Compression of Wireless Sensor Node Data for Transmission based on Minimalist, Adaptive, and Streaming Compression Algorithm

Abuda, Chad Ferrino P.; Caya, Meo Vincent C.; Cruz, Febus Reidj G.; Uy, Francis Aldrine A.

doi:10.1109/hnicem.2018.8666320

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…In the paper, we demonstrated that the proposed use of the O-MAS-R compression algorithm maintained a greater compression ratio than the MAS algorithm at a 53% reduction in data transmission power consumption [20]. As the compression ratio is directly proportional to data transmission power usage, implementing the O-MAS-R algorithm in wireless sensor network sensor nodes will result in even lower data transmission power consumption [21].…”

Section: Discussionmentioning

confidence: 93%

“…One of the signi cant limitations for the autonomous telehealth wheelchair is the battery life. The operating of biophysical sensors embedded in the wheelchair is limited by various resources, such as power supply, memory storage and processing capabilities [20] [21]. Continuous monitoring sensors produce a large amount of data and consume signi cant storage memory and transmission power.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, because it concentrates on computing oating point data, the Minimalist Adaptive and Streaming (MAS) method was recommended as resource e cient [25]. Among them, MAS and S-LZW algorithms have been widely applied in real-time sensing applications, such as monitoring wind speed, rainfall, temperature, humidity, soil moisture, pressure, and battery level [20][26]. The reduction of power consumption during data transmission of the MAS algorithm is between 53.55439% and 55.95118%, while that of the S-LZW is between 23.40953% and 33.96955%.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Autonomous Wheelchair with Health Monitoring System based on Internet of Thing

Hou,

Latif,

Mehryar

et al. 2024

Preprint

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Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on internet of thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users’ four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.

show abstract

Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Autonomous Wheelchair with Health Monitoring System based on Internet of Thing

Hou,

Latif,

Mehryar

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In the paper, we demonstrated that the proposed use of the O-MAS-R compression algorithm maintained a greater compression ratio than the MAS algorithm at a 53% reduction in data transmission power consumption 24 . As the compression ratio is directly proportional to data transmission power usage, implementing the O-MAS-R algorithm in wireless sensor network sensor nodes will result in even lower data transmission power consumption 25 .…”

Section: Discussionmentioning

confidence: 96%

An autonomous wheelchair with health monitoring system based on Internet of Thing

Hou,

Latif,

Mehryar

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on Internet of Thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users’ four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.

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Energy Efficiency in IoT-Based Smart Healthcare

Sangra¹,

Rana²,

Singh³

2022

Proceedings of Third International Conference on Computing, Communications, and Cyber-Security

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Compression of Wireless Sensor Node Data for Transmission based on Minimalist, Adaptive, and Streaming Compression Algorithm

Cited by 9 publications

References 10 publications

An Autonomous Wheelchair with Health Monitoring System based on Internet of Thing

An Autonomous Wheelchair with Health Monitoring System based on Internet of Thing

An autonomous wheelchair with health monitoring system based on Internet of Thing

Energy Efficiency in IoT-Based Smart Healthcare

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