Energy-aware collaborative sensing for multiple applications in mobile cloud computing

Loomba, Radhika; Shi, Lei; Jennings, Brendan; Friedman, Roy; Kennedy, John; Butler, Joe

doi:10.1016/j.suscom.2015.09.001

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…The cloud-offloaded global positioning system (CO-GPS) which provides sensing devices to assure duty cycle of the GPS receiver device and logging the millisecond raw data from the GPS signal for processing technique is helpful to conserve energy [17]. Collaborative sensing and aggregation of information using trusted middleware provide energy saving [18]. Sensing as a service mechanism provides energy efficiency, supports multiple applications in a flexible and secure loading on various platforms, and also supports the incentive mechanism [19].…”

Section: Sensing Techniquesmentioning

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: Sensing Techniquesmentioning

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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“…Radhika Loombaa et. al., [8]addressed the trade-off between the quality of the sensed data received by applications and the required energy to transfer data from the mobile handsets, by considering a scheme in which a collaborative sensing middleware intervene between multiple applications requiring sensed data and the mobile handsets located within a particular physical area. An algorithm, Info-Aggregation, has been presented which seeks to maximize the degree to which sensed data transferred from a mobile device can be served to more than one application.…”

Section: Related Workmentioning

confidence: 99%

Survey on Minimizing Energy Consumption in Mobile Cloud Computing

Arun¹,

Jaiganesh²

2016

IJCA

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Mobile cloud computing is the thrust research area in the field of recent communication paradigm. Eminent energy conservation strategies are proposed by various researchers in the field of mobile cloud computing. Optimizing energy consumption also plays an important role in mobile cloud computing. This paper reviews the existing research contributions on minimizing energy consumption in mobile cloud computing. It is inferred that maximum of 82% of energy can be conserved by making use of effective task scheduling method.

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