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BESDDFFS: Blockchain and EdgeDrone based secured data delivery for forest fire surveillance

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Abstract

Forest fires have been disastrous to civilizations since time immemorial causing damage to life and property. The human civilization has lived with a pressing need to devise a method to quickly detect and safely transmit fire alerts to minimize losses. This work presents a novel framework for Blockchain and EdgeDrone based Secured Data Delivery for Forest Fire Surveillance (BESDDFFS). A detailed three-layer edge computing architecture is proposed consisting of IoT Layer (wireless network Drones deployed in the forest), the Edge Layer (a Local Processing and Routing Station located at the edge of the IoT network) and a Cloud Server communicating continuously with the Edge Layer. The integration of an edge computing paradigm within a Blockchain setting thereby attaining enhanced levels of security and performance is a defining attribute of this work. Extensive experimentation has been undertaken at software and hardware experimental setups to gauge the efficacy of the proposed architecture. An energy-efficient prospective Leader selection algorithm is proposed as energy conservation is a crucial issue in a resource-constrained wildfire IoT environment. Moreover, an optimal and dynamic drone trajectory algorithm is also proposed to minimize energy consumption. The proposed BESFFSS architecture is compared with baseline algorithms of state-of-the-art approaches. A detailed analysis of QoS parameters reveals that the proposed system achieves up to 66% lesser delay, 36% greater throughput and a 12% greater ratio of successfully delivered packets. The proposed Leader selection (PRELS) algorithm achieves 0.5% lesser energy consumption per node and requires 8.6% lesser time to perform a fair selection of a Leader in a zone.

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References

  1. Mehta P, Gupta R, Tanwar S (2020) Blockchain envisioned UAV networks: Challenges, solutions and comparisons. Comput Commun 151. https://doi.org/10.1016/j.comcom.2020.01.023

  2. Gharibi M, Boutaba R, Waslander SL (2016) Internet of drones. IEEE Access 4:1148–1162. https://doi.org/10.1109/ACCESS.2016.2537208

    Article  Google Scholar 

  3. Demir K, Cicibaş H, Arica N (2015) Unmanned aerial vehicle domain: areas of research. Def Sci J 65:319–329. https://doi.org/10.14429/dsj.65.8631

    Article  Google Scholar 

  4. Greenwood WW, Lynch JP, Zekkos D (2019) Applications of UAVs in civil infrastructure. J Infrastruct Syst 25(2)

  5. Seo J, Duque L, Wacker JP (Jun. 2018) Field application of UAS-based bridge inspection. Transp Res Rec 2672(12):72–81

    Article  Google Scholar 

  6. Liu X, Gao L, Guang Z, Song Y (2013) A UAV allocation method for traffic surveillance in sparse road network. Journal of Highway and Transportation Research and Development (English Edition) 7(2):81–87

    Article  Google Scholar 

  7. Gu X, Abdel-Aty M, Xiang Q, Cai Q, Yuan J (Feb. 2019) Utilizing UAV video data for in-depth analysis of drivers’ crash risk at interchange merging areas. Accid Anal Prev 123:159–169

    Article  Google Scholar 

  8. Zwolenski M, Weatherill L (2014) The digital universe: Rich data and the increasing value of the Internet of Things. Austral J Telecommun Digit Econ 2(3):47

    Google Scholar 

  9. Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Internet Things J 3:1–1. https://doi.org/10.1109/JIOT.2016.2579198

    Article  Google Scholar 

  10. Mukherjee A, Dey N, De D (2020) EdgeDrone: QoS aware MQTT middleware for mobile edge computing in opportunistic Internet of Drone Things. Comput Commun 152:108. https://doi.org/10.1016/j.comcom.2020.01.039

    Article  Google Scholar 

  11. Doerr SH, Santı’n C. (2016) Global trends in wildfire and its impacts: perceptions versus realities in a changing world. Philos Trans R Soc B 371:20150345. https://doi.org/10.1098/rstb.2015.0345

    Article  Google Scholar 

  12. Hao WM, Ward DW, Olbu G, Baker SP (1996) Emissions of CO2, CO, and hydrocarbons from fires in diverse African savanna ecosystems. J Geophys Res 101:23577–23584

    Article  Google Scholar 

  13. Fearnside PM (2000) Climate Change 46:115–158

    Article  Google Scholar 

  14. Crutzen PJ, Andreae MO (1990) Biomass burning in the tropics: impact on atmospheric chemistry and biogeochemical cycles. Science 250:1669–1678

    Article  Google Scholar 

  15. FSI, State of the Forest Report, Forest Survey of India, Ministry of Environment and Forests, GoI, 2019

  16. Satendra, Kaushik AD (2014) Forest fire Diaster management. National Institute of disaster management. Ministry of Home Affairs, New Delhi

  17. Hefeeda M, Bagheri M Forest fire modeling and early detection using wireless sensor networks. Ad Hoc & Sensor Wireless Networks 7:169–224 https://www.cs.sfu.ca/~mhefeeda/Papers/ahswn09a.pdf

  18. Nakamoto S (2009) Bitcoin: A peer-to-peer electronic cash system, Cryptography Mailing list at https://metzdowd.com, 03

  19. Chanl R (2018) Blockchain data structure, https://www.linkedin.com/pulse/Blockchain-data-structure-ronald-chan

  20. Tanwar S, Bhatia Q, Patel P, Kumari A, Singh PK, Hong W (2020) Machine learning adoption in Blockchain-based smart applications: the challenges and a way forward. IEEE Access 8:474–488. https://doi.org/10.1109/ACCESS.2019.2961372

    Article  Google Scholar 

  21. Tanwar S, Parekh K, Evans R (2020) Blockchain-based electronic healthcare record system for healthcare 4.0 applications. J Inf Secur Appl 50:102407

    Google Scholar 

  22. Bodkhe U, Bhattacharya P, Tanwar S, Tyagi S, Kumar N, Obaidat MS (2019) Blohost: Blockchain enabled smart tourism and hospitality management, in: 2019 International Conference on Computer, Information and Telecommunication Systems, CITS, pp. 1–5, https://doi.org/10.1109/CITS.2019.8862001

  23. Singh SK, Rathore S, Park JH (2019) Blockiotintelligence: A Blockchain-enabled intelligent IoT architecture with artificial intelligence. Futur Gener Comput Syst

  24. Al-Jaroodi J, Mohamed N (2020) Blockchain in industries: A survey. IEEE Access 7:36500–36515

    Article  Google Scholar 

  25. García-Magariño I, Lacuesta R, Rajarajan M, Lloret J (2018) Security in networks of unmanned aerial vehicles for surveillance with an agent-based approach inspired by the principles of blockchain. Ad Hoc Netw 86:86–82. https://doi.org/10.1016/j.adhoc.2018.11.010

    Article  Google Scholar 

  26. Ferrag MA, Maglaras L (2019) DeliveryCoin: An IDS and Blockchain-based delivery framework for drone-delivered services. 8:58. https://doi.org/10.3390/computers8030058

  27. Liang X, Zhao J, Shetty S, Li D (2017) Towards data assurance and resilience in IoT using blockchain. https://doi.org/10.1109/MILCOM.2017.8170858

  28. Kuzmin A, Znak E (2018) Blockchain-base structures for a secure and operate network of semi-autonomous. Unmanned Aerial Vehicles:32–37. https://doi.org/10.1109/SOLI.2018.8476785

  29. Barka E, Kerrache C, Benkraouda H, Shuaib K, Ahmad F, Kurugollu F (2019) Towards a trusted unmanned aerial system using Blockchain for the protection of critical infrastructure. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.3706

  30. Ge C, Ma X, Liu Z, Xia J (2020) A semi-autonomous distributed blockchain-based framework for UAVs system. J Syst Archit 107:101728. https://doi.org/10.1016/j.sysarc.2020.101728

    Article  Google Scholar 

  31. Islam Abhi A, Shin S (2019) BUS: a blockchain-enabled data acquisition scheme with the assistance of UAV swarm in internet of things. IEEE Access. 7. 103231–103249. https://doi.org/10.1109/ACCESS.2019.2930774

  32. Jensen I, Selvaraj D, Prakash R (2019) Blockchain technology for networked swarms of unmanned aerial vehicles (UAVs). 1–7. https://doi.org/10.1109/WoWMoM.2019.8793027

  33. Rana T, Shankar A, Sultan M, Patan R, Balamurugan B (2019) An intelligent approach for UAV and drone privacy security using Blockchain Methodology. 162–167. https://doi.org/10.1109/CONFLUENCE.2019.8776613

  34. Aggarwal S, Shojafar M, Kumar N, Conti M (2019) A new secure data dissemination model in internet of drones. https://doi.org/10.1109/ICC.2019.8761372

  35. Al-Kaff A, Madridano Á, Campos S, Garcia F, Martín Gómez D, de la Escalera A (2020) Emergency support unmanned aerial vehicle for Forest fire surveillance. Electronics 9:260. https://doi.org/10.3390/electronics9020260

    Article  Google Scholar 

  36. Afghah F, Razi A, Chakareski J, Ashdown J (2019) Wildfire monitoring in remote areas using autonomous unmanned aerial vehicles

  37. Sengan S, Varadarajan V, Kumar C, Priya V, Logesh R, Subramaniyaswamy V (2019) Unmanned Aerial Vehicle (UAV) based forest fire detection and monitoring for reducing false alarms in forest-fires. Comput Commun 149. https://doi.org/10.1016/j.comcom.2019.10.007

  38. Alexandrov D, Pertseva E, Berman I, Pantiukhin I, Kapitonov A (2019) Analysis of machine learning methods for wildfire security monitoring with an unmanned aerial vehicles. 3–9. https://doi.org/10.23919/FRUCT.2019.8711917

  39. Rajeshwari S (2019). Effective forest fire detection system using visual images and unmanned aerial vehicle ijariie.2019.05.06

  40. Sherstjuk V, Zharikova M, Sokol I (2018). Forest fire monitoring system based on UAV team, remote sensing and image processing. 590–594. https://doi.org/10.1109/DSMP.2018.8478590

  41. Chamoso P, González-Briones A, Prieta FD, Corchado J (2018) Computer vision system for fire detection and report using UAVs. RSFF

  42. Wardihani E, Ramdhani M, Suharjono A, Setyawan TA, Hidayat SS, Helmy, Widodo, Sarono, Triyono E, Saifullah F (2018) Real-time forest fire monitoring system using unmanned aerial vehicle. J Eng Sci Technol 13:1587–1594

    Google Scholar 

  43. Yuan C, Liu Z, Zhang Y (2017) Aerial images-based forest fire detection for firefighting using optical remote sensing techniques and unmanned aerial vehicles. J Intell Robot Syst 88:88–654. https://doi.org/10.1007/s10846-016-0464-7

    Article  Google Scholar 

  44. EA, Yfantis (2017). An autonomous UAS with AI for Forest fire prevention, detection and real time advice and communication to and among firefighters J Comput Sci Appl Inform Technol. 2. 1–5. https://doi.org/10.15226/2474-9257/2/3/00120

  45. Khan N, Brohi S, Zaman N (2020). UAV’s applications, architecture, security issues and attack Scenarios: a survey. https://doi.org/10.1007/978-981-15-3284-9_86.

  46. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified. Real-Time Object Detection:779–788. https://doi.org/10.1109/CVPR.2016.91

  47. Pimont F, Dupuy J-L, Linn R (2012) Coupled slope and wind effects on fire spread with influences of fire size: A numerical study using FIRETEC. Int J Wildland Fire 21:828. https://doi.org/10.1071/WF11122

    Article  Google Scholar 

  48. Cruz M, Alexander M (2019) The 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread in forests and shrublands. Ann For Sci 76:44. https://doi.org/10.1007/s13595-019-0829-8

    Article  Google Scholar 

  49. Rothermel RC (1972). A mathematical model for predicting fire spread in wildland fuels. Res. Pap. INT-115. Ogden, UT: U.S. Department of Agriculture, Intermountain Forest and Range Experiment Station. 40 p

  50. Perdana RC, Wibowo FW (2016) Quality of service for XBee in implementation of wireless sensor network. Res J Appl Sci 11:692–697

    Google Scholar 

  51. Moridi MA, Kawamura Y, Sharifzadeh M, Chanda EK, Wagner M, Okawa H (2018) Performance analysis of ZigBee network topologies for underground space monitoring and communication systems. Tunn Undergr Space Technol 71:201–209

    Article  Google Scholar 

  52. Silva M, Souza E, Alsina P, Leite D, Morais M, Pereira D, Nascimento L, Medeiros A, Junior F, Nogueira M, Albuquerque G, Dantas J (2019) Performance evaluation of multi-UAV network applied to scanning rocket impact area. Sensors. 19:4895. https://doi.org/10.3390/s19224895

    Article  Google Scholar 

  53. Wheeb A, Morad A, Al-Tameemi M (2018) Performance evaluation of transport protocols for mobile. Ad Hoc Netw 13:5181–5185. https://doi.org/10.3923/jeasci.2018.5181.5185

    Article  Google Scholar 

  54. Horani M, Hasna MO (2018) Latency analysis of UAV based communication networks. 385–390. https://doi.org/10.1109/ICTC.2018.8539626

  55. Fan X, Huang C, Fu B, Wen S, Chen X (2018) UAV-assisted data dissemination in delay-constrained VANETs. Mob Inf Syst 2018:1–12. https://doi.org/10.1155/2018/8548301

    Article  Google Scholar 

  56. Poudel S, Moh S (2020) Energy-efficient and fast MAC protocol in UAV-aided wireless sensor networks for time-critical applications. Sensors. 20. https://doi.org/10.3390/s20092635

  57. Wardihani E, Ramdhani M, Suharjono A, Setyawan TA, Hidayat SS, Helmy, Widodo S, Triyono E, Saifullah F (2018) Real-time forest fire monitoring system using unmanned aerial vehicle. Journal of Engineering Science and Technology 13:1587–1594

    Google Scholar 

  58. Khan N, Zaman N, Brohi S, Usmani RSA, Nayyar A (2020) Smart traffic monitoring system using Unmanned Aerial Vehicles (UAVs). Comput Commun 157:434–443. https://doi.org/10.1016/j.comcom.2020.04.049

    Article  Google Scholar 

  59. Shi N, Liang T, Li W, Qi X, Yu K (2020) A blockchain-empowered AAA scheme in the large-scale HetNet. Digital Communications and Networks. https://doi.org/10.1016/j.dcan.2020.10.002

  60. Tan L, Xiao H, Yu K, Aloqaily M, Jararweh Y (2021) A Blockchain-empowered crowdsourcing system for 5G-enabled smart cities. Computer Standards & Interfaces 76:103517. https://doi.org/10.1016/j.csi.2021.103517

    Article  Google Scholar 

  61. Feng C, Yu K, Bashir A, AI-Otaibi Y, Lu Y, Chen S, Zhang D (2020) Efficient and secure data sharing for 5G flying drones: a blockchain-enabled approach. IEEE Netw 35. https://doi.org/10.1109/MNET.011.2000223

  62. Zhen L, Bashir A, Yu K, Al-Otaibi Y, Foh C, Xiao P (2020) Energy-efficient random access for LEO satellite-assisted 6G internet of remote things. IEEE Internet Things J:1–1. https://doi.org/10.1109/JIOT.2020.3030856

  63. Zhang J, Yu K, Wen Z, Qi X, Paul A (2021) 3D reconstruction for motion blurred images using deep learning-based intelligent systems. Computers, Materials & Continua 66:2087–2104. https://doi.org/10.32604/cmc.2020.014220

    Article  Google Scholar 

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Acknowledgments

This research is funded in parts by DST-SERB project ECR/2017/000983 grants. The authors would like to thank DST-SERB for this support.

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  1. Department of Computer Science and Engineering, National Institute of Technology, Patna, Bihar, 800005, India

    Sreemana Datta & Ditipriya Sinha

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  1. Sreemana Datta
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Correspondence to Sreemana Datta.

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This article is part of the Topical Collection: Special Issue on Blockchain for Peer-to-Peer Computing

Guest Editors: Keping Yu, Chunming Rong, Yang Cao, and Wenjuan Li

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Datta, S., Sinha, D. BESDDFFS: Blockchain and EdgeDrone based secured data delivery for forest fire surveillance. Peer-to-Peer Netw. Appl. 14, 3688–3717 (2021). https://doi.org/10.1007/s12083-021-01187-2

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