A New Intelligent Approach for Optimizing 6LoWPAN MAC Layer Parameters

Abstract

Fairness, low latency, and high throughput with low energy consumption are desired attributes for medium access control (MAC) protocols. The IEEE 802.15.4 standard defines the MAC and physical layers standard for IPv6 over low power personal area network (6LoWPAN). When a non-appropriate parameter setting is used, the default MAC parameters generate excessive collisions, packet losses, and high latency under high traffic when a large number of 6LoWPAN nodes being deployed. A search of the literature revealed few studies that investigate the impact of optimizing these parameters to achieve high throughput with minimum latency. This paper proposes a new intelligent approach to selecting the optimal 6LoWPAN MAC layer parameters set; the introduced mechanism depends on artificial neural networks, genetic algorithm, or particles swarm optimization to select and validate the optimized MAC parameters. The obtained simulation results showed that utilizing the optimal MAC parameters improved 6LoWPAN network throughput by 52-63% and reduced the end-to-end delay by 54-65% in which the enhancement percentage depends on the number of deployed sensor nodes in the network.

Keywords

• 6LoWPAN
• artificial neural network
• genetic algorithm
• particle swarm optimization
• MAC parameters