International Journal of Communication Networks and Distributed Systems

Kadu, Nanasaheb Bhausahe; Nirmal, Mahesh Dattatray; Bendre, Mininath Raosaheb; Bhosale, Sachin Sampatrao; Bhandwalkar, Kalyani Tukaram; Gholap, Nilesh Dilip

doi: 10.1504/ijcnds.2026.152112pmid: N/A

Cloud computing enables convenient access to computational resources, but managing them efficiently remains a challenge. Load balancing optimises performance and resource usage, but achieving efficiency in large-scale environments is complex. In this paper, the Enhancing Load Balancing Efficiency in Cloud Computing utilising Binary Kepler and Slap Swarm Optimised Approach (ELBF-CC-BK-SSOA) is proposed to overcome the challenges. This research proposes Binary Kepler optimisation (BKOA) as a load balancing solution for cloud computing. Initially, the System Efficiency using BKOA is used to find the best solution to problems. Next, resource allocation using MemeticSalp Swarm Optimisation Algorithm (MSSOA) is employed to distribute available resources. Experimental results demonstrate that ELBF-CC-BK-SSOA outperforms existing methods by achieving 33.13% lower response time, 28.27% higher throughput, 24.12% reduced CPU utilisation and 26.19% increased cost efficiency compared to existing techniques. This highlights the model's effectiveness in enhancing cloud performance, resource utilisation and reducing operational costs for dynamic cloud environments.

Nayak, Vasanth; Pawar, Sumathi; Kumar, B.L. Sunil

doi: 10.1504/ijcnds.2026.152133pmid: N/A

The rapid development of the Internet and communication technology has led to the expansion of large networks and data. In response to these threats, intrusion detection systems (IDS) were created to protect networks by analysing network traffic to ensure privacy, fairness, and security. The challenge remains correcting, reducing, and identifying new inputs. Recently, IDS based on machine learning (ML) and deep learning (DL) have been offered as effective techniques for detecting network vulnerabilities. This paper provides an overview of IDS and then classifies important ML and DL techniques for developing network-based IDS (NIDS) systems. Furthermore, this paper updates the techniques, evaluation methods, and data selection to reflect the current needs and advances in ML and DL-based NIDS. The limitations of the proposed method are analysed, key research issues are identified, and future research plans for NIDS based on ML as well as DL are provided.

Kishore, G. Shyam; Chandrasekhar, P.

doi: 10.1504/ijcnds.2026.152115pmid: N/A

This work presented an effective peak-to-average power ratio (PAPR) reduction with a hybrid grey code phase factor based partial transmit sequence (PTS) and tone reservation based deep convolutional neural network (CNN) technique (Hybrid Grey-PF-TRDCNN). In the first stage, the tone reservation network TRCNN reserves some of the tones to create the peak-cancelling signal. In the second stage, the resultant PAPR reduced signal of stage 1 is further reduced with the grey code phase factor using PTS. Here, the optimal phase sequence is selected by an efficient beetle swarm optimisation (BSO) technique to reduce the PAPR of the signal. The presented hybrid approach provides PAPR reduction in hybrid frequency-quadrature amplitude modulation (HFQAM) based F-OFDM signal. The MATLAB 2021a working platform with Xilinx 14.5 is used to implement the proposed technique. The experimental outcomes of the suggested strategy are contrasted with those of other existing approaches.

Tran, Huu Q.; Ngo, Sy

doi: 10.1504/ijcnds.2026.152114pmid: N/A

Micro-electro-mechanical systems (MEMS) have revolutionised technology by integrating microelectronics with mechanical systems to create versatile miniature devices. This review explores MEMS evolution, from early developments to recent advancements. It outlines core principles of MEMS design and fabrication, including lithography, deposition, and etching. The paper examines various MEMS devices - sensors, actuators, resonators, and microfluidic systems - emphasising design considerations, fabrication techniques, and performance metrics. It highlights MEMS applications in healthcare, automotive, aerospace, consumer electronics, and telecommunications, driving innovations in medical diagnostics, environmental sensing, and autonomous technologies. Emerging research on new materials, advanced fabrication methods, and integration with nanotechnology and biotechnology is discussed. Key challenges, such as scalability, reliability, and energy efficiency, are addressed, providing insights into future directions. This article serves as a valuable resource for understanding MEMS history, current state, and future opportunities for researchers and industry professionals.