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Image processing based data reduction technique in WVSN for smart agriculture

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Abstract

Nowadays, to improve animal well being in livestock farming application, a wireless video sensor network (WVSN) can be deployed to early detect injury and monitor animals. They are composed of small embedded video and camera motes that capture video frames periodically and send them to a specific node called a sink. Sending all the captured images to the sink consumes a lot of energy on every sensor and may cause a bottleneck at the sink level. Energy consumption and bandwidth limitation are two important challenges in WVSNs because of the limited energy resources of the nodes and the medium scarcity. In this work, we introduce two mechanisms to reduce the overall number of frames sensed and sent to the sink. The first approach is applied on each sensor node, where the FRABID algorithm, a joint data reduction, and frame rate adaptation on sensing and transmission phases mechanism is introduced. This approach reduces the number of sensed frames based on a similarity method. The aim is to adapt the number of sensed frames based on the degree of difference between two consecutive sensed frames in each period. This adaptation technique maintains the accuracy of the video while capturing frames holding new information. This approach is validated through simulations using real data-sets from video sensors (Wang et al. in: 2014 IEEE conference on computer vision and pattern recognition workshops, pp 393–400, 2014). The results show that the amount of sensed data is reduced by more than 70% compared to a recent algorithm in Christian et al. (Multimed Tools Appl 79(3):1801–1819, 2020) while guaranteeing the detection of all the critical events at the sensor node level. The second approach exploits the Spatio-temporal correlation between neighboring nodes to reduce the number of captured frames. For that purpose, Synchronization with Frame Rate Adaptation SFRA algorithm is introduced where overlapping nodes capture frames in a synchronized fashion every \(N-1\) periods, where N is the number of overlapping sensor nodes. The results show more than 90% data reduction, surpassing other techniques in the literature at the level of the number of sensed frames by 20% at least.

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  1. http://changedetection.net/

References

  1. Wang Y, Jodoin P-M, Porikli F, Konrad J, Benezeth Y, Ishwar P (2014) Cdnet 2014: an expanded change detection benchmark dataset. In: 2014 IEEE conference on computer vision and pattern recognition workshops, pp 393–400

  2. Christian S, Abdallah M, Raphaël C (2020) Energy-efficient secured data reduction technique using image difference function in wireless video sensor networks. Multimed Tools Appl 79(3):1801–1819

    Google Scholar 

  3. Ismael Waleed M, Gao Mingsheng, Al-Shargabi Asma A, Zahary Ammar (2019) An in-networking double-layered data reduction for internet of things (IoT). Sensors (Basel, Switzerland) 19(4):795

    Article  Google Scholar 

  4. Ghosal A, Halder S, Conti M (2019) Disc: a novel distributed on-demand clustering protocol for internet of multimedia things. In: 2019 28th international conference on computer communication and networks (ICCCN), pp 1–9. IEEE

  5. Akram J, Munawar HS, Kouzani AZ, Parvez MMA (2022) Using adaptive sensors for optimised target coverage in wireless sensor networks. Sensors, 22(3)

  6. Walid Osamy, AhmedM Khedr, Ahmed Salim, Ibrahim AlAli Amal, El-Sawy Ahmed A (2022) Recent studies utilizing artificial intelligence techniques for solving data collection, aggregation and dissemination challenges in wireless sensor networks: a review. Electronics 11(3):313

    Article  Google Scholar 

  7. Qin Zhenquan, Ma Can, Wang Lei, Jiaqi Xu, Bingxian Lu (2013) An overlapping clustering approach for routing in wireless sensor networks. Int J Distrib Sens Netw 9(3):867385

    Article  Google Scholar 

  8. Kumar Saurabh, Kim Hyungwon (2019) Energy efficient scheduling in wireless sensor networks for periodic data gathering. IEEE Access 7:11410–11426

    Article  Google Scholar 

  9. Maivizhi R, Yogesh P (2020) Spatial correlation based data redundancy elimination for data aggregation in wireless sensor networks. In: 2020 international conference on innovative trends in information technology (ICITIIT), pp 1–5

  10. Benzerbadj A, Bouabdellah K (2013) Redundancy and criticality based scheduling in wireless video sensor networks for monitoring critical areas. Procedia Comput Sci 21(234–241):12

    Google Scholar 

  11. Thorson JT, Arimitsu ML, Barnett LAK, Cheng W, Eisner LB, Haynie AC, Hermann AJ, Holsman K, Kimmel DG, Lomas MW et al (2021) Forecasting community reassembly using climate-linked spatio-temporal ecosystem models. Ecography 44(4):612–625

    Article  Google Scholar 

  12. Salim C, Makhoul A, Darazi R, Couturier R (2018) Kinematics based approach for data reduction in wireless video sensor networks. In: 2018 14th International conference on wireless and mobile computing, networking and communications (WiMob), pp 1–8

  13. Salim C, Mitton N (2021) Image similarity based data reduction technique in wireless video sensor networks for smart agriculture. In: AINA 2021—35th international conference on advanced information networking and applications, Toronto, Canada

  14. Salim Christian, Makhoul Abdallah, Darazi Rony, Couturier Raphael (2019) Similarity based image selection with frame rate adaptation and local event detection in wireless video sensor networks. Multimed Tools Appl 78(5):5941–5967

    Article  Google Scholar 

  15. Bou TG, Makhoul A, Demerjian J, Laiymani D (2018) A new autonomous data transmission reduction method for wireless sensors networks. In: Middle east and North Africa communications conference, Jounieh, Lebanon

  16. Monika R, Hemalatha R, Radha S (2018) Energy efficient surveillance system using WVSN with reweighted sampling in modified fast Haar wavelet transform domain. Multimed Tools Appl 77(23):30187–30203

    Article  Google Scholar 

  17. Luo Juan, Yin Luxiu, Jinyu Hu, Wang Chun, Liu Xuan, Fan Xin, Luo Haibo (2019) Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT. Futur Gener Comput Syst 97:50–60

    Article  Google Scholar 

  18. Rafi A, Ali G, Akram J et al. (2019) Efficient energy utilization in fog computing based wireless sensor networks. In: 2019 2nd international conference on computing, mathematics and engineering technologies (iCoMET), pp. 1–5. IEEE

  19. Ding Xu, Li Qun, Zhu Hongbo (2019) Energy-saving computation offloading by joint data compression and resource allocation for mobile-edge computing. IEEE Commun Lett 23(4):704–707

    Article  Google Scholar 

  20. Priyadarshini Sushree BB, Acharya BM, Das DS (2013) Redundant data elimination and optimum camera actuation in wireless multimedia sensor network (WMSN). Int J Eng Res Technol, 2(6)

  21. Abo-Zahhad M, Farrag M, Ali A, Amin O (2015) An energy consumption model for wireless sensor networks. In: 5th international conference on energy aware computing systems applications, pp 1–4

  22. Krummacker D, Fischer C, Alam K, Karrenbauer M, Melnyk S, Schotten HD, Chen P, Tang S (2020) Intra-network clock synchronization for wireless networks: from state of the art systems to an improved solution. In: 2020 IEEE 2nd international conference on computer communication and the internet (ICCCI), pp 36–44

  23. Malon T, Roman-Jimenez G, Guyot P, Chambon S, Charvillat V, Crouzil A, Péninou A, Pinquier J, Sèdes F, Sénac C (2018) Toulouse campus surveillance dataset: scenarios, soundtracks, synchronized videos with overlapping and disjoint views. In: Proceedings of the 9th ACM multimedia systems conference, pp 393–398

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Acknowledgements

This work was partially supported by a grant from CPER DATA and by LIRIMA Agrinet project.

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Authors and Affiliations

  1. Inria, Villeneuve-d’Ascq, France

    Jana Koteich & Nathalie Mitton

  2. Computer Science and Mathematics, Junia, 59000, Lille, France

    Christian Salim

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  1. Jana Koteich
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  2. Christian Salim
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Correspondence to Christian Salim.

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Koteich, J., Salim, C. & Mitton, N. Image processing based data reduction technique in WVSN for smart agriculture. Computing 105, 2675–2698 (2023). https://doi.org/10.1007/s00607-023-01198-2

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