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

Abstract

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.

Critical Instant for Probabilistic Timing Guarantees: Refuted and Revisited

“…For sensor networks, a series of results by Ren et al analyzes the response time distribution, and Theorem 3.1 from [31] as well as Theorem 1 from [32] are affected. Their analysis assumes that the unsound critical instant from [25] is the worst-case scenario for bounding the probability of response time.…”

“…Since then, the critical instant theorem under the probabilistic setup by Maxim and Cucu-Grosjean [25] has been widely used in the literature [26], [31], [32]. Due to an essential complication in probabilistic response-time analysis (i.e., convolution over multiple random variables), several techniques have been developed to tackle issues of intractability through various means, e.g., down-sampling [24], [25], [27], [30], concentration inequalities [8], [9], [35], task-level convolution [35], and Monte-Carlo simulation [6].…”

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

“…Other studies focused on the improvement of the performance of 'MAC protocol of WBANs' for the prioritisation of numerous vital signs to ensure data efficiency [88,89]. Finally, [90] presented probabilistic real-time systems in BASNs with fixed-priority pre-emptive scheduling.…”