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In network condition-based solutions, the typical examples leverage the receiver signal strength (RSS) or the signal-to-interference-and-noise ratio (SINR) measured by the mobile node.
In [2], the latter was analyzed in relation to the RSS of the mobile node, yielding an overall higher throughput experienced by the mobile node user.
This transition is confirmed based on stochastic probability λ sf, which is defined through the ratio of maximum number of packets dropped by the mobile node to the maximum number of packets received by the mobile node from its neighbours as given by (6) {lambda}_{mathrm{sf}}kern0.5em =frac{M_{mathrm{np}(d)}}{M_{mathrm{np}(r)}} (6).
The idea is to admit/not drop a call if it agrees to enter/continue using a codec lower than the one requested, if it is supported by the mobile node.
Similarly, in [7], the actual sample reputation score, Qn(c i ), is calculated by the mobile node upon entering or leaving a given network in the same fashion as the Qth.
In multiple attribute solutions, the authors improve upon the QoS perceived by the mobile node in the network selection process through the use of multiple attribute decision maker (MADM) algorithms which leverage multiple network conditions and performance metrics.
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The scores are sampled by the mobile nodes while within the Wi-Fi network at 40 km/h, between the prediction model and aggregate model.
The scores are sampled by the mobile nodes while within the WiMAX network at 40 km/h, between the prediction model and aggregate models.
This network connectivity highly depends on the degree of cooperation attributed by the mobile nodes present in the routing path established between the source and the destination [8].
The scores are sampled by the mobile nodes while within the WiMAX network at 40 km/h, between the two models.
The scores are sampled by the mobile nodes upon entering the Wi-Fi network at 40 km/h, between the prediction model and aggregate models.
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