The literature on programming sensor networks has, by and large, focused on providing higher-level abstractions for expressing local node behavior. Kairos is a natural next step in sensor network programming in that it allows the programmer to express, in a centralized fashion, the desired global behavior of a distributed computation on the entire sensor network. Kairos’ compile-time and runtime subsystems expose a small set of programming primitives, while hiding from the programmer the details of distributed code generation and instantiation, remote data access and management, and inter-node program flow coordination. Kairos ’ runtime is greatly simplified by assuming eventual consistency in node state; this assumption underlies many practical distributed computations proposed for sensor networks. In this paper, we describe Kairos ’ programming model, and the flexibility and robustness it affords programmers. We demonstrate its suitability, through actual implementation, for a variety of distributed programs—both infrastructure services and signal processing tasks—typically encountered in sensor network literature: routing tree construction, localization, and object tracking. Our experimental results suggest that Kairos does not adversely affect the performance or accuracy of distributed programs, while our implementation experiences suggest that it greatly raises the level of abstraction presented to the programmer.

This article describes information-based approaches to processing and organizing spatially distributed, multi-modal sensor data in a sensor network. Energy constrained networked sensing systems must rely on collaborative signal and information processing (CSIP) to dynamically allocate resources, maintain multiple sensing foci, and attend to new stimuli of interest, all based on task requirements and resource constraints. Target tracking is an essential capability for sensor networks and is used as a canonical problem for studying information organization problems in CSIP. After formulating a CSIP tracking problem in a distributed constrained optimization framework, the paper describes IDSQ and other techniques for tracking individual targets as well as combinatorial tracking problems such as counting targets. Results from simulations and experimental implementations have demonstrated that these information based approaches are scalable and make efficient use of scarce sensing and communication resources.

This article describes one of the major efforts in the sensor network community to build an integrated sensor network system for surveillance missions. The focus of this effort is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice, and energy consumption is narrowly accounted for within a single protocol. In this article, we describe the design and implementation of a complete running system, called VigilNet, for energyefficient surveillance. The VigilNet allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We evaluate VigilNet middleware components and integrated system extensively on a network of 70 MICA2 motes. Our results show that our surveillance strategy is adaptable and achieves a significant extension of

Event-driven programming is a popular model for writing programs for tiny embedded systems and sensor network nodes. While event-driven programming can keep the memory overhead down, it enforces a state machine programming style which makes many programs difficult to write, maintain, and debug. We present a novel programming abstraction called protothreads that makes it possible to write eventdriven programs in a thread-like style, with a memory overhead of only two bytes per protothread. We show that protothreads significantly reduce the complexity of a number of widely used programs previously written with event-driven state machines. For the examined programs the majority of the state machines could be entirely removed. In the other cases the number of states and transitions was drastically decreased. With protothreads the number of lines of code was reduced by one third. The execution time overhead of protothreads is on the order of a few processor cycles.

Sensor networks open a new frontier for embedded distributed computing. Paradigms for sensor network programming in the large have been identified as a significant challenge towards developing large-scale applications. Classical programming languages are too low-level. This paper presents the design, implementation, and evaluation of EnviroSuite, a programming framework that introduces a new paradigm, called environmentally immersive programming, to abstract distributed interactions with the environment. Environmentally immersive programming refers to an object-based programming model in which individual objects represent physical elements in the external environment. It allows the programmer to think directly in terms of environmental abstractions. EnviroSuite provides language primitives for environmentally immersive programming that map transparently into a support library of distributed algorithms for tracking and environmental monitoring. We show how nesC code of realistic applications is significantly simplified using EnviroSuite, and demonstrate the resulting system performance on Mica2 and XSM platforms.

Severely resource-constrained devices present a confounding challenge to the functional programmer: we are used to having powerful abstraction facilities at our fingertips, but how can we make use of these tools on a device with an 8- or 16-bit CPU and at most tens of kilobytes of RAM? Motivated by this challenge, we have developed Flask, a domain specific language embedded in Haskell that brings the power of functional programming to sensor networks, collections of highly resource-constrained devices. Flask consists of a staging mechanism that cleanly separates node-level code from the meta-language used to generate node-level code fragments; syntactic support for embedding standard sensor network code; a restricted subset of Haskell that runs on sensor networks and constrains program space and time consumption; a higher-level “data stream ” combinator library for quickly constructing sensor network programs; and an extensible runtime that provides commonly-used services. We demonstrate Flask through several small code examples as well as a compiler that generates node-level code to execute a network-wide query specified in a SQL-like language. We show how using Flask ensures constraints on space and time behavior. Through microbenchmarks and measurements on physical hardware, we demonstrate that Flask produces programs that are efficient in terms of CPU and memory usage and that can run effectively on existing sensor network hardware.

Clustering is an important characteristic of most sensor applications. In this paper we define COSMOS, the Cluster-based, heterOgeneouSMOdel for Sensor networks. The model assumes a hierarchical network architecture comprising of a large number of low cost sensors with limited computation capability, and fewer number of powerful clusterheads, uniformly distributed in a two dimensional terrain. The sensors are organized into single hop clusters, each managed by a clusterhead. The clusterheads are organized in a mesh-like topology. All sensors in a cluster are time synchronized, whereas the clusterheads communicate asynchronously. The sensors are assumed to have multiple power states and a wake-up mechanism to facilitate power management. To illustrate algorithm design using our model, we discuss implementation of algorithms for sorting and summing in sensor networks.

We present a new software system architecture for the implementation of hard real-time applications. The core of the system is a microkernel whose reactivity (interrupt handling as in synchronous reactive programs) and proactivity (task scheduling as in traditional RTOSs) are fully programmable. The microkernel, which we implemented on a Strong-ARM processor, consists of two interacting domain-specific virtual machines, a reactive E (Embedded) machine and a proactive S (Scheduling) machine. The microkernel code (or microcode) that runs on the microkernel is partitioned into E and S code. E code manages the interaction of the system with the physical environment: the execution of E code is triggered by environment interrupts, which signal external events such as the arrival of a message or sensor value, and it releases application tasks to the S machine. S code manages the interaction of the system with the processor: the execution of S code is triggered by hardware interrupts, which signal internal events such as the completion of a task or time slice, and it dispatches application tasks to the CPU, possibly preempting a running task. This partition of the system orthogonalizes the two main concerns of real-time implementations: E code refers to environment time and thus defines the reactivity of the system in a hardware- and scheduler-independent fashion; S code refers to CPU time and defines a system scheduler. If both time lines can be reconciled, then the code is called time safe; violations of time safety are handled again in a programmable way, by run-time exceptions. The separation of E from S code permits the independent programming, verification, optimization, composition, dynamic adaptation, and reuse of both reaction and scheduling mechanisms. Our measurements show that the system overhead is very acceptable even for large sets of task, generally in the 0.2–0.3 % range.

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.

Abstract — We introduce galsC, a language designed for programming event-driven embedded systems such as sensor networks. galsC implements the TinyGALS programming model. At the local level, software components are linked via synchronous method calls to form actors. At the global level, actors communicate with each other asynchronously via message passing, which separates the flow of control between actors. A complementary model called TinyGUYS is a guarded yet synchronous model designed to allow thread-safe sharing of global state between actors via parameters without explicitly passing messages. The galsC compiler extends the nesC compiler, which allows for better type checking and code generation. Having a well-structured concurrency model at the application level greatly reduces the risk of concurrency errors, such as deadlock and race conditions. The galsC language is implemented on the Berkeley motes and is compatible with the TinyOS/nesC component library. We use a multi-hop wireless sensor network as an example to illustrate the effectiveness of the language. I.