4 research outputs found

    Resource Management in Heterogeneous Wireless Sensor Networks

    Get PDF
    We propose a first approach in the direction of a general framework for resource management in wireless sensor networks (WSN). The basic components of the approach are a model for WSNs and a task model. Based on these models, a first version of an algorithm for assigning tasks to a WSN is presented. The models and the algorithm are designed in such a way that an extension to more complex models is possible. Furthermore, the developed approach to solve the RM problem allows an easy adaptation, to fit more complex models. In this way, a flexible approach is achieved, which may form the base for many RM approaches.\ud The possibilities and limitations of the presented approach are tested on randomly generated instances. The aim of these tests is to show that the chosen models and algorithm form a proper starting point to design RM tools

    Trade-offs for implementing DSP algorithms on the Montium architecture

    No full text

    Statistical quality analysis of schedulers under soft-real-time constraints

    Get PDF
    This paper describes an algorithm to determine the performance of real-time systems with tasks using stochastic processing times. Such an algorithm can be used for guaranteeing Quality of Service of periodic tasks with soft real-time constraints. We use a discrete distribution model of processing times instead of worst case times like in hard real-time systems. Such a model gives a more realistic view on the actual requirements of the system. The presented algorithm works for all deterministic scheduling systems, which makes it more general than existing 6algorithms and allows us to compare performance between these systems. To demonstrate our method, we make a comparison between the performance of the well known scheduling algorithms Earliest Deadline First and Rate Monotonic. We show that the complexity of our method can compete with other algorithms that work for a wide range of schedulers
    corecore