Consumption of Energy

2012 ◽  
Author(s):  
David E. Nye
Keyword(s):  
Energy Consumption ◽  
Complex Networks ◽  
Muscle Power ◽  
Archival Work

Anthropologists working within a functionalist tradition considered energy to be a fundamental need, along with food, water, and shelter. In 1949, Leslie White argued that systems of energy were so fundamental that societies could be classified according to how much light, heat, and power they had mastered. The society with the greatest access to energy was the most advanced. The most primitive were those that controlled nothing more than their own muscle power. By the 1980s, however, historians began to see consumers as actors whose decisions shaped which products succeeded in the market. The notion that advertisers controlled consumption collapsed after Roland Marchand's archival work revealed that agencies continually responded to changes in public taste, forced to follow trends beyond their control. Before it was possible to think of energy as something to be effortlessly consumed, complex networks of power had to be built into the very structure of cities. This article discusses energy consumption, and considers the establishment and growth of factories, as well as the use of energy in public lighting and transportation.

scholarly journals Zero-skipping in CapsNet. Is it worth it?

10.29007/cd8h ◽  
2020 ◽  
Author(s):  
Ramin Sharifi ◽  
Pouya Shiri ◽  
Amirali Baniasadi
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Complex Networks ◽  
General Purpose ◽  
Graphical Processing Units ◽  
Different Types ◽  
Graphical Processing ◽  
Save Energy ◽  
Time And Energy

Capsule networks (CapsNet) are the next generation of neural networks. CapsNet can be used for classification of data of different types. Today’s General Purpose Graphical Processing Units (GPGPUs) are more capable than before and let us train these complex networks. However, time and energy consumption remains a challenge. In this work, we investigate if skipping trivial operations i.e. multiplication by zero in CapsNet, can possibly save energy. We base our analysis on the number of multiplications by zero detected while training CapsNet on MNIST and Fashion- MNIST datasets.

scholarly journals A Reputation Value-Based Task-Sharing Strategy in Opportunistic Complex Social Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jia Wu ◽  
Fangfang Gou ◽  
Wangping Xiong ◽  
Xian Zhou
Keyword(s):  
Social Networks ◽  
Energy Consumption ◽  
Complex Networks ◽  
High Reliability ◽  
High Energy ◽  
Base Station ◽  
Particle Swarm Algorithm ◽  
Task Sharing ◽  
Sharing Strategy ◽  
Computationally Intensive

As the Internet of Things (IoT) smart mobile devices explode in complex opportunistic social networks, the amount of data in complex networks is increasing. Large amounts of data cause high latency, high energy consumption, and low-reliability issues when dealing with computationally intensive and latency-sensitive emerging mobile applications. Therefore, we propose a task-sharing strategy that comprehensively considers delay, energy consumption, and terminal reputation value (DERV) for this context. The model consists of a task-sharing decision model that integrates latency and energy consumption, and a reputation value-based model for the allocation of the computational resource game. The two submodels apply an improved particle swarm algorithm and a Lagrange multiplier, respectively. Mobile nodes in the complex social network are given the opportunity to make decisions so that they can choose to share computationally intensive, latency-sensitive computing tasks to base stations with greater computing power in the same network. At the same time, to prevent malicious competition from end nodes, the base station decides the allocation of computing resources based on a database of reputation values provided by a trusted authority. The simulation results show that the proposed strategy can meet the service requirements of low delay, low power consumption, and high reliability for emerging intelligent applications. It effectively realizes the overall optimized allocation of computation sharing resources and promotes the stable transmission of massive data in complex networks.

Complex Networks

2009 ◽  
Author(s):  
Reuven Cohen ◽  
Shlomo Havlin
Keyword(s):  
Complex Networks

Lay Perceptions of Energy Consumption

2009 ◽  
Author(s):  
Shahzeen Z. Attari ◽  
Michael L. DeKay ◽  
Cliff I. Davidson ◽  
Wandi Bruine de Bruin
Keyword(s):  
Energy Consumption ◽  
Lay Perceptions

Study on Energy Consumption by Land Cargo Transportation

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Shunquan Huang ◽  
Siqin Yu ◽  
Zhongmin Liu
Keyword(s):  
Energy Consumption ◽  
Cargo Transportation

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

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