scholarly journals Energy-Balanced Data Gathering and Aggregating in WSNs: A Compressed Sensing Scheme

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaofei Xing ◽  
Dongqing Xie ◽  
Guojun Wang
Keyword(s):  
Energy Efficiency ◽  
Compressed Sensing ◽  
Data Gathering ◽  
Sampling Technique ◽  
Data Recovery ◽  
Data Reconstruction ◽  
Sink Node ◽  
Data Sampling ◽  
Improve Energy Efficiency ◽  
Reconstruction Performance

Compressed sensing (CS) is an emerging sampling technique by which the data sampling and aggregating can be done simultaneously, which can be applied to many fields, including data processing in wireless sensor networks (WSNs). In WSNs, data aggregating can reduce data transmission cost and improve energy efficiency. Existing CS-based data gathering work in WSNs utilizes the centralized method to process the data by a sink node, which causes the load imbalance and “coverage hole” problems, and so forth. In this paper, we propose an energy-balanced data gathering and aggregating (EDGA) scheme that integrates a clustering hierarchical structure with the CS to optimize and balance the amount of data transmitted. We also design a data reconstruction algorithm to perform data recovery tasks by utilizing the orthogonal matching pursuit theory, which helps to reconstruct the original data accurately and effectively at sink node. The advantages of the proposed scheme compared with other state-of-the-art related methods are measured on the metrics of data recovery ratio and energy efficiency. We implement our scheme on a simulation platform using a real dataset from Intel lab. Simulation results demonstrate that the proposed data gathering and aggregating scheme guarantees accurate data reconstruction performance and obtains energy efficiency significantly compared to existing methods.

scholarly journals Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2654 ◽  
Author(s):  
Yuan Rao ◽  
Gang Zhao ◽  
Wen Wang ◽  
Jingyao Zhang ◽  
Zhaohui Jiang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressed Sensing ◽  
Data Acquisition ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Improve Energy Efficiency ◽  
Switching Mechanism ◽  
Acquisition Scheme

Due to the limited energy budget, great efforts have been made to improve energy efficiency for wireless sensor networks. The advantage of compressed sensing is that it saves energy because of its sparse sampling; however, it suffers inherent shortcomings in relation to timely data acquisition. In contrast, prediction-based approaches are able to offer timely data acquisition, but the overhead of frequent model synchronization and data sampling weakens the gain in the data reduction. The integration of compressed sensing and prediction-based approaches is one promising data acquisition scheme for the suppression of data transmission, as well as timely collection of critical data, but it is challenging to adaptively and effectively conduct appropriate switching between the two aforementioned data gathering modes. Taking into account the characteristics of data gathering modes and monitored data, this research focuses on several key issues, such as integration framework, adaptive deviation tolerance, and adaptive switching mechanism of data gathering modes. In particular, the adaptive deviation tolerance is proposed for improving the flexibility of data acquisition scheme. The adaptive switching mechanism aims at overcoming the drawbacks in the traditional method that fails to effectively react to the phenomena change unless the sampling frequency is sufficiently high. Through experiments, it is demonstrated that the proposed scheme has good flexibility and scalability, and is capable of simultaneously achieving good energy efficiency and high-quality sensing of critical events.

IOT and Industry 4.0 – Gateway towards improving Energy Efficiency

2017 ◽  
Vol 7 (9) ◽  
pp. 58
Author(s):  
Jait Purohit
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Real Time ◽  
Energy Management ◽  
Manufacturing Industry ◽  
Management Practices ◽  
Data Gathering ◽  
Time Data ◽  
Improve Energy Efficiency ◽  
Day To Day Operations

Energy efficiency (EE) has become an important benchmark in manufacturing industry due the increasing concerns about climate change and tightening of environmental regulations. However, most manufacturing and production industries today are only able to monitor aggregated energy consumption and lack the real-time visibility of EE on the shop floors. The ability to access energy information and effectively analyse such real-time data to extract key indicators is a crucial factor for successful energy management. While enabling real-time online monitoring of Energy Efficiency, it also applies data gathering analysis to detect abnormal energy consumption patterns and quantify energy efficiency gaps. Through a case study of a microfluidic device manufacturing line, we demonstrate how the application can assist energy managers in embedding best energy management practices in their day-to-day operations and improve Energy Efficiency by eliminating possible energy wastages on manufacturing shop floors.

scholarly journals Energy-Efficient Data Gathering Scheme Based on Broadcast Transmissions in Wireless Sensor Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Soobin Lee ◽  
Howon Lee
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Distributed Data ◽  
Improve Energy Efficiency ◽  
Wireless Medium ◽  
Important Challenge ◽  
Efficient Data

Improving energy efficiency is the most important challenge in wireless sensor networks. Because sensing information is correlated in many sensor network applications, some previous works have proposed ideas that reduce the energy consumption of the network by exploiting the spatial correlation between sensed information. In this paper, we propose a distributed data compression framework that exploits the broadcasting characteristic of the wireless medium to improve energy efficiency. We analyze the performance of the proposed framework numerically and compare it with the performance of previous works using simulation. The proposed scheme performs better when the sensing information is correlated.

EELEACH Clustering Approach to Improve Energy Efficiency in WSN

2019 ◽  
Vol 13 (2) ◽  
pp. 148-153
Author(s):  
Neha D. Desai ◽  
Shrihari D. Khatawkar
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Data Delivery ◽  
Sink Node ◽  
Improve Energy Efficiency ◽  
Packet Delivery ◽  
Head Node

Background: Wireless sensor network is self-organizing which consists of a large number of sensor nodes and one sink node according to recent patents. The most important characteristics of such a network are the restricted resources like battery power, consumption capacity and consumption range. Energy consumption is one of the important issues in the wireless sensor network and the challenge is to prolong the network lifespan. Objective: The objective of the proposed approach is to balance a consumption of energy at member node as well as head node of cluster during the data transmission stage and to improve energy efficiency and lifespan of the network. Methods: The aim of an energy efficient clustering method to deal with the homogenous distribution and deployment of tree structure is performed. The performance of network is enhanced by electing head node with data to the node with greater cluster rate and having lowest distance from sink node. The member node sends their data to the head node which forwards their data to the node with greater weight rate which is sent to the sink node in an energy balancing way. Results: A performance analysis of existing approach as LEACH and proposed approach as EELEACH is undertaken by considering different metrics such as energy consumption successful data delivery, throughput, routing overhead, packet delivery fraction and delay ratio. Conclusion: From result analysis, the proposed system as EELEACH shows successful data delivery, throughput, routing overhead, packet delivery fraction and delay ratio. Hence, the low energy consumption improved lifespan of the network and better data transfer rate.

scholarly journals Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

2021 ◽  
Vol 11 (4) ◽  
pp. 1435
Author(s):  
Xue Bi ◽  
Lu Leng ◽  
Cheonshik Kim ◽  
Xinwen Liu ◽  
Yajun Du ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Energy Criterion ◽  
Step Size ◽  
Relationship Analysis ◽  
Support Set ◽  
Reconstruction Performance ◽  
Sparsity Level

Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

scholarly journals Are Cross-Border Classes Feasible for Students to Collaborate in the Analysis of Energy Efficiency Strategies for Socioeconomic Development While Keeping CO2 Concentration Controlled?

2021 ◽  
Vol 13 (3) ◽  
pp. 1584
Author(s):  
Roberto Araya ◽  
Pedro Collanqui
Keyword(s):  
Energy Efficiency ◽  
Socioeconomic Development ◽  
Concept Maps ◽  
Research Question ◽  
Co2 Concentration ◽  
Asia Pacific ◽  
School Students ◽  
Improve Energy Efficiency ◽  
Cross Border ◽  
Science Classes

Education is critical for improving energy efficiency and reducing CO2 concentration, but collaboration between countries is also critical. It is a global problem in which we cannot isolate ourselves. Our students must learn to collaborate in seeking solutions together with others from other countries. Thus, the research question of this study is whether interactive cross-border science classes with energy experiments are feasible and can increase awareness of energy efficiency among middle school students. We designed and tested an interactive cross-border class between Chilean and Peruvian eighth-grade classes. The classes were synchronously connected and all students did experiments and answered open-ended questions on an online platform. Some of the questions were designed to check conceptual understanding whereas others asked for suggestions of how to develop their economies while keeping CO2 air concentration at acceptable levels. In real time, the teacher reviewed the students’ written answers and the concept maps that were automatically generated based on their responses. Students peer-reviewed their classmates’ suggestions. This is part of an Asia-Pacific Economic Cooperation (APEC) Science Technology Engineering Mathematics (STEM) education project on energy efficiency using APEC databases. We found high levels of student engagement, where students discussed not only the cross-cutting nature of energy, but also its relation to socioeconomic development and CO2 emissions, and the need to work together to improve energy efficiency. In conclusion, interactive cross-border science classes are a feasible educational alternative, with potential as a scalable public policy strategy for improving awareness of energy efficiency among the population.

scholarly journals Dimensionless Analysis on the Characteristics of Pneumatic Booster Valve with Energy Recovery

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Fan Yang ◽  
Kotaro Tadano ◽  
Gangyan Li ◽  
Toshiharu Kagawa
Keyword(s):  
Mathematical Model ◽  
Energy Efficiency ◽  
Energy Recovery ◽  
Operational Parameters ◽  
Local Pressure ◽  
Air Power ◽  
Improve Energy Efficiency ◽  
Dimensionless Analysis ◽  
Air Supply ◽  
Save Energy

Factories are increasingly reducing their air supply pressures in order to save energy. Hence, there is a growing demand for pneumatic booster valves to overcome the local pressure deficits in modern pneumatic systems. To further improve energy efficiency, a new type of booster valve with energy recovery (BVER) is proposed. The BVER principle is presented in detail, and a dimensionless mathematical model is established based on flow rate, gas state, and energy conservation. The mathematics model was transformed into a dimensionless model by accurately selecting the reference values. Subsequently the dimensionless characteristics of BVER were found. BVER energy efficiency is calculated based on air power. The boost ratio is found to be mainly affected by the operational parameters. Among the structural ones, the recovery/boost chamber area ratio and the sonic conductance of the chambers are the most influential. The boost ratio improves by 15%–25% compared to that of a booster valve without an energy recovery chamber. The efficiency increases by 5%–10% depending on the supply pressure. A mathematical model is validated by experiment, and this research provides a reference for booster valve optimisation and energy saving.

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