Architecture of Typical Sensor Agent for Learning and Classification Network

Abstract

Distributed decision making and learning procedures operate with distributed data sources. Intelligent access to distributed data sources is one of the important requirements to any distributed learning and decision making systems. In many such applications, input data arrive from spatially distributed sensors structured in sensor networks. An increasing interest to what is called intelligent sensors for which agent-based technology seems to be rather attractive implementation paradigm forms a marked trend in the sensor network area. Indeed, the latter provides a natural mapping from the set of nodes of intelligent sensor network to the set of collaborating agents and, at the same time, enriches the network nodes by the methods of collaboration developed under multi-agent approach and provides the network nodes with the capability to communicate in terms of high level language. The paper proposes a typical (reusable) architecture of the intelligent sensor agents as well as typical protocols supporting interaction of sensors with other software components of agent-based network intended for distributed learning and classification. The main solutions proposed in the paper are demonstrated via prototyping of two case studies.