@inproceedings {INPROC-2014-26,
author = {Andreas Benzing and Boris Koldehofe and Kurt Rothermel},
title = {{Bandwidth-Minimized Distribution of Measurements in Global Sensor Networks}},
booktitle = {In Proceedings of the 14th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS 2014)},
publisher = {Springer-Verlag},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
series = {Lecture Notes in Computer Science},
volume = {8460},
pages = {156--170},
type = {Conference Paper},
month = {June},
year = {2014},
doi = {10.1007/978-3-662-43352-2_13},
keywords = {Data Streams; Global Sensor Networks; Optimization},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2014-26/INPROC-2014-26.pdf,
http://link.springer.com/chapter/10.1007/978-3-662-43352-2_13},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Global sensor networks (GSN) allow applications to integrate huge amounts of
data using real-time streams from virtually anywhere. Queries to a GSN offer
many degrees of freedom, e.g. the resolution and the geographic origin of data,
and scaling optimization of data streams to many applications is highly
challenging. Existing solutions hence either limit the flexibility with
additional constraints or ignore the characteristics of sensor streams where
data points are produced synchronously. In this paper, we present a new
approach to bandwidth-minimized distribution of real-time sensor streams in a
GSN. Using a distributed index structure, we partition queries for bandwidth
management and quickly identify overlapping queries. Based on this information,
our relay strategy determines an optimized distribution structure which
minimizes traffic while being adaptive to changing conditions. Simulations show
that total traffic and user perceived delay can be reduced by more than 50\%.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2014-26&engl=1}
}
@inproceedings {INPROC-2012-30,
author = {Ben W. Carabelli and Andreas Benzing and Georg Seyboth and Rainer Blind and Mathias B{\"u}rger and Frank D{\"u}rr and Boris Koldehofe and Kurt Rothermel and Frank Allg{\"o}wer},
title = {{Exact Convex Formulations of Network-Oriented Optimal Operator Placement}},
booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC2012)},
publisher = {IEEE},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {3777--3782},
type = {Conference Paper},
month = {December},
year = {2012},
doi = {10.1109/CDC.2012.6426790},
keywords = {Optimization; Computer networks; Sensor networks},
language = {English},
cr-category = {G.1.6 Numerical Analysis Optimization},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2012-30/INPROC-2012-30.pdf,
http://dx.doi.org/10.1109/CDC.2012.6426790},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Data processing tasks are increasingly spread across the internet to account
for the spatially distributed nature of many data sources. In order to use
network resources efficiently, subtasks need to be distributed in the network
so data can be filtered close to the data sources. Previous approaches to this
operator placement problem relied on various heuristics to constrain the
complexity of the problem. In this paper, we propose two generic integer
constrained problem formulations: a topology aware version which provides a
placement including the specific network links as well as an end-to-end delay
aware version which relies on the routing capabilities of the network. A linear
programming relaxation for both versions is provided which allows exact and
efficient solution using common solvers.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2012-30&engl=1}
}
@inproceedings {INPROC-2011-30,
author = {Andreas Benzing and Boris Koldehofe and Kurt Rothermel},
title = {{Efficient Support for Multi-Resolution Queries in Global Sensor Networks}},
booktitle = {Proceedings of the Fifth International Conference on COMmunication System softWAre and middlewaRE: COMSWARE 2011},
publisher = {ACM},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {1--12},
type = {Conference Paper},
month = {July},
year = {2011},
doi = {10.1145/2016551.2016562},
keywords = {DSPS, global sensor network, indexing, query processing},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2011-30/INPROC-2011-30.pdf,
http://dl.acm.org/authorize?6553117},
contact = {andreas.benzing@ipvs.uni-stuttgart.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Stream processing has evolved as a paradigm for efficiently sharing and
integrating a massive amount of data into applications. However, the
integration of globally dispersed sensor data imposes challenges in the
effective utilization of the IT infrastructure that forms the global sensor
network. Especially, simulations require the integration of sensor streams at
widely differing spatial and temporal resolutions. For current stream
processing solutions it is necessary to generate a separate data stream for
each requested resolution. Therefore, these systems will suffer from high
redundancy in data streams, wasting a significant amount of bandwidth and
limiting their scalability.
This paper presents a new approach to scalable distributed stream processing of
data which stems from globally dispersed sensor networks. The approach supports
applications in establishing continuous queries for sensor data at different
resolutions and ensures efficient bandwidth usage of the data distribution
network. Unlike existing work in the context of video stream processing which
provides multiple resolutions by establishing separate channels for each
resolution, this paper presents a stream processing system that can efficiently
split/combine data streams in order to decrease/increase their resolution
without loss in data precision. In addition the system provides mechanisms for
load balancing of sensor data streams that allow efficient utilization of the
bandwidth of the global sensor network.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2011-30&engl=1}
}
@inproceedings {INPROC-2011-08,
author = {Gerald G. Koch and Andreas Benzing and Christoph P. Mayer},
title = {{An Approach for Urban Sensing with Quality-Aware Situation Detection and Efficient Communication}},
booktitle = {Proceedings of the Workshops der wissenschaftlichen Konferenz Kommunikation in verteilten Systemen 2011 (WowKiVS 2011); Kiel, Germany, March 11th, 2011},
editor = {Horst Hellbr{\"u}ck and Norbert Luttenberger and Volker Turau},
publisher = {EASST},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
series = {Electronic Communications of the EASST},
pages = {1--10},
type = {Workshop Paper},
month = {March},
year = {2011},
issn = {1863-2122},
keywords = {Urban sensing; Internet of Things; Complex Event Processing; CEP; Delay Tolerant Network; DTN; Distributed Diagnostic Simulation},
language = {English},
cr-category = {C.2.2 Network Protocols,
C.2.4 Distributed Systems,
I.6.3 Simulation and Modeling Applications},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2011-08/INPROC-2011-08.pdf},
contact = {gerald.koch@ipvs.uni-stuttgart.de andreas.benzing@ipvs.uni-stuttgart.de mayer@kit.edu},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Urban Sensing employs physical-world mining to create a digital model of the
physical world using a large number of sensors. Handling the large amount of
data generated by sensors is costly and requires energy-saving measures for
sensing and sensor data transmission. Such schemes often affect data quality
and message delay. However, the detection of real-world situations using
Complex Event Processing on sensor data has to be dependable and timely and
requires precise data. In this position paper, we propose an approach to
integrate the contradicting optimization goals of energy-efficient wireless
sensor networks and dependable situation detection. It separates the system
into the following tiers: First, to support energy-efficiency and allow sparse,
unconnected sensor networks, we exploit the mobility of people through Delay
Tolerant Networking for collecting sensor data. This frees sensor nodes from
energy-expensive routing. Second, we employ Diagnostic Simulation which
provides data that is complete, precise and in time and therefore supports
quality-aware situation detection.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2011-08&engl=1}
}
@inproceedings {INPROC-2010-69,
author = {Andreas Benzing and Boris Koldehofe and Marco V{\"o}lz and Kurt Rothermel},
title = {{Multilevel Predictions for the Aggregation of Data in Global Sensor Networks}},
booktitle = {Proceedings of the 14th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications},
publisher = {IEEE},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {169--178},
type = {Conference Paper},
month = {October},
year = {2010},
keywords = {Global Sensor Networks; Distributed Stream Processing; Predictors},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2010-69/INPROC-2010-69.pdf,
http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5636734},
contact = {andreas.benzing@ipvs.uni-stuttgart.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Real-time simulations are one challenging application domain that is expected
to introduce high requirements to global sensor applications. Besides having
hard constraints on latency bounds at which data needs to be processed,
simulation applications will impose high requirements with respect to available
bandwidth. Predictors, originally introduced in the domain of wireless sensor
networks for energy saving, are one appealing solution to provide real-time
estimates and at the same time significantly reduce the data rates. While in
the setting of wireless sensor networks many prediction models have been
analyzed, their behavior and use is unclear when applied to distributed data
streams where aggregation results are typically processed over multilevel
hierarchies.
In the context of weather simulations, we propose a distributed R-Tree-based
aggregation algorithm that allows for efficient reuse of aggregate queries. In
the setting of real temperature readings taken from weather stations during one
month, we study the trade-off between updates of the prediction model and the
precision of the predicted values. Our evaluations indicate that even in
situations where complex prediction models are expected to perform best, simple
prediction models give higher benefits with respect to saving bandwidth while
providing similar data accuracy.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2010-69&engl=1}
}
@inproceedings {INPROC-2010-18,
author = {Andreas Benzing and Boris Koldehofe and Kurt Rothermel},
title = {{Distributed Diagnostic Simulations for the Smart Grid}},
booktitle = {Accepted Poster at the 1st International Conference on Energy-Efficient Computing and Networking: E-Energy 2010},
publisher = {Online},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
pages = {1--3},
type = {Conference Paper},
month = {April},
year = {2010},
keywords = {Diagnostic Simulation; Global Sensor Grid},
language = {German},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2010-18/INPROC-2010-18.pdf},
contact = {andreas.benzing@ipvs.uni-stuttgart.de},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Energy efficiency is usually achieved by reducing the energy consumption as far
as possible. With the growing amount of renewable energy sources, energy
efficient usage also has to consider what kind of and when power is consumed.
By matching the availability of electrical power with the current demands, the
amount of unused energy and therefore overall energy production can be reduced.
The so called smart grid aims to provide this matching with a broad deployment
of smart meters to acquire the current demand. However, current approaches to
the smart grid cannot handle the huge amount of sensors and energy sources
involved in a scalable way. Most data acquisition systems focus on the lookup
and reading of single sensors and therefore do not fit the requirements of a
large scale power grid simulation. We propose a Global Sensor Grid (GSG) which
provides consumers with data preprocessed to their needs instead of delivering
raw sensor data. With this decoupling from the actual sensors, multiple
consumers can benefit from improvements in data acquisition and avoidance of
the redundant processing of data by each consumer. By integrating so-called
diagnostic simulations into the GSG, gaps in sensor coverage can be filled with
higher precision than normal interpolation.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2010-18&engl=1}
}
@inproceedings {INPROC-2009-04,
author = {Andreas Benzing and Klaus Herrmann and Boris Koldehofe and Kurt Rothermel},
title = {{Identifying the Challenges in Reducing Latency in GSN using Predictors}},
booktitle = {Workshops der Wissenschaftlichen Konferenz Kommunikation in Verteilten Systemen 2009 (WowKiVS 2009)},
editor = {Tiziana Margaria and Julia Padberg and Gabriele Taentzer},
address = {Kassel},
publisher = {EASST},
institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
series = {Electronic Communications of the EASST},
volume = {17},
pages = {1--6},
type = {Workshop Paper},
month = {March},
year = {2009},
issn = {1863-2122},
keywords = {Global Sensor Networks, Wireless Sensor Networks, Predictors},
language = {English},
cr-category = {C.2.4 Distributed Systems},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2009-04/INPROC-2009-04.pdf},
department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems},
abstract = {Simulations based on real-time data continuously gathered from sensor networks
all over the world have received growing attention due to the increasing
availability of measured data. Furthermore, predictive techniques have been
employed in the realm of such networks to reduce communication for
energy-efficiency. However, research has focused on the high amounts of data
transferred rather than latency requirements posed by the applications. We
propose using predictors to supply data with low latency as required for
accurate simulations. This paper investigates requirements for a successful
combination of these concepts and discusses challenges that arise.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2009-04&engl=1}
}