When physical communication network infrastructures fail, infrastructure-less communication networks such as mobile ad-hoc networks (MANET), can provide an alternative. This, however, requires MANETs to be adaptable to dynamic contexts characterized by the changing density and mobility of devices and availability of energy sources. To address this challenge, this paper proposes a decentralized context-adaptive topology control protocol. The protocol consists of three algorithms and uses preferential attachment based on the energy availability of devices to form a loop-free scale-free adaptive topology for an ad-hoc communication network. The proposed protocol has a number of advantages. First, it is adaptive to the environment, hence applicable in scenarios where the number of participating mobile devices and their availability of energy resources is always changing. Second, it is energy-efficient through changes in the topology. This means it can be flexibly combined with different routing protocols. Third, the protocol requires no changes on the hardware level. This means it can be implemented on all current phones, without any recalls or investments in hardware changes. The evaluation of the protocol in a simulated environment confirms the feasibility of creating and maintaining a self-adaptive ad-hoc communication network, consisting of multitudes of mobile devices for reliable communication in a dynamic context.
Complex adaptive systems provide a significant number of concepts such as reaction, interaction, adaptation, and evolution. In general, these concepts are modelled employing different techniques which give an inexplicit vision on the system. Therefore, all concepts must be carefully modelled using the same approach to avoid contradiction and guarantee system homogeneity and correctness. However, developing a computing system that includes all these concepts using the same approach is not an easy task and requires a perfect understanding of the system's behaviour. In this paper, we contribute as stepwise towards proposing an approach to model the most important concepts of complex adaptive systems while ensuring homogeneity and the correctness of models. For this aim, we present five standard agent-based models formalizing agent properties, reaction, interaction, adaptation, and evolution. These models are adapted to all cases of complex adaptive systems since they include an abstract description of these concepts. To implement our approach formally, we choose the Event-B method due to the strong assurance of bugs' absence that it guarantees. Besides, it supports horizontal and vertical refinement which facilitates the specification process. Furthermore, the approach of this paper addresses the very abstract level of modelling which expand the use of this approach to other formal methods and tools.
One of the basic approaches in science views behavioral products as a process within a dynamic system. The mechanism might be seen as a representation of many instances of centralized control in real time. Many real systems, however, exhibit autonomy by denying statically treated mechanisms. This study addresses the issues related to the identification of dynamic systems and suggests how determining the basic principles of a collective structure may be the key to understanding complex behavioral processes. A fundamental model is derived to assess the advantages of this perspective using a basic methodology. The connection between perspective and technique demonstrates certain aspects within their actual context while also clearly including the framework of actual dynamic system identification.
Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years. Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion. It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed. As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation.
Book detailsGreg Conradi SmithWilliam & Mary, Williamsburg, VACellular Biophysics and Modeling: A Primer on the Computational Biology of Excitable CellsCambridge University Press 2019© Greg Conradi Smith 2019DOI: 10.1017/9780511793905ISBN 978-1-107-00536-5 HardbackISBN 978-0-521-18305-5 Paperback
