The proliferation of multi-vendor autonomous systems (MVAS), such as shared warehouses, necessitates robust cooperation frameworks for robotic agents from different vendors. Ensuring operational integrity in these environments is a critical challenge, as the misbehaviour of a single agent can disrupt the entire system. This paper proposes a decentralised framework for secure and trustworthy multi-robot cooperation. Our approach leverages distributed ledger technology (DLT) to create an immutable record of operations and introduces a novel, lightweight Proof of Location (PoL) mechanism. This PoL allows agents to verify task completions both physically and cryptographically with minimal computational overhead. The framework is further strengthened by a real-time, algorithmic trust and reputation system that continuously evaluates agent behaviour. A key advantage over traditional methods is our support for asynchronous verification, which enhances both scalability and efficiency by removing centralised bottlenecks. Extensive simulations in a warehouse setting demonstrate that our framework effectively detects and mitigates malicious activities, thereby improving the security and performance of MVAS. The broader applicability of our approach is further illustrated through a conceptual case study in a smart city parking scenario.

The application of machine learning (ML) in data analytics has become widespread across various fields, including healthcare, finance, marketing, and many others. Current research has extensively explored ML model markets, which are typically coordinated by a single broker acting as an intermediary between data providers and model buyers. However, this solution poses significant challenges due to the monopolistic control it grants the broker. Therefore, we propose a multi-broker market model to overcome these issues. The decision-making processes among multiple brokers are formulated as a multi-stage Stackelberg game designed to maximize the profits of each broker. Subsequently, the existence and uniqueness of nash Equilibrium for brokers’ strategies are proven. We conduct simulation experiments using four types of cost functions for two brokers. The simulation results demonstrate that brokers can reach a Nash Equilibrium through the proposed market model.