18,826 research outputs found
Towards a Formal Framework for Mobile, Service-Oriented Sensor-Actuator Networks
Service-oriented sensor-actuator networks (SOSANETs) are deployed in
health-critical applications like patient monitoring and have to fulfill strong
safety requirements. However, a framework for the rigorous formal modeling and
analysis of SOSANETs does not exist. In particular, there is currently no
support for the verification of correct network behavior after node failure or
loss/addition of communication links. To overcome this problem, we propose a
formal framework for SOSANETs. The main idea is to base our framework on the
\pi-calculus, a formally defined, compositional and well-established formalism.
We choose KLAIM, an existing formal language based on the \pi-calculus as the
foundation for our framework. With that, we are able to formally model SOSANETs
with possible topology changes and network failures. This provides the basis
for our future work on prediction, analysis and verification of the network
behavior of these systems. Furthermore, we illustrate the real-life
applicability of this approach by modeling and extending a use case scenario
from the medical domain.Comment: In Proceedings FESCA 2013, arXiv:1302.478
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Self-Evaluation Applied Mathematics 2003-2008 University of Twente
This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008
SolarStat: Modeling Photovoltaic Sources through Stochastic Markov Processes
In this paper, we present a methodology and a tool to derive simple but yet
accurate stochastic Markov processes for the description of the energy
scavenged by outdoor solar sources. In particular, we target photovoltaic
panels with small form factors, as those exploited by embedded communication
devices such as wireless sensor nodes or, concerning modern cellular system
technology, by small-cells. Our models are especially useful for the
theoretical investigation and the simulation of energetically self-sufficient
communication systems including these devices. The Markov models that we derive
in this paper are obtained from extensive solar radiation databases, that are
widely available online. Basically, from hourly radiance patterns, we derive
the corresponding amount of energy (current and voltage) that is accumulated
over time, and we finally use it to represent the scavenged energy in terms of
its relevant statistics. Toward this end, two clustering approaches for the raw
radiance data are described and the resulting Markov models are compared
against the empirical distributions. Our results indicate that Markov models
with just two states provide a rough characterization of the real data traces.
While these could be sufficiently accurate for certain applications, slightly
increasing the number of states to, e.g., eight, allows the representation of
the real energy inflow process with an excellent level of accuracy in terms of
first and second order statistics. Our tool has been developed using Matlab(TM)
and is available under the GPL license at[1].Comment: Submitted to IEEE EnergyCon 201
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