Reliable sensor network software is difficult to create: applications are concurrent and distributed, hardware-based memory protection is unavailable, and severe resource constraints necessitate the use of unsafe, low-level languages. Our work improves this situation by providing efficient memory and type safety for TinyOS 2 applications running on the Mica2, MicaZ, and TelosB platforms. Safe execution ensures that array and pointer errors are caught before they can corrupt RAM. Our contributions include showing that aggressive optimizations can make safe execution practical in terms of resource usage; developing a technique for efficiently enforcing safety under interrupt-driven concurrency; extending the nesC language and compiler to support safety annotations; finding previously unknown bugs in TinyOS; and, finally, showing that safety can be exploited to increase the availability of sensor networks applications even when memory errors are left unfixed. Categories and Subject Descriptors C.3 [Special-Purpose and Application-Based Systems]: real-time and embedded systems; C.4 [Performance of Systems]:

TinyOS applications are built with software components that communicate through narrow interfaces. Since components enable finegrained code reuse, this approach has been successful in creating applications that make very efficient use of the limited code and data memory on sensor network nodes. However, the other important benefit of components—rapid application development through black-box reuse—remains largely unrealized because in many cases interfaces have implied usage constraints that can be the source of frustrating program errors. Developers are commonly forced to read the source code for components, partially defeating the purpose of using components in the first place. Our research helps solve these problems by allowing developers to explicitly specify and enforce component interface contracts. Due to the extensive reuse of the most common interfaces, implementing contracts for a small number of frequently reused interfaces permitted us to extensively check a number of applications. We uncovered some subtle and previously unknown bugs in applications that have been in common use for years.

Sensor networks have a significant potential in diverse applications some of which are already beginning to be deployed in areas such as environmental monitoring. As the application logic becomes more complex, programming difficulties are becoming a barrier to adoption of these networks. The difficulty in programming sensor networks is not only due to their inherently distributed nature but also the need for mechanisms to address their harsh operating conditions such as unreliable communications, faulty nodes and extremely constrained resources. Researchers have proposed different programming models to overcome these difficulties with the ultimate goal of making programming easy while making full use of available resources. In this paper, we first explore the requirements for programming models for sensor networks. Then we present a taxonomy of the programming models, classified according to the level of abstractions they provide. We present an evaluation of various programming models for their responsiveness to the requirements. Our results point to promising efforts in the area and a discussion of the future directions of research in this area.

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.

We argue that the principal cause of sensornet deployment and development difficulty is an inability to observe a network’s internal operation. We further argue that this lack of visibility is due to the activity and resource constraints enforced by limited energy. We present the Mote Network (MNet) architecture, which elevates visibility to be its dominant design principle. We propose a quantitative metric for network visibility and explain why network isolation and fairness are critical concerns. We describe the Fair Waiting Protocol (FWP), MNet’s single-hop protocol and show how its fairness and isolation can improve throughput and efficiency. We present the Pull Collection Protocol as a case study in designing multihop protocols in the architecture. 1

Persistent storage offers multiple advantages for sensor networks, yet the available storage systems have been unwieldy because of their complexity and device-specific designs. We present the Coffee file system for flashbased sensor devices. Coffee provides a programming interface for building efficient and portable storage abstractions. Unlike previous flash file systems, Coffee uses a small and constant RAM footprint per file, making it scale elegantly with workloads consisting of large files or many files. In addition, the performance overhead of Coffee is low: the throughput is at least 92 % of the achievable direct flash driver throughput. We show that network layer components such as routing tables and packet queues can be implemented on top of Coffee, leading to increased performance and reduced memory requirements for routing and transport protocols.

We advocate a novel programming approach we call slotted programming that not only addresses the specific hardware capabilities of sensor nodes, but also facilitates coding through a truly modular design. The approach is based on the temporal decoupling of the different tasks of a sensor node such that at any time at most one task is active. In contrast to traditional sensor network programming, slotted programming guarantees that each of these tasks can be implemented as an independent software module, simplifying not only the coding and testing phase, but also the code reuse in a different context. In addition, we believe that the proposed approach is highly qualified for energy efficient and real time applications. To substantiate our claims, we have implemented slotos, an extension to TinyOS that supports slotted programming. Within this framework, we demonstrate the advantages of the slotted programming paradigm.

Abstract. We propose FIT, a flexible, light-weight and real-time scheduling system for wireless sensor platforms. There are three salient features of FIT. First, its two-tier hierarchical framework supports customizable application-specific scheduling policies, hence FIT is very flexible. Second, FIT is light-weight in terms of minimizing thread number to reduce preemptions and memory consumption while at the same time ensuring system schedulability. We propose a novel Minimum Thread Scheduling Policy (MTSP) exploration algorithm within FIT to achieve this goal. Finally, FIT provides a detailed real-time schedulability analysis method to help check if application’s temporal requirements can be met. We implemented FIT on MICAz motes, and carried out extensive evaluations. Results demonstrate that FIT is indeed flexible and light-weight for implementing real-time applications, at the same time, the schedulability analysis provided can predict the real-time behavior. FIT is a promising scheduling system for implementing complex real-time applications in sensor networks. 1

Virtual memory has been successfully used in different domains to extend the amount of memory available to applications. We have adapted this mechanism to sensor networks, where, traditionally, RAM is a severely constrained resource. In this paper we show that the overhead of virtual memory can be significantly reduced with compile-time optimizations to make it usable in practice, even with the resource limitations present in sensor networks. Our approach, ViMem, creates an efficient memory layout based on variable access traces obtained from simulation tools. This layout is optimized to the memory access patterns of the application and to the specific properties of the sensor network hardware. Our implementation is based on TinyOS. It includes a pre-compiler for nesC code that translates virtual memory accesses into calls of ViMem’s runtime component. ViMem uses flash memory as secondary storage. In order to evaluate our system we have modified nontrivial existing applications to make use of virtual memory. We show that its runtime overhead is small even for large data sizes.

Software components are modular and can enable post-deployment update, but their high overhead in runtime and memory is prohibitive for many embedded systems. This paper proposes to minimize such overhead by exploiting behavioral transparency in models of computation. In such a model (e.g., synchronous dataflow), the state of buffer requirements is determined completely by the firing sequence of the actors without requiring functional simulation of the actors. Instead of dedicating space to each channel or actor statically, our dispatcher passes buffer pointers to an actor upon firing. Straightforward implementations are counterproductive, as fine-grained allocation incurs high pointer overhead while coarse-grained allocation suffers from fragmentation. To address this problem, we propose medium-grained, “access-contiguous” buffer allocation scheme. We formulate the problem as 2-D tiles that represent the lifetime of the buffers over time and define operators for their translation and transformations to minimize their memory occupation spatially and temporally. Experimental results on real-life applications show up to 70 % data memory reduction compared to existing techniques. Our technique retains code modularity for dynamic configuration and, more importantly, enables many more applications that otherwise would not fit if implemented using previous state-of-the-art techniques. Categories and Subject Descriptors C.3 [Special-purpose and application-based systems]: Real-time