DaaS: Towards Energy-Efficient Data Collection Optimization for Data as a Service in IoT networks

Abstract

The Internet of Things (IoT) networks have been adopted ubiquitously to support domain applications. Specially, social robot devices have become an important part of IoT networks as smart devices, where gathering sensory Data functionality can be encapsulated as a IoT Services (denote DaaS), as a foundation for supporting widely domain applications. In this setting, gathering sensory data from service-aware social robots in an energy-efficient manner is of importance for prolonging the network lifetime and promoting proper decision-making. Considering the large-scale and spatial-temporal evolutional characteristic of IoT networks, social robots roam over time to deal with tasks, especially considering the fact that it may hardly be predicted for the regions and time durations that certain anomalies may occur. Therefore, this paper proposes to adopt mobile edge computing to support sensory data gathering. Edge nodes in edge networks gather sensory data from their subordinating social robots in a periodic manner. We design an edge network division method by constructing an improved Sort-Tile-Recursive (STR) tree, which can cluster the edge nodes and decrease unnecessary energy consumption. Experimental results show that our technique is more efficient than traditional ones in decreasing energy consumption.