Fig 4 - uploaded by Marios Kountouris
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The interplay between J SoI and the number of selected realizations k 1 and k 2 for the EDT case with n = 100, λ = 1, and δ = 0.5.
Source publication
We study a multiuser system in which an information source provides status updates to two monitors with heterogeneous goals. Semantic filtering is first performed to select the most useful realizations for each monitor. Packets are then encoded and sent so that each monitor can timely fulfill its goal. In this regard, some realizations are importan...
Contexts in source publication
Context 1
... due to spending time for sending insignificant realizations. Likewise, the derived optimal values of k 1 and k 2 for the LDT and PDT cases with different arrivals rates are listed in Table I. We see that the exponential penalty results in the lowest and highest values for optimal k 1 and k 2 , respectively, compared to the other forms. Fig. 4 shows the effects of weight parameters in the objective function J SoI and the number of selected realizations for the ...
Context 2
... = 1, and δ = 0.5. Varying each weight parameter alters the optimal values of k 1 and k 2 , hence J SoI . Notably, giving ten times more weight to the arrivals of monitor 2 compared to those of monitor 1 equalizes the optimal k 1 and k 2 . The transmitter equally filters around 33% of frequent and infrequent arrivals. The obtained information from Fig. 4 and from its extension for δ = 0.25 is given in Table II. We observe that higher erasure probability results in fewer selected packets since the transmitter spends more time to retransmit the unsuccessful packets. The same conclusions hold for the LDT and PDT cases. Fig. 5 presents the objective function J SoI versus the arrival rate λ ...
Context 3
... due to spending time for sending insignificant realizations. Likewise, the derived optimal values of k 1 and k 2 for the LDT and PDT cases with different arrivals rates are listed in Table I. We see that the exponential penalty results in the lowest and highest values for optimal k 1 and k 2 , respectively, compared to the other forms. Fig. 4 shows the effects of weight parameters in the objective function J SoI and the number of selected realizations for the ...
Context 4
... = 1, and δ = 0.5. Varying each weight parameter alters the optimal values of k 1 and k 2 , hence J SoI . Notably, giving ten times more weight to the arrivals of monitor 2 compared to those of monitor 1 equalizes the optimal k 1 and k 2 . The transmitter equally filters around 33% of frequent and infrequent arrivals. The obtained information from Fig. 4 and from its extension for δ = 0.25 is given in Table II. We observe that higher erasure probability results in fewer selected packets since the transmitter spends more time to retransmit the unsuccessful packets. The same conclusions hold for the LDT and PDT cases. Fig. 5 presents the objective function J SoI versus the arrival rate λ ...
