Resilient Distributed Constraint Reasoning to Autonomously Configure and Adapt IoT Environments

Abstract

In this paper, we investigate multi-agent techniques to install autonomy and adaptation in IoT-based smart environment settings, like smart home scenarios. We particularly make use of the smart environment configuration problem (SECP) framework, and map it to a distributed optimization problem (DCOP). This consists in enabling smart objects to coordinate and self-configure as to meet both user-defined requirements and energy efficiency, by operating a distributed constraint reasoning process over a computation graph. As to cope with the dynamics of the environment and infrastructure (e.g. by adding or removing devices), we also specify the k -resilient distribution of graph-structured computations supporting agent decisions, over dynamic and physical multi-agent systems. We implement a self-organizing distributed repair method, based on a distributed constraint optimization algorithm to adapt the distribution as to ensure the system still performs collective decisions and remains resilient to upcoming changes. We provide a full stack of mechanisms to install resilience in operating stateless DCOP solution methods, which results in a robust approach using a fast DCOP algorithm to repair any stateless DCOP solution methods at runtime. We experimentally evaluate the performances of these techniques when operating stateless DCOP algorithms to solve SECP instances.

A Fault‐tolerant model for tuple space coordination in distributed environments

“…In Reference 35, the limitations of fault tolerance using partial replication of the system are addressed, and a hybrid approach is given that uses both a central server and client‐to‐client communication and aims to provide causal consistency. In Reference 36, the authors presented a distributed repair method based on distributed optimization that provides tolerance against any faults. Replication and relocation technique is used to provide resilience.…”