Application of the Cell-DEVS Paradigm for Cell Spaces Modelling and Simulation

Abstract

We present the results obtained when using the Cell-DEVS paradigm for cell spaces modelling and simulation. This formalism allows one to model and simulate cell spaces, including delay functions, to specify their timing behavior. Cell spaces can be defined in an automated fashion, simplifying the construction of new models, and easing the verification of the structural models. The approach was implemented in a development tool, showing that development times can improve by several orders of magnitude. The main results of development experiences are presented, showing the usefulness of the approach.

DEVS modeling of mobile wireless ad hoc networks

“…The reason is that in the above scenario, when the mobile node located at cell (19,10) in Fig. 31 with hopcount value of 60 moves to a new cell (18,9) in Fig.…”

“…Nine different collision scenarios are created from this initial topology. Four of these collisions are between static and mobile nodes (potential collisions between node pairs located at cells: (0, 0)-(1, 1), (1, 4)-(0, 5), (9, 10)- (10,11), (18,12)- (19,13)), three of them are between two mobile nodes (potential collisions between node pairs located at cells: (2, 12)- (4,14), (6, 1)-(8, 1), (12,3)- (12,5)) and two of them are between a mobile node and a gateway (potential collisions between node pairs located at cells (14,18)- (15,19), (16,18)- (15,19)). Fig.…”

“…31 shows the state of the model after 22 iterations at Time 22:000. In the figure, when the mobile node located at cell (17,12) with direction SW and hop count value 10 comes closer to another mobile node located at cell (19,10), the mobile node located at cell (19,10) should update its hop-count value to 20. Figs.…”

“…The toolkit has been built as a set of independent software pieces, each of them independent of the operating environment chosen. Our goal was to build models of ad hoc networks using the advantages of DEVS and Cell-DEVS for the following reasons: efficiency (by describing a high level specification of the problem to be modeled, we have reduced the effort needed in developing the application) and high performance (the models execute using a discrete-event approach, which, as shown in [18][19][20], provide higher precision and speedups than the discrete time approaches). In [18], the authors showed that the discrete-event nature of DEVS models combined with parallel simulation techniques can produce speedups of up to 1000 times.…”

“…In [18], the authors showed that the discrete-event nature of DEVS models combined with parallel simulation techniques can produce speedups of up to 1000 times. In [19,20], we showed that Cell-DEVS also provides these advantages. DEVS also provides a formal framework that can be used to validate and verify the models, opening the door to re-using the models as well as integrating them with other models based on different formalisms (for instance, using Petri Nets or Finite State Machines to specify the behavior of traffic lights or railway controllers).…”