Delay-Aware Reverse Approach for Data Aggregation Scheduling in Wireless Sensor Networks

Area of Science:

• Computer Science
• Network Engineering
• Wireless Communication

Background:

• Time-sensitive applications demand low-latency data aggregation in wireless sensor networks.
• The minimum latency aggregation scheduling problem is NP-hard, hindering optimal solutions.
• Existing methods often use local information, leading to suboptimal schedules.

Purpose of the Study:

• To develop an efficient algorithm for minimum latency data aggregation scheduling in wireless sensor networks.
• To address the limitations of existing scheduling approaches that rely on local network information.
• To minimize overall data transmission delay in wireless sensor networks.

Main Methods:

• Proposing RADAS: a delay-aware Reverse Approach for Data Aggregation Scheduling.
• Determining sensor transmission sequences in reverse order, from the last time slot to the first.
• Maximizing concurrent transmissions per time slot, prioritizing senders with higher potential aggregation delays.

Main Results:

• RADAS demonstrates superior performance compared to state-of-the-art schemes.
• The algorithm achieves up to 30% delay reduction, particularly in large and dense networks.
• Prioritizing high-delay senders optimizes subsequent scheduling and shortens overall schedule length.

Conclusions:

• RADAS offers an effective solution for the minimum latency aggregation scheduling problem in wireless sensor networks.
• The reverse scheduling approach and delay-aware prioritization are key to its performance gains.
• The algorithm is particularly beneficial for large-scale and densely deployed wireless sensor networks.