Summary We describe an interaction based approach for computer modeling and simulation of large integrated biological, information, social and technical (BIST) systems 1 Examples of such systems are urban regional transportation systems, the national electrical power markets and grids, gene regulatory networks, the worldwide Internet, infectious diseases, vaccine design and deployment, theater war, etc. These systems are composed of large numbers of interacting human, physical, informational and technological components. These components adapt and learn, exhibit perception, interpretation, reasoning, deception, cooperation and non-cooperation, and have economic motives as well as the usual physical properties of interaction. The theoretical foundation of our approach consists of two parts: (i) mathematics of complex interdependent dynamic networks, and (ii) mathematical and computational theory of a class of finite discrete dynamical systems called Sequential Dynamical Systems (SDSs). We then consider engineering principles based on such a theory. As with the theoretical foundation, they consist of two basic parts: (i) Efficient data manipulation, including synthesis, integration, storage and regeneration and (ii) high performance computing oriented system design, development and implementation. The engineering methods allow us to specify, design, and analyze simulations of extremely large systems and implement them on massively parallel architectures. As an illustration of our approach, an interaction based computer modeling and simulation framework to study very large interdependent societal infrastructures is described. 1

Abstract. Many complex systems can be modeled as multiagent systems in which the constituent entities (agents) interact with each other. The global dynamics of such a system is determined by the nature of the local interactions among the agents. Since it is difficult to formally analyze complex multiagent systems, they are often studied through computer simulations. While computer simulations can be very useful, results obtained through simulations do not formally validate the observed behavior. Thus, there is a need for a mathematical framework which one can use to represent multiagent systems and formally establish their properties. This work contains a brief exposition of some known mathematical frameworks that can model multiagent systems. The focus is on one such framework, namely that of finite dynamical systems. Both, deterministic and stochastic versions of this framework are discussed. The paper contains a sampling of the mathematical results from the literature to show how finite dynamical systems can be used to carry out a rigorous study of the properties of multiagent systems and it is shown how the framework

Abstract. In this paper a synthetic mobility model which represents behavior and movement pattern of heterogeneous units in disaster relief and battlefield scenarios is proposed. These operations usually take place in environment without preexisting communication infrastructure and units thus have to be connected by wireless communication network. Units cooperate to fulfill common tasks and communication network has to serve high amount of communication requests, especially data, voice and video stream transmissions. To verify features of topology control, routing and interaction protocols software simulations are usually used, because of their scalability, repeatability and speed. Behavior of all these protocols relies on the mobility model of the network nodes, which has to resemble real-life movement pattern. Proposed mobility model is goaldriven and provides support for various types of units, group mobility and realistic environment model with obstacles. Basic characteristics of the mobility model like node spatial distribution and average node degree were analyzed.

Most routing protocols in MANET adopt the popular Random Waypoint model for its simplicity and suitability for theoretical study and analysis. Recently, several entity, group and scenario based mobility models and frameworks have been proposed to model much more realistic and practical movements of mobile nodes in real scenarios. Although some work exists in evaluating routing protocols based on such specific scenarios, and some effort in adapting a protocol to suit mobility has been made, there does not exist any protocol that makes direct use of mobility information to route packets within a MANET. In this work, we first develop a practical mobility model that recognizes an orbital pattern in the sociological movement of mobile users, and then propose a novel Orbit Based Routing (OBR) protocol, that leverages the underlying orbital mobility to accurately determine a set of likely regions containing any node in the MANET. By forming a distributed location database among acquaintances and employing a scalable geographic routing to forward packets among nodes, OBR emerges as a clear choice for MANET routing in the face of practical mobility. We propose three different schemes of OBR and compare their performance against an

A major hurdle in evaluating routing protocols for a

Abstract. Motivated by realistic sensor network scenarios that have mis-informed nodes and variable network topologies, we propose an approach to routing that combines the best features of limited-flooding and information-sensitive path-finding protocols into a reliable, low-power method that can make delivery guarantees independent of parameter values or information noise levels. We introduce Parametric Probabilistic Sensor Network Routing Protocols, afamily of light-weight and robust multi-path routing protocols for sensor networks in which an intermediate sensor decides to forward a message with a probability that depends on various parameters, such as the distance of the sensor to the destination, the distance of the source sensor to the destination, or the number of hops a packet has already traveled. We propose two protocol variants of this family and compare the new methods to other probabilistic and deterministic protocols, namely constant-probability gossiping, uncontrolled flooding, random wandering, shortest path routing (and a variation), and a load-spreading shortest-path protocol inspired by (Servetto and Barrenechea, 2002). We consider sensor networks where a sensor’s knowledge of the local or global information is uncertain (parametrically noised) due to sensor mobility, and investigate the trade-off between robustness of the protocol as measured by quality of service (in particular, successful delivery rate and delivery lag) and use of resources (total network load). Our results for networks with randomly placed nodes and realistic urban networks with varying density show that the multi-path protocols are less sensitive to misinformation, and suggest that in the presence of noisy data, a limited flooding strategy will actually perform better and use fewer resources than an attempted single-path routing strategy, with the Parametric Probabilistic Sensor Network Routing Protocols outperforming other protocols. Our results also suggest that protocols using network information perform better than protocols that do not,

This technical report describes five synthetic data sets that are being released to the larger academic community for research. The data sets are based on detailed microscopic simulation-based modeling and integration techniques and are being released as Data Set Version 1.0. We expect to make available new and enhanced synthetic data products, including new cities and regions, on a regular basis. The data set provided represent