International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 7, Issue 1 (January 2020), Pages: 125-138

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 Review Paper

 Title: Algorithms and methods for energy harvesting in wireless networks: A review

 Author(s): Siti Nurhafizza Maidin 1, Kamilia Kamardin 2, 3, *, Noor Azurati Ahmad 1, Irfanuddin Shafi Ahmed 1, Hazilah Mad Kaidi 1, 3, Nurul Aini Bani 1, Sumiaty Ambran 2

 Affiliation(s):

 1Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
 2Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
 3Wireless Communication Centre, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-5317-714X

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2020.01.013

 Abstract:

The element of energy harvesting in the wireless network is popular nowadays due to its effectiveness in terms of performance. The effectiveness can be seen in the way it can prolong the battery life as well as increase the performance of the network. In order to have a better understanding of how energy harvesting happens in a wireless network, this paper will present different algorithms and methods used for energy harvesting on the wireless network. The algorithms such as EHWA, SSPCA, NEEC, ANCAEE, QL-SEP and PSO are discussed and reviewed. The methods discussed and reviewed in this paper are spectrum and energy-harvesting technique, Fair Packet Ratio Distributed Computation Method, Power Feedback Control MPPT, TEH technique and Electric Field Energy Harvesting technique. The review on energy harvesting will also include simulation approaches, methods and the result of the different energy harvesting simulations. By conducting this paper review, it will help researchers to see the energy harvesting in different aspects of algorithms and methods. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Wireless network, Energy harvesting, Algorithm, Methods

 Article History: Received 14 June 2019, Received in revised form 1 September 2019, Accepted 19 November 2019

 Acknowledgment:

This work is supported by Universiti Teknologi Malaysia (UTM) and the Ministry of Education (MOE) under grant no. 06G45, 17H58, 06G43 and 5F063. The authors would like to thank UTM and MOE for realizing and supporting this research work.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Maidin SN, Kamardin K, and Ahmad NA et al. (2020). Algorithms and methods for energy harvesting in wireless networks: A review. International Journal of Advanced and Applied Sciences, 7(1): 125-138

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10

 Fig. 11 Fig. 12 Fig. 13 Fig. 14 Fig. 15 Fig. 16 Fig. 17 Fig. 18

 Tables

 Table 1 Table 2 Table 3

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 References (35) 

  1. Abidoye AP, Azeez NA, Adesina AO, and Agbele KK (2011). ANCAEE: A novel clustering algorithm for energy efficiency in wireless sensor networks. Wireless Sensor Network, 3: 307-312. https://doi.org/10.4236/wsn.2011.39032   [Google Scholar]
  2. Akyildiz IF, Lee WY, Vuran MC, and Mohanty S (2008). A survey on spectrum management in cognitive radio networks. IEEE Communications Magazine, 46(4): 40-48. https://doi.org/10.1109/MCOM.2008.4481339   [Google Scholar]
  3. Ansari N and Han T (2016). Freenet: Spectrum and energy harvesting wireless networks. IEEE Network, 30(1): 66-71. https://doi.org/10.1109/MNET.2016.7389833   [Google Scholar]
  4. Ansari N, Zhang C, Rojas-Cessa R, Sakarindr P, Hou ES, and De S (2008). Networking for critical conditions. IEEE Wireless Communications, 15(2): 73-81. https://doi.org/10.1109/MWC.2008.4492980   [Google Scholar]
  5. Anwar T and Mui WGP (2007). Design and implementation of a wireless network system in a smart campus. CommIT (Communication and Information Technology) Journal, 1(2): 127-139. https://doi.org/10.21512/commit.v1i2.474   [Google Scholar]
  6. Aoudia FA, Gautier M, and Berder O (2017). Distributed computation of fair packet rates in energy harvesting wireless sensor networks. IEEE Wireless Communications Letters, 6(5): 626-629. https://doi.org/10.1109/LWC.2017.2724512   [Google Scholar]
  7. Argyriou A and Alay Ö (2016). Distributed estimation in wireless sensor networks with an interference canceling fusion center. IEEE Transactions on Wireless Communications, 15(3): 2205-2214. https://doi.org/10.1109/TWC.2015.2500231   [Google Scholar]
  8. Bergonzini C, Brunelli D, and Benini L (2009). Algorithms for harvested energy prediction in batteryless wireless sensor networks. In the 3rd International Workshop on Advances in sensors and Interfaces, IEEE, Trani, Italy: 144-149. https://doi.org/10.1109/IWASI.2009.5184785   [Google Scholar]
  9. Bozorgi SM, Rostami AS, Hosseinabadi AAR, and Balas VE (2017). A new clustering protocol for energy harvesting-wireless sensor networks. Computers and Electrical Engineering, 64: 233-247. https://doi.org/10.1016/j.compeleceng.2017.08.022   [Google Scholar]
  10. Cadambe VR and Jafar SA (2008). Interference alignment and spatial degrees of freedom for the k user interference channel. In the IEEE International Conference on Communications, IEEE, Beijing, China: 971-975. https://doi.org/10.1109/ICC.2008.190   [Google Scholar]
  11. Chang KS, Kang SM, Park KJ, Shin SH, Kim HS, and Kim HS (2012). Electric field energy harvesting powered wireless sensors for smart grid. Journal of Electrical Engineering and Technology, 7(1): 75-80. https://doi.org/10.5370/JEET.2012.7.1.75   [Google Scholar]
  12. Chen X and Yuen C (2014). Performance analysis and optimization for interference alignment over MIMO interference channels with limited feedback. IEEE Transactions on Signal Processing, 62(7): 1785-1795. https://doi.org/10.1109/TSP.2014.2304926   [Google Scholar]
  13. Deorankar ML and Markande SD (2014). Adaptive switching techniques of power scavenging in WSN for industries. In the 2014 International Conference on Power, Automation and Communication, IEEE, Amravati, India: 71-75. https://doi.org/10.1109/INPAC.2014.6981138   [Google Scholar]
  14. Du P, Yang Q, Shen Z, and Kwak KS (2017). Distortion minimization in wireless sensor networks with energy harvesting. IEEE Communications Letters, 21(6): 1393-1396. https://doi.org/10.1109/LCOMM.2017.2674680   [Google Scholar]
  15. Gong P, Xu Q, and Chen TM (2014). Energy harvesting aware routing protocol for wireless sensor networks. In the 2014 9th International Symposium on Communication Systems, Networks and Digital Sign, IEEE, Manchester, UK: 171-176. https://doi.org/10.1109/CSNDSP.2014.6923819   [Google Scholar] PMCid:PMC4290461
  16. Heinzelman WR, Chandrakasan A, and Balakrishnan H (2000). Energy-efficient communication protocol for wireless microsensor networks. In the 33rd Annual Hawaii International Conference on System Sciences, IEEE, Maui, USA: 3005-3014. https://doi.org/10.1109/HICSS.2000.926982   [Google Scholar]
  17. Hoang DC, Tan YK, Chng HB, and Panda SK (2009). Thermal energy harvesting from human warmth for wireless body area network in medical healthcare system. In the International Conference on Power Electronics and Drive Systems, IEEE, Taipei, Taiwan: 1277-1282. https://doi.org/10.1109/PEDS.2009.5385814   [Google Scholar]
  18. Jafar SA (2011). Interference alignment: A new look at signal dimensions in a communication network. Foundations and Trends® in Communications and Information Theory, 7(1): 1-134. https://doi.org/10.1561/0100000047   [Google Scholar]
  19. Kausar AZ, Reza AW, Saleh MU, and Ramiah H (2014). Energizing wireless sensor networks by energy harvesting systems: Scopes, challenges and approaches. Renewable and Sustainable Energy Reviews, 38: 973-989. https://doi.org/10.1016/j.rser.2014.07.035   [Google Scholar]
  20. Kosunalp S (2016). A new energy prediction algorithm for energy-harvesting wireless sensor networks with Q-learning. IEEE Access, 4: 5755-5763. https://doi.org/10.1109/ACCESS.2016.2606541   [Google Scholar]
  21. Little FE, McSpadden JO, Chang K, and Kaya N (1998). Toward space solar power: Wireless energy transmission experiments past, present and future. In the AIP Conference Proceedings, AIP, 420(1): 1225-1233. https://doi.org/10.1063/1.54957   [Google Scholar]
  22. Malathi L, Gnanamurthy RK, and Chandrasekaran K (2015). Energy efficient data collection through hybrid unequal clustering for wireless sensor networks. Computers and Electrical Engineering, 48: 358-370. https://doi.org/10.1016/j.compeleceng.2015.06.019   [Google Scholar]
  23. Martinez G, Li S, and Zhou C (2014). Wastage-aware routing in energy-harvesting wireless sensor networks. IEEE Sensors Journal, 14(9): 2967-2974. https://doi.org/10.1109/JSEN.2014.2319741   [Google Scholar]
  24. Nguyen TD, Khan JY, and Ngo DT (2017). An effective energy-harvesting-aware routing algorithm for WSN-based IoT applications. In the IEEE International Conference on Communications, IEEE, Paris, France: 1-6. https://doi.org/10.1109/ICC.2017.7996888   [Google Scholar]
  25. Panatik KZ, Kamardin K, Shariff SA, Yuhaniz SS, Ahmad NA, Yusop OM, and Ismail S (2016). Energy harvesting in wireless sensor networks: A survey. In the 2016 IEEE 3rd International Symposium on Telecommunication Technologies, IEEE, Kuala Lumpur, Malaysia: 53-58. https://doi.org/10.1109/ISTT.2016.7918084   [Google Scholar]
  26. Rasan B and Al-Nafiey A (2019). The application of artificial intelligence for power flow problem in power networks. Annals of Electrical and Electronic Engineering, 2(5): 1-5. https://doi.org/10.21833/AEEE.2019.05.001   [Google Scholar]
  27. Stark I (2006). Invited talk: Thermal energy harvesting with thermo life. In the International Workshop on Wearable and Implantable Body Sensor Networks, IEEE, Cambridge, USA: 19-22. https://doi.org/10.1109/BSN.2006.37   [Google Scholar]
  28. Vilardebo GJ and Gündüz D (2014). Competitive analysis of energy harvesting wireless communication systems. In the European Wireless 2014; 20th European Wireless Conference, VDE, Barcelona, Spain: 1-6.   [Google Scholar]
  29. Wei D, Kaplan S, and Chan HA (2008). Energy efficient clustering algorithms for wireless sensor networks. In the ICC Workshops-IEEE International Conference on Communications Workshops, IEEE, Beijing, China: 236-240. https://doi.org/10.1109/ICCW.2008.50   [Google Scholar]
  30. Wu Y and Liu W (2013). Routing protocol based on genetic algorithm for energy harvesting-wireless sensor networks. IET Wireless Sensor Systems, 3(2): 112-118. https://doi.org/10.1049/iet-wss.2012.0117   [Google Scholar]
  31. Yang J and Zhang D (2009). An energy-balancing unequal clustering protocol for wireless sensor networks. Information Technology Journal, 8(1): 57-63. https://doi.org/10.3923/itj.2009.57.63   [Google Scholar]
  32. Younis O and Fahmy S (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4): 366-379. https://doi.org/10.1109/TMC.2004.41   [Google Scholar]
  33. Yucek T and Arslan H (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials, 11(1): 116-130. https://doi.org/10.1109/SURV.2009.090109   [Google Scholar]
  34. Zhao N, Yu FR, and Leung VC (2015). Wireless energy harvesting in interference alignment networks. IEEE Communications Magazine, 53(6): 72-78. https://doi.org/10.1109/MCOM.2015.7120020   [Google Scholar]
  35. Zhao N, Yu FR, Sun H, Nallanathan A, and Yin H (2013). A novel interference alignment scheme based on sequential antenna switching in wireless networks. IEEE Transactions on Wireless Communications, 12(10): 5008-5021. https://doi.org/10.1109/TWC.2013.090413.121731   [Google Scholar]