Smart home and spaces with multiple stakeholders: automation, conflicts, security and recommender systems

When managing multiple smart homes and spaces with multiple stakeholders, key questions arise about creating an architecture that supports automation, encompassing aspects such as monitoring and control, ensuring privacy and security, and analysis and recommendation from shared accumulated data. To address those issues, we designed a rule language based on separating concerns, allowing users to specify desired outcomes without addressing technical implementation details. The separation of concerns resulted in a layered architecture that converts high-level decisions into home-specific actions, and sensor data into high-level information for decision making. A critical component, a rule engine, was designed and implemented. It evaluates and carries out the rules according to the state and context of the home and its residents, recognizing and resolving conflicting rules. A recommender system can suggest rules based on learning the residents’ behavior or by including rules of similar residents or households. A structured, policy-driven process addresses rule management issues: conflict resolution, security, privacy and distribution of authority. Our architecture supports seamless integration of rule or hardware changes and long-term data collection facilities, enabling learning residents’ behavior patterns to refine and automate decision-making. In this article, we examine the challenge of smart spaces, which typically involve multiple stakeholders with different levels of authority and responsibility. We analyze the problem from various perspectives and propose a comprehensive system architecture and design, incorporating novel algorithms to meet the requirements of such systems. In addition, we address issues related to roles and authority, user hierarchy, conflict detection and resolution, auto-negation, and full automation, where rules can be seamlessly applied across diverse environments.