Neighborhood Detection in Mobile Ad-Hoc Network Using Colored Petri Net

Abstract

Colored Petri Nets (CPNs) [2] is a language for the modeling and validation of systems in which concurrency, communication [6], and synchronization play a major role. Colored Petri Nets is a discrete-event modeling language combining Petri nets with the functional programming language Standard ML. Petri nets provide the foundation of the graphical notation and the basic primitives for modeling concurrency, communication, and synchronization. Standard ML provides the primitives for the definition of data types, describing data manipulation, and for creating compact and parameterizable models. A CPN model of a system is an executable model representing the states of the system and the events (transitions) that can cause the system to change state [4]. The CPN language makes it possible to organize a model as a set of modules, and it includes a time concept for representing the time taken to execute events in the modeled system.
In a mobile ad-hoc network(MANET) mobile nodes directly send messages to each other via wireless transmission. A node can send a message to another node beyond its transmission range by using other nodes as relay points, and thus a node can function as a router [1]. Typical applications of MANETS include defense systems such as battlefield survivability and disaster recovery. The research on MANETs originates from part of the Advanced Research Projects Agency(ARPA) project in the 1970s [1]. With the explosive growth of the Internet and mobile communication networks, challenging requirements have been introduced into MANETs and designing routing protocols has become more complex. One approach for ensuring correctness of an existing routing protocol is to create a formal model for the protocol and analyze the model to determine if indeed the protocol provides the defined service correctly. Colored Petri Nets are a suitable modeling language for this purpose as it can conveniently express non-determinism, concurrency and different levels of abstraction that are inherent in routing protocols.
However, it is not easy to build a CPN model of a MANET because a node can move in and out of its transmission range and thus the MANET‟s topology dynamically changes. In this paper we propose an algorithm for addressing such mobility problem of a MANET [1]. Using this algorithm a node can find its neighbors ,which are dynamically changing, at any instant of time .