Fernando Castillo

Digital societies increasingly rely on trustworthy proofs of physical presence for services such as supply-chain tracking, e-voting, ride-sharing, and location-based rewards. Yet, traditional localization methods often lack cryptographic guarantees of where and when an entity was present, leaving them vulnerable to spoofing, replay, or collusion attacks. In response, research on Proof-of-Location (PoL) has emerged, with recent approaches combining distance bounding, distributed consensus, and privacy-enhancing techniques to enable verifiable, tamper-resistant location claims. As the design space for PoL systems grows in complexity, this paper provides a unified framework to help practitioners navigate diverse application needs. We first propose a taxonomy identifying four core domains: (1) cryptographic guarantees, (2) spatio-temporal synchronization, (3) trust and witness models, and (4) interaction and overhead. Building on this, we introduce a methodology to map application-specific requirements onto appropriate PoL architectures. We illustrate this process through three use cases (retail e-coupons, supply chain auditing, and physical e-voting), each showing how different constraints shape protocol choices. Overall, this work offers a structured approach to building secure, scalable, and interoperable PoL systems.

Decentralized Autonomous Machines (DAMs) represent a transformative paradigm in automation economy, integrating artificial intelligence (AI), blockchain technology, and Internet of Things (IoT) devices to create self-governing economic agents participating in Decentralized Physical Infrastructure Networks (DePIN). Capable of managing both digital and physical assets and unlike traditional Decentralized Autonomous Organizations (DAOs), DAMs extend autonomy into the physical world, enabling trustless systems for Real and Digital World Assets (RDWAs). In this paper, we explore the technological foundations, and challenges of DAMs and argue that DAMs are pivotal in transitioning from trust-based to trustless economic models, offering scalable, transparent, and equitable solutions for asset management. The integration of AI-driven decision-making, IoT-enabled operational autonomy, and blockchain-based governance allows DAMs to decentralize ownership, optimize resource allocation, and democratize access to economic opportunities. Therefore, in this research, we highlight the potential of DAMs to address inefficiencies in centralized systems, reduce wealth disparities, and foster a post-labor economy.