A general decision framework for SNBIS unifying the information fusion of sensor network, decision and control action is the most crucial theory for implementation of SNBIS in various environments to efficiently execute tasks. The intelligent decision framework proposed in this paper lays the foundation of the mathematical structure and techniques for the unified general decision process of SNBIS. By further exploring the relationship of observation and task execution in SNBIS, this framework separate the traditional event to observation mapping into two mappings, event to physical quantity mapping and physical quantity to observation mapping. Based on the two mappings, we derive the new decision mapping and use the firefighting robot navigation problem to illustrate the application of intelligent decision framework. In this example, we also investigate the special case degenerated to traditional state space estimation problem to show the relationship between our framework and traditional decision techniques. Then we extend the intelligent
decision framework to accommodate observations from multiple kinds of physical quantities. Under this framework, optimal decision and Observation Selection for limited knowledge of correlations among physical quantities are formulated and their equivalence condition is derived. Fuzzy logic controller, which is widely used with less strict-sense mathematical structure, can be derived by degenerating Observation Selection scheme. Simulation results show that the intelligent decision outperforms traditional decision schemes and is more robust to environment or system parameter variations in both single observation and multiple observation scenarios.
In addition to SNBIS, we also apply the framework to CR spectrum sensing to mitigate the hidden terminal problem in a novel way. Unlike the traditional cooperative spectrum sensing which relies on cooperation of other nodes to obtain independent sensing data, the proposed spectrum sensing scheme senses the PS-Rx to create another sensing channel and obtain the “complete” observation of the primary transmission. By taking the geographical position information into consideration, we can improve the information fusion of spectrum sensing to achieve the diversity which is traditionally applied in cooperative spectrum sensing.
Based on this framework, research topics, including gathering more information of the conditional distributions by learning, blind estimation without knowing the distribution in this framework, etc, is able to be explored. By sophisticated and accurate decision process, SNBIS is able to be applied in more fascinating future life scenarios like smart home, intelligent health care and medical system to realize them.
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