LAUSR

Edge computing promises to facilitate the collaboration of smart sensors at the network edge, in order to satisfy the delay constraints of certain requests, and decrease the transmission of large-volume sensory data from the edge to the cloud. Generally, the functionalities provided by smart sensors are encapsulated as services, and the satisfaction of certain requests is reduced to the composition of services configured upon smart sensors in edge networks. Considering the dynamics and nonpredictability of incoming requests, an adaptive and online service configuration mechanism is essential, especially when various temporal constraints are prescribed by requests and satisfied by configured services. In this article, we formulate this problem in terms of a continuous-time Markov decision process model based on the state–action–reward mechanism. A temporal-difference learning approach is developed to optimize the service configuration while taking long-term delay sensitivity and energy efficiency into consideration. Extensive experiments are conducted, and evaluation results show that our approach outperforms the state-of-art's techniques for achieving close-to-optimal service configuration, and improving the temporal satisfaction of user requests.