Maximizing Lifetime of Range-Adjustable Wireless Sensor Networks: A Neighborhood-Based Estimation of Distribution...

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) are crucial for monitoring, but their lifetime is limited by sensor activity scheduling.
• Existing methods often assume fixed sensor sensing ranges, which is insufficient for modern networks with adjustable ranges.
• Adjustable sensing ranges introduce challenges in search space expansion and complex energy allocation, hindering lifetime maximization.

Purpose of the Study:

• To address the lifetime maximization problem for WSNs with range-adjustable sensors (LM-RAS).
• To propose a novel algorithm, Neighborhood-based Estimation of Distribution Algorithm (NEDA), to efficiently solve LM-RAS.
• To enhance sensor activity scheduling for prolonged WSN operational lifespan.

Main Methods:

• Developed a Neighborhood-based Estimation of Distribution Algorithm (NEDA) to recursively solve the LM-RAS problem.
• Integrated a Linear Programming (LP) model to assign activation times and maximize network lifetime based on current sensor coverage schemes.
• Employed a neighborhood sampling strategy for diverse scheme exploration and a heuristic repair strategy for efficiency improvement.

Main Results:

• NEDA effectively optimizes network lifetime by iteratively evolving coverage schemes and solving LP models.
• Experimental results demonstrate NEDA's superior performance compared to state-of-the-art approaches across various WSN scales.
• The algorithm successfully handles the complexities introduced by adjustable sensor sensing ranges.

Conclusions:

• NEDA provides an effective solution for prolonging WSN lifetime with range-adjustable sensors.
• The proposed framework is adaptable for other flexible LP problems with similar structural characteristics.
• This research advances sensor activity scheduling for more efficient and longer-lasting wireless sensor networks.