Energy Saving in TSCH Networks by Means of Proactive Reduction of Idle Listening

2020 ◽  
pp. 131-144
Author(s):  
Stefano Scanzio ◽  
Gianluca Cena ◽  
Adriano Valenzano ◽  
Claudio Zunino
Keyword(s):  
Energy Saving ◽  
Idle Listening

An Energy-Saving Routing Algorithm for Opportunistic Networks Based on Asynchronous Sleep Approach

2013 ◽  
Vol 441 ◽  
pp. 1001-1004 ◽  
Author(s):  
Yu Kun Yao ◽  
Wen Hui Liu ◽  
Wen Xi Zheng ◽  
Zhi Ren
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Energy Saving ◽  
Network Lifetime ◽  
Network Topology ◽  
Routing Algorithm ◽  
Opportunistic Networks ◽  
Low Power Consumption ◽  
Saving Energy ◽  
Idle Listening

The sparse and mobility of nodes in opportunistic networks will cause network topology often fracturing, resulting that a large number of isolated nodes appear in opportunistic networks. These isolated nodes will waste much energy at idle listening stage, so this paper proposes an Efficient Routing algorithm for opportunistic networks based on Asynchronous Sleep Approach-ERASA. ERASA achieves the goal of saving energy by making the isolated nodes enter the low power consumption dormancy state. Then they will be timely awaken when there are other nodes entering into their communication range. Compared with the classical Epidemic routing algorithm, the several aspects performance of ERASA algorithm are improved such as network lifetime.

scholarly journals An improved asynchronous energy-saving mechanism for IEEE 802.15.5-based networks

2018 ◽  
Vol 14 (9) ◽  
pp. 155014771880218
Author(s):  
Li-yong Yuan ◽  
Fei-long Lin ◽  
Jun-ke Lv
Keyword(s):  
Energy Saving ◽  
Destination Node ◽  
Simulation Evaluation ◽  
Multiple Routes ◽  
Routing Mechanism ◽  
Sleep Schedule ◽  
Idle Listening ◽  
Ieee 802.15.5 ◽  
Reliability And Robustness ◽  
Performance Analysis And Simulation

IEEE 802.15.5 standard introduces the mesh routing which improves reliability and robustness of routing in wireless sensor network since there are multiple routes from source node to destination node. To achieve energy efficiency for a long network lifetime, the asynchronous energy saving is proposed in IEEE 802.15.5, which includes the receiver-initiated unicast mechanism and the sender-initiated broadcast mechanism. In this article, an improved asynchronous energy saving named semi-synchronous energy saving is proposed to deal with the energy inefficiency from idle listening existing in asynchronous energy saving. In semi-synchronous energy saving, every node maintains the wake/sleep schedule information of nodes in its two-hop neighborhood and uses this information to calculate the wake-up time of an interested neighbor that the data will be sent to, which reduces idle listening time in both unicast and broadcast mechanisms. A low delay routing mechanism is also proposed based on the wake/sleep schedule information of neighbors. Furthermore, the optimal guard time to guarantee receiving the wake-up notification from neighbor in the presence of clock drift is investigated for saving energy. The performance analysis and simulation evaluation show that semi-synchronous energy saving saves more than 80% of energy with less than 30% memory cost compared to asynchronous energy saving, and it also improves the time of completing broadcast and the end-to-end delay.

Subjective Time Preference and Willingness to Pay for an Energy-Saving Durable Good

2001 ◽  
Vol 32 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Gerrit Antonides ◽  
Sophia R. Wunderink
Keyword(s):  
Willingness To Pay ◽  
Energy Saving ◽  
Subjective Time ◽  
Durable Good ◽  
Discount Function ◽  
Hyperbolic Discount ◽  
The One ◽  
Two Parameter ◽  
Difference Effect ◽  
Better Than

Summary: Different shapes of individual subjective discount functions were compared using real measures of willingness to accept future monetary outcomes in an experiment. The two-parameter hyperbolic discount function described the data better than three alternative one-parameter discount functions. However, the hyperbolic discount functions did not explain the common difference effect better than the classical discount function. Discount functions were also estimated from survey data of Dutch households who reported their willingness to postpone positive and negative amounts. Future positive amounts were discounted more than future negative amounts and smaller amounts were discounted more than larger amounts. Furthermore, younger people discounted more than older people. Finally, discount functions were used in explaining consumers' willingness to pay for an energy-saving durable good. In this case, the two-parameter discount model could not be estimated and the one-parameter models did not differ significantly in explaining the data.

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

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