So far, numerous application-driven developments for the experimental design for DPSs related to the sensor scheduling in monitoring networks have been carefully studied. Nevertheless, all problems considered by no means exhaust all potential situations motivated by practical settings, and many difficult issues still remain open, posing challenges to researchers concerned with distributed measurement systems. In this chapter, two important extensions of the foregoing results to other experimental settings related to difficult design problems encountered in identification of real-world processes are discussed. The first one is the sensor scheduling problem for observations collected in a series for different realizations of processes with random parameters. Both the theoretical background and an algorithm for calculating optimum group experimental designs are provided to address this issue. The theory is applicable to those practical situations in which a dynamic system is sensitive to sampling or gives a different response at each run of the experiment. Together with the definition of group designs that is also introduced, this structure leads to a practical and numerically tractable representation of optimum designs for estimation of the mean values of the parameters. The second setting of great practical relevance which is investigated in this chapter is the problem of realization of the observational process under the presence of spatially correlated measurements. The task is extremely difficult, since information from different sensor nodes cannot be separated during the data fusion, leading to a far higher level of complexity compared to the uncorrelated setting.
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- Extensions toward Challenging Problems of Network Scheduling
- Springer Berlin Heidelberg