EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Area of Science:

• Computer Science
• Electrical Engineering
• Urban Planning

Background:

• Smart cities leverage modern technologies to enhance residents' quality of life.
• Wireless Sensor Networks (WSNs) are crucial for smart city infrastructure.
• Effective deployment of Internet of Things (IoT) sensors in WSNs is a critical network design challenge.

Purpose of the Study:

• To introduce a novel algorithm, EDTD-SC, for optimizing sensor and sink placement in WSNs for smart cities.
• To enhance coverage, connectivity, and robustness while considering budget constraints and network obstacles.
• To address the limitations of random and regular deployment strategies.

Main Methods:

• Utilizes Delaunay triangulation and k-means clustering for optimal sensor and sink localization.
• Evaluates sensor distribution considering coverage, connectivity, and robustness in the presence of obstacles.
• Applies the algorithm to real-world urban environments, including Midtown Manhattan, New York.

Main Results:

• The EDTD-SC algorithm demonstrates superior performance compared to random and regular deployment methods.
• Achieved a 29.6% improvement in area coverage and a 29.7% reduction in end-to-end delay.
• Exhibits significant resilience against network attacks.

Conclusions:

• The EDTD-SC algorithm offers an effective solution for sensor deployment in smart city WSNs.
• Optimized deployment leads to enhanced network performance, including coverage, delay, and resilience.
• This approach provides a practical framework for designing robust and efficient smart city networks.