A platform for power management based on indoor localization in smart buildings using long short‐term neural networks

Elbes, Mohammed; Alrawashdeh, Thamer A.; Almaita, Eyad; AlZu’bi, Shadi; Jararweh, Yaser

doi:10.1002/ett.3867

Cited by 28 publications

(14 citation statements)

References 31 publications

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“…For example, the widely used technology of dynamic voltage and frequency scaling (DVFS) dynamically adjusts the power supply voltage and the running frequency of the CPU according to the task demand, which leads to different levels of power states. However, in today's embedded system design, to achieve the purpose of power optimization, the intelligent terminal uses different levels of power management design [52], [53] in the system and driver, resulting in the poor accuracy of this power model in utilization-based power estimation. Another popular non-utilization-based method [37], [41], [43] is event-driven and system-call based.…”

Section: B Comparisonmentioning

confidence: 99%

A Survey of Energy Consumption Measurement in Embedded Systems

Guo

Zhou

et al. 2021

IEEE Access

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The low-power design of software and hardware is crucial for the efficiency and sustainability of embedded systems. Accurate energy consumption measurement plays a vital role in evaluating the energy performance of software and hardware, which also provides design insights to develop new paradigms for algorithm or code optimization in terms of energy efficiency and system cost. A few literature has surveyed this research field, but it lacks multi-faceted comparisons and comprehensive analysis. In this paper, we first show the necessity of accurate energy consumption analysis for embedded systems. Then, we study major methods in literature for measuring energy consumption of embedded systems, which can be summarized with three categories: 1) measurement-based energy profiling, 2) model-based energy estimation, and 3) simulator-based energy estimation. Some subcategories are further made based on characteristics of these apporaches. The pros and cons of each category have been reviewed and evaluated through multi-faceted comparisons. Finally, for transparent energy analysis and improving the energy efficiency of embedded systems, we come up with some contributing factors that matter the energy consumption measurements and discuss the challenges and future research directions. INDEX TERMSInternet of Things (IoT), embedded system, energy consumption, power consumption

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Section: B Comparisonmentioning

confidence: 99%

A Survey of Energy Consumption Measurement in Embedded Systems

Guo

Zhou

et al. 2021

IEEE Access

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“…It is the process of acquiring an object or user’s location through intelligent devices (sensors) in an indoor or outdoor environment. It is a critical requirement in most smart applications [ 19 , 20 ]. An exponential increase in smartphones, wristwatches, and other intelligent wireless IoT devices is motivating researchers to develop efficient localization schemes.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Location Aware Schemes in the Internet of Things

Khan

Saboor

Kim

et al. 2021

Sensors

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The rapid development in wireless technologies is positioning the Internet of Things (IoT) as an essential part of our daily lives. Localization is one of the most attractive applications related to IoT. In the past few years, localization has been gaining attention because of its applicability in safety, health monitoring, environment monitoring, and security. As a result, various localization-based wireless frameworks are being presented to improve such applications’ performances based on specific key performance indicators (KPIs). Therefore, this paper explores the recently proposed localization schemes in IoT. Initially, this paper explains the major KPIs of localization. After that, a thorough comparison of recently proposed localization schemes based on the KPIs is presented. The comparison includes an overview, architecture, network structure, performance parameters, and target KPIs. At the end, possible future directions are presented for the researchers working in this domain.

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“…In recent years, convolutional neural networks (CNNs) had achieved great success in many fields, such as health care [5,6], marketing [7], power management [8], civil engineering [9], distributed database [10], cyber security [11] and so on. The field of computer vision semantic segmentation is no exception.…”

Section: Introductionmentioning

confidence: 99%

Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image

Ding

Weng

Xia

et al. 2021

IJGI

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Building and road extraction from remote sensing images is of great significance to urban planning. At present, most of building and road extraction models adopt deep learning semantic segmentation method. However, the existing semantic segmentation methods did not pay enough attention to the feature information between hidden layers, which led to the neglect of the category of context pixels in pixel classification, resulting in these two problems of large-scale misjudgment of buildings and disconnection of road extraction. In order to solve these problem, this paper proposes a Non-Local Feature Search Network (NFSNet) that can improve the segmentation accuracy of remote sensing images of buildings and roads, and to help achieve accurate urban planning. By strengthening the exploration of hidden layer feature information, it can effectively reduce the large area misclassification of buildings and road disconnection in the process of segmentation. Firstly, a Self-Attention Feature Transfer (SAFT) module is proposed, which searches the importance of hidden layer on channel dimension, it can obtain the correlation between channels. Secondly, the Global Feature Refinement (GFR) module is introduced to integrate the features extracted from the backbone network and SAFT module, it enhances the semantic information of the feature map and obtains more detailed segmentation output. The comparative experiments demonstrate that the proposed method outperforms state-of-the-art methods, and the model complexity is the lowest.

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A platform for power management based on indoor localization in smart buildings using long short‐term neural networks

Cited by 28 publications

References 31 publications

A Survey of Energy Consumption Measurement in Embedded Systems

A Survey of Energy Consumption Measurement in Embedded Systems

A Systematic Review of Location Aware Schemes in the Internet of Things

Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image

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