Wireless sensor networks (WSNs) are attracting great interest in a number of application domains concerned with monitoring and control of physical phenomena, as they enable dense and untethered deployments at low cost and with unprecedented flexibility. However, application development is still one of the main hurdles to a wide adoption of WSN technology. In current real-world WSN deployments, programming is typically carried out very close to the operating system, therefore requiring the programmer to focus on low-level system issues. This not only shifts the focus of the programmer away from the application logic, but also requires a technical background that is rarely found among application domain experts. The need for appropriate high-level programming abstractions, capable to simplify the programming chore without sacrificing efficiency, has been long recognized and several solutions have been hitherto proposed, which differ along many dimensions. In this paper, we survey the state-of-the-art in programming approaches for WSNs. We begin by presenting a taxonomy of WSN applications, to identify the fundamental requirements programming platforms must deal with. Then, we introduce a taxonomy of WSN programming approaches that captures the fundamental differences among existing solutions, and constitutes the core contribution of this paper. Our presentation style relies on concrete examples and code snippets taken from programming platforms representative of the taxonomy dimensions being discussed. We use the taxonomy to provide an exhaustive classification of existing approaches. Moreover, we also map existing approaches back to the application requirements, therefore providing not only a complete view of the state-of-the-art, but also useful insights for selecting the programming abstraction most appropriate to the application at hand.

This paper presents Pixie, a new sensor node operating system designed to support the needs of data-intensive applications. These applications, which include high-resolution monitoring of acoustic, seismic, acceleration, and other signals, involve high data rates and extensive in-network processing. Given the fundamentally resourcelimited nature of sensor networks, a pressing concern for such applications is their ability to receive feedback on, and adapt their behavior to, fluctuations in both resource availability and load. The Pixie OS is based on a dataflow programming model based on the concept of resource tickets, a core abstraction for representing resource availability and reservations. By giving the system visibility and fine-grained control over resource management, a broad range of policies can be implemented. To shield application programmers from the burden of managing these details, Pixie provides a suite of resource brokers, which mediate between low-level physical resources and higher-level application demands. Pixie is implemented in NesC and supports limited backwards compatibility with TinyOS. We describe Pixie in the context of two applications: limb motion analysis for patients undergoing treatment for motion disorders, and acoustic target detection using a network of microphones. We present a range of experiments demonstrating Pixie’s ability to accurately account for resource availability at runtime and enable a range of both generic and application-specific adaptations.

Functional Reactive Programming (FRP) is an approach to reactive programming where systems are structured as networks of functions operating on signals. FRP is based on the synchronous dataflow paradigm and supports both continuous-time and discrete-time signals (hybrid systems). What sets FRP apart from most other languages for similar applications is its support for systems with dynamic structure and for higher-order reactive constructs. Statically guaranteeing correctness properties of programs is an attractive proposition. This is true in particular for typical application domains for reactive programming such as embedded systems. To that end, many existing reactive languages have type systems or other static checks that guarantee domain-specific properties, such as feedback loops always being well-formed. However, they are limited in their capabilities to support dynamism and higher-order data-flow compared with FRP. Thus, the onus of ensuring such properties of FRP programs has so far been on the programmer as established static techniques do not suffice. In this paper, we show how dependent types allow this concern to be addressed. We present an implementation of FRP embedded in the dependently-typed language Agda, leveraging the type system of the host language to craft a domain-specific (dependent) type system for FRP. The implementation constitutes a discrete, operational semantics of FRP, and as it passes the Agda type, coverage, and termination checks, we know the operational semantics is total, which means our type system is safe. Categories and Subject Descriptors D.3.2 [Programming Languages]: Language Classifications—applicative (functional) languages, data-flow languages, specialized application languages General Terms Languages Keywords dependent types, domain-specific languages, DSELs, FRP, functional programming, reactive programming, synchronous data-flow

Modern networks provide a variety of interrelated services including routing, traffic monitoring, load balancing, and access control. Unfortunately, the languages used to program today’s networks lack modern features—they are usually defined at the low level of abstraction supplied by the underlying hardware and they fail to provide even rudimentary support for modular programming. As a result, network programs tend to be complicated, error-prone, and difficult to maintain. This paper presents Frenetic, a high-level language for programming distributed collections of network switches. Frenetic provides a declarative query language for classifying and aggregating network traffic as well as a functional reactive combinator library for describing high-level packet-forwarding policies. Unlike prior work in this domain, these constructs are—by design—fully compositional, which facilitates modular reasoning and enables code reuse. This important property is enabled by Frenetic’s novel runtime system which manages all of the details related to installing, uninstalling, and querying low-level packet-processing rules on physical switches. Overall, this paper makes three main contributions: (1) We analyze the state-of-the art in languages for programming networks and identify the key limitations; (2) We present a language design that addresses these limitations, using a series of examples to motivate and validate our choices; (3) We describe an implementation of the language and evaluate its performance on several benchmarks.

Abstract. In this paper we present the first investigation into the implementation of a Functional Hybrid Modelling language for non-causal modelling and simulation of physical systems. In particular, we present a simple way to handle connect constructs: a facility for composing model fragments present in some form in most non-causal modelling languages. Our implementation is realised as a domain-specific language embedded in Haskell. The method of embedding employs quasiquoting, thus demonstrating the effectiveness of this approach for languages that are not suitable for embedding in more traditional ways. Our implementation is available on-line, and thus the first publicly available prototype implementation of a Functional Hybrid Modelling language. 1

Abstract. This paper explores how to implement an iteratively staged domain-specific language (DSL) by embedding into a functional language. The domain is modelling and simulation of physical systems where models are expressed in terms of non-causal differential-algebraic equations; i.e., sets of constraints solved through numerical simulation. What sets our language apart is that the equational constraints are first class entities allowing for an evolving model structure characterised by repeated generation of updated constraints. Hence iteratively staged. Our DSL can thus be seen as a combined functional and constraint programming language, albeit a two-level one, with the functional language chiefly serving as a meta language. However, the two levels do interact throughout the simulation. The embedding strategy we pursue is a mixture of deep and shallow, with the deep embedding enabling justin-time (JIT) compilation of the constraints as they are generated for efficiency, while the shallow embedding is used for the remainder for maximum leverage of the host language. The paper is organised around a specific DSL, but our implementation strategy should be applicable for iteratively staged languages in general. Our DSL itself is further a novel variation of a declarative constraint programming language. 1

Abstract—Service modeling and composition is a fundamental method for offering advanced functionality by combining a set of primitive services provided by the system. Unlike in the case of web services for which there is an abundance of reliable resources, in sensor networks, the resources are constrained and communication among nodes is error-prone and unreliable. Such a dynamic environment requires a continuous adaptation of the composition of services. In this paper, we first propose a graph-based model of sensor services that maps to the operational model of sensor networks and is amenable to analysis. Based on this model, we formulate the process of sensor service composition as a cost-optimization problem, which is NP-complete. We then propose two heuristic methods for its solution, the top-down and the bottom-up, and discuss their centralized and distributed implementations. Using simulations, we evaluate their performance.

Functional Reactive Programming (FRP) is an approach to reactive programming where systems are structured as networks of functions operating on time-varying values (signals). FRP is based on the synchronous data-flow paradigm and supports both continuous-time and discretetime signals (hybrid systems). What sets FRP apart from most other reactive languages is its support for systems with highly dynamic structure (dynamism) and higher-order reactive constructs (higher-order data-flow). However, the price paid for these features has been the loss of the safety and performance guarantees provided by other, less expressive, reactive languages. Statically guaranteeing safety properties of programs is an attractive proposition. This is true in particular for typical application domains for reactive programming such as embedded systems. To that end, many existing reactive languages have type systems or other static checksthatguaranteedomain-specificconstraints, suchasfeedbackbeingwell-formed(causality analysis). However, comparedwithFRP,theyarelimitedintheircapacitytosupportdynamism andhigher-orderdata-flow. Ontheotherhand, asestablishedstatictechniquesdonotsufficefor highly structurally dynamic systems, FRP generally enforces few domain-specific constraints, leaving the FRP programmer to manually check that the constraints are respected. Thus, there

This paper presents our experience using a domain-specific functional language, WaveScript, to build embedded sensing applications used in scientific research. We focus on a recent computervision application for detecting birds in their natural environment. The application was ported from a prototype in C++. In reimplementing the application, we gained a much cleaner factoring of its functionality (through higher-order functions and better interfaces to libraries) and a near-linear parallel speed-up with no additional effort. These benefits are offset by one substantial downside: the lack of familiarity with the language of the original vision researchers, who understandably tried to use the language in the familiar way they use C++ and thus ran into various problems. Categories and Subject Descriptors: D.3.2 Concurrent, distributed, and parallel languages; Applicative (functional) languages; Data-flow languages

Staging is a powerful language construct that allows a program at one stage to manipulate and specialize a program at the next. We propose 〈ML 〉 as a new staged calculus designed with novel features for staged programming in modern computing platforms such as embedded systems. A distinguishing feature of 〈ML 〉 is a model of process separation, whereby different stages of computation are executed in different process spaces. Our language also supports dynamic type specialization via type abstraction, dynamic type construction, and a limited form of type dependence. 〈ML 〉 is endowed with a largely standard metatheory, including type preservation and type safety results. We discuss the utility of our language via code examples from the domain of wireless sensor network programming.