Efficient aggregation of multiple classes of information in wireless sensor networks.

Area of Science:

• Computer Science
• Network Engineering
• Wireless Communication

Background:

• Congestion in Wireless Sensor Networks (WSNs) leads to critical issues like buffer overflow, resource waste, and data loss.
• Efficient congestion control is vital for maintaining network performance and data integrity in WSNs.

Purpose of the Study:

• To propose and evaluate the Priority-based Coverage-aware Congestion Control (PCC) algorithm for WSNs.
• To enhance event throughput, coverage fidelity, and fairness in congested WSNs.
• To generalize PCC for multi-type data collection using a novel Pricing System.

Main Methods:

• Development of the Priority-based Coverage-aware Congestion Control (PCC) algorithm.
• Implementation of a priority-distinct, fair queue scheduler for selective packet dropping.
• Generalization of PCC with a Pricing System for multi-valued data collection.
• Extensive simulation analysis to evaluate performance metrics.

Main Results:

• PCC effectively alleviates congestion, significantly improving event throughput and coverage fidelity.
• The generalized PCC with the Pricing System achieves higher throughput for high-priority packets and ensures fairness among data categories.
• The Pricing System optimizes data collection under fixed capacity by prioritizing valued information.

Conclusions:

• PCC offers an efficient solution for congestion control in WSNs, enhancing overall system performance.
• The Pricing System effectively manages multi-type data collection in congested WSNs, aligning with sink-defined data value.
• The proposed methods demonstrate improved WSN performance metrics, including throughput, fairness, and coverage fidelity.