| Type: | Package |
| Title: | Routing Distribution, Broadcasts, Transmissions and Receptions in an Opportunistic Network |
| Version: | 1.2 |
| Date: | 2017-06-27 |
| Author: | Christian E. Galarza, Jonathan M. Olate |
| Maintainer: | Christian E. Galarza <cgalarza88@gmail.com> |
| Description: | Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Suggests: | hopbyhop, endtoend |
| NeedsCompilation: | no |
| Packaged: | 2017-06-27 21:39:12 UTC; ra143711 |
| Repository: | CRAN |
| Date/Publication: | 2017-06-27 21:44:06 UTC |
Theoretical broadcasts/transmissions/receptions for an Opportunistic model
Description
This function computes the probability of success and the expected values of the number of broadcasts, transmissions and receptions for an Opportunistic model.
Usage
Expected(p)
Arguments
p |
vector of probabilities of length |
Value
A matrix with the probabilities and expected values for an Opportunistic model for all hops sizes <= N
Author(s)
Christian E. Galarza and Jonathan M. Olate
References
Biswas, S., & Morris, R. (2004). Opportunistic routing in multi-hop wireless networks. ACM SIGCOMM Computer Communication Review, 34(1), 69-74.
See Also
Examples
#An N=3 Opportunistic system with probabilities p = c(0.0,0.4,0.1)
res1 = Expected(p=c(0.9,0.4,0.1))
res1
Monte Carlo broadcasts/transmissions/receptions for an Opportunistic model
Description
This function estimates via Monte Carlo the probability of success and the expected values of the number of broadcasts, transmissions and receptions for an Opportunistic model.
Usage
MonteCarlo(p, M = 10^4)
Arguments
p |
vector of probabilities of length |
M |
Total number of Monte Carlo simulations |
Details
N is computed from p length. M is code10^4 by default.
Value
A vector with the success probability and expected values (broadcast, transmissions and receptions) for an N Opportunistic model.
Author(s)
Christian E. Galarza and Jonathan M. Olate
References
Biswas, S., & Morris, R. (2004). Opportunistic routing in multi-hop wireless networks. ACM SIGCOMM Computer Communication Review, 34(1), 69-74.
See Also
Examples
#Monte Carlo simulation for an N=3 Opportunistic system with probabilities
#p = c(0.0,0.4,0.1)
res2 = MonteCarlo(p=c(0.9,0.4,0.1),M=10^4)
res2
Routing distribution for an Opportunistic network
Description
It provides the different
possible routes, their frequency as well as their respective
probabilities when considering uncertain probabilities lying
on a interval p +- delta.
Usage
routes(p, delta = 0)
Arguments
p |
vector of probabilities of length |
delta |
Delta value when considering uncertain probabilities. The interval is of the type |
Details
By default, delta is considered to be zero disregarding uncertainty.
Value
A data frame containing the routes, frequencies, and respective probabilities.
Author(s)
Christian E. Galarza and Jonathan M. Olate
See Also
Examples
## Not run:
#An N=7 Opportunistic system with probabilities p1 = 0.7,...,p7 = 0.1
> p = seq(0.7,0.1,length.out = 7)
> routes(p)
Freq Probability Value
route 1 1 p1^7 0.08235
route 2 6 p1^5*p2 0.10084
route 3 10 p1^3*p2^2 0.12348
route 4 4 p1*p2^3 0.1512
route 5 5 p1^4*p3 0.12005
route 6 12 p1^2*p2*p3 0.147
route 7 3 p2^2*p3 0.18
route 8 3 p1*p3^2 0.175
route 9 4 p1^3*p4 0.1372
route 10 6 p1*p2*p4 0.168
route 11 2 p3*p4 0.2
route 12 3 p1^2*p5 0.147
route 13 2 p2*p5 0.18
route 14 2 p1*p6 0.14
route 15 1 p7 0.1
Total 64
## End(Not run)