Security and Efficiency Challenges in Cloud-Integrated Wireless Sensor Networks: A Comprehensive Review
DOI:
https://doi.org/10.29070/wcgh4r32Keywords:
Wireless Sensor Networks, Cloud Computing, Secure Architecture, Efficient Architecture, Data Security, Energy Efficiency, Cloud-Based WSNsAbstract
Environmental monitoring, healthcare, smart cities, and industrial automation are just a few of the many applications that have seen the widespread use of Wireless Sensor Networks (WSNs). Issues with scalability, security, and data management arise from sensor nodes' limited resources, including power, computing power, and storage space. One approach is to combine WSNs with cloud computing, which offers elastic computing resources, large-scale storage, and the ability to do complex data analysis. Although there are numerous advantages to this technology, cloud-based WSNs still face challenges such as complex system architecture, decreased performance, and new security risks. Ensuring the safety and efficiency of WSN architectural design in a cloud environment is the primary emphasis of this work. Secure data transmission, robust authentication, and access control are the main tenets of the suggested architecture, which aim to shield sensor data from cyber attacks and illegal access. Also tuned for increased efficiency are cloud-based communication, data aggregation, and resource management. The architecture's goals include reducing data transmission times, increasing network longevity, enhancing system performance, and protecting data integrity and secrecy. To address one of the primary challenges in merging traditional and cloud WSNs, the model incorporates design principles of both efficiency and security. Building on previous work, this study proposes a novel architectural design for cloud-based wireless sensor networks that improves their scalability and flexibility. It also paves the way for future work in this area.
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References
1. Rani, S., Sai, V., & Maheswar, R. (Eds.). (2022). IoT and WSN Based Smart Cities: A Machine Learning Perspective. Springer.
2. Nayak, B., Pani, S. K., Choudhury, T., Satpathy, S., & Mohanty, S. N. (Eds.). (2022). Wireless Sensor Networks and the Internet of Things: Future Directions and Applications. Apple Academic Press.
3. Ambika, N. (2023). IoT-based WSN security. In B. Bhushan, S. K. Sharma, R. Kumar, & I. Priyadarshini (Eds.), 5G and Beyond. Springer.
4. Mushtaq, M. U., Hong, J., Owais, M., & Danso, S. A. (2023). Enhancing security and energy efficiency in wireless sensor network routing with IoT challenges: A thorough review. LC International Journal of STEM, 4(3), 1-24. https://doi.org/10.5281/zenodo.10184917 — Review summarizing security and energy-efficient routing challenges in IoT-integrated WSNs.
5. Behera, T. M., Mohapatra, S. K., Samal, U. C., Khan, M. S., Daneshmand, M., & Gandomi, A. H. (2019). Residual energy-based cluster-head selection in WSNs for IoT applications. IEEE Internet of Things Journal, 6(3), 5132-5139. — Discusses cluster head selection based on residual energy for improved WSN lifetime.
6. Roy, P. K., & Bhattacharya, A. (2022). SDIWSN: A software-defined networking-based authentication protocol for real-time data transfer in industrial WSNs. IEEE Transactions on Network and Service Management, 19(3), 3465-3477. — Explores trust and authentication mechanisms to ensure secure data transmission in WSNs.
7. Khan, T., Shanmugavel, S., & Rajesh, A. (2016). Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm and Evolutionary Computation, 30, 1-10. — Presents a hybrid heuristic for efficient cluster head selection to improve energy performance.
8. Behera, T. M. (2022). Energy-Efficient Routing Protocols for Wireless Sensor Networks: A Survey. Electronics, 11(15), 2282. — A detailed survey of classical and bio-inspired routing protocols for energy efficiency in WSNs.
9. Bangotra, D. K. (2022). Energy-Efficient and Secure Opportunistic Routing Protocol for Wireless Sensor Networks. PMC Article. — Focuses on opportunistic routing and security in WSNs using nature-inspired optimization approaches.
10. Mahmake, N., Mathonsi, T. E., Muchenje, T., & Du Plessis, D. (2023). A hybrid algorithm to enhance wireless sensor network security on the IoT. arXiv preprint. — Proposes a lightweight security algorithm combining SPINS and IoT security to reduce power consumption and maintain performance.
11. Ameer Ahmad, I., Al-Nayar, M. M. J., & Mahmood, A. M. (2022). Investigation of energy efficient clustering algorithms in WSNs: A review. MMEP. — Reviews clustering techniques aimed at energy reduction in WSNs.
12. Samara, G., & Aljaidi, M. (2019). Efficient energy, cost reduction, and QoS based routing protocol for wireless sensor networks. arXiv preprint. — Introduces a comprehensive routing protocol focused on energy, cost, and QoS optimization.
13. Alam, A. (2022). Energy-Efficient Adaptive Routing in Heterogeneous Wireless Sensor Networks via Hybrid PSO and Dynamic Clustering. Journal — Presents hybrid swarm optimization for adaptive routing and energy efficiency (published in 2022).
14. Shah, S., Munir, A., Waheed, A., Alabrah, A., Mukred, M., Amin, F., & Salam, A. (2023). Enhancing security and efficiency in underwater wireless sensor networks: A lightweight key management framework. Symmetry, 15(8), 1484. — Though focused on underwater WSNs, provides insight into lightweight security applicable to WSN environments.
15. Ajeesh, S., & Cherian, R. (2023). A comprehensive review – energy efficient wireless sensor network. International Journal of Engineering Research & Technology (IJERT), 11(01). — Broad review of energy-efficient strategies in WSNs.
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