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Docta Complutense author Zapater, Marina author Sanchez, Cesar author Ayala Rodrigo, José Luis author Moya, Jose M. author Risco Martín, José Luis 2023-06-20T01:05:03Z 2023-06-20T01:05:03Z 2012-08-03 1424-8220 10.3390/s120810659 https://hdl.handle.net/20.500.14352/43286 https://doi.org/10.3390/s120810659 https://www.mdpi.com/1424-8220/12/8/10659 Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time. eng Atribución 3.0 España Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments journal article URL https://docta.ucm.es/bitstreams/6276f344-e4a5-42a0-bf7c-8a76fbb5abc3/download File MD5 448bf4088b90cd32ab943fb6edec5bcc 600851 application/pdf sensors-12-10659.pdf URL https://docta.ucm.es/bitstreams/b74f416a-4bff-463c-b26f-86e24b25ed94/download File MD5 793d893e0e8a340c286f97f19ceb35b8 53087 text/plain sensors-12-10659.pdf.txt