Wireless Sensor Networks in Edge Computing: Exploring the integration of wireless sensor networks with edge computing architectures
Keywords:
Future Directions, Edge Computing,Abstract
Wireless Sensor Networks (WSNs) have emerged as a key technology for collecting data in various fields, including environmental monitoring, healthcare, and smart cities. However, traditional WSNs face challenges in handling the massive amounts of data generated and processing them in real-time. Edge computing, with its proximity to the data source, has the potential to address these challenges by offloading data processing tasks from the cloud to the edge of the network. This paper explores the integration of WSNs with edge computing architectures, focusing on the benefits, challenges, and potential applications of this integration. We discuss the architecture of WSNs in edge computing, the role of edge nodes, data processing techniques, and security considerations. Furthermore, we present case studies and real-world examples to illustrate the practical implementation of WSNs in edge computing. Finally, we provide insights into future research directions and potential advancements in this field.
Downloads
References
Tatineni, S., and A. Katari. “Advanced AI-Driven Techniques for Integrating DevOps and MLOps: Enhancing Continuous Integration, Deployment, and Monitoring in Machine Learning Projects”. Journal of Science & Technology, vol. 2, no. 2, July 2021, pp. 68-98, https://thesciencebrigade.com/jst/article/view/243.
Shahane, Vishal. "Optimizing Cloud Resource Allocation: A Comparative Analysis of AI-Driven Techniques." Advances in Deep Learning Techniques 3.2 (2023): 23-49.
Abouelyazid, Mahmoud. "Comparative Evaluation of SORT, DeepSORT, and ByteTrack for Multiple Object Tracking in Highway Videos." International Journal of Sustainable Infrastructure for Cities and Societies 8.11 (2023): 42-52.
K. Joel Prabhod, “ASSESSING THE ROLE OF MACHINE LEARNING AND COMPUTER VISION IN IMAGE PROCESSING,” International Journal of Innovative Research in Technology, vol. 8, no. 3, pp. 195–199, Aug. 2021, [Online]. Available: https://ijirt.org/Article?manuscript=152346
Tatineni, Sumanth, and Sandeep Chinamanagonda. “Leveraging Artificial Intelligence for Predictive Analytics in DevOps: Enhancing Continuous Integration and Continuous Deployment Pipelines for Optimal Performance”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Feb. 2021, pp. 103-38, https://aimlstudies.co.uk/index.php/jaira/article/view/104.
