logo

SCIENCE CHINA Information Sciences, Volume 62, Issue 4: 042305(2019) https://doi.org/10.1007/s11432-018-9751-5

A low complexity online controller using fuzzy logic in energy harvesting WSNs

More info
  • ReceivedJul 24, 2018
  • AcceptedDec 6, 2018
  • PublishedFeb 26, 2019

Abstract

In this paper, we present a fuzzy logic based scheme for a two hop energy harvesting (EH) wireless sensor network (WSN). Incorporating data and energy causality constraints, discrete transmission rates, finite energy and data buffers, a fuzzy model is developed which uses network throughput, battery level and channel gain as inputs. The fuzzy scheme is then compared with optimum, modified optimum, and Markov decision process (MDP) schemes in terms of computational complexity, throughput, battery level and data buffer capacity. The throughput results show that the fuzzy online scheme preforms closely to the compared schemes and avoids battery depletion even when the number of discrete transmission rates are increased.


References

[1] Ephremides A. Energy concerns in wireless networks. IEEE Wireless Commun, 2002, 9: 48-59 CrossRef Google Scholar

[2] Jiang X, Polastre J, Culler D. Perpetual environmentally powered sensor networks. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, Boise, 2005. 463--468. Google Scholar

[3] Yeatman E M. Advances in power sources for wireless sensor nodes. In: Proceedings of International Workshop on Wearable Implantable BSN, 2004. Google Scholar

[4] Powercast Lifetime Power® Energy Harvesting Kit Deutschland. 2017. https://www.mouser.de/new/powercast/powercastlifetimepower/. Google Scholar

[5] Ulukus S, Yener A, Erkip E. Energy Harvesting Wireless Communications: A Review of Recent Advances. IEEE J Sel Areas Commun, 2015, 33: 360-381 CrossRef Google Scholar

[6] Medepally B, Mehta N B. Voluntary energy harvesting relays and selection in cooperative wireless networks. IEEE Trans Wireless Commun, 2010, 9: 3543-3553 CrossRef Google Scholar

[7] Kashef M, Ephremides A. Optimal Partial Relaying for Energy-Harvesting Wireless Networks. IEEE/ACM Trans Networking, 2016, 24: 113-122 CrossRef Google Scholar

[8] Tutuncuoglu K, Yener A. Optimum Transmission Policies for Battery Limited Energy Harvesting Nodes. IEEE Trans Wireless Commun, 2012, 11: 1180-1189 CrossRef Google Scholar

[9] Chen H, Li Y H, Luiz Rebelatto J. Harvest-Then-Cooperate: Wireless-Powered Cooperative Communications. IEEE Trans Signal Process, 2015, 63: 1700-1711 CrossRef ADS arXiv Google Scholar

[10] Nasir A A, Zhou X, Durrani S. Wireless-Powered Relays in Cooperative Communications: Time-Switching Relaying Protocols and Throughput Analysis. IEEE Trans Commun, 2015, 63: 1607-1622 CrossRef Google Scholar

[11] Ishibashi K, Ochiai H, Tarokh V. Energy harvesting cooperative communications. In: Proceedings of the 23rd International Symposium on Personal, Indoor and Mobile Radio Communications, 2012. 1819--1823. Google Scholar

[12] Minasian A, ShahbazPanahi S, Adve R S. Energy Harvesting Cooperative Communication Systems. IEEE Trans Wireless Commun, 2014, 13: 6118-6131 CrossRef Google Scholar

[13] Ait Aoudia F, Gautier M, Berder O. RLMan: An Energy Manager Based on Reinforcement Learning for Energy Harvesting Wireless Sensor Networks. IEEE Trans Green Commun Netw, 2018, 2: 408-417 CrossRef Google Scholar

[14] Cong Y, Zhou X. Event-Trigger Based Robust-Optimal Control for Energy Harvesting Transmitter. IEEE Trans Wireless Commun, 2017, 16: 744-756 CrossRef Google Scholar

[15] Liu W, Zhou X, Durrani S. Energy Harvesting Wireless Sensor Networks: Delay Analysis Considering Energy Costs of Sensing and Transmission. IEEE Trans Wireless Commun, 2016, 15: 4635-4650 CrossRef Google Scholar

[16] Li T, Fan P, Chen Z. Optimum Transmission Policies for Energy Harvesting Sensor Networks Powered by a Mobile Control Center. IEEE Trans Wireless Commun, 2016, 15: 6132-6145 CrossRef Google Scholar

[17] Kang X, Ho C K, Sun S. Full-Duplex Wireless-Powered Communication Network With Energy Causality. IEEE Trans Wireless Commun, 2015, 14: 5539-5551 CrossRef Google Scholar

[18] Kravets P, Kyrkalo R. Fuzzy logic controller for embedded systems. In: Proceedings of International Conference on Perspective Technologies and Methods in MEMS Design, Ukraine, 2009. Google Scholar

[19] Jiang H, Sun Y, Sun R. Fuzzy-Logic-Based Energy Optimized Routing for Wireless Sensor Networks. Int J Distributed Sens Networks, 2013, 9: 216561 CrossRef Google Scholar

[20] Aoudia F A, Gautier M, Berder O. Fuzzy power management for energy harvesting wireless sensor nodes. In: Proceedings of International Conference on Communications, Kuala Lumpur, 2016. Google Scholar

[21] Yousaf R, Ahmad R, Ahmed W. Fuzzy Power Allocation for Opportunistic Relay in Energy Harvesting Wireless Sensor Networks. IEEE Access, 2017, 5: 17165-17176 CrossRef Google Scholar

[22] Li S, Murch R D. An Investigation Into Baseband Techniques for Single-Channel Full-Duplex Wireless Communication Systems. IEEE Trans Wireless Commun, 2014, 13: 4794-4806 CrossRef Google Scholar

[23] Wang D X, Zhang R Q, Cheng X, et al. Relay selection in two-way full-duplex energy-harvesting relay networks. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), Washington, 2016. Google Scholar

[24] Novák V, Perfilieva I, Movckovr J. Mathematical Principles of Fuzzy Logic. Dodrecht: Kluwer Academic, 1999. Google Scholar

[25] Siddique N, Adeli H. Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing. Hoboken: Wiley, 2013. Google Scholar

[26] Mamdani E H, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Machine Studies, 1975, 7: 1-13 CrossRef Google Scholar

[27] Kallenberg L. Handbook of Markov Decision Processes: Methods and Applications. Berlin: Springer, 2002. Google Scholar

[28] Mashrgy M A, Bdiri T, Bouguila N. Robust simultaneous positive data clustering and unsupervised feature selection using generalized inverted Dirichlet mixture models. Knowledge-Based Syst, 2014, 59: 182-195 CrossRef Google Scholar

[29] Littman M L, Dean T L, Kaelbling L P, et al. On the complexity of solving Markov decision problems. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Montréal, 1995. Google Scholar

[30] Suraweera H A, Smith P J, Shafi M. Capacity Limits and Performance Analysis of Cognitive Radio With Imperfect Channel Knowledge. IEEE Trans Veh Technol, 2010, 59: 1811-1822 CrossRef Google Scholar

  • Figure 1

    (Color online) Basic cooperative communications model with EH.

  • Figure 2

    (Color online) Mamdani inference method [24].

  • Figure 3

    (Color online) Flowchart of fuzzy logic controlled relay.

  • Figure 4

    (Color online) Membership functions.

  • Figure 5

    (Color online) Comparison of battery energy level over time. (a) Battery level of optimum, fuzzy I, and MDP I schemes; (b) battery level of modified IV, fuzzy IV, and MDP IV schemes.

  • Figure 6

    (Color online) Comparisons of buffer size. (a) Buffer size of fuzzy I and MDP I schemes; (b) buffer size of fuzzy IV and MDP IV schemes.

  • Figure 7

    (Color online) Comparisons of sent data over time. (a) Sent data of optimum, modified I, MDP I, and fuzzy I schemes; (b) sent data of optimum, modified IV, MDP IV, and fuzzy IV schemes.

  • Table 1   Fuzzy rules for determining transmission rate
    Energy Throughput Channel gain Rate
    LowLowHigh Low
    LowLowLow Low
    LowHigh High Low
    LowHighLow Low
    HighLowHigh High
    HighLowLow Low
    HighHighHigh High
    HighHighLow High
  • Table 2   Table of results
    Algorithm Available rate Average rate Average buffer Max buffer Buffer standard deviation
    Optimum N/A 698.1 N/A N/A N/A
    Modified I0, 250, 1000 650.8 N/A N/A N/A
    Modified II0, 250, 500, 1000 660 N/A N/A N/A
    Modified III0, 250, 1000, 1250 647 N/A N/A N/A
    Modified IV0, 250, 500, 1000, 1250 679 N/A N/A N/A
    MDP I 0, 250, 1000 646.8 58.6 1875 255
    MDP II 0, 250, 500, 1000 649.7 31.3 2000 166
    MDP III 0, 250, 1000, 1250 647.1 77.6 6100 149
    MDP IV 0, 250, 500, 1000, 1250 652.6 56.3 2125 176
    Fuzzy I 0, 250, 1000 635.4 79.9 3000 296
    Fuzzy II 0, 250, 500, 1000 639.8 35.4 1875 145
    Fuzzy III0, 250, 1000, 1250 649.8 72 5500 122
    Fuzzy IV0, 250, 500, 1000, 1250 653.5 31.8 1875 136

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1