Abstract
The proliferation of smart sensor nodes for IoT deployments comes with requirements of energy efficiency and to fulfil functional requirements, but it also demands a fast time to market. As a result, we need to facilitate the design of these IoT nodes, while providing the required performance. In this article, we introduce a design space exploration method focusing on IoT nodes that are data intensive due to the inherent complexity of their high data volume. The proposed method aims to identify areas of the design where processing optimisation would have a greater impact on the overall node energy consumption, define an energy budget for prospective additional tasks in the processing pipeline, and in conclusion evaluate the optimal node offloading configuration.
Highlights
The Internet of Things (IoT) has attracted significant attention in recent years, resulting in the deployment of a variety of sensor devices, supported by ubiquitous computing [1]–[4]
The analysis of this design space exploration (DSE) method is based on four design examples: two traditional image processing systems, and two CNN-based systems
CONTRIBUTION AND COMPARISON OF OUR METHOD TO OTHER DSE METHODS In our literature review, we have identified a vacancy in DSE methods for IoT nodes
Summary
The Internet of Things (IoT) has attracted significant attention in recent years, resulting in the deployment of a variety of sensor devices, supported by ubiquitous computing [1]–[4]. Focusing on the sensor node, we need to facilitate its deployment for the different scenarios, batteries and energy harvesters are frequently selected as energy sources. This imposes tight constraints on the energy consumption of the sensor node, which has to be considered in the architecture of the WSN for the computational and communication configurations [5], [6]. In the analysis of IoT sensor nodes, a widely used assumption is that IoT sensor nodes are devices that sense, store and communicate scalar sensor values This approach limits the analysis to simple sensors, such as pressure, temperature or humidity sensors, leaving out sensors that provide vectors of data such as sound or vision sensors.
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