ABSTRACT
The role of networks is to simplify matters in business and life. Business decisions are made more quickly and accurately. This motivates the demand for the construction of temporary networks, where it becomes necessary to expand networks across different terrains. Such interconnection between mobile computers has produced the concept of ad hoc networks.
These networks and devices support computing anywhere, anytime, in a trend known as computing-on-move, mobile computing, or nomadic computing. The use of mobile devices has become essential in our daily life. Modern mobile technology involves solving a number of hard optimization problems which may pertain to situations that can only be imprecisely modeled. Deterministic algorithms perform poorly in such situations. For these problems, methods inspired by nature sometimes work very efficiently and effectively. Although the solutions obtained by these methods do not always equal mathematically strict solutions, near-optimal solutions are sometimes enough in most practical purposes.
Soft computing techniques based on genetic algorithms, neural networks, and fuzzy logic, mimic the human decision-making process to provide heuristic solutions to some challenging problems in mobile computing. Soft computing methods applied to real-world problems offer more robust, tractable, and less costly solutions than those obtained by more conventional mathematical techniques. Soft computing techniques, such as evolutionary computation (EC) and grammatical evolution (GE) are proposed to detect ad hoc flooding and route disruption attacks in mobile ad hoc networks. In Chapter 5, grammatical evolution evolves programs written in Backus-Naur form (BNF) grammar. The problem is elaborated from protecting mobile ad hoc networks from different kinds of attacks. The performance of grammatical evolution is analyzed with ad hoc flooding and route disruption attacks on various mobility patterns in the nodes on the network.
