Abstract
In wireless sensor networks (WSNs), sensor nodes are deployed for collecting and analyzing data. These nodes use limited energy batteries for easy deployment and low cost. The use of limited energy batteries is closely related to the lifetime of the sensor nodes when using wireless sensor networks. Efficient-energy management is important to extending the lifetime of the sensor nodes. Most effort for improving power efficiency in tiny sensor nodes has focused mainly on reducing the power consumed during data transmission. However, recent emergence of sensor nodes equipped with multi-cores strongly requires attention to be given to the problem of reducing power consumption in multi-cores. In this paper, we propose an energy efficient scheduling method for sensor nodes supporting a uniform multi-cores. We extend the proposed T-Ler plane based scheduling for global optimal scheduling of a uniform multi-cores and multi-processors to enable power management using dynamic power management. In the proposed approach, processor selection for a scheduling and mapping method between the tasks and processors is proposed to efficiently utilize dynamic power management. Experiments show the effectiveness of the proposed approach compared to other existing methods.
Highlights
wireless sensor networks (WSNs) consist of a number of moblile sensor nodes which are tiny, multi-functional, and low-power
Power management among sensor nodes is of critical importance for several reasons: limited energy batteries and ensuring longevity [2,3,4], meeting performance requirements [2,5,6], inefficiency arising because of over provisioning resources [2], power challenges posed by CMOS scaling [2,7], and enabling green computing [2]
The lifetime of WSNs is closely related to the management of sensor nodes operating at limited energy
Summary
WSNs consist of a number of moblile sensor nodes which are tiny, multi-functional, and low-power. Sensor nodes in WSNs have evolved for multimedia streaming and image processing In response to these high performance demands, sensor nodes with multi-processors have emerged. A multi-processor sensor node platform, mPlatform, which is capable of parallel processing for computationally intensive signal processing, was proposed by Lymberopoulos et al [1]. Power management among sensor nodes is of critical importance for several reasons: limited energy batteries and ensuring longevity [2,3,4], meeting performance requirements [2,5,6], inefficiency arising because of over provisioning resources [2], power challenges posed by CMOS scaling [2,7], and enabling green computing [2]. The scheduling algorithms must be able to keep battery lifetime longer while meeting the time constraints
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