2,932 research outputs found
DyPS: Dynamic Processor Switching for Energy-Aware Video Decoding on Multi-core SoCs
In addition to General Purpose Processors (GPP), Multicore SoCs equipping
modern mobile devices contain specialized Digital Signal Processor designed
with the aim to provide better performance and low energy consumption
properties. However, the experimental measurements we have achieved revealed
that system overhead, in case of DSP video decoding, causes drastic
performances drop and energy efficiency as compared to the GPP decoding. This
paper describes DyPS, a new approach for energy-aware processor switching (GPP
or DSP) according to the video quality . We show the pertinence of our solution
in the context of adaptive video decoding and describe an implementation on an
embedded Linux operating system with the help of the GStreamer framework. A
simple case study showed that DyPS achieves 30% energy saving while sustaining
the decoding performanc
Semantic Compression for Edge-Assisted Systems
A novel semantic approach to data selection and compression is presented for
the dynamic adaptation of IoT data processing and transmission within "wireless
islands", where a set of sensing devices (sensors) are interconnected through
one-hop wireless links to a computational resource via a local access point.
The core of the proposed technique is a cooperative framework where local
classifiers at the mobile nodes are dynamically crafted and updated based on
the current state of the observed system, the global processing objective and
the characteristics of the sensors and data streams. The edge processor plays a
key role by establishing a link between content and operations within the
distributed system. The local classifiers are designed to filter the data
streams and provide only the needed information to the global classifier at the
edge processor, thus minimizing bandwidth usage. However, the better the
accuracy of these local classifiers, the larger the energy necessary to run
them at the individual sensors. A formulation of the optimization problem for
the dynamic construction of the classifiers under bandwidth and energy
constraints is proposed and demonstrated on a synthetic example.Comment: Presented at the Information Theory and Applications Workshop (ITA),
February 17, 201
Distributed Database Management Techniques for Wireless Sensor Networks
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Xplore. Authors shall not post the final, published versions of their papers.In sensor networks, the large amount of data generated by sensors greatly influences the lifetime of the network. In order to manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.This work was partially supported by the Instituto de Telcomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Ministerio de Ciencia e Innovacion, through the Plan Nacional de I+D+i 2008-2011 in the Subprograma de Proyectos de Investigacion Fundamental, project TEC2011-27516, by the Polytechnic University of Valencia, though the PAID-05-12 multidisciplinary projects, by Government of Russian Federation, Grant 074-U01, and by National Funding from the FCT-Fundacao para a Ciencia e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.Diallo, O.; Rodrigues, JJPC.; Sene, M.; Lloret, J. (2013). Distributed Database Management Techniques for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems. PP(99):1-17. https://doi.org/10.1109/TPDS.2013.207S117PP9
Energy efficient and latency aware adaptive compression in wireless sensor networks
Wireless sensor networks are composed of a few to several thousand sensors deployed over an area or on specific objects to sense data and report that data back to a sink either directly or through a series of hops across other sensor nodes. There are many applications for wireless sensor networks including environment monitoring, wildlife tracking, security, structural heath monitoring, troop tracking, and many others. The sensors communicate wirelessly and are typically very small in size and powered by batteries. Wireless sensor networks are thus often constrained in bandwidth, processor speed, and power. Also, many wireless sensor network applications have a very low tolerance for latency and need to transmit the data in real time. Data compression is a useful tool for minimizing the bandwidth and power required to transmit data from the sensor nodes to the sink; however, compression algorithms often add a significant amount of latency or require a great deal of additional processing. The following papers define and analyze multiple approaches for achieving effective compression while reducing latency and power consumption far below what would be required to process and transmit the data uncompressed. The algorithms target many different types of sensor applications from lossless compression on a single sensor to error tolerant, collaborative compression across an entire network of sensors to compression of XML data on sensors. Extensive analysis over many different real-life data sets and comparison of several existing compression methods show significant contribution to efficient wireless sensor communication --Abstract, page iv
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