A distributed approach to gas detection and source localization using heterogeneous information

This chapter introduces a system for early detection of gaseous substances and coarse source localization by using heterogeneous sensor measurements and human reports. The system is based on Distributed Perception Networks, a Multi-agent system framework implementing distributed Bayesian reasoning. Causal probabilistic models are exploited in several complementary ways. They support uniform and efficient integration of very heterogeneous information sources, such as different static and mobile sensors as well as human reports. In principle, modular Bayesian networks allow creation of complex probabilistic observation models which adapt to changing constellations of information sources at runtime. On the other hand, Bayesian networks are used also for coarse modeling of transitions in the gas propagation processes. By combining dynamic models of gas propagation processes with the observation models, we obtain adaptive Bayesian systems which correspond to Hidden Markov Models. The resulting systems facilitate seamless combination of prior domain knowledge and heterogeneous observations.