Complex adaptive communication networks and environments: Part 2

Due to recent rapid advancements in social, pervasive and mobile communication network technologies, the topologies as well as interaction of components in modern networks often involve complex communication of personal as well as sensory data. An exponential increase in human usage of networks can result in a set of unprecedented as well as unpredictable effects, not just on the network structure but also as a reflection back on the lives of individual human users and the society. As a result, modern communication networks tend to exhibit properties associated with living or lifelike artificial systems, often classified as Complex Adaptive Systems (CAS). CAS are systems with numerous nonlinear interacting components often leading to emergent phenomena. CAS are considered as a special class of systems because it is often impossible to model them using traditional analytical techniques due to a lack of linearity as well as a high number of variables (or agents) in the system. This often results in a system with characteristics that are unpredictable if evaluated based solely on an examination of the individual components. In any domain, the absence of well-established modeling and simulation techniques makes it difficult to quantify or classify problems or present solutions in that domain. Being able to model and simulate the environment and not just the network gives designers the ability to predict outcomes as well as to perform a systematic simluationbased validation of real-world network deployments. Modeling can be particularly useful in the domain of online and offline social networks, both of which have shown extensive growth in the recent past as modeled by Zhu et al. While simulation of computer networks has always played an important role in the design and development of networks as well as protocols and algorithms, due to the above-mentioned increase in the scale and order of complexity, there is a need for newer and more effective techniques and paradigms for modeling and simulation of large-scale networks. As a follow-up to the first part of the special issue on Complex Adaptive COmmunicatiOn Networks and environmentS (CACOONS), this second part presents a selection of four peer-reviewed papers on the use of two complexity-related multidisciplinary modeling and simulation techniques, namely, agent-based modeling (ABM) and complex networks–based modeling (CN).