Flexible real-time transmission scheduling for wireless networks with non-deterministic workloads

Wireless Sensor Networks (WSNs) are increasingly used in industrial applications such as the Internet-of-Things, Smart City technologies and critical infrastructure monitoring. Industrial WSNs often operate in a cluster or star configuration. To ensure real-time and predictable performance, link access is typically managed using time-slotted superframe methods. These methods generally use static and potentially inefficient slot assignments. In this paper, we propose to dynamically readjust time slot lengths as a technique to minimize overall energy consumption. Our approach combines real-time performance guarantees with energy conservation methods through a set of dynamic modulation based adaptive packet transmission scheduling algorithms that are designed to reclaim unused slot times. To support our reclaiming method in a wireless environment we introduce a novel low-power listening technique called reverse-low-power listening (RLPL) as part of an overall Hybrid Low-Power Listening (HLPL) protocol. We evaluate our algorithms using Castalia simulator against an oracle-based approach, and show that our dynamic slot reclaiming approach, coupled with HLPL, can introduce substantial power savings without sacrificing real-time support which may be a new approach towards improving industrial wireless standards.