Towards efficient real-time decision support at the edge
Kyoung‐Don Kang · 2019
In the emerging Internet of Things (IoT) paradigm, the need for real-time decision support is increasing fast to support key applications, such as smart health, disaster recovery, or battlefield monitoring. In these applications, it is essential to efficiently retrieve fresh sensor data and process them in a timely manner to aid in decision making. However, related work is relatively scarce. In this paper, we formulate the problem of real-time decision support and explore a suite of scheduling methodologies to efficiently retrieve and process sensor data for real-time decision support subject to timing and data freshness constraints at the network edge and discuss open research issues.