I think the sizing of the network depends much on its application. How the sensors are distributed in a specific area will determine the number of nodes and the distance between the nodes. In addition the function of every sensor node will set the data produced in a specific time and so its traffic. The power and the bandwidth and the sensitivity of the receiver is dictated by the internode distance and the data rate. The processing power required in every node is determined by the signal processing required in every node. However there are number of generic wireless sensor networks that can be used as a reference for the parameters. On the other side there are data published about practical networks.
You can survey them to get the range of these parameters.
As an example please refer to the paper: Article Comparison of Routing Protocols in Wireless Sensor Networks ...
I think the sizing of the network depends much on its application. How the sensors are distributed in a specific area will determine the number of nodes and the distance between the nodes. In addition the function of every sensor node will set the data produced in a specific time and so its traffic. The power and the bandwidth and the sensitivity of the receiver is dictated by the internode distance and the data rate. The processing power required in every node is determined by the signal processing required in every node. However there are number of generic wireless sensor networks that can be used as a reference for the parameters. On the other side there are data published about practical networks.
You can survey them to get the range of these parameters.
As an example please refer to the paper: Article Comparison of Routing Protocols in Wireless Sensor Networks ...
Event-Based Variance-Constrained H∞ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements. (IEEE TRANSACTIONS ON CYBERNETICS, VOL. 48, NO. 3, MARCH 2018 1007)
Please see TABLE I FILTER PARAMETERS.
From the same paper read: By implementing Algorithm 1
and using MATLAB (with the YALMIP 3.0), the LMIs in
Theorem 3 can be solved recursively and some of the desired
filter parameters are obtained as shown in Table I.
If you are facing some dificulties in simulation MATLAB block; kindly contact Authors; they are cooperative if you ask exact question(s)