SUMMARYAdvances in real-time system and wireless communication have led to the deployment of body area sensor networks (BASNs) for effective real-time healthcare applications. Real-time systems in BASNs tend increasingly to be probabilistic and mixed critical to meet stringent requirements on space, weight, and power consumption. Response-time analysis is an important and challenging task for BASNs to provide some critical services. In this paper, we propose a request-based compositional probabilistic response-time analysis framework for probabilistic real-time task models with fixed-priority preemptive scheduling in BASNs. In this method, each probabilistic real-time task is abstracted as a probabilistic request function. Rough response-time distribution is computed first based on the cumulative request distribution and then exact response-time distribution is obtained by refinement based on the request increase distribution. Our strategy can effectively improve performance by reducing repetitive computational overhead for the probabilistic response-time analysis of all tasks in the system. Our evaluation demonstrates that our proposed method significantly outperforms the existing probabilistic response-time analysis algorithm in terms of analysis duration.

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