PHY-MAC Cross-Layer Approach to Energy-Efficiency and Packet-Loss Trade-off in Low-Power, Low-Rate Wireless Communications

2013 ◽  
Vol 17 (4) ◽  
pp. 661-664 ◽  
Author(s):  
Nikola Zogovic ◽  
Goran Dimic ◽  
Dragana Bajic
Keyword(s):  
Energy Efficiency ◽  
Wireless Communications ◽  
Low Power ◽  
Packet Loss ◽  
Cross Layer ◽  
Trade Off ◽  
Low Rate

scholarly journals Trade-off analysis of PHY-Aware MAC in low-rate low-power UWB networks

2005 ◽  
Vol 43 (12) ◽  
pp. 147-155 ◽  
Author(s):  
A. El Fawal ◽  
J.Y. Le Boudec ◽  
R. Merz ◽  
B. Radunovic ◽  
J. Widmer ◽  
...  
Keyword(s):  
Low Power ◽  
Trade Off ◽  
Low Rate

The Study on Power Consumption Models for Low-Power Wireless Communications

2011 ◽  
Vol 268-270 ◽  
pp. 1691-1696
Author(s):  
Xiao Ling Mo ◽  
Mei Xiang Peng
Keyword(s):  
Energy Efficiency ◽  
Wireless Communications ◽  
Power Consumption ◽  
Low Power ◽  
Path Loss ◽  
Energy Consumption Optimization ◽  
Channel Loss ◽  
Transmission Energy Consumption ◽  
Transmission Energy ◽  
Efficiency Consideration

Power consumption models for low-power wireless communications, where transmitter and receiver electronics power consumption is comparable to PA power consumption, are based on channel path loss, depending on distance between transmitter and receiver, making them suitable for energy-efficiency consideration of multi-hop vs. single-hop communication. We propose L – model, based on total channel-loss, that is more suitable for transmission energy consumption optimization in the sense of different modulation and coding techniques than d – models. Since total channel loss information is available at current transceivers in terms of RSSI and LQI, L – model is more suitable for TPC techniques optimization than d – models.

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

scholarly journals Energy Efficiency in Short and Wide-Area IoT Technologies—A Survey

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 22
Author(s):  
Eljona Zanaj ◽  
Giuseppe Caso ◽  
Luca De Nardis ◽  
Alireza Mohammadpour ◽  
Özgü Alay ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Low Power ◽  
Energy Efficient ◽  
Wide Area ◽  
Wide Area Networks ◽  
The Internet ◽  
Extensive Review ◽  
Research Directions ◽  
The Internet Of Things

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions.

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