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Probabilistic response‐time analysis for real‐time systems in body area sensor networks

Advances 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. Copyright © 2015 John Wiley & Sons, Ltd.