Related Experiment Video
Updated: Feb 7, 2026
05:30
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
1.2K
Optimal Time Assignment Policy for Maximizing Throughput in Cognitive Sensor Network with Energy Harvesting
1The 63rd Institute, National University of Defense Technology, Nanjing 210007, China. whao1983@126.com.
Sensors (Basel, Switzerland)
|August 8, 2018
Summary
Cognitive sensor networks with energy harvesting (EH-CSN) improve spectrum and energy efficiency. This study optimizes EH-CSN performance by balancing harvesting, sensing, and transmission times for maximum throughput while avoiding interference.
Area of Science:
- Wireless communication networks
- Energy harvesting technologies
- Cognitive radio systems
Background:
- Cognitive sensor networks (CSNs) offer solutions for spectrum and energy efficiency challenges.
- Energy harvesting (EH) enables sustainable operation of CSNs, allowing sensors to gather energy from the environment.
- Cognitive sensors (CSs) can opportunistically access licensed spectrum bands when idle, enhancing spectrum utilization.
Purpose of the Study:
- To investigate the performance of cognitive sensor networks with energy harvesting (EH-CSN).
- To optimize the time fractions for harvesting, sensing, and transmission in EH-CSNs.
- To maximize the average throughput of EH-CSNs under collision and energy constraints.
Main Methods:
- Formulation of an optimization problem to maximize average throughput.
- Derivation of lower and upper bounds for the energy harvesting time fraction.
- Mathematical analysis to prove the uniqueness and existence of optimal time fractions.
- Verification of theoretical analysis through numerical simulations.
Main Results:
- Identification of optimal time fractions for energy harvesting, sensing, and transmission.
- Demonstration of improved average throughput in EH-CSNs.
- Validation of the proposed model and optimization strategy through simulations.
- Analysis of the trade-offs between energy harvesting, sensing, and transmission durations.
Conclusions:
- The proposed model effectively balances energy harvesting and data transmission for cognitive sensor networks.
- Optimal time fraction allocation is crucial for maximizing throughput and ensuring reliable communication.
- EH-CSNs present a viable approach for efficient spectrum and energy utilization in wireless networks.
More Related Videos
08:42Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
Published on: May 5, 2015
12.6K
09:01A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
Published on: May 7, 2014
10.6K
Related Concept Videos
00:48Optimal Foraging
13.9K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
13.9K
01:38Cognitive Dissonance
37.5K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
37.5K
02:26Protein Networks
4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
04:10What is Energy?
59.2K
The universe is composed of matter in different forms, and all forms of matter contain energy. The different forms of energy on Earth originate from the Sun — the ultimate energy source. Plants capture light energy from the Sun, and, via the process of photosynthesis, convert it into chemical energy. This stored energy from plants can be harnessed in many ways. For example, eating plant products as food provides energy for our body to function, and burning wood or coal (fossilized...
59.2K
01:21Free Energy
52.1K
Free energy—abbreviated as G for the scientist Gibbs who discovered it—is a measurement of useful energy that can be extracted from a reaction to do work. It is the energy in a chemical reaction that is available after entropy is accounted for. Reactions that take in energy are considered endergonic and reactions that release energy are exergonic. Plants carry out endergonic reactions by taking in sunlight and carbon dioxide to produce glucose and oxygen. Animals, in turn, break...
52.1K
02:18Network Covalent Solids
16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K