Mohammad A Al Khaldy¹, Ahmad Nabot², Ahmad Al-Qerem^3,*, Mohammad Alauthman⁴, Amina Salhi^5,*, Suhaila Abuowaida⁶, Naceur Chihaoui⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 673-697, 2025, DOI:10.32604/cmes.2025.070004 - 30 October 2025

Abstract The exponential growth of Internet of Things (IoT) devices has created unprecedented challenges in data processing and resource management for time-critical applications. Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems, while pure edge computing faces resource constraints that limit processing capabilities. This paper addresses these challenges by proposing a novel Deep Reinforcement Learning (DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments. Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency. The framework introduces three key innovations: (1)… More >

Valentin Stangaciu^*, Mihai-Vladimir Ghimpau, Adrian-Gabriel Sztanarec

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3213-3229, 2025, DOI:10.32604/cmc.2025.068844 - 23 September 2025

Abstract Along with process control, perception represents the main function performed by the Edge Layer of an Internet of Things (IoT) network. Many of these networks implement various applications where the response time does not represent an important parameter. However, in critical applications, this parameter represents a crucial aspect. One important sensing device used in IoT designs is the accelerometer. In most applications, the response time of the embedded driver software handling this device is generally not analysed and not taken into account. In this paper, we present the design and implementation of a predictable real-time More >

Rashmi Sharma¹, Nitin Nitin², Deepak Dahiya^3,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5197-5213, 2022, DOI:10.32604/cmc.2022.025059 - 21 April 2022

Abstract Failure is a systemic error that affects overall system performance and may eventually crash across the entire configuration. In Real-Time Systems (RTS), deadline is the key to successful completion of the program. If tasks effectively meet the deadline, it means the system is working in pristine order. However, missing the deadline means a systemic fault due to which the system can crash (hard RTS) or degrade inclusive performance (soft RTS). To fine-tune the RTS, tolerance is the critical issue and must be handled with extreme care. This article explains the context of fault tolerance with… More >

Fengxiang Zhang^∗

Computer Systems Science and Engineering, Vol.34, No.4, pp. 171-178, 2019, DOI:10.32604/csse.2019.34.171

Abstract This paper addresses a number of mathematical issues related to multiprocessor global EDF platforms. We present a deadline-d all busy period and backward interference which are important concepts for multiprocessor EDF systems, and some general schedulability conditions for any studied job are proposed. We formally prove that at most m-1 different tasks’ jobs could contribute their execution time to an interval starting with a P_busy−d, and we propose an approach for computing an exact upper bound of the total deadline-d task load in a given interval. Therefore, the proposed results are important foundations for constructing exact More >

Awais Qasim^a, Syed Asad Raza Kazmi^b

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 49-63, 2019, DOI:10.31209/2018.100000012

Abstract The paradigm of multi-agent systems is very expressive to model distributed real-time systems. These real-time multi-agent systems by their working nature have temporal constraints as they need to operate in pervasive, dynamic and unpredictable environments. To achieve better fault-tolerance, they need to have the ability of self-adaptivity making them adaptable to the failures. Presently there is a lack of vocabulary for the formal modelling of real-time multi-agent systems with self-adaptive ability. In this research we proposed a framework named SMARTS for the formal modelling of self-adaptive real-time multi-agent systems. Our framework integrates MAPE-K interfaces, reflection More >