Energy efficient OFDMA: Trade-off between computation and transmission energy

2011 ◽  
Author(s):  
Feng-Seng Chu ◽  
Kwang-Cheng Chen
Keyword(s):  
Energy Efficient ◽  
Trade Off ◽  
Transmission Energy

EA Based Approach for Resource Allocation in Cloud Computing

2017 ◽  
Vol 7 (11) ◽  
pp. 98
Author(s):  
Ritika . ◽  
Gantavya Talwar
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Efficient ◽  
Energy Savings ◽  
Trade Off ◽  
Positioning Systems ◽  
Turn On ◽  
User Activity

Appealing to the requirement of energy savings, many approaches of energy-efficient locating sensing have been explored. Methods beyond the action of locating are somehow auxiliary, and most of the attentions are focused on locating sensing based methods. A class of lightweight positioning systems has been developed to explore a large part of the energy-accuracy trade-off space. These systems either reduce accuracy requirements, or aggressively use other cues to determine when and where to turn on EA. Implicitly or explicitly, these systems generally make several assumptions about the environment or about user activity. In this research, we proposed an energy efficient cloud based VM in which tasks can be achieved using better SLA and less energy.

Foraging patterns of Water Pipits (Anthus spinoletta) with nestlings

1987 ◽  
Vol 65 (6) ◽  
pp. 1522-1529 ◽  
Author(s):  
Paul Hendricks
Keyword(s):  
Foraging Behavior ◽  
Energy Efficient ◽  
Niche Partitioning ◽  
Delivery Rate ◽  
Female Dominance ◽  
Behavior Changes ◽  
Foraging Patterns ◽  
Trade Off ◽  
Alpine Habitat ◽  
Nest Distribution

Foraging patterns of pairs of Water Pipits (Anthus spinoletta) nesting in alpine habitat in Wyoming were examined for sex-specific differences in division of space, how foraging behavior changes with nestling age, and how foraging behavior is modified to reduce the risk of nest detection by predators. Parental investment (measured by number of deliveries to nestlings, fecal sac removal, and time spent incubating and brooding) was not useful in predicting patterns of sexual niche partitioning of foraging space. There were no consistent patterns as to which sex foraged farthest from the nest. Distribution of the orientation of trip departures from nests, however, was significantly different between pair members in all cases. The mechanism(s) maintaining this pattern of spatial segregation is not known, but it may be the result of female dominance during the breeding season. Delivery rate of food to nestlings was positively correlated to nestling age. There was a concurrent positive correlation between delivery rate and percentage of foraging trips less than 50 m from the nest. Adult pipits flew significantly longer distances from nests when departing with fecal sacs. This is probably an adaptation to reduce the probability of nest detection by predators, and represents a trade-off between energy-efficient foraging and reproductive success.

scholarly journals A low-power 10-bit 6.66 MS/s CMOS SAR ADC with built-in digital calibration dedicated to Wireless Sensor Networks applications

2018 ◽  
Vol 13 (3) ◽  
pp. 1-8
Author(s):  
Felipe Makara ◽  
Lucas Mangini da Silva ◽  
Luis Henrique Assumpção Lolis ◽  
Andre Mariano
Keyword(s):  
Wireless Sensor Networks ◽  
Input Signal ◽  
Energy Efficient ◽  
Sar Adc ◽  
Digital Calibration ◽  
Wireless Sensor ◽  
Trade Off ◽  
Iot Applications ◽  
Improve Accuracy ◽  
Binary Weighted

In this paper, an energy-efficient SAR ADC for IoT applications is presented. The proposed ADC relies on a built-in calibration circuit to improve accuracy and introduces an original DAC that merges the concepts of binary-weighted and C/2C arrays in order to achieve a favorable trade-off between area, accuracy and power consumption. The system consumes 58 µW per conversion cycle sampling at a frequency of 6.66 MHz with an SNDR of 49.78 dB for a 1MHz input signal. With an ENOB of 8 bits, the resulting FOM is 34fJ/conversion-step.

scholarly journals Energy-Efficient UAV-Enabled MEC System: Bits Allocation Optimization and Trajectory Design

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4521 ◽  
Author(s):  
Linpei Li ◽  
Xiangming Wen ◽  
Zhaoming Lu ◽  
Qi Pan ◽  
Wenpeng Jing and Zhiqun Hu
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Efficient ◽  
High Mobility ◽  
Trajectory Design ◽  
Total Energy Consumption ◽  
Computing Services ◽  
Aerial Vehicle ◽  
Transmission Energy Consumption ◽  
Transmission Energy

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system is attracting a lot of attentions for the potential of low latency and low transmission energy consumption, due to the advantages of high mobility and easy deployment. It has been widely applied to provide communication and computing services, especially in Internet of Things (IoT). However, there are still some challenges in the UAV-enabled MEC system. Firstly, the endurance of the UAV is limited and further impacts the performance of the system. Secondly, mobile devices are battery-powered and the batteries of some devices are hard to change. Therefore, in this paper, a UAV-enabled MEC system in which the UAV is empowered to have computing capability and provides tasks offloading service is studied. The total energy consumption of the UAV-enabled system, which includes the energy consumption of the UAV and the energy consumption of the ground users, is minimized under the constraints of the UAV’s energy budget, the number of each task’s bits, the causality of the data and the velocity of the UAV. The bits allocation of uploading data, computing data, downloading data and the trajectory of the UAV are jointly optimized with the goal of minimizing the total energy consumption. Moreover, a two-stage alternating algorithm is proposed to solve the non-convex formulated problem. Finally, the simulation results show the superiority of the proposed scheme compared with other benchmark schemes. Finally, the performance of the proposed scheme is demonstrated under different settings.

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