LAUSR

With the emergence of Internet of Things, the number of connected devices has been dramatically increasing, causing severe spectrum shortage problem. To fully explore the spectrum resources, big data, and cloud computing can be employed by cognitive radio networks, to make efficient use of various sensing results from different sensing sources. However, the massive growth of sensing data brings tremendous load pressure on the data center, resulting in long service response time and poor Quality of Experience. Edge computing and fog computing deal with these issues by placing computation resources at the network edge. However, compared with the data center, the capabilities at edge servers are limited. Therefore, a services routing-based caching scheme (SRCS) is proposed, which can greatly lighten the load on the data center and maintain the advantages of global intelligent computing of traditional cloud computing. Specifically, SRCS first introduces the concept of transmitting service flow. At the edge layer, data are converted to service flow by network hardware and software, thus achieving the network architecture centered on service computing. Then, SRCS proposes a service routing based on service similarity, transmits similar services through the same path, and service data are fused on the path to minimize transmission load. Moreover, SRCS caches services in content routers (CRs). When the service is requested again, CRs are used as service providers to return data, thus achieving the nearest access to the content. Both theoretical analysis and experiment results demonstrate that comparing existing schemes, SRCS improves service response time by 13.67%–51.15%, reduces transmitting data amount by 23.62%–30.3%, and makes energy consumption more balanced.