Yiming Zhang¹, Wei Zhang^1,2,*, Cong Shen³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3185-3211, 2025, DOI:10.32604/cmc.2025.067563 - 23 September 2025

Abstract Distributed data fusion is essential for numerous applications, yet faces significant privacy security challenges. Federated learning (FL), as a distributed machine learning paradigm, offers enhanced data privacy protection and has attracted widespread attention. Consequently, research increasingly focuses on developing more secure FL techniques. However, in real-world scenarios involving malicious entities, the accuracy of FL results is often compromised, particularly due to the threat of collusion between two servers. To address this challenge, this paper proposes an efficient and verifiable data aggregation protocol with enhanced privacy protection. After analyzing attack methods against prior schemes, we implement… More >

Aadil Mushtaq Pandith¹, Manni Kumar², Naveen Kumar³, Nitin Goyal^4,*, Sachin Ahuja², Yonis Gulzar⁵, Rashi Rastogi⁶, Rupesh Gupta⁷

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 259-279, 2025, DOI:10.32604/cmc.2025.062608 - 26 March 2025

Abstract Underwater wireless sensor networks (UWSNs) rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink. However, many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments. Additionally, conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink, commonly known as the energy hole issue. Moreover, cluster-based aggregation methods face significant challenges such as cluster head (CH) failures and collisions within clusters that degrade overall network performance. To address these limitations,… More >

Tien-Wen Sung¹, Wei Li¹, Chao-Yang Lee^2,*, Yuzhen Chen¹, Qingjun Fang¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 407-434, 2025, DOI:10.32604/cmc.2025.061694 - 26 March 2025

Abstract To transmit customer power data collected by smart meters (SMs) to utility companies, data must first be transmitted to the corresponding data aggregation point (DAP) of the SM. The number of DAPs installed and the installation location greatly impact the whole network. For the traditional DAP placement algorithm, the number of DAPs must be set in advance, but determining the best number of DAPs is difficult, which undoubtedly reduces the overall performance of the network. Moreover, the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the… More >

Chao-Hsien Hsieh¹, Qingqing Yang^2,*, Dehong Kong², Fengya Xu², Hongmei Wang²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2869-2891, 2025, DOI:10.32604/cmc.2024.058122 - 17 February 2025

Abstract The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of… More >

Congcong Wang¹, Chen Wang^2,3,*, Wenying Zheng^4,*, Wei Gu⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 799-816, 2025, DOI:10.32604/cmc.2024.057975 - 03 January 2025

Abstract As smart grid technology rapidly advances, the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection. Current research emphasizes data security and user privacy concerns within smart grids. However, existing methods struggle with efficiency and security when processing large-scale data. Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge. This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities. The approach optimizes data preprocessing, More >

Dong Chen¹, Tanping Zhou^1,2,3,*, Xu An Wang^1,2, Zichao Song¹, Yujie Ding¹, Xiaoyuan Yang^1,2

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2477-2497, 2023, DOI:10.32604/iasc.2023.039238 - 21 June 2023

Abstract Smart grid (SG) brings convenience to users while facing great challenges in protecting personal private data. Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value, preventing the leakage of personal data while ensuring its availability. Recently, a flexible subset data aggregation (FSDA) scheme based on the Paillier homomorphic encryption was first proposed by Zhang et al. Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset. In this paper, firstly, an efficient attack with both theorems… More >

Siguang Chen^1,2,*, Li Yang^1,2, Yanhang Shi^1,2, Qian Wang¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3781-3796, 2023, DOI:10.32604/cmc.2023.036437 - 31 March 2023

Abstract As an essential component of intelligent transportation systems (ITS), electric vehicles (EVs) can store massive amounts of electric power in their batteries and send power back to a charging station (CS) at peak hours to balance the power supply and generate profits. However, when the system collects the corresponding power data, several severe security and privacy issues are encountered. The identity and private injection data may be maliciously intercepted by network attackers and be tampered with to damage the services of ITS and smart grids. Existing approaches requiring high computational overhead render them unsuitable for… More >

Ahmad O. Aseeri¹, Sajjad Hussain Chauhdary^2,*, Mohammed Saeed Alkatheiri³, Mohammed A. Alqarni⁴, Yu Zhuang⁵

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1901-1918, 2023, DOI:10.32604/cmc.2023.028777 - 06 February 2023

Abstract IoT devices rely on authentication mechanisms to render secure message exchange. During data transmission, scalability, data integrity, and processing time have been considered challenging aspects for a system constituted by IoT devices. The application of physical unclonable functions (PUFs) ensures secure data transmission among the internet of things (IoT) devices in a simplified network with an efficient time-stamped agreement. This paper proposes a secure, lightweight, cost-efficient reinforcement machine learning framework (SLCR-MLF) to achieve decentralization and security, thus enabling scalability, data integrity, and optimized processing time in IoT devices. PUF has been integrated into SLCR-MLF to… More >

Arslan Iftikhar¹, M. A. Elmagzoub², Ansar Munir^1,*, Hamad Abosaq Al Salem², Mahmood ul Hassan³, Jarallah Alqahtani², Asadullah Shaikh²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1153-1168, 2023, DOI:10.32604/csse.2023.030802 - 20 January 2023

Abstract In every network, delay and energy are crucial for communication and network life. In wireless sensor networks, many tiny nodes create networks with high energy consumption and compute routes for better communication. Wireless Sensor Networks (WSN) is a very complex scenario to compute minimal delay with data aggregation and energy efficiency. In this research, we compute minimal delay and energy efficiency for improving the quality of service of any WSN. The proposed work is based on energy and distance parameters as taken dependent variables with data aggregation. Data aggregation performs on different models, namely Hybrid-Low… More >

P. Nalayini^1,*, R. Arun Prakash²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2033-2047, 2023, DOI:10.32604/csse.2023.028269 - 01 August 2022

Abstract Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in… More >