There has been increasing interest in incorporating sensing systems into objects or the environment for monitoring purposes. In this work we compare approaches to performing fully-distributed anomaly detection as a means of detecting security threats for objects equipped with sensing and communication abilities. With the desirability of increased visibility into the cargo in the transport chain and the goal of improving security, we consider the approach of equipping cargo with sensing and communication capabilities as a means of ensuring the security of the cargo as a key application. We have gathered real sensor test data from a rail trial and used the collected data to test the feasibility of the anomaly detection approach. The results demonstrate the effectiveness of our approach.

The Mobius framework is an environment for supporting multiple modeling formalisms and solution techniques. Models expressed in formalisms that are compatible with the framework are translated into equivalent models using Mobius framework components. This translation preserves the structure of the models, allowing efficient solutions. The framework is implemented in the tool by a well-defined abstract functional interface. Models and solution techniques interact with one another through the use of the standard interface, allowing them to interact with Mobius framework components, not formalism components. This permits novel combinations of modeling techniques, and will be a catalyst for new research in modeling techniques. This paper describes our approach, focusing on the "atomic model". We describe the formal description of the Mobius components as well as their implementations in our software tool.

High level modeling representations, such as stochastic Petri nets, frequently generate very large state spaces and corresponding state transition rate matrices. We propose a new steady state solution approach that avoids explicit storing of the matrix in memory. This method does not impose any structural restrictions on the model, uses Gauss Seidel and variants as the numerical solver, and uses less memory than current state of the art solvers. An implementation of these ideas shows that one can realistically solve very large, general models in relatively little memory.