Internet of Things (IoT) is expanding at a rapid rate where it allows for virtually endless opportunities and connections to take place. In general, IoT opens the door to a myriad of applications but also to many challenges. One of the major challenges is how to efficiently retrieve the sensory data from “resources-limited” IoT devices. Such devices typically have a restricted energy budget, which broadly hinders their direct connection to the Internet. In this realm, modern mobile devices, e.g. smartphones, tablets, smartwatches, have been harnessed to bridge between the low-power IoT devices and the Internet. However, the current vision which mainly relies on designing siloed gateways, i.e. a separate gateway/App for each IoT device, is certainly impractical, especially with the rapid growth in the number of IoT devices. Furthermore, energy efficiency of the smart mobile devices hosting the IoT gateways has to be thoroughly considered.

To tackle these challenges, we introduce GaaS (Gateway as a Service), a cross-platform gateway architecture for opportunistically retrieving sensory data from the low-power IoT sensors. Through Bluetooth low energy radios, GaaS is capable of simultaneously connecting to several nearby IoT sensors. To this end, we devise two distinct priority-based scheduling algorithms, namely the EP-WSM and FEP-AHP schedulers, which rank the detected IoT sensors, before estimating the connection time for each IoT sensor. The intuition behind ranking the IoT sensors is to improve the data retrieval rate from these sensors together with reducing the energy overhead on the mobile devices. Additionally, GaaS encompasses a self-adaptive engine to automatically balance the trade-off between energy efficiency and data retrieval rate through switching between schedulers according to the runtime dynamics. To demonstrate the effectiveness of GaaS, we implemented an IoT testbed to evaluate the energy consumption, the latency, and the data retrieval rate. The results show that using GaaS, compared to siloed gateways, we can identify up to 18% savings in the consumed energy while requiring much less data retrieval time.