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iGridEdgeDrone: Hybrid Mobility Aware Intelligent Load Forecasting by Edge Enabled Internet of Drone Things for Smart Grid Networks

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Abstract

With the growing prevalence of Internet connectivity in the civilized world, smart grid technology has become more practically relevant to implement. The smart electric grid is more than just a generation and transmission infrastructure. Modernizing such electric grids to automate the process of tracking the electricity consumption at multiple locations, while intelligently managing the supply is an exciting transformation, which offers both challenges as well as opportunities. Further, it must consider the change in prices with demand throughout the day. Advanced communication policy and intelligent sensing mechanism and decision making must be adapted to collect, monitor, and analyze real-time information, performing automatic metering, home automation, and inter-grid communication within vast geographical distance. In this paper, two crucial issues in the domain of smart grid management and communication have been addressed and the potential solution is provided. Firstly Delay Tolerant Network assisted the Internet of Drone Things based communication paradigm has been modeled for smart-grid communication in intermittent connective smart grid networks. The hybrid cluster-based 3D mobility has been engineered to that pursue the information sharing and offloading within IoT based cloud infrastructure. The routing mechanism results in 97% message delivery in 5 MB buffer size and about 8.5 × 106 J data transmission energy dissipation. In the latter half of this work, a load forecasting strategy is proposed, combining the mathematically robust gradient boosting strategies and a popular Deep Learning methodology, termed the Long Short-Term Memory approach. A hybrid architecture is developed for enhanced prediction in the presence of noise and faulty transmission of data from the physical layer. Further, the proposed model is also capable of generalization to a variegated set of data and produces forecast results with 0.167 MSE score, 7.231 MAE score, and 4.9% resource utilization which are better than the conventional frameworks on resource-constrained edge computing platforms.

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References

  1. Muralitharan, K., Rathinasamy, S., Vishnuvarthan, R.: Neural network-based optimization approach for energy demand prediction in a smart grid. Neurocomputing 273, 199–208 (2018)

    Article  Google Scholar 

  2. Mukherjee, A., Prateeti, M., Nilanjan, D., Debashis, D., Panigrahi, B.K.: Lightweight sustainable intelligent load forecasting platform for smart grid applications. Sustain. Comput. Inform. Syst. 25, 100356 (2020)

    Google Scholar 

  3. Bahuguna, Y., Punetha, D., Verma, P.: An analytic study of the key factors influencing the design and routing techniques of a wireless sensor network. Int. J. Interact. Multimed. Artif. Intell. 4, 11–15 (2017)

    Google Scholar 

  4. Takano, T.: Wireless power transfer from space to earth. IEICE Trans. Electron. 96(10), 1218–1226 (2013)

    Article  Google Scholar 

  5. Hao, R., Yang, H., Zhou, Z.: Driving behavior evaluation model base on big data from internet of vehicles. Int. J. Ambient Comput. Intell. (IJACI) 10(4), 78–95 (2019)

    Article  Google Scholar 

  6. Mukherjee, A., Dey, N., De, D.: EdgeDrone: QoS aware MQTT middleware for mobile edge computing in opportunistic Internet of Drone Things. Comput. Commun. 152, 93–108 (2020)

    Article  Google Scholar 

  7. Vimal, S., Khari, M., Crespo, R.G., Kalaivani, L., Dey, N., Kaliappan, M.: Energy enhancement using Multiobjective Ant colony optimisation with Double Q learning algorithm for IoT based cognitive radio networks. Comput. Commun. 154, 481–490 (2020)

    Article  Google Scholar 

  8. Vimal, S., Khari, M., Dey, N., Crespo, R.G., Robinson, Y.H.: Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT. Comput. Commun. 151, 355–364 (2020)

    Article  Google Scholar 

  9. Solanki, V.K., Venkaesan, M., Katiyar, S.: Conceptual model for smart cities: irrigation and highway lamps using IoT. Int. J. Interact. Multimed. Artif. Intell. 4, 28–33 (2017)

    Google Scholar 

  10. Velusamy, D., Pugalendhi, G.K.: Water cycle algorithm tuned fuzzy expert system for trusted routing in smart grid communication network. IEEE Trans. Fuzzy Syst. 28, 1167–1177 (2020)

    Article  Google Scholar 

  11. Alam, S., Aqdas, N., Qureshi, I.M., Ghauri, S.A., Sarfraz, M.: Joint power and channel allocation scheme for IEEE 802.11 af based smart grid communication network. Future Gener Comput Syst 95, 694–712 (2019)

    Article  Google Scholar 

  12. Jat, D.S., Bishnoi, L.C., Nambahu, S.: An intelligent wireless QoS technology for big data video delivery in WLAN. Int. J. Ambient Comput. Intell. (IJACI) 9(4), 1–14 (2018)

    Article  Google Scholar 

  13. Khan, M.W., Zeeshan, M.: QoS-based dynamic channel selection algorithm for cognitive radio based smart grid communication network. Ad Hoc Netw. 87, 61–75 (2019)

    Article  Google Scholar 

  14. Mukherjee, A., Dey, N., Kumar, R., Panigrahi, B.K., Hassanien, A.E., Manuel, J., Tavares, R.S.: Delay-Tolerant network assisted flying Ad-Hoc network scenario: modeling and analytical perspective. Wirel. Netw. 25(5), 2675–2695 (2019)

    Article  Google Scholar 

  15. Saha, S., Nandi, S., Paul, P.S., Shah, V.K., Roy, A., Das, S.K.: Designing delay constrained hybrid ad hoc network infrastructure for post-disaster communication. Ad Hoc Netw. 25, 406–429 (2015)

    Article  Google Scholar 

  16. Na, Z., Zhang, M., Wang, J., Gao, Z.: UAV-assisted wireless powered Internet of Things: joint trajectory optimization and resource allocation. Ad Hoc Netw. 98, 102052 (2020)

    Article  Google Scholar 

  17. Kumar, S., Hussain, L., Banarjee, S., Reza, M., Energy load forecasting using deep learning approach-LSTM and GRU in spark cluster. In: 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), Kolkata, 2018, pp. 1–4

  18. Tian, C., Ma, J., Zhang, C., Zhan, P.: A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies 11, 3493 (2018)

    Article  Google Scholar 

  19. Mohammad, F., Kim, Y.-C.: Energy load forecasting model based on deep neural networks for smart grids. Int. J. Syst. Assur. Eng. Manag. 11, 824–834 (2019)

    Article  Google Scholar 

  20. Singh, A.K., Khatoon, S., Muazzam, M., Chaturvedi, D.K., Load forecasting techniques and methodologies: a review. In: 2012 2nd International Conference on Power, Control and Embedded Systems, Allahabad, pp. 1–10 (2012)

  21. Chen, H., Lundberg, S., Lee, S.-I.: Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data. arXiv preprint arXiv:1801.07384 (2018)

  22. Vrablecová, P., Ezzeddine, A.B., Rozinajová, V., Šárik, S., Sangaiah, A.K.: Smart grid load forecasting using online support vector regression. Comput. Electr. Eng. 65, 102–117 (2018)

    Article  Google Scholar 

  23. He, C., Wang, R., Tan, Z.: Energy-aware collaborative computation offloading over mobile edge computation empowered fiber-wireless access networks. IEEE Access 8, 24662–24674 (2020)

    Article  Google Scholar 

  24. Komerath, N., Komerath, P.: Implications of inter-satellite power beaming using a space power grid. In: 2011 Aerospace Conference, pp. 1–11. IEEE, (2011)

  25. Bu, S., Yu, F.R.: A game-theoretical scheme in the smart grid with demand-side management: towards a smart cyber-physical power infrastructure. IEEE Trans. Emerg. Topi. Comput. 1(1), 22–32 (2013)

    Article  Google Scholar 

  26. Prateek, K., Arvind, N., Alaria, S.K.: MANET-evaluation of DSDV, AODV and DSR routing protocol. Int. J. Innov. Eng. Technol. 2(1), 99–104 (2013)

    Google Scholar 

  27. Rathi, D., Welekar, R.R.: Performance evaluation of AODV routing protocol in VANET with NS2. Int. J. Interact. Multimed. Artif. Intell. 4, 23–27 (2017)

    Google Scholar 

  28. Tyagi, S., Som, S., Rana, Q.P.: Trust based dynamic multicast group routing ensuring reliability for ubiquitous environment in MANETs. Int. J. Ambient Comput. Intell. (IJACI) 8(1), 70–97 (2017)

    Article  Google Scholar 

  29. Deng, J., Tirkkonen, O., Freij-Hollanti, R., Chen, T., Nikaein, N.: Resource allocation and interference management for opportunistic relaying in integrated mmWave/sub-6 GHz 5G networks. IEEE Commun. Mag. 55(6), 94–101 (2017)

    Article  Google Scholar 

  30. Ejaz, W., Anpalagan, A., Imran, M.A., Jo, M., Naeem, M., Qaisar, S.B., Wang, W.: Internet of Things (IoT) in 5G wireless communications. IEEE Access 4, 10310–10314 (2016)

    Article  Google Scholar 

  31. Esswie, A.A., Pedersen, K.I.: Opportunistic spatial preemptive scheduling for URLLC and eMBB coexistence in multi-user 5G networks. IEEE Access 6, 38451–38463 (2018)

    Article  Google Scholar 

  32. Panigrahi, B., Rath, H.K., Jagyasi, B., Simha, A.: D2D-and DTN-based efficient data offloading techniques for 5G networks. In: Resource Allocation in Next-Generation Broadband Wireless Access Networks, pp. 190–209. IGI Global (2017)

  33. Kuai, M., Hong, X., Qiangyuan, Yu.: Delay-tolerant forwarding strategy for named data networking in vehicular environment. Int. J. Ad Hoc Ubiquitous Comput. 31(1), 1–12 (2019)

    Article  Google Scholar 

  34. Harrati, Y., Abdali, A.: MaxHopCount: a new drop policy to optimize messages delivery rate in delay tolerant networks. Int. J. Interact. Multimed. Artif. Intell. 4, 37–41 (2016)

    Google Scholar 

  35. Bekmezci, I., Sahingoz, O.K.: Ş Temel (2013) Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw. 11(3), 1254–1270 (2013)

    Article  Google Scholar 

  36. Mukherjee, A., Keshary, V., Pandya, K., Dey, N., Satapathy, S.C.: Flying ad hoc networks: a comprehensive survey. In: Satapathy, S.C., Tavares, J.M.R.S., Bhateja, V., Mohanty, J.R. (eds.) Information and Decision Sciences, pp. 569–580. Springer, Singapore (2018)

    Chapter  Google Scholar 

  37. Islam, M.S., Thaky, S.I., Hossen, M.S.: Performance evaluation of delay-tolerant routing protocols on Bangladesh map. In: Pati, B., Panigrahi, C.R., Buyya, R., Li, K.-C. (eds.) Advanced Computing and Intelligent Engineering, pp. 461–471. Springer, Singapore (2020)

    Chapter  Google Scholar 

  38. Sharma, A., Singh, A.: A Contact Based Routing Protocol for High Mobility Scenario in DTN. In: 2019 Amity International Conference on Artificial Intelligence (AICAI), pp. 371–379. IEEE (2019)

  39. Sharif, H.M.: DTN routing protocols on two distinct geographical regions in an opportunistic network: an analysis. Wirel. Pers. Commun. 108(2), 839–851 (2019)

    Article  Google Scholar 

  40. Mukherjee, A., Dey, N., Kausar, N., Ashour, A.S., Taiar, R., Hassanien, A.E.: A disaster management specific mobility model for flying ad-hoc network. In: Khosrow-Pour, M. (ed.) Emergency and Disaster Management: Concepts, Methodologies, Tools, and Applications, pp. 279–311. IGI Global (2019)

  41. Khan, Z., Fan, P., Abbas, F., Chen, H., Fang, S.: Two-level cluster based routing scheme for 5G V2X communication. IEEE Access 7, 16194–16205 (2019)

    Article  Google Scholar 

  42. Senturk, I.F., Kebe, G.Y.: A novel shortest path routing algorithm for wireless data collection in transportation networks. In: 2019 4th International Conference on Computer Science and Engineering (UBMK), pp. 1–5. IEEE (2019)

  43. Acharjya, D., Anitha, A.: A comparative study of statistical and rough computing models in predictive data analysis. Int. J. Ambient Comput. Intell. (IJACI) 8(2), 32–51 (2017)

    Article  Google Scholar 

  44. Zhang, W., Qi, Q., Deng, J.: Building intelligent transportation cloud data center based on SOA. Int. J. Ambient Comput. Intell. (IJACI) 8(2), 1–11 (2017)

    Article  Google Scholar 

  45. Ziel, F.: Modeling public holidays in load forecasting: a German case study. J. Mod. Power Syst. Clean Energy 6, 191–207 (2018). https://doi.org/10.1007/s40565-018-0385-5

    Article  Google Scholar 

  46. Kumar, S., Kumar-Solanki, V., Choudhary, S.K., Selamat, A., Gonzalez-Crespo, R.: Comparative study on ant colony optimization (ACO) and K-means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT). Int. J. Interact. Multimed. Artif. Intell. 6, 107–116 (2020)

    Google Scholar 

  47. Spaho, E., Dhoska, K., Barolli, L., Kolici, V., Takizawa, M.: Enhancement of Binary Spray and Wait Routing Protocol for Improving Delivery Probability and Latency in a Delay Tolerant Network. In: International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 105–113. Springer, Cham (2019)

  48. Darroudi, S.M., Caldera-Sànchez, R., Gomez, C.: Bluetooth mesh energy consumption: a model. Sensors 19(5), 1238 (2019)

    Article  Google Scholar 

  49. Baquerizo, G., Pablo, J., Suárez, A., Macias, E., Salas, E.: Hardware mechanism for energy saving in WiFi access points. Sensors 19(21), 4745 (2019)

    Article  Google Scholar 

  50. Wang, Y., Sun, T., Rao, G., Li, D.: Formation tracking in sparse airborne networks. IEEE J. Sel. Areas Commun. 36(9), 2000–2014 (2018)

    Article  Google Scholar 

  51. Komerath, N.M., Komerath, P.P.: Terrestrial micro renewable energy applications of space technology. Phys. Procedia 20, 255–269 (2011)

    Article  Google Scholar 

  52. Okay, F.Y., Ozdemir, S.: A fog computing based smart grid model. In: 2016 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–6. IEEE (2016)

  53. McDaniel, P., McLaughlin, S.: Security and privacy challenges in the smart grid. IEEE Secur. Priv. 7(3), 75–77 (2009)

    Article  Google Scholar 

  54. Ray, P.P.: An introduction to dew computing: definition, concept and implications. IEEE Access 6, 723–737 (2017)

    Article  Google Scholar 

  55. Mengelkamp, E., Notheisen, B., Beer, C., Dauer, D., Weinhardt, C.: A blockchain-based smart grid: towards sustainable local energy markets. Comput. Sci. Res. Dev. 33(1–2), 207–214 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering and BSH, Institute of Engineering and Management, Salt Lake, Sector-5, Kolkata, West Bengal, India

    Amartya Mukherjee

  2. Department of Computer Science and Engineering, Institute of Engineering and Management, Salt Lake, Sector-5, Kolkata, West Bengal, India

    Prateeti Mukherjee

  3. Department of CSE, Moulana Abul Kalam Azad University of Technology, Kolkata, Haringhata, Nadia, West Bengal, 741249, India

    Debashis De

  4. Department of IT, Techno International New Town, Rajarhat, Kolkata, West Bengal, India

    Nilanjan Dey

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  1. Amartya Mukherjee
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Correspondence to Amartya Mukherjee.

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Mukherjee, A., Mukherjee, P., De, D. et al. iGridEdgeDrone: Hybrid Mobility Aware Intelligent Load Forecasting by Edge Enabled Internet of Drone Things for Smart Grid Networks. Int J Parallel Prog 49, 285–325 (2021). https://doi.org/10.1007/s10766-020-00675-x

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