Applicability of Multi-Agent Systems and Constrained Reasoning for Sensor-Based Distributed Scenarios: A Systematic Mapping Study on Dynamic DCOPs

Context: At present, sensor-based systems are widely used to solve distributed problems in changing environments where sensors are controlled by intelligent agents. On Multi-Agent Systems, agents perceive their environment through such sensors, acting upon that environment through actuators in a continuous cycle. These problems have not always been addressed from an ad-hoc perspective, designed specifically for the circumstances of the problem at hand. Instead, they have been modelled under a common mathematical framework as distributed constrained optimisation problems (DCOP). Objective: The question to answer is how sensor-based scenarios have been modelled as DCOPs in changing environments known as Dynamic DCOP and what their trends, gaps, and progression are. Method: A systematic mapping study of Dynamic DCOPs has been conducted, considering the scattered literature and the lack of consensus in the terminology. Results: Given the high complexity of distributed constraint-based problems, priority is given to obtaining sub-optimal but fast responses with a low communication cost. Other trending aspects are the scalability and guaranteeing the solution over time. Conclusion: Despite some lacks in the analysis and experimentation in real-world scenarios, a large set that is applicable to changing sensor-based scenarios is evidenced, along with proposals that allow the integration of off-the-shell constraint-based algorithms.