Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
DVFS-Based Long-Term Task Scheduling for Dual-Channel Solar-Powered Sensor Nodes
Ist Teil von
IEEE transactions on very large scale integration (VLSI) systems, 2017-11, Vol.25 (11), p.2981-2994
Ort / Verlag
IEEE
Erscheinungsjahr
2017
Quelle
IEEE Electronic Library (IEL)
Beschreibungen/Notizen
Solar-powered sensor nodes (SCSNs) with energy storages have the greatest potential and are widely used in the coming era of the Internet of Things, since they avoid tedious battery maintenance tasks. However, because the solar energy source is unstable and limited, the sensor nodes suffer from high deadline miss ratio (DMR). To achieve better DMR, the existing scheduling algorithms find the best scheduling scheme in a single period of the recurring task queue and, hence, ignore the long-term performance. To tackle this challenge, this paper proposes a three-level dynamic voltage-frequency scaling (DVFS)-based scheduling strategy to minimize long-term DMR for dual-channel SCSNs. This approach includes a day-level scheduler to achieve a coarse-grained task arrangement, two artificial neural networks to determine the task priorities, and a DVFS-based task selection algorithm for slot-level execution. Experiments show that the proposed scheduler reduces DMR by over 30% on average.