An Energy-Saving and Efficient Deployment Strategy for Heterogeneous Wireless Sensor Networks Based on Improved...

Area of Science:

• Environmental Science
• Computer Science
• Engineering

Background:

• The Internet of Things (IoT) offers convenient data acquisition for environmental monitoring, but initial sensor deployment often leads to coverage issues.
• Traditional data acquisition methods can cause invasive damage, highlighting the need for efficient wireless sensor network (WSN) deployment.

Purpose of the Study:

• To address coverage blind zones and redundancy in heterogeneous IoT sensor networks.
• To propose an adaptive cooperative optimization seagull algorithm (PSO-SOA) for optimal sensor node deployment.

Main Methods:

• Developed an adaptive cooperative optimization seagull algorithm (PSO-SOA) incorporating a scaling factor and random opposite learning.
• Calculated individual fitness based on node count, coverage radius, and area dimensions to maximize coverage rate.
• Optimized sensor node positions through iterative updates and fine-tuning to improve exploration, exploitation, and global optimum seeking.

Main Results:

• The PSO-SOA algorithm achieved 6.1%, 4.8%, and 1.2% higher coverage than PSO, GWO, and basic SOA algorithms, respectively.
• Network energy consumption was reduced by 86.8%, 68.4%, and 52.6% compared to PSO, GWO, and basic SOA.
• The proposed method effectively avoids coverage blind zones and redundancy in heterogeneous sensor networks.

Conclusions:

• The adaptive cooperative optimization seagull algorithm provides an effective optimal deployment strategy for heterogeneous sensor networks in IoT.
• This approach significantly enhances network coverage and reduces operational costs while improving data acquisition efficiency.
• The PSO-SOA algorithm demonstrates superior performance in overcoming limitations of random sensor deployment.