Seque: Lean and Energy-aware Data Management for IoT Gateways

Abstract

IoT systems with multiple deployed sensor nodes often use gateways to gather, fuse, transform and transmit diverse data acquired from the sensor nodes, e.g., to a cloud server. When being deployed in remote environments, not only the memory and storage, but also energy can be scarce and supply be time-dependent and often unpredictable, e.g. when obtained by energy harvesting. In this realm, this paper proposes a lean and energy-aware methodology called Seque for data management for such gateways. Rather than processing multiple sensor requests at a time and being unconscious of the level of available energy, Seque schedules only one request at a time. Moreover, Seque dynamically decides whether to directly process and transmit data of a request to a cloud server, or alternatively compress and persist data locally on the gateway in expectation of a power failure to postpone the upload to time of recovery from a power shortage. With this scheduling technique, a guarantee can be given that no sensor request admitted will suffer from partial or full loss of data. A reference implementation of Seque is provided with scheduling decisions being calibrated based on energy models of sensor interfaces, CPU system and upload interfaces of a real embedded gateway platform. Presented analysis on whether energy can be sent most by selective compression of data. Finally, the lightweight approach is evaluated in terms of energy consumption, and storage footprint and compared with commercially available database management systems including MongoDB and SQLite. The evaluation shows that Seque provides on average between 51% and 63% lower energy consumption for different data schemas per sensor request and also between 63% and 78% of lower storage requirements, pronouncing its leanness.