246 research outputs found
A Smart Checkpointing Scheme for Improving the Reliability of Clustering Routing Protocols
In wireless sensor networks, system architectures and applications are designed to consider both resource constraints and scalability, because such networks are composed of numerous sensor nodes with various sensors and actuators, small memories, low-power microprocessors, radio modules, and batteries. Clustering routing protocols based on data aggregation schemes aimed at minimizing packet numbers have been proposed to meet these requirements. In clustering routing protocols, the cluster head plays an important role. The cluster head collects data from its member nodes and aggregates the collected data. To improve reliability and reduce recovery latency, we propose a checkpointing scheme for the cluster head. In the proposed scheme, backup nodes monitor and checkpoint the current state of the cluster head periodically. We also derive the checkpointing interval that maximizes reliability while using the same amount of energy consumed by clustering routing protocols that operate without checkpointing. Experimental comparisons with existing non-checkpointing schemes show that our scheme reduces both energy consumption and recovery latency
Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning
End-to-end learning has become a popular method to optimize a constellation
shape of a communication system. When the channel model is differentiable,
end-to-end learning can be applied with conventional backpropagation algorithm
for optimization of the shape. A variety of optimization algorithms have also
been developed for end-to-end learning over a non-differentiable channel model.
In this paper, we compare gradient-free optimization method based on the
cubature Kalman filter, model-free optimization and backpropagation for
end-to-end learning on a fiber-optic channel modeled by the split-step Fourier
method. The results indicate that the gradient-free optimization algorithms
provide a decent replacement to backpropagation in terms of performance at the
expense of computational complexity. Furthermore, the quantization problem of
finite bit resolution of the digital-to-analog and analog-to-digital converters
is addressed and its impact on geometrically shaped constellations is analysed.
Here, the results show that when optimizing a constellation with respect to
mutual information, a minimum number of quantization levels is required to
achieve shaping gain. For generalized mutual information, the gain is
maintained throughout all of the considered quantization levels. Also, the
results implied that the autoencoder can adapt the constellation size to the
given channel conditions
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Transiently Powered Computers
Demand for compact, easily deployable, energy-efficient computers has driven the development of general-purpose transiently powered computers (TPCs) that lack both batteries and wired power, operating exclusively on energy harvested from their surroundings.
TPCs\u27 dependence solely on transient, harvested power offers several important design-time benefits. For example, omitting batteries saves board space and weight while obviating the need to make devices physically accessible for maintenance. However, transient power may provide an unpredictable supply of energy that makes operation difficult. A predictable energy supply is a key abstraction underlying most electronic designs. TPCs discard this abstraction in favor of opportunistic computation that takes advantage of available resources. A crucial question is how should a software-controlled computing device operate if it depends completely on external entities for power and other resources? The question poses challenges for computation, communication, storage, and other aspects of TPC design.
The main idea of this work is that software techniques can make energy harvesting a practicable form of power supply for electronic devices. Its overarching goal is to facilitate the design and operation of usable TPCs.
This thesis poses a set of challenges that are fundamental to TPCs, then pairs these challenges with approaches that use software techniques to address them. To address the challenge of computing steadily on harvested power, it describes Mementos, an energy-aware state-checkpointing system for TPCs. To address the dependence of opportunistic RF-harvesting TPCs on potentially untrustworthy RFID readers, it describes CCCP, a protocol and system for safely outsourcing data storage to RFID readers that may attempt to tamper with data. Additionally, it describes a simulator that facilitates experimentation with the TPC model, and a prototype computational RFID that implements the TPC model.
To show that TPCs can improve existing electronic devices, this thesis describes applications of TPCs to implantable medical devices (IMDs), a challenging design space in which some battery-constrained devices completely lack protection against radio-based attacks. TPCs can provide security and privacy benefits to IMDs by, for instance, cryptographically authenticating other devices that want to communicate with the IMD before allowing the IMD to use any of its battery power. This thesis describes a simplified IMD that lacks its own radio, saving precious battery energy and therefore size. The simplified IMD instead depends on an RFID-scale TPC for all of its communication functions.
TPCs are a natural area of exploration for future electronic design, given the parallel trends of energy harvesting and miniaturization. This work aims to establish and evaluate basic principles by which TPCs can operate
REMIND: A Framework for the Resilient Design of Automotive Systems
In the past years, great effort has been spent on enhancing the security and safety of vehicular systems. Current advances in information and communication technology have increased the complexity of these systems and lead to extended functionalities towards self-driving and more connectivity. Unfortunately, these advances open the door for diverse and newly emerging attacks that hamper the security and, thus, the safety of vehicular systems. In this paper, we contribute to supporting the design of resilient automotive systems. We review and analyze scientific literature on resilience techniques, fault tolerance, and dependability. As a result, we present the REMIND resilience framework providing techniques for attack detection, mitigation, recovery, and resilience endurance. Moreover, we provide guidelines on how the REMIND framework can be used against common security threats and attacks and further discuss the trade-offs when applying these guidelines
Exascale storage systems: an analytical study of expenses
The computational power and storage capability of supercomputers are growing at a different pace, with storage lagging behind; the widening gap necessitates new approaches to keep the investment and running costs for storage systems at bay. In this paper, we aim to unify previous models and compare different approaches for solving these problems.
By extrapolating the characteristics of the German Climate Computing Center's previous supercomputers to the future, cost factors are identified and quantified in order to foster adequate research and development.
Using models to estimate the execution costs of two prototypical use cases, we are discussing the potential of three concepts: re-computation, data deduplication and data compression
09061 Abstracts Collection -- Combinatorial Scientific Computing
From 01.02.2009 to 06.02.2009, the Dagstuhl Seminar 09061 ``Combinatorial Scientific Computing \u27\u27 was held in Schloss Dagstuhl -- Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
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