Abstract
In the era of the Internet of Things (IoT), the interest and demand for embedded systems have been explosively increasing. In particular, vehicular sensor networks are one of the fields where IoT-oriented embedded devices (also known as IoT devices) are being actively used. These IoT devices are widely deployed in and out of the vehicle to check vehicle conditions, prevent accidents, and support autonomous driving, forming a vehicular sensor network. In particular, such sensor networks mainly consist of third-party devices that operate independently of the vehicle and run on their own batteries. After all, like all battery-powered embedded devices, the IoT devices for the vehicular sensor network also suffer from limited power sources, and thus research on how to design/operate them energy-efficiently is drawing attention from both academia and industry. This paper notes that the vehicular sensor network may be the best application for ultra-low power system on-chips (ULP SoCs). The ULP SoCs are mainly designed based on ultra-low voltage operating (ULV) circuits, and this paper aims to realize the energy-optimized driving of the network by applying state of the art (SoA) low-power techniques exploiting the unique characteristics of ULV circuits to the IoT devices in the vehicular sensor network. To this end, this paper proposes an optimal task assignment algorithm that can achieve the best energy-efficient drive of the target network by fully utilizing the SoA low power techniques for ULV circuits. Along with a detailed description of the proposed algorithm, this paper demonstrates the effectiveness of the proposed method by providing an in-depth evaluation process and experimental results for the proposed algorithm.
Highlights
As Internet of Things (IoT) has grown significantly, numerous sensors and embedded systems have been developed explosively and are being released as IoT devices [1,2,3].One of the areas where IoT market trends are most prominent is the automotive industry.Vehicles that are transforming into a second main living space following customers’ homes are using various auxiliary systems for safety and security as well as various conveniences.Some of these auxiliary systems have been produced by vehicle manufacturers and are already deployed to the vehicle during the vehicle manufacturing phase
We determined that temperature effect inversion (TEI)-FS is the most appropriate method for our target application, and decided to utilize this technique in this paper
We noted that while the demand for vehicular sensor networks based on third-party IoT devices is continuously increasing, development is being delayed due to the limitation that these IoT devices must be operated with limited power sources
Summary
Vehicles that are transforming into a second main living space following customers’ homes are using various auxiliary systems for safety and security as well as various conveniences. Some of these auxiliary systems have been produced by vehicle manufacturers and are already deployed to the vehicle during the vehicle manufacturing phase. Third-party IoT devices for vehicles are fitted to various places in the vehicle for various purposes, forming a single independent sensor network. These devices operate separately from the vehicle’s engine control unit (ECU) and are powered by their own internal battery rather than by the power source in a vehicle. They cannot be free from the inconvenience of charging, the biggest issue of battery-powered devices, which means how long they can be used on a single charge is the most important factor in determining their usefulness
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