In modern times, wireless sensor networks (WSNs) are necessary for remote system surveillance and information exchange. Despite the advantages of WSNs, node lifetime and data loss limit their use. Several researchers use energy-efficient protocols to solve the aforementioned issues in WSNs. However, the majority of authors focused exclusively on the energy-efficient model for solving the issues. Yet, this results in high energy consumption and repetitive processes. So, this research developed a cluster head (CH) selection method using the fuzzy model for effective data transmission. The main contribution of the proposed work is to perform secure data transmission within the WSN by reducing and balancing energy consumption. Initially, the necessary network attributes are gathered from the available resources. The adaptive fuzzy C-means clustering (AFCMC) concept is employed in clustering the WSN. In the cluster-based WSN, CH is optimally chosen with the support of the improved black widow optimization algorithm (IBWOA). Here, the parameter in AFCMC is optimally tuned by the same IBWOA. The tuned parameters include the fuzziness parameter (2-20), epsilon (1-10), and the number of iterations (10-100). Subsequently, the data transmission is conducted with the support of the deep state-action-reward-selection algorithm (Deep SARSA). In this, the suggested Deep SARSA helped to perform the data transmission securely through the shortest path and improved the detection of malicious nodes. The newly designed approach is compared with the solutions in the literature. The established IBWOA-AFCMC models in terms of residual energy are 16.66%, 21.73%, 19.14%, and 55.55% more effective than KMC, hierarchical clustering, biclustering, and FCM in the 100th number of nodes. This dual-layer approach significantly enhances network longevity and data integrity in dynamic WSN environments.
The cooperative non-orthogonal multiple access (CNOMA) system illustrates a prospective multiple access approach for future wireless communications. Line-of-sight communication is becoming more difficult to meet the needs of a modern society. As a result, reconfigurable intelligent surfaces (RIS) technology has emerged in proposed system. Consequently, this technology improves the efficiency of conventional CNOMA system by including RIS components. Efficiency of proposed RIS-aided CNOMA system is assessed using () generalized fading channel. The evaluation is carried out with different fading elements including nonlinearity () and multipath clusters (). The current research specifies the performance of proposed system with one-sided Gaussian, Rayleigh, Nakagami, and Weibull generalized fading channels for different fading components. The evaluation explores the effect of perfect and imperfect channel state conditions on proposed system through simulation results. Finally, the obtained results of the proposed RIS-Aided CNOMA system are compared with the traditional CNOMA system under () fading channels. The findings demonstrate that the significance of RIS technology to improve the efficiency of traditional CNOMA system in terms of ergodic capacity, bit error rate, and outage probability with the channel state information. Monte Carlo simulation is used for the simulation to get the accurate results.
The emergence of 5G and the imminent deployment of 6G networks have revolutionized wireless communication by enabling ultra-reliable low-latency communication, enhanced mobile broadband, and massive machine-type communication. While these diverse applications foster increasingly complex congestion scenarios due to highly dynamic traffic patterns, diverse requirements of different services, and ultra-dense deployments of the network, efficient congestion control has become a pressing challenge. Conventional methods cannot address the nonlinear and multi-relational characteristics of data traffic. Therefore, with the rapid scaling of connected devices with heterogeneous service demands, bottlenecks will be experienced, which leads to packet loss, increased latency, and poor QoS. Given the above challenges, this paper introduces an advanced congestion control framework developed on top of intelligent graph-based learning and optimization strategies. The input data is from the DeepSense 6G dataset and preprocessed using Z-score and min-max normalization to enhance the stability and uniformity of the preprocessed data. The feature selection is further fine-tuned through the Orchard Algorithm (OA), ensuring highly relevant feature selection. An innovative combination of Multi-Relational Skeleton Graph Attention Networks is proposed to cater to the challenge of understanding multiple relational behaviors in data traffic. Furthermore, the performance of the integrated model is optimized with the Greater Cane Rat Optimization Algorithm to enhance learning efficiency and the precision of congestion control. Experimental analysis demonstrates an outstanding accuracy of 99.9%, ensuring the proposed approach effectively mitigates congestion in 5G/6G networks.
Underwater wireless sensor networks (UWSN) have been proven essential in providing solutions for various purposes ranging from aquatic surveillance to marine exploration and defense. However, such networks suffer from the drawback of being hindered by several aspects including lengthy propagation delay times, scarcity of energy supplies, changing network structures, and unstable acoustic mediums-issues that heuristic and static methods cannot resolve. Driven by these challenges, the Optimized Clustering and Energy-Aware Network Protocol called OCEANet has been proposed in this research. This protocol is based on a double intelligence approach for a self-adapting dynamic underwater network environment. Its unique feature lies in combining the evolutionary biology-based clustering technique with software-defined radio configuration through artificial intelligence techniques. The OCEANet system comprises two operational phases that complement each other. The clustering and evolutionary bio-beaconswarm routing algorithm selects the appropriate cluster heads considering their residual energy, location, and link quality and thus helps create the optimal multi-hop route toward the sink node. The Self-Updating Reconfigurable Framework uses the Deep Q-Network reinforcement learning technique along with Graph Convolutional Network-based transfer learning. The proposed OCEANet outperforms the other models with a packet delivery ratio of 94% and throughput of 189.18 kbps when there are 100 nodes. Also, while lowering the energy usage and delays, and by increasing the network life cycle, OCEANet establishes it as a reliable, scalable solution for next-generation underwater sensor networks.
Content-based network slicing is a key technology to enable the transition from the network as infrastructure to network-as-services and offer services to business verticals. Its efficient use of limited network resources makes it a promising future wireless technique. This article presents a novel network slicing-based model for optimal cell planning while maximizing the content-based fairness utility. The users are divided in two categories: (i) emergency users (EUs) and (ii) normal users (NUs). Here, emergency services are limited to text and voice to prevent congestion, whereas NUs have access to all services (text, voice, and video). Both user types are considered across two emergency scenarios: zone-specific and random. Random EUs are those needing medical, safety, or security assistance, whereas zone-specific EUs are users within a disaster-affected area. Simulations are performed to observe the trade-off between the NU and EU association with the optimal BS while satisfying the demanded rate of service. The effectiveness of the proposed hybrid Dragonfly Algorithm with Differential Evolution (DADE) and Buyer Inspired Meta-heuristic Algorithm (BIMA) with Fuzzy C-means algorithm is demonstrated using simulation results. Comparative simulations show that the proposed DADE-based algorithm significantly outperforms other state-of-the-art algorithms in maximizing network utility.
