scholarly journals An energy-efficient quality adaptive framework for multi-modal sensor context recognition

2011 ◽  
Author(s):  
Nirmalya Roy ◽  
Archan Misra ◽  
Christine Julien ◽  
Sajal K. Das ◽  
Jit Biswas
Keyword(s):  
Energy Efficient ◽  
Context Recognition ◽  
Adaptive Framework

scholarly journals Adaptive Framework Combining Sensors and IoT for Data Monitoring in Restricted Areas

2019 ◽  
Vol 8 (11) ◽  
pp. 564-567 ◽  
Keyword(s):  
Energy Efficiency ◽  
Data Transmission ◽  
Energy Efficient ◽  
Data Transfer ◽  
Sensor Data ◽  
Data Monitoring ◽  
Security Measures ◽  
Improve Energy Efficiency ◽  
Location Data ◽  
Adaptive Framework

The deployment of Internet-of-Things (IoT) enables an even richer variety of sensors at a much larger scale. Where offloading both the evaluation and the polling of IoT sensor data to the cloud would improve energy efficiency and data transfer costs for the mobile. We build an energy efficient framework for Combining Sensors and IoT to help developers easily builds applications that evaluate sensor data on the server via data transmission. We built a advanced framework to compress data i.e Novel Data Compression Approach that helps the user to know the regular movement of particular person with the sensor within the limited premises and the location surveillance of the host will be saving the location data with some security measures We also implement our protocol and compare it with the certificate-based scheme to illustrate its feasibility.

Enabling energy-efficient context recognition with configuration folding

2012 ◽  
Author(s):  
Muhammad Umer Iqbal ◽  
Marcus Handte ◽  
Stephan Wagner ◽  
Wolfgang Apolinarski ◽  
Pedro Jose Marron
Keyword(s):  
Energy Efficient ◽  
Context Recognition

scholarly journals A General Framework for Making Context-Recognition Systems More Energy Efficient

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 766
Author(s):  
Vito Janko ◽  
Mitja Luštrek
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Energy Use ◽  
Energy Savings ◽  
Expert Knowledge ◽  
High Energy ◽  
Research Area ◽  
Efficient Solutions ◽  
Sensor Data ◽  
Context Recognition

Context recognition using wearable devices is a mature research area, but one of the biggest issues it faces is the high energy consumption of the device that is sensing and processing the data. In this work we propose three different methods for optimizing its energy use. We also show how to combine all three methods to further increase the energy savings. The methods work by adapting system settings (sensors used, sampling frequency, duty cycling, etc.) to both the detected context and directly to the sensor data. This is done by mathematically modeling the influence of different system settings and using multiobjective optimization to find the best ones. The proposed methodology is tested on four different context-recognition tasks where we show that it can generate accurate energy-efficient solutions—in one case reducing energy consumption by 95% in exchange for only four percentage points of accuracy. We also show that the method is general, requires next to no expert knowledge about the domain being optimized, and that it outperforms two approaches from the related work.

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