The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round-robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper, we compare these scheduling schemes with the optimal scheduling scheme produced with two different modeling approaches: an approach based on a priced-timed automaton model (implemented and evaluated in Uppaal Cora), as well as an analytical approach (partly formulated as nonlinear optimization problem) for a slightly adapted scheduling problem. We show that in some cases the results of the simple scheduling schemes (round-robin, and best-first) are close to optimal. However, the optimal schedules, computed according to both methods, also clearly show that in a variety of scenarios, the simple schedules are far from optimal.

The use of mobile devices is limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries over the load to exploit recovery properties usually extends the system lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper we compare these scheduling schemes with the optimal scheduling scheme produced with a priced timed automaton battery model (implemented and evaluated in Uppaal Cora). We see that in some cases the results of the simple scheduling schemes are close to optimal. However, the optimal schedules also clearly show that there is still room for improving the battery lifetimes.

We investigate the problem of specification based testing with dense sets of inputs and outputs, in particular with imprecision as they might occur due to errors in measurements, numerical instability or noisy channels. Using quantitative transition systems to describe implementations and specifications, we introduce implementation relations that capture a notion of correctness "up to ε", allowing deviations of implementation from the specification of at most ε. These quantitative implementation relations are described as Hausdorff distances between certain sets of traces. They are conservative extensions of the well-known ioco relation. We develop an on-line and an off-line algorithm to generate test cases from a requirement specification, modeled as a quantitative transition system. Both algorithms are shown to be sound and complete with respect to the quantitative implementation relations introduced.

This paper presents Modest (MOdeling and DEscription language for Stochastic Timed systems), a formalism that is aimed to support (i) the modular description of reactive system's behaviour while covering both (ii) functional and (iii) nonfunctional system aspects such as timing and quality-of-service constraints in a single specification. The language contains features such as simple and structured data types, structuring mechanisms like parallel composition and abstraction, means to control the granularity of assignments, exception handling, and non-deterministic and random branching and timing. Modest can be viewed as an overarching notation for a wide spectrum of models, ranging from labeled transition systems, to timed automata (and probabilistic variants thereof) as well as prominent stochastic processes such as (generalized semi-)Markov chains and decision processes. The paper describes the design rationales and details of the syntax and semantics.

This paper is concerned with the analysis and redesign of a distributed algorithm to monitor the availability of nodes in self-configuring networks. The simple scheme to regularly probe a node - "are you still there?" - may easily lead to over- or underloading. The essence of the algorithm is therefore to automatically adapt the probing frequency. We show that a self-adaptive scheme to control the probe load, originally proposed as an extension to the UpnP/spl trade/ (universal plug and play) standard, leads to an unfair treatment of nodes: some nodes probe fast while others almost starve. An alternative distributed algorithm is proposed that overcomes this problem and that tolerates highly dynamic network topology changes. The algorithm is very simple and can be implemented on large networks of small computing devices such as mobile phones, PDAs, and so on.

Functional Interface (AFI). Models and solution techniques interact with one another through the use of the standard interface, allowing them to interact with M OBIUS framework components, not formalism components. This permits novel combinations of modeling techniques. The AFI uses abstract classes to implement the framework components. The most basic model in the M OBIUS framework is an atomic model, and is made up of state variables that hold information about the state of a model and actions that are used for changing model state. Stochastic activity networks (SANs) and the stochastic process algebra PEPA are example atomic models that have been successfully implemented in the M OBIUS tool.

This paper investigates the tradeoff between reliability and effectiveness for the IPv4 Zeroconf protocol, proposed by Cheshire/Adoba/Guttman in 2002, dedicated to the selfconfiguration of IP network interfaces. We develop a simple stochastic cost model of the protocol, where reliability is measured in terms of the probability to avoid an address collision after configuration, while effectiveness is viewed as the average penalty perceived by a user. We derive an analytical expression for the user penalty which we use to derive optimal configuration parameters of the network, restricting to those parameters which are under the control of a consumer electronics manufacturer. In particular we show that minimal cost and maximal reliability are qualities that cannot be achieved at the same time.