An Optimal CDG Framework for Energy Efficient WSNs

Yan, Wenjie; Dong, Yongfeng; Zhang, Suqi; Wang, Zijuan

doi:10.1049/cje.2016.10.017

Cited by 6 publications

(4 citation statements)

References 26 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rout and Ghosh [13] attempted to improve the energy efficiency of the bottleneck zone in a WSN by combining duty cycle and network coding. Yan et al [14] studied the energy efficiency issue of compressed sensing in WSN, and proposed an optimal compressed data gathering framework and its corresponding algorithm. Chen et al [15] designed a scheme of task offloading and frequency scaling for mobile devices in mobile edge computing environments in order to improve their energy efficiency.…”

Section: Background and Related Workmentioning

confidence: 99%

A Multi‐queue Approach of Energy Efficient Task Scheduling for Sensor Hubs

Huang

Zhang

2020

Chin. j. electron.

View full text Add to dashboard Cite

Section: Background and Related Workmentioning

confidence: 99%

A Multi‐queue Approach of Energy Efficient Task Scheduling for Sensor Hubs

Huang

Zhang

2020

Chin. j. electron.

View full text Add to dashboard Cite

“…matrix optimization technique to reduce the data redundancy resulted from the Spatial-temporal correlation, such as the Spatio-Temporal Hierarchical Data Aggregation using Compressive Sensing (ST-HDACS) [241] , the Spatial-Temporal Compressive Data Gathering algorithm (ST-CDGA) [242] , and the Spatio-Temporal Kronecker Compressive Sensing method (STKCS) [243] . In addition, the Dispersion Wavelet Transform Matrix (DWTM) is applied in the Measurement Matrix Optimization Algorithm (MMOA) to achieve Spatial-temporal Compressive Sensing [244] . With the DWTM s acting as the sparse basis matrix, an Optimized Data Fusion Tree was constructed based on both of the CS theory and the routing topology.…”

Section: Acm T S Nmentioning

confidence: 99%

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

Lin

Wang

Min

et al. 2020

ACM Trans. Sen. Netw.

View full text Add to dashboard Cite

A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) which are not equipped with any energy harvesting modules was conducted in this paper. Firstly, a novel generic mathematical definition of Energy Efficiency was proposed, which took the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, it is for the first time that the Energy Efficiency of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network were expounded at length. Accordingly, the concepts concerning the Energy Efficiency, namely the Energy-efficient Means, the Energy-efficient Tier, and the Energy-efficient Perspective, were proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 were tracked and reviewed. Specifically, they were classified into five categories, i.e., the Energy-Efficient Media Access Control (EEMAC) protocol, the Mobile Node Assistance Scheme (MNAS), the Energy-Efficient Clustering Scheme (EECS), the Energy-Efficient Routing Scheme (EERS), and the Compress Sensing based Scheme (CSS) respectively. A detailed elaboration on both the basic principle and the evolution of them was made. Finally, further analysis on the categories was made and the related conclusion was drawn. To be specific, the interdependence among them, the relationships between each of them and the Energy-efficient Means, the Energy-efficient Tier as well as and the Energy-efficient Perspective were analyzed in detail. Besides, the specific applicable scenarios for each of them and the relevant statistics analysis were detailed. The proportion and the number of citations of each category were illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm, and the feasible direction concerning the energy efficiency in the future were pointed out.

show abstract

“…Yan et, al. presented an optimal Compressed Data Gathering (CDG) framework [35] which adopted a Diffusion Wavelet Transform Matrix (DWTM) as the sparse representation of the compressed data. Besides, they proposed a novel Measurement Matrix Optimization Algorithm (MMOA) for the process of acquiring the compressed data.…”

Section: Ntmentioning

confidence: 99%

An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs

et al. 2019

View full text Add to dashboard Cite

A novel algorithm which combined the merits of the Clustering strategy and the Compressive Sensing-based (CS-based) scheme was proposed in this paper. The lemmas for the relationship between any two adjacent layers, the optimal size of clusters, the optimal distribution of the Cluster Head (CH) and the corresponding proofs were presented firstly. In addition, to alleviate the "Hot Spot Problem" and reduce the energy consumption resulted from the rotation of the role of CHs, a third role of Backup Cluster Head (BCH) as well as the corresponding mechanism to rotate the roles between the CH and BCH were proposed. Subsequently, the Energy-Efficient Compressive Sensing-based clustering Routing (EECSR) protocol was presented in detail. Finally, extensive simulation experiments were conducted to evaluate its energy performance. Comparisons with the existing clustering algorithms and the CS-based algorithm verified the effect of EECSR on improving the energy efficiency and extending the lifespan of WSNs.

show abstract

An Optimal CDG Framework for Energy Efficient WSNs

Cited by 6 publications

References 26 publications

A Multi‐queue Approach of Energy Efficient Task Scheduling for Sensor Hubs

A Multi‐queue Approach of Energy Efficient Task Scheduling for Sensor Hubs

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs

Contact Info

Product

Resources

About