In this section, the different functionalities of the network planner will be illustrated: optimal sink selection, establishing symbiotic cooperation between networks, setting and optimizing node parameters, and path loss model optimization.
This section illustrates the optimal solution to the optimization problem (3).
Furthermore, Fig. 2 illustrates that optimal rotation period decreases as the harvesting area increases.
The last case study illustrates the optimal design of experiments for estimating model parameters with maximal accuracy.
Results obtained illustrates that optimal SPP and smart decision-making on the incentive value can remarkably raise the aggregator's profit and improve the system reliability.
The structure and locomotion of 5-TET are described in Section 2. Section 3 illustrates the optimal locomotion based on kinematic and dynamic analyses.
Figure 6 illustrates the optimal scheduling and power allocation policies in the case in which our algorithm converges for the same scenario as in Fig. 5 a. Fig. 6 Optimal scheduling and power allocation when Algorithm 1 converges.
Moreover, Fig. 3 illustrates the optimal solution of the examples after the grand coalition (i.e. S 15 = {1, 2, 3, 4}) and demonstrates that each customer is allocated to which depots.
Figure 7 illustrates the optimal performance of Hungarian algorithm when it is employed to assign channels among the femtocells in a physical cluster based on a minimum interference generation criterion.
The paper also illustrates the optimal conditions for increasing accuracy in recovering data thanks to fact that once a few numbers of point are known, it is possible to predict the trend of values of PCBs in unknown locations of the considered area.
