Abstract
In recent future, wireless sensor networks (WSNs) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of WSNs facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers (DCs). The high economical and environmental impact of the energy consumption in DCs requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed.
In this context, this paper shows the following on-going research lines and obtained results. In the field of WSNs: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of DCs: energy-optimal workload assignment policies in heterogeneous DCs, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.
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Zapater, M., Ayala, J.L., Moya, J.M. (2012). GreenDisc: A HW/SW Energy Optimization Framework in Globally Distributed Computation. In: Bravo, J., López-de-Ipiña, D., Moya, F. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2012. Lecture Notes in Computer Science, vol 7656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35377-2_1
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DOI: https://doi.org/10.1007/978-3-642-35377-2_1
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