HDS: Heterogeneity-aware dual-interface scheduling for energy-efficient delay-constrained data collection in IoT

Abstract

With the advancements in low-power and miniature electronics, various smart devices are deployed and interconnected as the Internet of Things (IoT), collecting a massive amount of data from surrounding environments. Despite the popularity of ZigBee for low-power communications in IoT, WiFi has recently been recommended for data collection in IoT for its high data rate, high reliability, native IP compatibility, and vastly-deployed infrastructures. However, it is well known that WiFi is energy-consuming. Although many schemes have been designed to reduce WiFi energy consumption, they usually suffer from the dilemma that a longer (shorter) sleep of WiFi gives a lower (higher) energy consumption but a larger (smaller) latency, hindering the use of WiFi in a wide range of IoT applications that require a certain level of quality of service (QoS). To this end, we propose a Heterogeneity-aware Dual-interface Scheduling (HDS) scheme to fully exploit the heterogeneity between ZigBee and WiFi to realize energy-efficient and delay-constrained data collection in a tree-based IoT network, where each device is equipped with a ZigBee and a WiFi interface. The low-power feature of ZigBee is utilized as much as possible for high energy efficiency, while the high-reliability advantage of WiFi is leveraged when the ZigBee link quality is low for delay guarantee. Under network dynamics, HDS jointly allocates ZigBee and WiFi schedules to strike a balance between energy and delay for optimized performance. A prototype system is built atop an IoT platform integrated with commercial off-the-shelf ZigBee and WiFi modules. Experiment results show that the energy consumption of HDS is 80.3% and 43.6% lower than the standard power saving protocol and a state-of-the-art dual-interface scheme, respectively, under a moderate delay constraint. Additionally, the percentage of data packets that satisfy the delay constraint is above 98.6%.