We present a methodology for scalable exploration of cyber-physical system architectures. We propose a mathematical formulation of the architecture exploration problem as an optimized mapping problem that includes joint selection of system topologies and components taken from predefined libraries. Using a graph-based representation of an architecture, we introduce novel compact encodings of mapping constraints and path constraints that significantly improve the scalability of the formulation. We use the new encodings to instantiate design requirements, such as interconnection, routing, timing, and energy constraints, on the architecture model. We implement our methods in an extensible architecture exploration toolbox, and provide a pattern-based language for formal, yet flexible, requirement specification. Numerical evaluations on a set of design problems from wireless sensor networks, reconfigurable manufacturing systems, and electrical power systems demonstrate the effectiveness of our approach.

We address the problem of synthesizing safety-critical embedded and cyber-physical system architectures to minimize a cost function while guaranteeing the desired reliability. We represent a system architecture as a configurable graph in which both the nodes (components) and edges (interconnections) may fail. We then propose a compact analytical formalism to efficiently reason about the reliability of the overall system based on the failure probabilities of the components, and provide expressions of the design constraints that avoid exhaustive enumeration of failure cases on all possible graph configurations. Based on these constraints, we cast the synthesis problem as an optimization problem and propose monolithic and iterative optimization schemes to decrease the problem complexity. We implement the proposed algorithms in the ArchEx framework, leveraging a pattern-based specification language to facilitate problem formulation. Design problems from aircraft electric power distribution networks and reconfigurable industrial manufacturing systems illustrate the effectiveness of our approach.

We address the design space exploration of wireless networks to jointly select topology and component sizing. We formulate the exploration problem as an optimized mapping problem, where network elements are associated with components from pre-defined libraries to minimize a cost function under correctness guarantees. We express a rich set of system requirements as mixed integer linear constraints over path variables, denoting the presence or absence of paths between network nodes, and propose an algorithm for efficient, compact encoding of feasible paths that can reduce by orders of magnitude the complexity of the optimization problem. We incorporate our methods in a system-level design space exploration toolbox and evaluate their effectiveness on design examples from data collection and localization networks.

We present ArchEx, a framework for cyber-physical system architecture exploration. We formulate the exploration problem as a mapping problem, where "virtual" components are mapped into "real" components from pre-defined libraries to minimize an objective function while guaranteeing that system requirements are satisfied. ArchEx leverages an extensible set of patterns to enable formal, yet flexible, requirement specification, a graph-based internal representation of the system architecture, and algorithms based on mixed integer linear programming to solve the mapping problem. Its effectiveness is demonstrated on two industrial case studies: an aircraft power distribution network and a reconfigurable automated production line.

In this paper we present the results and the assessment of a statistical analysis of the wireless sensor networks (WSN) radio channel in indoor office scenarios. The work is based on an extensive set of received signal strength (RSS) measurements collected within different typical indoor spaces and node placement scenarios. Results are compared with corresponding values from the log-distance model, which is the most widely used in WSN simulators. Channel temporal stability is also analyzed. The analysis reveals interesting regularities within the measured data and as expected we observe a highly non-stationary behavior of the RSS. We also propose several improvements for the channel model of WSN simulators based on our observations. They can be beneficial for running more accurate network simulations for such applications as localization.