Convey intelligence to edge aggregation analytics

Abstract

In Internet of Things (IoT) environments, networks of sensors, actuators, and computing devices are responsible to locally process contextual data, reason and collaboratively support aggregation analytics tasks. We rest on the edge computing paradigm where pushing processing and inference to the edge of the IoT network allows the complexity of analytics to be distributed into many smaller and more manageable pieces and to be physically located at the source of the contextual information it needs to work on. This enables a huge amount of rich contextual data to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized cloud/back-end processing system. We propose a lightweight, distributed, predictive intelligence mechanism that supports communication efficient aggregation analytics within the edge network. Our idea is based on the capability of the edge nodes to perform sensing and locally determine (through prediction) whether to disseminate contextual data in the edge network or to locally re-construct undelivered contextual data in light of minimizing the required communication interaction at the expense of accurate analytics tasks. Based on this decision making, we eliminate data transfer at the edge of the network, thus saving network resources for sensing and receiving data, by exploiting the nature of the captured contextual data. We provide comprehensive experimental evaluation of the proposed mechanism over a real contextual dataset and show the benefits stemmed from its adoption in edge computing environments.