Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free ZigBee wireless smart plug network with RSSI multi-lateration-based proximity estimation and parallelised machine learning capabilities for demand response

© The Institution of Engineering and Technology
Received 05/03/2018, Accepted 29/06/2020, Revised 29/04/2020, Published 16/07/2020

This study explores how wireless ZigBee technology may be applied to automation of electric loads in residential and commercial spaces, allowing to participate in demand response initiatives. The authors discuss development of a custom smart plug with sensing, wireless communication, and electric load actuation capabilities along with several innovative upgrades. There are many commercially available smart plugs that contain multiple sensors and relays. However, very few provide the ability to effectively estimate the proximity between modules or the ability to perform robust system-wide optimisation. The authors propose two innovative smart plug eco-system improvements. One is the use of a received signal strength indicator (RSSI) multi-lateration-based method to estimate the relative proximities of modules. The RSSI values for almost all transmission paths within the ZigBee network are acquired via the authors' forced network reconfiguration algorithm, addressing the limitations of RSSI observation within a star structure. A second innovation is the development of a parallelised neural network training method for application to load automation. The authors use a k-means clustering algorithm to divide training data into subsets such that training may be parallelised.

Inspec keywords: learning (artificial intelligence); sensor fusion; power engineering computing; optimisation; pattern clustering; Zigbee; indoor communication; neural nets; wireless sensor networks

Other keywords: commercially available smart plugs; RSSI multilateration-based proximity estimation; load automation; innovations complement; innovative smart plug eco-system improvements; partitioned neural networks; RSSI values; multiple networks; relative proximities; demand response initiative; wireless communication; residential space; parallelised machine learning capabilities; ZigBee network; received signal strength indicator; authors; electric loads; parallelised neural network training method; custom smart plug; multiple sensors; robust system-wide optimisation; ZigBee technology; electric load actuation capabilities; commercial space; ZigBee wireless smart plug network; residential spaces; commercial spaces; RSSI observation; innovative upgrades; innovation

Subjects: Other topics in statistics; Knowledge engineering techniques; Other topics in statistics; Power engineering computing; Radio links and equipment; Wireless sensor networks

References

    1. 1)
      • 14. Ghai, B., Sharma, S., Bindalkar, G., et al: ‘Energy efficient dynamic nearest node election for localizations of mobile node in wireless sensor networks’. IEEE Int. Conf. on Computational Intelligence and Computing, Madurai, India, 2015.
    2. 2)
      • 4. Wang, S., Bi, S., Zhang, Y.-J.A.: ‘Demand response management for profit maximizing energy loads in real-time electricity market’, IEEE Trans. Power Syst., 2018, 33, (6), p. 9.
    3. 3)
      • 34. Li, C., Cai, W., Yu, C., et al: ‘Electricity consumption behaviour analysis based on adaptive weighted-feature K-means-AP clustering’, IET Gener. Transm. Distrib. J., 2019, 13, (12), p. 1200.
    4. 4)
      • 16. Guan, Z., Zhang, B., Zhang, Y., et al: ‘Delaunay triangulation-based localization scheme’. 27th Annual Chinese Control and Decision Conf. (CCDC), Chongqing, China, 2017.
    5. 5)
      • 19. Zhao, C., Wang, B.: ‘A MLE-PSO indoor localization algorithm based on RSSI’. 36th Annual Chinese Control Conf., Dalian, China, 2017.
    6. 6)
      • 25. Deese, A., Carrigan, B., Klein, E., et al: ‘Automation of residential load in power distribution systems with focus on demand response’, IET Res. J. Gener. Transm. Distrib., 2013, 7, (4), pp. 357–365.
    7. 7)
      • 10. Raju, M., Oliveira, T., Agrawal, D.: ‘A practical distance estimator through distributed RSSI/LQI processing – an experimental study’. 2012 IEEE Int. Conf. on Communications (ICC), Ottawa, Canada, 2012.
    8. 8)
      • 28. Digi-Corporation.: ‘Digi application notes on indoor path loss for XBee radios’ (Digi Corporation, New York, NY, 2018).
    9. 9)
      • 15. Al Shayokh, M., Alkasi, U., Partal, H.: ‘Performance improvement techniques for RSSI-based localization methods’. IEEE Int. Conf. on Electrical Information and Communication Technology, Khulna, Bangladesh, 2013.
    10. 10)
      • 7. Mohsenian-Rad, A.H., Wong, V., Jatskevich, J., et al: ‘Autonomous demand-side management based on game-theoretic energy consumption scheduling for future smart grids’, IEEE Trans. Smart Grid, 2010, 1, (3), pp. 320–331.
    11. 11)
      • 20. Lijina, P., Kumaar, N.: ‘Bluetooth RSSI-based collision avoidance in multi-robot environment’. IEEE Int. Conf. on Advances in Computing, Communications, and Informatics (ICACCI), Jaipur, India, 2016.
    12. 12)
      • 17. Du, Z.-G., Pan, J.-S., Chu, S.-C., et al: ‘Quasi-affine transformation evolutionary algorithm with communication schemes for application of RSSI in wireless sensor networks’, IEEE Access, 2020, 1, (1), p. 12.
    13. 13)
      • 26. Gill, K., Yang, S.-H., Yao, F.: ‘A ZigBee-based home automation system’, IEEE Trans. Smart Grid, 2009, 1, (2), pp. 422–430.
    14. 14)
      • 36. Xu, Z., Wu, Z., Xuan, H.: ‘Scaling information-theoretic text clustering: a sampling-based approximate method’. Second Int. Conf. on Advanced Cloud and Big Data, Washington DC, 2014.
    15. 15)
      • 23. Romanos, P., Hatziargyriou, N., Schmid, J., et al: ‘Single agents in smart grids’. 7th Mediterranean Conf. and Exhibition on Power Generation, Transmission, Distribution, and Energy Conversion, Athens, Greece, 2010.
    16. 16)
      • 13. Ren, J., Wang, Y., Niu, C., et al: ‘An improved indoor positioning algorithm basd on RSSI filtering’. 17th IEEE Int. Conf. on Communication Technology, Beijing, China, 2017.
    17. 17)
      • 33. Rojas, R.: ‘Neural networks: a systematic approach’ (Springer-Verlag Publishing, New York, NY USA, 1996).
    18. 18)
      • 18. Campeon, J., Lopez, S., Melean, S., et al: ‘Indoor positioning based on RSSI of WiFi signals: how accurate can it be?’. IEEE Biennial Congress of Argentina, Argentina, 2018.
    19. 19)
      • 2. Chen, X., Wang, J., Xie, J., et al: ‘Demand response potential evaluation for residential air conditioning loads’, IET Gener. Transm. Distrib. J., 2018, 12, (19), p. 9.
    20. 20)
      • 12. Luciani, D., Davis, A.: ‘RSSI-based range analysis of near-ground nodes in Wi-Fi crowded environments’. IEEE Conf. on Technologies for Homeland Security, Waltham, Mass USA., 2013.
    21. 21)
      • 21. Beaudeau, J., Bugallo, M., Djuric, P.: ‘RSSI-based multi-target tracking by cooperative agents using fusion of cross-target information’, IEEE Trans. Signal Process., 2015, 63, (19), p. 5033.
    22. 22)
      • 8. DoGru, E.: ‘Methods for minimizing error in location estimation using received signal strength’. 26th IEEE Signal Processing and Communications Applications Conf., Izmir, Turkey, 2018.
    23. 23)
      • 9. Wozniak, M., Polap, D.: ‘Intelligent home systems for ubiquitous user support by using neural networks and rule-based approach’, IEEE Trans. Ind. Inf., 2020, 16, (4), p. 854.
    24. 24)
      • 32. Huang, C.-C., Mahn, H.: ‘RSS-based indoor positioning based on multi-dimensional kernel modeling and weighted average tracking’, IEEE Sens. J., 2016, 16, (9), p. 953.
    25. 25)
      • 1. Vu, D. H., Muttaqi, K.M., Agalgaonkar, A.P., et al: ‘Customer reward-based demand response program to improve demand elasticity and minimise financial risk during price spikes’, IET Gener. Transm. Distrib. J., 2018, 12, (15), p. 12.
    26. 26)
      • 22. Valecce, G., Micoli, G., Boccadoro, P., et al: ‘Robotic-aided IoT: automated deployment of a 6TiSCH network using an UGV’, IET Wirel. Sens. Syst. J., 2019, 9, (6), p. 611.
    27. 27)
      • 3. Varghese, S., Kurian, C., George, V., et al: ‘Comparative study of ZigBee topologies for IoT-based lighting automation’, IET Wirel. Sens. Syst. J., 2019, 9, (4), p. 425.
    28. 28)
      • 30. Chen, C., Feng, K.: ‘Enhanced distance and location estimation for broadband wireless networks’, IEEE Trans. Mob. Comput., 2015, 4, (11), p. 1111.
    29. 29)
      • 6. Luo, Q., Lam, H., Wang, D., et al: ‘Towards a wireless building management system with minimum change to building protocols’. IEEE/ACM Third Int. Conf. on Cyber-Physical Systems (ICCPS), Beijing, China, 2012.
    30. 30)
      • 29. Pagano, S., Peirani, S., Valle, M.: ‘Indoor ranging and localisation algorithm based on received signal strength indicator using statistic parameters for wireless sensor networks’, IET Wirel. Sens. Syst. J., 2015, 5, (5), p. 567.
    31. 31)
      • 5. Lee, D.L., Ksu, C.L.: ‘An implementation of intelligent energy saving system’. IEEE/ACM Int. Conf. on Green Computing and Communications, Sichuan, China, 2011.
    32. 32)
      • 35. Hua, M., Lau, M. K., Pei, J., et al: ‘Continuous k-means monitoring with low reporting cost in sensor networks’, IEEE Trans. Knowl. Data Eng., 2009, 21, (12), p. 10.
    33. 33)
      • 11. Rajalakshmii, A., Netad, V., Yoshida, M., et al: ‘Centroid-based 3D localization technique using RSSI with a mobile robot’. 17th Int. Symp. on Wireless Personal Multimedia Communications (WPMC), Hyderabad, India, 2014.
    34. 34)
      • 31. Bandirmali, N., Torlak, M.: ‘BLUE-based real-time location estimation using indoor RSSI measurements’. 23rd Signal Processing and Communications Applications Conf. (SIU), Istanbul, Turkey, 2015.
    35. 35)
      • 27. Song, C., Feng, C., Wang, C., et al: ‘Simulation and experimental analysis of a ZigBee sensor network with fault detection and reconfiguration mechanism’. 8th Asian Control Conf. (ASCC), Kaohsiung, Taiwan, 2011.
    36. 36)
      • 24. Fauldi, R.: ‘Building wireless sensor networks with ZigBee, XBee, arduino, and processing’ (O'Reilly Publishing, New York, NY, 2010).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-wss.2018.5047
Loading

Related content

content/journals/10.1049/iet-wss.2018.5047
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address