Abstract. The ability to update the program code installed on wireless sensor nodes plays an import role in the highly dynamic environments sensor networks are often deployed in. Such code update mechanisms should support flexible reconfiguration and adaptation of the sensor nodes but should also operate in an energy and time efficient manner. In this paper, we present FlexCup, a flexible code update mechanism that minimizes the energy consumed on each sensor node for the installation of arbitrary code changes. We describe two different versions of FlexCup and show, using a precise hardware emulator, that our mechanism is able to perform updates up to 8 times faster than related code update algorithms found in the literature, while consuming only an eighth of the energy.
Abstract-With the proliferation of sensor networks and sensor network applications, the overall complexity of such systems is continuously increasing. Sensor networks are now heterogeneous in terms of their hardware characteristics and application requirements even within a single network. In addition, the requirements of currently supported applications are expected to change over time. All of this makes developing, deploying, and optimizing sensor network applications an extremely difficult task. In this paper, we present the architecture of TinyCubus, a flexible and adaptive cross-layer framework for TinyOSbased sensor networks that aims at providing the necessary infrastructure to cope with the complexity of such systems. TinyCubus consists of a data management framework that selects and adapts both system and data management components, a cross-layer framework that enables optimizations through cross-layer interactions, and a configuration engine that installs components dynamically. Furthermore, we show the feasibility of our architecture by describing and evaluating a code distribution algorithm that uses application knowledge about the sensor topology in order to optimize its behavior.
In this paper we present Levels, a programming abstraction for energy-aware sensor network applications. Unlike most previous work it does not try to maximize network lifetime but rather helps to meet user-defined lifetime goals while maximizing application quality. Levels is targeted to applications where there is no redundancy and no node should fail early.With our programming abstraction the application developer defines so-called energy levels. These energy levels form a stack and can be deactivated from top to bottom if the lifetime goal cannot be met otherwise. Each code block within an energy level contains information about its energy consumption, which can be obtained from simulation tools without much effort. The runtime system then uses the data about the energy consumption of the different levels to compute an optimal level assignment for the time remaining. As we show in the evaluation, applications using Levels can accurately meet given lifetime goals and offer good application quality. In addition, the runtime overhead of our system is almost negligible.
Short abstractWe define three of the key issues related to efficient management and configuration of sensor networks: the distribution and management of roles within the network, efficient code distribution algorithms, and efficient on-the-fly code update algorithms. We present some results for each of these issues as we have dealt with them within the TinyCubus project. Full abstractIn this paper, we define three of the key issues that need to be solved in order to provide efficient management and configuration of applications and system software in sensor networks: the distribution and management of roles within the network, efficient code distribution algorithms, and efficient on-the-fly code update algorithms for sensor networks. The first issue is motivated by the increasing heterogeneity of sensor network applications and their need for more complex (non-homogeneous) network topologies and structures. The second one is motivated by the intrinsic energy constraint issues and, in general, the resource limitation of sensor networks. Finally, the third one is needed due to the nature of monitoring applications and optimization needs from applications that should be able to efficiently incorporate code updates so that the network can adapt to its surroundings on the fly. In this paper we present related work and some results for each of these issues as we have dealt with them within the TinyCubus project.
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.
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