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JSDP 2023, 20(2): 195-210 |
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Ghaderi M R, Tabataba Vakili V, Sheikhan M. Energy Consumption Analysis based on Compressive Sensing Model in Wireless Sensor Networks. JSDP 2023; 20 (2) : 12
URL: http://jsdp.rcisp.ac.ir/article-1-1019-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1019-en.html
Islamic Azad University, South Tehran Branch
Abstract: (744 Views)
Nowadays, wireless sensor networks (WSNs) have found many applications in a variety of topics. The main purpose of these networks is to measure environmental phenomena and to send read data in multi-hop paths to the sink to be exploited by users. The most important challenge in WSNs is to minimize energy consumption in sensor batteries and increase network lifetime. One of the most important techniques for reducing energy consumption in WSNs is the compressive sensing (CS) technique. CS reduces network energy consumption by reducing data transmission in the network and increasing the network lifetime. The use of CS technique in a WSN results in the production of different models of CS signals. These models are based on spatial, temporal and spatio-temporal sensors readings. On the other hand, in order to overcome the challenge of energy consumption, the exact recognition of energy resources in the network is essential.
Energy consumption in a sensor node can be divided into two parts: (a) the energy used for computing; and (b) the energy consumed by the communication. The energy used for the computing consists of three components: 1. sensor energy consumption (data reading), 2. background energy consumption, and 3. energy consumption for processing. The power consumption of the communication includes the following: 1. energy consumption for data transmission; 2. energy consumption for data receiving; 3. energy consumption for sending messages; and 4. energy consumption for receiving messages. Hence, the existence of a model for analyzing energy consumption in a CS-based WSN is necessary. Several models have been developed to analyze energy consumption in a WSN, but there is not a complete model for analyzing energy consumption in a CS-based WSN.
In this paper, we study all energy consumption components mentioned above in a CS-based WSN and present a complete model for energy consumption analysis. This model can optimize the design of CS-based WSNs energy efficiency improvement approach. To evaluate the proposed model, we use this model to analyze energy consumption in the compressive data gathering technique which is a CS-based data aggregation method. Using this model can optimize the design of CS-based WSNs.
Energy consumption in a sensor node can be divided into two parts: (a) the energy used for computing; and (b) the energy consumed by the communication. The energy used for the computing consists of three components: 1. sensor energy consumption (data reading), 2. background energy consumption, and 3. energy consumption for processing. The power consumption of the communication includes the following: 1. energy consumption for data transmission; 2. energy consumption for data receiving; 3. energy consumption for sending messages; and 4. energy consumption for receiving messages. Hence, the existence of a model for analyzing energy consumption in a CS-based WSN is necessary. Several models have been developed to analyze energy consumption in a WSN, but there is not a complete model for analyzing energy consumption in a CS-based WSN.
In this paper, we study all energy consumption components mentioned above in a CS-based WSN and present a complete model for energy consumption analysis. This model can optimize the design of CS-based WSNs energy efficiency improvement approach. To evaluate the proposed model, we use this model to analyze energy consumption in the compressive data gathering technique which is a CS-based data aggregation method. Using this model can optimize the design of CS-based WSNs.
Article number: 12
Type of Study: Research |
Subject:
Paper
Received: 2019/05/19 | Accepted: 2020/10/20 | Published: 2023/10/22 | ePublished: 2023/10/22
Received: 2019/05/19 | Accepted: 2020/10/20 | Published: 2023/10/22 | ePublished: 2023/10/22
References
1. [1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: a survey," Computer Networks, vol. 38, pp. 393-422, 2002. [DOI:10.1016/S1389-1286(01)00302-4]
2. [2] Z. Xiong, A. Liveris, and S. Cheng, "Distributed source coding for sensor networks," IEEE Signal Processing Magazine, vol. 21, pp. 80-94, 2004. [DOI:10.1109/MSP.2004.1328091]
3. [3] J. Lee, and W. Cheng, "Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication," IEEE Sensors Journal, vol. 12, pp. 2891- 2897, 2012. [DOI:10.1109/JSEN.2012.2204737]
4. [4] M. A. Zahhad, O. Amin, M. Farrag, and A. Ali, "An energy consumption model for wireless sensor networks," IEEE 5th International Conference on Energy Aware Computing Systems & Applications, Cairo, Egypt, Dec. 2015.
5. [5] J. Haupt, W. Bajwa, M. Rabbat, and R. Nowak, "Compressed sensing for networked data," Signal Processing Magazine, vol. 25, pp. 92-101, 2008. [DOI:10.1109/MSP.2007.914732]
6. [6] E. Candes and M. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, vol. 25, pp. 21-30, 2008. [DOI:10.1109/MSP.2007.914731]
7. [7] M. B. Wakin, M. F. Duarte, S. Sarvotham, D. Baron, and R. G. Baraniuk, "Recovery of jointly sparse signals from few random," in Proc. 15th ACM MobiCom, Beijing, China, pp. 145-156, Sep. 2009.
8. [8] J. Tropp and A. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Transactions on Information Theory, vol. 53, pp. 4655-4666, Dec. 2007. [DOI:10.1109/TIT.2007.909108]
9. [9] A. Kulkarni and T. Mohsenin, "Low overhead architectures for OMP compressive sensing reconstruction algorithm," IEEE Transactions on Circuits and Systems, vol. 64, pp. 1468-1480, 2017. [DOI:10.1109/TCSI.2017.2648854]
10. [10] D. L. Donoho, M. Elad, and V. N. Temlyakov, "Stable recovery of sparse over complete representations in the presence of noise," IEEE Transactions on Information Theory, vol. 52, no. 1, pp. 6-18, Jan. 2006. [DOI:10.1109/TIT.2005.860430]
11. [11] E. Candes and J. Romberg, "Sparsity and incoherence in compressive sampling," Inverse Problems, vol. 23, pp. 969-985, Apr. 2007. [DOI:10.1088/0266-5611/23/3/008]
12. [12] C. Luo, et al. , "Compressive data gathering for large-scale wireless sensor networks," in Proc. of the 15th annual international conference on Mobile computing and networking (Mobicom), pp. 145-156, 2009. [DOI:10.1145/1614320.1614337]
13. [13] K. C Lan and M. Z. Wei, "A compressibility-based clustering algorithm for hierarchical compressive data gathering," IEEE Sensors Journal, vol. 17, pp. 2550-2562, Apr. 2017. [DOI:10.1109/JSEN.2017.2669081]
14. [14] B. Ali, N. Pissinou, and K. Makki, "Identification and validation of spatio-temporal associations in wireless sensor networks," in Proc. SENSORCOMM, Athens, Greece, pp. 496-501, Jun. 2009. [DOI:10.1109/SENSORCOMM.2009.83] [PMID]
15. [15] M. Leinonen and S. Member, "Sequential compressed sensing with progressive signal reconstruction in wireless sensor networks," IEEE Transactions on Wireless Communication, vol. 14, pp. 1622-1635, Mar. 2015. [DOI:10.1109/TWC.2014.2371017]
16. [16] M. Duarte and R. Baraniuk, "Kronecker product matrices for compressive sensing," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., Dallas, TX, USA, pp. 3650-3653, Mar. 2010. [DOI:10.1109/ICASSP.2010.5495900]
17. [17] M. Mahmudimanesh, A. Khelil, and N. Suri, "Balanced spatio-temporal compressive sensing for multi-hop wireless sensor networks," IEEE 9th Int. Conf. on Mobile Ad hoc and Sensor Systems, Las Vegas, USA, Oct. 2012. [DOI:10.1109/MASS.2012.6502539]
18. [18] X. Li, X. Tao, and Z. Chen, "Spatio-temporal compressive sensing based data gathering in wireless sensor networks," IEEE Wireless Communications Letters, vol. 7, pp. 198-201, Apr. 2018. [DOI:10.1109/LWC.2017.2764899]
19. [19] M. A. Zahhad, O. Amin, M. Farrag, and A. Ali, "Survey on energy consumption models in wireless sensor networks," Open Transactions on Wireless Communications, vol. 1, pp. 63-79, 2014.
20. [20] C. Karakus, A. C. Gurbuz, and B. Tavli, "Analysis of energy efficiency of compressive sensing in wireless sensor networks," IEEE Sensors Journal, vol. 13, pp. 1999-2008, May 2013. [DOI:10.1109/JSEN.2013.2244036]
21. [21] W. Heinzelman, A. Chandrakasan, H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Transactions on Wireless Communications, vol. 1, pp. 660-670, 2002. [DOI:10.1109/TWC.2002.804190]
22. [22] C. Zhou, M. Wang, W. Qu, and Z. Lu, "A wireless sensor network model considering energy consumption balance," Mathematical Problems in Engineering, vol. 2018, pp. 1-8, 2018. [DOI:10.1155/2018/8592821]
23. [23] A. Ali, M. Abo-Zahhad, and M. Farrag, "Modeling of wireless sensor networks with minimum energy consumption," Arabian Journal for Science and Engineering, vol. 42, pp. 2631-2639, Jul. 2017. [DOI:10.1007/s13369-016-2281-5]
24. [24] M. Ahmad Jan, P. Nanda, and X. He, "Energy evaluation model for an improved centralized clustering hierarchical algorithm in WSN," in Proc. International Conference on Wired/Wireless Internet Communication, WWIC, pp. 154-167, 2013. [DOI:10.1007/978-3-642-38401-1_12]
25. [25] V. Shnayder, M. Hempstead, B. Chen, G. W. Allen, and M. Welsh, "Simulating the power consumption of large-scale sensor network applications," in Proc. ACM Conf. Embedded Netw. Sensor Syst., pp. 188-200, 2004. [DOI:10.1145/1031495.1031518]
26. [26] F. Z. Djiroun and D. Djenouri, "MAC protocols with wake-up radio for wireless sensor networks: a review," IEEE Communications Surveys & Tutorials, vol. 19, pp. 587-618, 2017. [DOI:10.1109/COMST.2016.2612644]
27. [27] A. Rasul and T. Erlebach, "Reducing idle listening during data collection in wireless sensor networks," 10th International Conference on Mobile Ad-hoc and Sensor Networks, Maui, HI, USA, 2014. [DOI:10.1109/MSN.2014.10]
28. [28] N. N. Minh and M. K. Kim, "Reducing idle listening time in pipeline-forwarding MAC protocols of wireless sensor networks," IEEE International Conference on Advanced Technologies for Communications (ATC), Hanoi, Vietnam, 2016. [DOI:10.1109/ATC.2016.7764771]
29. [29] S.H. Lee and L. Choi, "ZeroMAC: Toward a zero sleep delay and zero idle listening media access control protocol with ultralow power radio frequency wakeup sensor," International Journal of Distributed Sensor Networks, vol. 13, pp. 1-21, 2017. [DOI:10.1177/1550147717716397]
30. [30] M. R. Ghaderi, V. T. Vakili, and M. Sheikhan, "FGAF CDG: fuzzy geographic routing protocol based on compressive data gathering in wireless sensor networks," Journal of Ambient Intelligence and Humanized Computing, pp. 1-23, Published online 17 May 2019 (
https://doi.org/10.1007/s12652-019-01314-1 [DOI:10.1007/s12652-019-01314-1).]
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