A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

Magsi, Hina; Sodhro, Ali Hassan; Al-Rakhami, Mabrook; Zahid, Noman; Pirbhulal, Sandeep; Wang, Lei

doi:10.3390/electronics10040367

Cited by 42 publications

(21 citation statements)

References 29 publications

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“…The main difference between previous related IoT HMS proposals, such as [ 14 , 15 , 16 ], and our approach, is that the former presented ad hoc solutions that achieved highly optimized results for particular devices, and thus required significant manual development to introduce new devices. In contrast, MoSIoT delegates the development of such devices to third parties, which implies a possible loss of optimization, but focuses more on the ability of the IoT HMS to be flexible to changes in the scenario.…”

Section: Discussionmentioning

confidence: 98%

“…In recent years, several IoT proposals have been defined for health monitoring of patients with different diseases; for example, [ 14 , 15 ] aimed to reduce the energy cost and improve the efficiency of healthcare devices, thus reducing the need for maintenance, which is essential when dealing with patients with disabilities. However, to our knowledge, no proposals exist that propose IoT HMS frameworks dedicated specifically to people with disabilities.…”

Section: Introductionmentioning

confidence: 99%

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MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities

Meliá

Nasabeh

Luján-Mora

2021

IJERPH

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The need to remotely monitor people with disabilities has increased due to growth in their number in recent years. The democratization of Internet of Things (IoT) devices facilitates the implementation of healthcare-monitoring systems (HMSs) that are capable of supporting disabilities and diseases. However, to achieve their full potential, these devices must efficiently address the customization demanded by different IoT HMS scenarios. This work introduces a new approach, called Modeling Scenarios of Internet of Things (MoSIoT), which allows healthcare experts to model and simulate IoT HMS scenarios defined for different disabilities and diseases. MoSIoT comprises a set of models based on the model-driven engineering (MDE) paradigm, which first allows simulation of a complete IoT HMS scenario, followed by generation of a final IoT system. In the current study, we used a real scenario defined by a recognized medical publication for a patient with Alzheimer’s disease to validate this proposal. Furthermore, we present an implementation based on an enterprise cloud architecture that provides the simulation data to a commercial IoT hub, such as Azure IoT Central.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities

Meliá

Nasabeh

Luján-Mora

2021

IJERPH

View full text Add to dashboard Cite

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“…From this perspective, advances in the technologies of sensors, microcontrollers, and communication protocols have made the mass production of IoT platforms with many connectivity options possible, and at affordable prices [ 3 , 4 , 5 ]. Due to the low cost of IoT hardware, the sensors are being deployed on a large scale to monitor machines and, in general, public and residential spaces [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…For example, accelerometers embedded in vehicles are constantly collecting vibration patterns for various types of roads. Likewise, several other sensors such as magnetometer, pressure, temperature, humidity, and ambient light sensors, among many others, measure the physical conditions wherever they are installed, in addition to those that have been developed for the health area [ 8 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

Signoretti

Silva

Andrade

et al. 2021

Sensors

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Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

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“…In the past decades, multi-sensor information fusion technology has received great attention and has been widely used in various fields, including military [ 1 , 2 ], medical [ 3 , 4 ], and financial [ 5 ] fields and so on [ 6 , 7 ]. In order to efficiently integrate information from different sources, it is crucial to choose an appropriate strategy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Determine Basic Probability Assignment Based on Adaboost and Its Application in Classification

Wang

2021

Entropy

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In the framework of evidence theory, one of the open and crucial issues is how to determine the basic probability assignment (BPA), which is directly related to whether the decision result is correct. This paper proposes a novel method for obtaining BPA based on Adaboost. The method uses training data to generate multiple strong classifiers for each attribute model, which is used to determine the BPA of the singleton proposition since the weights of classification provide necessary information for fundamental hypotheses. The BPA of the composite proposition is quantified by calculating the area ratio of the singleton proposition’s intersection region. The recursive formula of the area ratio of the intersection region is proposed, which is very useful for computer calculation. Finally, BPAs are combined by Dempster’s rule of combination. Using the proposed method to classify the Iris dataset, the experiment concludes that the total recognition rate is 96.53% and the classification accuracy is 90% when the training percentage is 10%. For the other datasets, the experiment results also show that the proposed method is reasonable and effective, and the proposed method performs well in the case of insufficient samples.

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A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

Cited by 42 publications

References 29 publications

MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities

MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

A Novel Method to Determine Basic Probability Assignment Based on Adaboost and Its Application in Classification

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