Smart Home IoT Sensors: Principles and Applications A Review of Low-Cost and Low-Power Solutions

Gazis, Alexandros; Katsiri, Eleftheria

doi:10.51626/ijeti.2021.02.00007

Cited by 15 publications

(5 citation statements)

References 14 publications

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“…Unlike more expensive alternatives, low-cost inclinometers offer a cost-effective solution for expanding the array of monitoring elements, thereby supplying valuable data for home automation [57]. Examples of low-cost inclinometers for smart homes include the work of Wang et al [58], who developed a screen-printed flexible radio frequency identification (RFID) sensor for measuring inclinations in smart home objects.…”

Section: Low-cost Inclinometers In Smart Homesmentioning

confidence: 99%

Enhancing the Accuracy of Low-Cost Inclinometers with Artificial Intelligence

Lozano,

Emadi,

Komarizadehasl

et al. 2024

Buildings

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The development of low-cost structural and environmental sensors has sparked a transformation across numerous fields, offering cost-effective solutions for monitoring infrastructures and buildings. However, the affordability of these solutions often comes at the expense of accuracy. To enhance precision, the LARA (Low-cost Adaptable Reliable Anglemeter) system averaged the measurements of a set of five different accelerometers working as inclinometers. However, it is worth noting that LARA’s sensitivity still falls considerably short of that achieved by other high-accuracy commercial solutions. There are no works presented in the literature to enhance the accuracy, precision, and resolution of low-cost inclinometers using artificial intelligence (AI) tools for measuring structural deformation. To fill these gaps, artificial intelligence (AI) techniques are used to elevate the precision of the LARA system working as an inclinometer. The proposed AI-driven tool uses Multilayer Perceptron (MLP) to glean insight from high-accuracy devices’ responses. The efficacy and practicality of the proposed tools are substantiated through the structural and environmental monitoring of a real steel frame located in Cuenca, Spain.

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Section: Low-cost Inclinometers In Smart Homesmentioning

confidence: 99%

Enhancing the Accuracy of Low-Cost Inclinometers with Artificial Intelligence

Lozano,

Emadi,

Komarizadehasl

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Additionally, Huang’s Law [ 17 ] observes that advancements in graphics processing units (GPUs) also follow a similar pattern, offering a chance for device properties to evolve at a steady and even faster rate than CPUs. These observations suggest that existing low-cost and low-power devices could offer alternative means to host and develop more complex and resource-intensive operations [ 18 ]. Notably, these capabilities are utilized by “edge computing”, an emerging technology that enables networks and devices to process data closer to where it is generated, either embedded on the node or after real-time processing on the computer’s node [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Gazis,

Katsiri

2024

Sensors

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This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design’s innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis’ residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.

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“…These appliances, being powered by fossil fuels, emit gases such as CO, which, due to its characteristics (odourless, invisible, tasteless, and non-irritating to the eyes), is challenging to detect and is therefore one of the most dangerous gases. In fact, CO is considered as the silent killer [22]. This problem, along with the consequences of the possible misuse of space heaters, has concerned several governments, such as those of Argentina [23] and Ecuador [24], among others.…”

Section: Introductionmentioning

confidence: 99%

Development and Assessment of an Indoor Air Quality Control IoT-Based System

et al. 2023

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Good health and well-being are primary goals within the list of Sustainable Development Goals (SDGs) proposed by the United Nations (UN) in 2015. New technologies, such as Internet of Things (IoT) and Cloud Computing, can aid to achieve that goal by enabling people to improve their lifestyles and have a more healthy and comfortable life. Pollution monitoring is especially important in order to avoid exposure to fine particles and to control the impact of human activity on the natural environment. Some of the sources of hazardous gas emissions can be found indoors. For instance, carbon monoxide (CO), which is considered a silent killer because it can cause death, is emitted by water heaters and heaters that rely on fossil fuels. Existing solutions for indoor pollution monitoring suffer from some drawbacks that make their implementation impossible for households with limited financial resources. This paper presents the development of IdeAir, a low-cost IoT-based air quality monitoring system that aims to reduce the disadvantages of existing systems. IdeAir was designed as a proof of concept to capture and determine the concentrations of harmful gases in indoor environments and, depending on their concentration levels, issue alarms and notifications, turn on the fan, and/or open the door. It has been developed following the Test-Driven Development Methodology for IoT-based Systems (TDDM4IoTS), which, together with the tool (based on this methodology) used for the automation of the development of IoT-based systems, has facilitated the work of the developers. Preliminary results on the functioning of IdeAir show a high level of acceptance by potential users.

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Smart Home IoT Sensors: Principles and Applications A Review of Low-Cost and Low-Power Solutions

Cited by 15 publications

References 14 publications

Enhancing the Accuracy of Low-Cost Inclinometers with Artificial Intelligence

Enhancing the Accuracy of Low-Cost Inclinometers with Artificial Intelligence

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Development and Assessment of an Indoor Air Quality Control IoT-Based System

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