Energy-efficient sensory data gathering in IoT networks with mobile edge computing

The I nternet o f T hings ( IoT ) networks have been adopted ubiquitously to support domain applications. Specially, social robots have become an important part of IoT networks as smart devices and are widely used to support versatile domain applications. In this setting, gathering sensory data from social-aware mobile 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 when considering 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 S ort- T ile- R ecursive ( 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.