We consider a directed network in which every edge possesses a latency function that specifies the time needed to traverse the edge given its congestion.
Let G V,E) denote a transportation network with a rate of traffic between each pair of nodes, and a latency function for each edge (road section).
The model may, of course, be extended by defining the vertex presence function ((psi,Vtimes mathcal {T}rightarrow {0,1})), and vertex latency function ((phi,Vtimes mathcal {T}rightarrow {0,1})).
(rho, E times mathcal{T} rightarrow {0,1}) is the edge presence function, which indicates whether a given edge is available at a given time; (zeta ,E times mathcal{T} rightarrow {mathbb T}) is the latency function, which indicates the time it takes to cross a given edge if starting at a given date.
In order to simplify the calculation, without loss of generality, we assume the service curve β(t) of the sensor node is a rate-latency function β R,T (t) given by β ( t ) = β R, T ( t ) = R ⋅ ( t - T ), ∀ t > T > 0, (8).
We use semi-Markov model to derive the packet loss rate and channel access latency as functions of packet aggregation number.
Thus, we apply a semi-Markov process [13] to model the random access in LTE/LTE-A and subsequently to derive the packet loss rate and channel access latency as functions of the number of aggregated packets.
Linear regression lines were determined for H-reflex latencies as functions of height and age.
Another mechanism by which viral latency activators function is by activating PKC.
Linear regression lines were determined for H-reflex latencies as function of height and this correlation is illustrated in Figure 1.
