Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3169-3189, 2025, DOI:10.32604/cmes.2025.070225 - 30 September 2025

Abstract Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures More >

Muhammad Salman Qamar^1,*, Muhammad Fahad Munir²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5463-5485, 2025, DOI:10.32604/cmc.2025.064442 - 30 July 2025

Abstract Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as clustering, multi-hop routing, and data aggregation, face challenges, including uneven energy depletion, high computational demands, and suboptimal cluster head (CH) selection. To address these limitations, this paper proposes a hybrid methodology that optimizes energy consumption (EC) while maintaining network performance. The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic (LEACH-D) protocol using More >

Muhammad Rashid^*, Majid Alotaibi

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6849-6865, 2023, DOI:10.32604/cmc.2023.032849 - 28 December 2022

Abstract We have proposed a flexible coprocessor key-authentication architecture for 80/112-bit security-related applications over field by employing Elliptic-curve Diffie Hellman (ECDH) protocol. Towards flexibility, a serial input/output interface is used to load/produce secret, public, and shared keys sequentially. Moreover, to reduce the hardware resources and to achieve a reasonable time for cryptographic computations, we have proposed a finite field digit-serial multiplier architecture using combined shift and accumulate techniques. Furthermore, two finite-state-machine controllers are used to perform efficient control functionalities. The proposed coprocessor architecture over and is programmed using Verilog and then implemented on Xilinx Virtex-7 FPGA More >

A. Jameer Basha¹, S. Aswini¹, S. Aarthini¹, Yunyoung Nam^2,*, Mohamed Abouhawwash^3,4

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1451-1466, 2023, DOI:10.32604/csse.2023.025503 - 15 June 2022

Abstract Wireless Sensor Network (WSN) technology is the real-time application that is growing rapidly as the result of smart environments. Battery power is one of the most significant resources in WSN. For enhancing a power factor, the clustering techniques are used. During the forward of data in WSN, more power is consumed. In the existing system, it works with Load Balanced Clustering Method (LBCM) and provides the lifespan of the network with scalability and reliability. In the existing system, it does not deal with end-to-end delay and delivery of packets. For overcoming these issues in WSN,… More >

Mudita Uppal¹, Deepali Gupta¹, Divya Anand², Fahd S. Alharithi³, Jasem Almotiri³, Arturo Mansilla^4,5, Dinesh Singh⁶, Nitin Goyal^1,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1799-1814, 2022, DOI:10.32604/cmc.2022.025330 - 24 February 2022

Abstract The rapid expansion of Internet of Things (IoT) devices deploys various sensors in different applications like homes, cities and offices. IoT applications depend upon the accuracy of sensor data. So, it is necessary to predict faults in the sensor and isolate their cause. A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults. This technique identifies the faulty sensor and determines the correct working of the sensor. Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are… More >