Abstract not found

From experience with wireless sensor networks it has become apparent that dynamic reprogramming of the sensor nodes is a useful feature. The resource constraints in terms of energy, memory, and processing power make sensor network reprogramming a challenging task. Many different mechanisms for reprogramming sensor nodes have been developed ranging from full image replacement to virtual machines. We have implemented an in-situ run-time dynamic linker and loader that use the standard ELF object file format. We show that run-time dynamic linking is an effective method for reprogramming even resource constrained wireless sensor nodes. To evaluate our dynamic linking mechanism we have implemented an application-specific virtual machine and a Java virtual machine and compare the energy cost of the different linking and execution models. We measure the energy consumption and execution time overhead on real hardware to quantify the energy costs for dynamic linking. Our results suggest that while in general the overhead of a virtual machine is high, a combination of native code and virtual machine code provide good energy efficiency. Dynamic run-time linking can be used to update the native code, even in heterogeneous networks.

Sensor networks are notoriously difficult to program, given that they encompass the complexities of both distributed and embedded systems. To address this problem, we present the design and implementation of a declarative sensor network platform, DSN: a declarative language, compiler and runtime suitable for programming a broad range of sensornet applications. We demonstrate that our approach is a natural fit for sensor networks by specifying several very different classes of traditional sensor network protocols, services and applications entirely declaratively – these include tree and geographic routing, link estimation, data collection, event tracking, version coherency, and localization. To our knowledge, this is the first time these disparate sensornet tasks have been addressed by a single high-level programming environment. Moreover, the declarative approach accommodates the desire for architectural flexibility and simple management of limited resources. Our results suggest that the declarative approach is well-suited to sensor networks, and that it can produce concise and flexible code by focusing on what the code is doing, and not on how it is doing it.

A main challenge with developing applications for wireless embedded systems is the lack of visibility and control during execution of an application. In this paper, we present a tool suite called Marionette that provides the ability to call functions and to read or write variables on pre-compiled, embedded programs at run-time, without requiring the programmer to add any special code to the application. This rich interface facilitates interactive development and debugging at minimal cost to the node.

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.

In-situ reconfiguration of software is indispensable in embedded networked sensing systems. It is required for re-tasking a deployed network, fixing bugs, introducing new features and tuning the system parameters to the operating environment. We present a system that supports software reconfiguration in embedded sensor networks at multiple levels. The system architecture is based on an operating system consisting of a fixed tiny static kernel and binary modules that can be dynamically inserted, updated or removed. On top of the operating system is a command interpreter, implemented as a dynamically extensible virtual machine, that can execute high-level scripts written in portable byte code. Any binary module dynamically inserted into the operating systems can register custom extensions in the virtual machine interpreter, thus allowing the high-level scripts executed by the virtual machine to efficiently access services exported by a module, such as tuning module parameters. Together these system mechanisms permit the flexibility of selecting the most appropriate level of reconfiguration. In addition to detailing the system architecture and the design choices, the paper presents a systematic analysis of flexibility versus cost tradeoffs provided by these mechanisms.

Abstract. Wireless Sensor Networks (WSNs) are increasingly being proposed in scenarios whose requirements cannot be fully predicted, or where the system functionality must adapt to changing conditions. In these scenarios, the ability to reconfigure portions of the software running on WSN nodes becomes imperative. At the same time, recent WSN proposals often employ heterogeneous nodes (e.g., sensors and actuators), which require the deployment of different code on different devices, based on their characteristics. Unfortunately, existing work in the field largely focuses on simpler scenarios where the same, monolithic program is distributed to all the nodes in the WSN. In this paper we present FIGARO, a programming model supported by an efficient run-time system and distributed protocols, collectively enabling an unprecedented fine-grained control over what is being reconfigured, and where. Using FIGARO, the programmer can deal explicitly with component dependencies and version constraints, as well as select precisely the subset of nodes targeted by reconfiguration, leaving the others unaltered. We show that our run-time support imposes a very limited processing and memory overhead, while the communication overhead lies within 9 % of the theoretical optimum. 1

The programming and retasking of sensor nodes could benefit greatly from the use of a virtual machine (VM) since byte code is compact, can be loaded on demand, and interpreted on a heterogeneous set of devices. The challenge is to ensure good programming tools and a small footprint for the virtual machine to meet the memory constraints of typical WSN platforms. To this end we propose Darjeeling, a virtual machine modelled after the Java VM and capable of executing a substantial subset of the Java language, but designed specifically to run on 8- and 16-bit microcontrollers with 2-10 KB of RAM. The Darjeeling VM uses a 16- rather than a 32-bit architecture, which is more efficient on the targeted platforms. Darjeeling features a novel memory organisation with strict separation of reference from non-reference types which eliminates the need for run-time type inspection in the underlying compacting garbage collector. Darjeeling uses a linked stack model that provides light-weight threads, and supports synchronisation. The VM has been implemented on three different platforms and was evaluated with micro benchmarks and a realworld application. The latter includes a pure Java implementation of the collection tree routing protocol conveniently programmed as a set of cooperating threads, and a reimplementation of an existing environmental monitoring application. The results show that Darjeeling is a viable solution for deploying large-scale heterogeneous sensor networks.

This article presents Agilla, a mobile agent middleware designed to support self-adaptive applications in wireless sensor networks. Agilla provides a programming model in which applications consist of evolving communities of agents that share a wireless sensor network. Coordination among the agents and access to physical resources are supported by a tuple space abstraction. Agents can dynamically enter and exit a network and can autonomously clone and migrate themselves in response to environmental changes. Agilla’s ability to support self-adaptive applications in wireless sensor networks has been demonstrated in the context of several applications, including fire detection and tracking, monitoring cargo containers, and robot navigation. Agilla, the first mobile agent system to operate in resource-constrained wireless sensor platforms, was implemented on top of TinyOS. Agilla’s feasibility and efficiency was demonstrated by experimental evaluation on two physical testbeds consisting of Mica2 and TelosB nodes.

Tapper is a lightweight scripting engine for highly constrained wireless sensor nodes. Many such nodes run on 8-bit microcontrollers (MCUs) with only a few kilobytes of on-chip memory, often with segmented memory and unorthogonal registers that make it difficult to perform even the most basic tasks. To facilitate development of software routines and enable dynamic reconfigurability, Tapper provides a software layer that can interpret human readable commands either interactively or in batch. It can invoke compiled routines ranging from timer and interrupt configurations and task scheduling to accessing I/O devices. Experiments on platforms including 8051, ATMega 169V, and Freescale MC9S12NE64 with built-in Ethernet show that the Tapper setup has the same modular, dynamic benefits as middleware systems but without the high overhead in code size and data memory.