Navjeet Kaur¹, Ayush Mittal², Saad Alahmari^3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079991 - 08 May 2026

Abstract Mobile fog computing must support latency-sensitive applications under dynamic user mobility and time-varying network conditions. Existing mobility-aware scheduling approaches are largely reactive and often ignore prediction uncertainty, resulting in service disruptions and inefficient task migration. This paper proposes an uncertainty-aware digital twin-based orchestration framework for proactive mobility-aware fog computing. The framework maintains real-time synchronized digital twins of users and fog nodes and integrates a hybrid Gated Recurrent Unit-Exponentially Weighted Moving Average (GRU-EWMA) mobility prediction model with fog-load forecasting to enable joint mobility- and load-aware decision-making. An entropy-based confidence mechanism is introduced to regulate proactive handover More >

Salahuddin¹, Khalid Haseeb^1,*, Mansoor Nasir¹, NZ Jhanjhi², Mamoona Humayun³

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079584 - 08 May 2026

Abstract Intelligent Transportation System (ITS) interconnects smart technologies for the advancement in communication and autonomous decision making in vehicle interactions. It manages traffic control infrastructure, analyses road conditions, and supports cooperative awareness in a crucial environment. The sensors continuously collect real-time vehicle data, process it, and forward it to analysis servers to predict the behavior of Vehicular Ad hoc Networks (VANETs). Many approaches have been proposed to address research challenges in routing and improve communication for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems. However, due to dynamic topology, the network becomes disturbed and loses established connections, leading… More >

Chaobin Wang^1,2, Xianghong Tang^1,2,*, Jianguang Lu^1,2, Jing Yang^1,2, Panliang Yuan^1,2

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078998 - 08 May 2026

Abstract Mobile Edge Computing (MEC) facilitates the rapid response and energy-efficient execution of tasks on mobile devices. However, determining whether and where to offload tasks remains a significant challenge due to the constantly changing character of workloads in MEC environments. To address this issue, this paper proposes PreAlloc-A2C—a deep reinforcement learning actor-critic-based framework that calculates allocation scores by leveraging both task features (task size, required completion time, and waiting time) and server features (queue length and historical workload). This design enables fully distributed task offloading decisions without centralized coordination. Additionally, a Long Short-Term Memory (LSTM) network More >

Afzal Badshah^1,*, Mona Eisa², Omar Alghushairy², Riad Alharbey², Manal Linjawi³, Ali Daud^4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077465 - 08 May 2026

Abstract The Internet of Things (IoT) devices generate massive data that leads to network congestion, propagation delays, and suboptimal resource allocation. Traditional Cloud Computing (CC) offers scalable resources required for that data; however, it has a long delay and communication overhead. On the other hand, Edge Computing (EC) guarantees low latency but has limited computational capacity. In this paper, we propose an intermediate paradigm, Regional Computing (RC), combined with a Fuzzy Logic System (FLS) for dynamic, multi-criteria offloading across edge, regional, and cloud. The FLS takes task size, cost, and computational demand as input metrics. It More >

Oluwatosin Ahmed Amodu¹, Zurina Mohd Hanapi^1,*, Raja Azlina Raja Mahmood¹, Faten A. Saif², Huda Althumali³, Chedia Jarray⁴, Umar Ali Bukar⁵, Mohammed Sani Adam⁶

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.074678 - 08 May 2026

Abstract Non-terrestrial networks (NTNs), encompassing unmanned aerial vehicles (UAVs), low-/high-altitude platforms (LAPs/HAPs), and satellite systems, are increasingly enabling Internet of Things (IoT) applications beyond the limits of terrestrial infrastructure. By combining UAV mobility with satellite and HAP coverage, NTN-assisted IoT supports diverse use cases, including remote sensing, smart cities, intelligent transportation, and emergency response. This paper presents a systematic mapping of machine learning (ML) research in NTN-assisted IoT with a focus on security-related aspects. A keyword co-occurrence analysis of over 2000 publications identifies twelve thematic clusters, including three clusters directly related to security, privacy, and trust.… More >

Nishu Gupta^1,*, Manuel J. C. S. Reis²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.082568 - 27 April 2026

Abstract This article has no abstract. More >

Jiajia Liu^1,2, Shuchen Pang³, Peng Xie³, Haitao Zhou³, Chenxi Du³, Haoran Hu³, Bo Tang³, Jianhua Liu³, Fei Jia¹, Huibing Zhang^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077824 - 09 April 2026

Abstract In the unmanned aerial vehicle (UAV) assisted edge computing system, the broadcast characteristics of the UAV signal, the high mobility of the UAV, and the limited airborne energy make the task offloading strategy face challenges such as increased risk of information disclosure, limited computing resources, and the trade-off between energy consumption and flight time. To address these issues, we propose a K-means in-depth reinforcement learning algorithm based on Soft Actor-Critic (SAC). The proposed method first leverages the K-means clustering algorithm to determine the optimal deployment of ground jammers based on the final distribution of mobile… More >

Chung-Wei Kuo^1,2,*, Cheng-Xuan Wu¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076767 - 09 April 2026

Abstract The rapid proliferation of the Internet of Things (IoT) has not only reshaped the digital ecosystem but also significantly widened the attack surface, leading to a surge in network traffic and diverse security threats. Deploying effective defense mechanisms in such environments is challenging, as conventional Intrusion Detection Systems (IDS) often struggle to balance computational efficiency with the reliable detection of low-frequency, high-impact threats, particularly within the tight resource constraints of edge devices. To address these limitations, we propose a lightweight, high-efficiency IDS framework specifically optimized for edge-based IoT applications, incorporating Mutual Information (MI)-based feature selection… More >

Wenxuan Yu¹, Wenjing Gao¹, Jiuru Wang², Rong Hao^1,*, Jia Yu^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076731 - 09 April 2026

Abstract Non-negative Matrix Factorization (NMF) is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally. Privacy-preserving outsourcing allows clients to offload heavy computing tasks to powerful servers, effectively solving the problem of local computing difficulties. However, the existing privacy-preserving NMF outsourcing schemes only allow one server to perform outsourcing computation, resulting in low efficiency on the server side. In order to improve the efficiency of outsourcing computation, we propose a privacy-preserving parallel NMF outsourcing scheme with multiple edge servers. We adopt the matrix blocking technique to divide the computation task into multiple subtasks, More >

Hitesh Mohapatra^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076726 - 09 April 2026

Abstract This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge. Existing literature shows that edge-based distributed intelligence reduces cloud dependency. It addresses transmission latency, device energy use, and bandwidth limits. Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination. Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8% and energy efficiency gains of 27.4% compared to conventional cloud-centric models.… More >