable Python Framework for Automated Translation and Testing of Industrial PLC Programs (Jul 2025)
Mikael Ebrahimi Salari, Eduard Paul Enoiu, Alessio Bucaioni, Wasif Afzal, Cristina Seceleanu
49th IEEE International Conference on Computers, Software, and Applications ( COMPSAC-2025)

Artificial Intelligence for Software Architecture: Literature Review and the Road Ahead (Jun 2025)
Alessio Bucaioni, Martin Weyssow , Junda He , Yunbo Lyu , David Lo
2030 Software Engineering - 2025 (2030 SE - 2025)

Benchmarking Large Language Models for Autonomous Run-time Error Repair: Toward Self-Healing Software Systems (Jun 2025)
Alessio Bucaioni, Gabriele Gualandi, Johan Toma
International Conference on Evaluation and Assessment in Software Engineering (EASE'25)

Model Transformations Using LLMs Out-of-the-Box: Can Accidental Complexity Be Reduced? (Jun 2025)
Gabriel Kazai , Ronnie Agyeiwaa Osei , Alessio Bucaioni, Antonio Cicchetti
First Workshop on Large Language Models For Generative Software Engineering (LLM4SE 2025)

AI-Powered Semantic Search for Historical Documentation: A Collaborative Research with Hitachi Energy (Apr 2025)
Ivan Hansson , Edvin Wiklund , Alessio Bucaioni, Luciana Provenzano
22nd International Conference on Information Technology: New Generations (ITNG 2025)

Enhancing IoT Edge Platforms: Selecting an MQTT-Compatible Broker for Kubernetes Environments (Apr 2025)
Bahareh Aghajanpour , Alessio Bucaioni, Gabriele Capannini
22nd International Conference on Information Technology: New Generations (ITNG 2025)