Cloud-based query evaluation for energy-efficient mobile sensing

Mo, Tianli; Lim, Lipyeow; Sen, Sougata; Misra, Archan; Balan, Rajesh Krishna; Lee, Mi Kyung

doi:10.1016/j.pmcj.2016.12.005

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Data Processing. Cloud-based query management and optimization techniques are used to lower the cost involved in sensing, reduces the maximum uncertainty, and propagates the query-evaluated result intelligently to reduce energy consumption [33]. A framework of integrated WSN and cloud is proposed which avoids network disruption, reduces loss of data, and increases network lifetime.…”

Section: Advanced System Designingmentioning

confidence: 99%

Survey of Energy-Efficient Techniques for the Cloud-Integrated Sensor Network

Das

Darji

et al. 2018

Journal of Sensors

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The sensor cloud is a combination of cloud computing with a wireless sensor network (WSN) which provides an easy to scale and efficient computing infrastructure for real-time application. A sensor cloud should be energy efficient as the life of the battery in the sensor is limited and there is a huge consumption of energy in the data centre in running the servers to provide storage. In this paper, we have classified energy-efficient techniques for sensor cloud into different categories and analyzed each technology by using various parameters. Usage percentage of each parameter for every technology is calculated and for all technologies on average is also calculated. From our analysis, we found that most of the energy-efficient techniques ignore quality of service (QoS) parameters, scalability, and network lifetime. Multiparameter optimization including other QoS parameters along with energy may be the future direction of research. Our study will be helpful for researchers to get information regarding current methods used for an energy-efficient sensor cloud and also to build advanced systems in the future.

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Section: Advanced System Designingmentioning

confidence: 99%

Survey of Energy-Efficient Techniques for the Cloud-Integrated Sensor Network

Das

Darji

et al. 2018

Journal of Sensors

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“…The amount of drain is dependent on the sensor (e.g., the GPS sensor drains the battery faster than the accelerometer), the sampling rate, the rate at which the sensor is turned on/off etc. Various techniques have been proposed to optimize the battery consumption of personalized devices -e.g., the use of contextual correlations within [7] and across individuals [6] to probabilistically infer context without actual sensing, or the use of infrastructure-based context [12] to limit the energy consumption of wearable devices are some examples of energy optimization using contextual cues. Alternatively, there has been works such as [13] and [4] where cheaper sensors have been used to trigger more expensive sensors.…”

Section: Challengesmentioning

confidence: 99%

“…However, while reducing the energy consumption, accuracy might be compromised. We investigated the trade-off between energy consumption and accuracy in context identification [6] and found that energy saving of up-to 60% could be achieved when we used crossuser correlation instead of querying every sensor of every individual. We also calculated the percentage deviation from actual sensor data and we found that accuracy was lost in 4% situations.…”

Section: Contributionsmentioning

confidence: 99%

Opportunities and challenges in multi-modal sensing for regular lifestyle tracking

Sen

2015

2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

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With the availability of various publicly available and personal sensors, recording and profiling of activities of daily living (ADL) is becoming a reality. The sensors are omnipresent -in smartphones, smartwatches, and smartglasses and even in the environment around us in the form of peer smartphones or even infrastructure sensors such as bluetooth low energy beacons. However, there are various challenges pertaining to the sensor data processing, which makes creation of activities of daily life tracker challenging. In this work, we discuss about some of these challenges. We also discuss about some ADL tracking systems that we have developed and how we have addressed some of the challenges in building these systems. We further discuss about how various ADL trackers can be combined into a framework which can allow individuals to select a custom set of ADLs for self-tracking.

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