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A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems.

Issam Al-Nader¹, Aboubaker Lasebae¹, Rand Raheem¹

¹Faculty of Science & Technology, Department of Computer Science, Middlesex University, The Burroughs, London NW4 4BT, UK.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

This summary is machine-generated.

A new bat algorithm optimizes wireless sensor networks (WSNs) for coverage, connectivity, and lifetime. This bio-inspired approach significantly enhances network performance compared to existing methods.

Keywords:

IoT sensor system QoS in WSNs WSN dependable WSN real-time systems scheduling algorithms smart sensing for safety

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Area of Science:

Computer Science
Electrical Engineering
Network Optimization

Background:

Multi-objective optimization (MOO) in wireless sensor networks (WSNs) is crucial for balancing coverage, connectivity, and network lifetime.
Existing algorithms often address only one or two MOO dimensions, with recent advancements like Hidden Markov Model (HMM)-based algorithms showing improvement but potentially limited by local optima.

Purpose of the Study:

To introduce a novel scheduling algorithm for WSNs utilizing bio-inspired computation, specifically the bat algorithm.
To overcome the limitations of previous algorithms by exploring the full search space for optimal sleep and wake-up schedules.
To enhance network lifetime, coverage, and connectivity in safety-critical WSNs.

Main Methods:

Developed a bat algorithm-based scheduling approach defining fitness and objective functions over the entire search space of node schedules.
Employed the Pareto sorting algorithm to organize the scheduling solution space based on node distance to the base station and residual energy.
Implemented and compared the bat algorithm with the HMM algorithm in MATLAB for performance evaluation.

Main Results:

The bat algorithm demonstrated significant improvements: 30% increase in network lifetime, 40% enhancement in coverage, and 26.7% improvement in connectivity.
These results indicate superior performance over the HMM-based scheduling algorithm.
The bat algorithm provides a globally optimized solution for WSN scheduling.

Conclusions:

The proposed bat algorithm offers a superior method for multi-objective optimization in WSNs.
This approach effectively balances and enhances network lifetime, coverage, and connectivity.
The bat algorithm is a promising solution for ensuring the dependability of safety-critical WSNs.