Secure & Resilient Cyber-Physical Systems (SECPS) Laboratory

AtoNet represents a step toward self-organizing, intelligent IoT ecosystems, where trust, adaptability, and topology awareness merge to ensure continuous operation, security, and autonomy in large decentralized infrastructures.

AtoNet – Adaptive Topology and Self-Organizing Networks for Decentralized IoT Systems

AtoNet explores how large-scale IoT and edge systems can autonomously reconfigure their topology and adapt roles dynamically in response to changing conditions such as failures, congestion, or malicious behavior. The project aims to create fully decentralized, trust-driven networks capable of maintaining performance, resilience, and coordination without centralized control.

Our ongoing research on AtoNet investigates:

• Adaptive topology evolution, where the network transitions across multiple structures (star, mesh, tree, cluster, hybrid) according to real-time conditions such as latency, load, and trust.
• Decentralized role management, allowing nodes to be promoted, demoted, or reassigned autonomously based on behavioral trust and capability scores.
• Behavioral validation and fault differentiation, distinguishing benign resource faults (e.g., low battery, disconnection) from malicious anomalies through challenge–response analysis.
• Resilient recovery and continuity mechanisms, where nodes elect new leaders and rebuild clusters locally after failures without global synchronization.
• Scalable adaptation across heterogeneous devices, bridging IoT sensors, edge nodes, and embedded platforms under dynamic and constrained environments.

TRACED – Trust-Aware Clustering and Adaptive Security for Edge Systems

The TRACED project investigates how Edge and IoT networks can self-organize securely without centralized control. The goal is to build adaptive, resilient, and trust-aware distributed systems capable of responding in real time to malicious nodes, faults, and behavioral changes.

Our current research expands on TRACED to explore:

• Dynamic trust-based clustering, where node roles (leaders, workers) evolve over time based on reliability, capability, and behavioral metrics.
• Distributed behavioral analysis, using lightweight challenge–response protocols and continuous heartbeat monitoring to detect anomalies and deviations.
• Defense against internal and Sybil attacks, through identity verification, public-key validation, and admission rate control.
• Fully decentralized fault and role management, allowing clusters to reconfigure autonomously without downtime.
• Integration with lightweight learning and heterogeneous testbeds, to validate TRACED in real-world, mobile, and industrial environments.

TRACED lays the foundation for a new generation of autonomous and trustworthy edge systems, where each node continuously evaluates and adapts its behavior locally, contributing to the collective security and stability of the network.

AQUILA represents a research infrastructure for the future of distributed systems, where heterogeneous devices operate under a shared orchestration layer to enable real-world experimentation, performance analysis, and validation of novel algorithms for reliability, security, and energy efficiency across the cloud–edge continuum.

AQUILA: A Flexible Architecture Guideline for Building Custom Distributed Systems Testbeds

The AQUILA project investigates how to build, interconnect, and orchestrate heterogeneous computing devices – from FPGAs to IoT nodes – into unified, scalable testbeds for distributed and edge systems research. Its goal is to make experimental infrastructures flexible, modular, and accessible, bridging the gap between large industrial platforms and purely simulated environments.

Building upon the AQUILA framework, our ongoing research focuses on:

• Cross-domain orchestration, enabling seamless task distribution and coordination across cloud, hybrid, and edge environments.
• Dynamic resource management, where devices with different capabilities (CPUs, FPGAs, microcontrollers) collaborate autonomously under a lightweight task manager.
• Heterogeneous communication protocols, combining UDP, MQTT, and serial bridges to integrate both IP-capable and resource-constrained nodes in a unified network.
• Hardware-software co-design, using real devices to emulate realistic network and compute behaviors that are not captured in simulators.
• Scalable experimentation methodology, allowing researchers to reproduce distributed workloads, failure scenarios, and adaptive control strategies in cost-effective setups.