Copyright © 2006, by the author(s).

Sensor networks are gaining a central role in the research community. This paper addresses some of the issues arising from the use of sensor networks in control applications. Classical control theory proves to be insufficient in modeling distributed control problems where issues of communication delay, jitter, and time synchronization between components are not negligible. After discussing our hardware and software platform and our target application, we review useful models of computation and then suggest a mixed model for design, analysis, and synthesis of control algorithms within sensor networks. We present a hierarchical model composed of continuous time-trigger components at the low level and discrete event-triggered components at the high level. Keywords—Distributed control, distributed pursuit–evasion game (DPEG), embedded, Mica, mote, NesC, pursuit–evasion game (PEG), sensor network, TinyOS. I.

The science of computation has systematically abstracted away the physical world. Embedded software systems, however, engage the physical world. Time, concurrency, liveness, robustness, continuums, reactivity, and resource management must be remarried to computation. Prevailing abstractions of computational systems leave out these "non-functional" aspects. This chapter explains why embedded software is not just software on small computers, and why it therefore needs fundamentally new views of computation. It suggests component architectures based on a principle called "actor-oriented design," where actors interact according to a model of computation, and describes some models of computation that are suitable for embedded software. It then suggests that actors can define interfaces that declare dynamic aspects that are essential to embedded software, such as temporal properties. These interfaces can be structured in a "system-level type system" that supports the sort of design-time and run-time type checking that conventional software benefits from.

Abstract. We present the design and implementation of the type system for Ptolemy II, which is a tool for component-based heterogeneous modeling and design. This type system combines static typing with run-time type checking. It supports polymorphic typing of components, and allows automatic lossless type conversion at run-time. To achieve this, we use a lattice to model the lossless type conversion relation among types, and use inequalities defined over the type lattice to specify type constraints in components and across components. The system of inequalities can be solved efficiently, with existence and uniqueness of a solution guaranteed by fixed-point theorems. This type system increases the safety and flexibility of the design environment, promotes component reuse, and helps simplify component development and optimization. The infrastructure we have built is generic in that it is not bound to one particular type lattice. The type system can be extended in two ways: by adding more types to the lattice, or by using different lattices to model different system properties. Higher-order function types and extended types can be accommodated in this way. 1

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Systems with both continuous and discrete behaviors can be modeled using a mixed-signal style or a hybrid systems style. This paper presents a component-based modeling and simulation framework that supports both modeling styles. The component framework, based on an actor meta-model, takes a hierarchical approach to manage heterogeneity in modeling complex systems. We describe how ordinary differential equations, discrete-event systems, and finite state machines can be built under this meta-model. A mixed-signal system is a hierarchical composition of continuous-time and discrete-event models, and a hybrid system is a hierarchical composition of continuous-time and finite-state-machine models. Hierarchical composition and information hiding help building clean models and efficient execution engines. Simulation technologies, in particular, the interaction between a continuous-time ODE solving engine and various discrete simulation engines are discussed. A signal type system is introduced to schedule hybrid components inside a continuous-time environment. Breakpoints are used to control the numerical integration step sizes so that discrete events are handled properly. A "refiring" mechanism and a "rollback" mechanism are designed to manage continuous components inside a discrete-event environment. The technologies are implemented in the Ptolemy II software environment. Examples are given to show the applications of this framework in mixed-signal and hybrid systems.

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Embedded systems typically include reactive and transformative functions, often described in different languages and semantics which are well established in their respective application domains. Additionally, a large part of the system functionality and components is reused from previous designs including legacy code. There is little hope that a single language will replace this heterogeneous set of languages. A design process must be able to bridge the semantic differences for verification and synthesis and should account for limited knowledge of system properties. This paper presents the SPI model (System Property Intervals) which employs behavioral intervals and process modes to allow the common representation of different languages and semantics. This model is the basis of a workbench which is targeted at the design of heterogeneously specified embedded systems.