Wireless Body Area Networks (WBANs) is an emerging platform for most of the human-centered applications, ranging from sports to medical performance monitoring. Cloudlet is the next generation computing architectural entity which emerges from the convergence of Cloud and mobile computing. Integrating WBANs with Cloudlet introduces an edge-of-things computing environment for pervasive health-care systems. The variation in the number of active WBANs nodes and data transmission rate requires optimal computing resources to avoid performance degradation and data loss. We argue the research gap in two-folds: (i) no simulation toolkit available for addressing the Cloud-enabled WBANs environments, and (ii) lack of an optimal resource provisioning mechanism to predict and adjust the computing resources in order to deal with the fluctuating demands of WBANs services. In this paper, we design and develop Cloud-WBAN, an experimental framework that not only brings the cloud computing system closer to the WBANs user (edge-of-things computing) but also automatically adjust the computing resources (at cloudlet) so as to maintain the service level agreements (SLAs) of the WBANs users on the basis of its sensory data volume and application's type. The Cloud-WBAN framework is built over CloudSim toolkit with autonomic resource provisioning framework inspired by the cloud layer model. We also propose a hybrid autonomic resource provisioning framework which is the combination of autonomic computing and queuing model. Finally, the effectiveness of the proposed approach is evaluated and the experimental results shows that the proposed approach improves the resource utilization by at least 26% and response time by at least 49% as compared with other approaches.
