A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks
Abstract
:1. Introduction
- (1)
- We model the optimal clustering problem as a separable convex optimization problem and solve it analytically to obtain the optimal clustering size and the optimal transmission radius.
- (2)
- We design a cluster heads-linking algorithm based on the pseudo Hilbert curve to collect the compressed sensed data among cluster heads in a collaborative and accumulative manner.
- (3)
- We design a distributed cluster-constructing algorithm to construct the inter-cluster data collection structure around virtual cluster heads in a wireless sensor network.
2. Related Work
3. System Model and Clustering Analysis
3.1. Overview of the Compressed Sensing Theory
3.2. System Model
3.3. Clustering Analysis
- (1)
- All sensor nodes are randomly distributed in the surveillance area with an independent and identical distribution, which can be modelled as a Poisson point process with parameter .
- (2)
- All sensor nodes are set to the same level of data transmission power and data transmission rate. Therefore, the data transmission range of all sensor nodes is identical.
- (3)
- Every sensor node is aware of its location. A number of sensor localization algorithms for WSNs can be used for this purpose [32].
4. The Collaborative Data Collection Scheme
4.1. The Cluster Heads-Linking Algorithm Based on the Pseudo Hilbert Curve
Algorithm 1 Cluster heads-linking algorithm based on the pseudo Hilbert curve. |
|
4.2. The Distributed Cluster Constructing Algorithm
Algorithm 2 Distributed cluster-constructing algorithm. |
|
5. Performance Evaluations
5.1. Performance Analysis
5.2. Performance Comparison
6. Conclusions and Future Research
Author Contributions
Funding
Conflicts of Interest
References
- Rawat, P.; Singh, K.D.; Chaouchi, H.; Bonnin, J.M. Wireless sensor networks: A survey on recent developments and potential synergies. J. Supercomput. 2014, 68, 1–48. [Google Scholar] [CrossRef]
- Borges, L.M.; Velez, F.J.; Lebres, A.S. Survey on the characterization and classification of wireless sensor network applications. IEEE Commun. Surv. Tutor. 2014, 16, 1860–1890. [Google Scholar] [CrossRef]
- Yong, W.; Yang, Z.; Zhang, J.; Feng, L.; Wen, H.; Shen, Y. CS2-Collector: A new approach for data collection in wireless sensor networks based on two-dimensional compressive sensing. Sensors 2016, 16, 1318. [Google Scholar]
- Abdul-Salaam, G.; Abdullah, A.H.; Anisi, M.H.; Gani, A.; Alelaiwi, A. A comparative analysis of energy conservation approaches in hybrid wireless sensor networks data collection protocols. Telecommun. Syst. 2016, 61, 159–179. [Google Scholar] [CrossRef]
- Middya, R.; Chakravarty, N.; Naskar, M.K. Compressive sensing in wireless sensor networks—A survey. IETE Tech. Rev. 2016, 33, 1–13. [Google Scholar] [CrossRef]
- Luo, J.; **ang, L.; Rosenberg, C. Does compressed sensing improve the throughput of wireless sensor networks? In Proceedings of the IEEE International Conference on Communications, Cape Town, South Africa, 23–27 May 2010; pp. 1–6. [Google Scholar]
- Sucasas, V.; Radwan, A.; Marques, H.; Rodriguez, J.; Vahid, S.; Tafazolli, R. A survey on clustering techniques for cooperative wireless networks. Ad Hoc Netw. 2016, 47, 53–81. [Google Scholar] [CrossRef]
- Jan, B.; Farman, H.; Javed, H.; Montrucchio, B.; Khan, M.; Ali, S. Energy efficient hierarchical clustering approaches in wireless sensor networks: A survey. Wirel. Commun. Mobile Comput. 2017, 2017, 1–14. [Google Scholar] [CrossRef]
- Singh, V.K.; Kumar, M. A compressed sensing approach to resolve the energy hole problem in large scale WSNs. Wirel. Pers. Commun. 2018, 99, 185–201. [Google Scholar] [CrossRef]
- Singh, V.K.; Kumar, M. In-network data processing in wireless sensor networks using compressed sensing. Int. J. Sens. Netw. 2018, 26, 174–189. [Google Scholar] [CrossRef]
- Lan, K.C.; Wei, M.Z. A compressibility-based clustering algorithm for hierarchical compressive data gathering. IEEE Sens. J. 2017, 17, 2550–2562. [Google Scholar] [CrossRef]
- Qiao, J.; Zhang, X. Compressive data gathering based on even clustering for wireless sensor networks. IEEE Access 2018, 6, 24391–24410. [Google Scholar] [CrossRef]
- Zhao, C.; Zhang, W.; Yang, Y.; Yao, S. Treelet-based clustered compressive data aggregation for wireless sensor networks. IEEE Trans. Veh. Technol. 2015, 64, 4257–4267. [Google Scholar] [CrossRef]
- Li, X.; Tao, X.; Mao, G. Unbalanced expander based compressive data gathering in clustered wireless sensor networks. IEEE Access 2017, 5, 7553–7566. [Google Scholar] [CrossRef]
- Bajwa, W.; Haupt, J.; Sayeed, A.; Nowak, R. Joint source-channel communication for distributed estimation in sensor networks. IEEE Trans. Inf. Theory 2007, 53, 3629–3653. [Google Scholar] [CrossRef]
- Luo, C.; Wu, F.; Sun, J.; Chen, C.W. Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the International Conference on Mobile Computing and Networking, Bei**g, China, 20–25 September 2009; pp. 145–156. [Google Scholar]
- **ang, L.; Luo, J.; Rosenberg, C. Compressed data aggregation: energy-efficient and high-fidelity data collection. IEEE/ACM Trans. Netw. 2013, 21, 1722–1735. [Google Scholar] [CrossRef]
- Zheng, H.; Yang, F.; Tian, X.; Gan, X.; Wang, X.; **ao, S. Data gathering with compressive sensing in wireless sensor networks: A random walk based approach. IEEE Trans. Parallel Distrib. Syst. 2015, 26, 35–44. [Google Scholar] [CrossRef]
- Hammoudeh, M.; Newman, R. Information extraction from sensor networks using the Watershed transform algorithm. Inf. Fusion 2015, 22, 39–49. [Google Scholar] [CrossRef] [Green Version]
- Quan, L.; **ao, S.; Xue, X.; Lu, C. Neighbor-aided spatial-temporal compressive data gathering in wireless sensor networks. IEEE Commun. Lett. 2016, 20, 578–581. [Google Scholar] [CrossRef]
- Cheng, J.; Ye, Q.; Jiang, H.; Wang, D. STCDG: An efficient data gathering algorithm based on matrix completion for wireless sensor networks. IEEE Trans. Wirel. Commun. 2013, 12, 850–861. [Google Scholar] [CrossRef]
- Piao, X.; Hu, Y.; Sun, Y.; Yin, B.; Gao, J. Correlated spatio-temporal data collection in wireless sensor networks based on low rank matrix approximation and optimized node sampling. Sensors 2014, 14, 23137–23158. [Google Scholar] [CrossRef] [PubMed]
- **e, K.; Ning, X.; Wang, X.; **e, D.; Cao, J.; **e, G.; Wen, J. Recover corrupted data in sensor networks: A matrix completion solution. IEEE Trans. Mobile Comput. 2017, 16, 1434–1448. [Google Scholar] [CrossRef]
- Candes, E.J.; Wakin, M.B. An introduction to compressive sampling. IEEE Signal Process. Mag. 2008, 25, 21–30. [Google Scholar] [CrossRef]
- Donoho, D.L. Compressed sensing. IEEE Trans. Inf. Theory 2006, 52, 1289–1306. [Google Scholar] [CrossRef]
- Qaisar, S.; Bilal, R.M.; Iqbal, W.; Naureen, M. Compressive sensing: From theory to applications, a survey. J. Commun. Netw. 2013, 15, 443–456. [Google Scholar] [CrossRef]
- Campobello, G.; Segreto, A.; Serrano, S. Data gathering techniques for wireless sensor networks: A comparison. Int. J. Distrib. Sens. Netw. 2016, 12, 1–17. [Google Scholar] [CrossRef]
- Candes, E.; Romberg, J.; Tao, T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 2006, 52, 489–509. [Google Scholar] [CrossRef]
- Figueiredo, M.A.T.; Nowak, R.D.; Wright, S.J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Signal Process. 2008, 1, 586–597. [Google Scholar] [CrossRef]
- Soussen, C.; Idier, J.; Duan, J.; Brie, D. Homotopy based algorithms for l0-regularized least-squares. IEEE Trans. Signal Process. 2014, 63, 3301–3316. [Google Scholar] [CrossRef]
- Jain, P.; Tewari, A.; Dhillon, I.S. Partial hard thresholding. IEEE Trans. Inf. Theory 2017, 63, 3029–3038. [Google Scholar] [CrossRef]
- Han, G.; Xu, H.; Duong, T.Q.; Jiang, J.; Hara, T. Localization algorithms of wireless sensor networks: A survey. Telecommun. Syst. 2013, 52, 2419–2436. [Google Scholar] [CrossRef]
- Xu, G.; Zhu, M.; Luo, X.; Wu, M.; Ren, F. An unequal clustering algorithm based on energy balance for wireless sensor networks. IEEJ Trans. Electr. Electron. Eng. 2012, 7, 402–407. [Google Scholar] [CrossRef]
- Li, H.; Liu, Y.; Chen, W.; Jia, W.; Li, B.; **ong, J. COCA: Constructing optimal clustering architecture to maximize sensor network lifetime. Comput. Commun. 2013, 36, 256–268. [Google Scholar] [CrossRef]
- Zhang, J.; Feng, X.; Liu, Z. A grid-based clustering algorithm via load analysis for industrial Internet of things. IEEE Access 2018, 6, 13117–13128. [Google Scholar] [CrossRef]
- Wu, C.; Chang, Y. Approximately even partition algorithm for coding the Hilbert curve of arbitrary-sized image. IET Image Process. 2012, 6, 746–755. [Google Scholar] [CrossRef]
- **e, R.; Jia, X. Transmission-efficient clustering method for wireless sensor networks using compressive sensing. IEEE Trans. Parallel Distrib. Syst. 2014, 25, 806–815. [Google Scholar]
Parent Orientation | ||||
---|---|---|---|---|
I | II | IV | I | I |
II | I | II | III | II |
III | III | III | II | IV |
IV | IV | I | IV | III |
Scheme | Number of Nodes | ||||
---|---|---|---|---|---|
200 | 400 | 600 | 800 | 1000 | |
Proposed | 0.1012 | 0.3536 | 0.7777 | 1.43234 | 2.0637 |
Cluster with CS | 0.1065 | 0.3557 | 0.8078 | 1.4724 | 2.1138 |
Cluster w/oCS | 0.3663 | 1.2067 | 2.5087 | 4.4584 | 6.5277 |
SPT with CS | 0.1441 | 0.4922 | 1.2334 | 2.0445 | 3.4418 |
SPT | 0.3571 | 1.0301 | 2.3671 | 3.8858 | 5.6929 |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, G.; Chen, H.; Peng, S.; Li, X.; Wang, C.; Yu, S.; Yin, P. A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks. Sensors 2018, 18, 2487. https://doi.org/10.3390/s18082487
Li G, Chen H, Peng S, Li X, Wang C, Yu S, Yin P. A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks. Sensors. 2018; 18(8):2487. https://doi.org/10.3390/s18082487
Chicago/Turabian StyleLi, Guorui, Haobo Chen, Sancheng Peng, **nguang Li, Cong Wang, Shui Yu, and Pengfei Yin. 2018. "A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks" Sensors 18, no. 8: 2487. https://doi.org/10.3390/s18082487