Reservation frame slotted ALOHA for multi-class IOT networks

Abstract

The Internet of Things (IoT) is a promising technology capable of revolutionizing our work and daily lives. ALOHA based medium access schemes are widely used in IoT applications due to their low complexity despite lower throughput figures. In this study, we aim to improve the performance of Frame Slotted Aloha (FSA) for a single hop IoT network without increasing the overall complexity. Duty cycling is a key concept for managing energy consumption of wireless networks with battery powered nodes having maximum duty cycle constraints. The goal of this study is to improve the performance of frame slotted Aloha by exploiting duty cycle patterns in these networks and using reservations in advance. We discuss the system model for a single class IoT network and study via simulations the performance of Reservation Frame Slotted Aloha (RFSA) as compared to FSA, as well as the performance implications of different system parameters related to traffic patterns. With the insight gained from this preliminary study, we next study a multi-class IoT network with nodes belonging to different classes with different duty cycle constraints. Adopting RFSA for such a network requires different schemes for allocating channel resources for each class. We propose several static and dynamic channel allocation schemes based on our traffic model and study their performance as compared to FSA. Static partitioning has better performance for low traffic loads but dynamic partitioning offers better throughput at higher traffic loads. Selection of an appropriate channel allocation scheme can vary according to the load as well as several system parameters of the network.