Design of Integrated Artificial Intelligence Techniques for Video Surveillance on IoT Enabled Wireless Multimedia Sensor Networks.

Romany F. Mansour; Carlos Soto; Roosvel Soto Díaz; José Escorcia Gutiérrez; Deepak Gupta; Ashish Khanna

doi:10.9781/ijimai.2022.08.005

Authors

Romany F. Mansour New Valley University
Carlos Soto Universidad Autónoma del Caribe
Roosvel Soto Díaz Universidad Simón Bolívar
José Escorcia Gutiérrez Universidad Autónoma del Caribe
Deepak Gupta Maharaja Agrasen University
Ashish Khanna Maharaja Agrasen University

DOI:

https://doi.org/10.9781/ijimai.2022.08.005

Keywords:

Artificial Intelligence, Technological Surveillance, Internet of things, Object Detection, Wireless Sensor Networks

Abstract

The recent advancements in the Internet of Things (IoT) and Wireless Multimedia Sensor Networks (WMSN) made high-speed multimedia streaming, data processing, and essential analytics processes with minimal delay. Multimedia sensors used in WMSN-based surveillance applications are beneficial helpful in attaining accurate and elaborate details. However, it has become essential to design an effective and lightweight solution for data traffic management in WMSN owing to the massive quantities of data, generated by multimedia sensors. The development of Artificial Intelligence (AI) and Machine Learning (ML) techniques can be leveraged to investigate, collect, store, and process multimedia streaming data for decision-making in real-time scenarios. In this aspect, the current study develops an Integrated AI technique for Video Surveillance in IoT-enabled WMSN, called IAIVS-WMSN. The proposed IAIVS-WMSN technique aims to design a practical scheme for object detection and data transmission in WMSN. The proposed IAIVS-WMSN approach encompasses three stages: object detection, image compression, and clustering. The Mask Regional Convolutional Neural Network (Mask RCNN) technique is primarily utilized for object detection in the target region. Besides, Neighbourhood Correlation Sequence-based Image Compression (NCSIC) technique is applied to reduce data transmission. Finally, Artificial Flora Algorithm (AFA)-based clustering technique is designed for the election of Cluster Heads (CHs) and construction clusters. The design of object detection with compression and clustering techniques for WMSN shows the novelty of the work. These three processes’ designs enable one to accomplish effective data transmission in IoT-enabled WMSN. The researchers conducted multiple simulations to highlight the supreme performance of the IAIVS-WMSN approach. The simulation outcomes inferred the enhanced performance of the IAIVS-WMSN algorithm to the existing approaches.

Downloads

Download data is not yet available.

References

I. F. Akyildiz, T. Melodia, and K. R. Chowdhury, “A survey on wireless multimedia sensor networks,” Computer networks, vol. 51, no. 4, pp. 921–960, 2007.

W. Fang, D. H. Wang, and Y. Wang, “Energy-efficient distributed target tracking in wireless video sensor networks,” International Journal of Wireless Information Networks, pp. 1–11, 2015.

M. Koyuncu, A. Yazici, M. Civelek, A. Cosar and M. Sert, “Visual and auditory data fusion for energy-efficient and improved object recognition in wireless multimedia sensor networks,” IEEE Sensors Journal, 19(5), pp.1839-1849, 2018.

H. ZainEldin, M. A. Elhosseini and H. A. Ali, “Image compression algorithms in wireless multimedia sensor networks: A survey,” Ain Shams Engineering Journal, vol. 6, pp. 481–490, 2015.

Q. Lu, W. Luo, J. Wanga and B. Chen, “Low-complexity and energy efficient image compression scheme for wireless sensor networks,” Computer Networks, vol. 52, pp. 2594–2603, 2008.

Donoho DL. Compressed sensing. IEEE Trans Inform Theory 2006; 52:1289–306.

T. Sheltami, M. Musaddiq and E. Shakshuki, “Data compression techniques in Wireless Sensor Networks,” Future Generation Computer Systems, vol. 64, pp. 151-162, 2016.

D. M. Pham and S. M. Aziz, “Object extraction scheme and protocol for energy efficient image communication over wireless sensor networks,” Computer Networks, vol. 57, pp. 2949–2960, 2013.

M. Civelek and A. Yazici, “Automated moving object classification in wireless multimedia sensor networks,” IEEE Sensors Journal, vol. 17, pp. 1116-1131, 2017.

Y.A.U. Rehman, M. Tariq and T. Sato, “A novel energy efficient object detection and image transmission approach for wireless multimedia sensor networks,” IEEE Sensors Journal, 16(15), pp.5942-5949, 2016.

Q. Guo, “Enhanced compressed sensing for visual target tracking in wireless visual sensor networks,” Journal of Electronic Imaging, 26(6), p.063028.

M. Koyuncu, A. Yazici, M. Civelek, A. Cosar and M. Sert, “Visual and auditory data fusion for energy-efficient and improved object recognition in wireless multimedia sensor networks,” IEEE Sensors Journal, 19(5), pp.1839-1849, 2018.

A.N. Sukumaran, R. Sankararajan, and M. Swaminathan, “Compressed sensing based foreground detection vector for object detection in wireless visual sensor networks,” AEU-International Journal of Electronics and Communications, 72, pp.216-224, 2017.

D. Wang, J. Liu, D. Yao and I.E.E.E. Member, “An energy-efficient distributed adaptive cooperative routing based on reinforcement learning in wireless multimedia sensor networks,” Computer Networks, 178, p. 107313.

M. Akter, M.O. Rahman, M.N. Islam, M.M. Hassan, A. Alsanad, and A.K. Sangaiah, “Energy-efficient tracking and localization of objects in wireless sensor networks,” IEEE Access, 6, pp.17165-17177.

Y. Shao, “Wireless multimedia sensor network video object detection method using dynamic clustering algorithm,” Multimedia Tools and Applications, 79(23), pp.16927-16940.

P. Sathyaprakash, and P. Prakasam, “Boltzmann randomized clustering algorithm for providing quality of evolution in wireless multimedia sensor networks,” Wireless Personal Communications, 112(4), pp.2335-2349.

S. Heng, Aimtongkham, P., Vo, V.N., Nguyen, T.G. and So-In, C., 2020. Fuzzy Adaptive-Sampling Block Compressed Sensing for Wireless Multimedia Sensor Networks. Sensors, 20(21), p. 6217.

B. A. Y. Alqaralleh, S. N. Mohanty, D. Gupta, A. Khanna, K. Shankar and T. Vaiyapuri, “Reliable Multi-Object Tracking Model Using Deep Learning and Energy Efficient Wireless Multimedia Sensor Networks,” in IEEE Access, vol. 8, pp. 213426-213436, 2020, doi: 10.1109/ACCESS.2020.3039695.

C. Xu, G. Wang, S. Yan, J. Yu, B. Zhang, S. Dai, Y. Li and L. Xu, “Fast Vehicle and Pedestrian Detection Using Improved Mask R-CNN,” Mathematical Problems in Engineering, 2020.

X. Wu, H. Shao, S. Wang and W. Wang, “A Multi-objective Artificial Flora Optimization Algorithm,” In International Conference on Simulation Tools and Techniques (pp. 267-277). Springer, Cham.

J. Uthayakumar, M. Elhoseny and K. Shankar, “Highly reliable and low-complexity image compression scheme using neighborhood correlation sequence algorithm in WSN,” IEEE Transactions on Reliability, 69(4), pp.1398-1423, Dec. 2020.