Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 891-898· 0 citations· 25 references
Abstract
This paper proposes an Intelligent Link Failure Prediction and QoS-Aware Routing framework for Mobile Ad Hoc Networks (MANETs) using the Harris Hawk Optimization (HHO) algorithm to achieve reliable and efficient data transmission under highly dynamic network conditions. The proposed model integrates proactive link failure prediction with HHO-based multi-objective route optimization to select stable, energy-efficient, and QoS-compliant paths. The framework is implemented and evaluated using the SimPy simulation environment under a realistic node mobility and traffic workload generated using Random Waypoint mobility with CBR and VBR traffic patterns, which is widely adopted for MANET performance evaluation. The proposed method is compared with Multi-Agent Deep Learning (MADL), Multi-Agent Deep Reinforcement Learning (MADRL), and the Communication-Aware Hierarchical Routing Framework (CAHRF). Experimental results demonstrate that the HHO-based approach significantly improves network performance by increasing the Packet Delivery Ratio (PDR) by 9.8-15.6%, reducing Link Failure Recovery Time by 21.4-34.7%, extending Network Lifetime by 18.2-27.9%, and decreasing Control Packet Cost by 16.5-25.3% compared to the benchmark methods. These improvements confirm that the proposed HHO-driven intelligent routing framework provides a robust, scalable, and QoS-aware solution for reliable communication in highly dynamic MANET environments.
Mobile Ad-hoc Network (MANET) are highly dynamic and infrastructure-less wireless network in which frequent topology changes, node mobility, packet collision, and energy constraints significantly affect routing performance and network reliability. This research suggests a Deep Reinforcement Learning (DRL)-based Optimized Multi-Path Relay Node Selection method for dependable and energy-efficient MANET routing. The suggested method takes into account important network metrics such as residual energy, node mobility, link stability, congestion level, and packet collision probability in order to intelligently choose the best relay nodes and different routing options using a Deep Q-Network (DQN)-based learning model. In comparison to traditional MANET routing protocols, simulation results show that the suggested DRL-based relay node selection technique greatly improves network lifetime, Packet Delivery Ratio (PDR), throughput and routing stability while lowering packet collision, end-to-end delay and energy consumption.
A. A. Samathu, G. Ravi, A. R. Mohamed Shanavas· THE SCIENTIFIC TEMPER· 0 citations
The paper introduces RML-ZEREM to solve existing limitations, which functions as a Reinforcement Learning (RL) based Zone-Based Leader-Aware Energy-Efficient Routing Protocol for MANETs, which serves next-generation MANET applications.
Rani Sahu, Babita Rathore· Journal of Intelligent Compu...· 0 citations
Simulation results position D3QN-PER as a strong candidate for deployment as a near-RT RIC xApp within the O-RAN architecture, advancing the vision of AI-native mobility management for 6G.
Kalpesh Popat, Divyakant T. Meva· Telecommunications Systems· 0 citations
Simulation results obtained demonstrate that Q-WeCBR outperforms CBR, DSDV, and GPSR in terms of packet delivery ratio and throughput, confirming the effectiveness of clustering combined with learning-based routing for dynamic vehicular networks.
Ahlam Boussadia· International journal of inf...· 0 citations
Autonomous-vehicle perception systems increasingly operate across heterogeneous compute and communication
environments, where the most accurate model is not necessarily the model that provides the best end-to-end service quality. A
high-capacity perception model may improve recognition quality while increasing inference time, CPU consumption, memory
demand, and sensitivity to network conditions. This paper proposes a Quality-of-Service (QoS)-Aware Intelligent Model
Selection framework that predicts the expected QoS of candidate perception models from model, network, and hardware context
and dynamically selects a feasible model according to application priorities. The framework extends a network-aware
autonomous-vehicle benchmarking foundation with a QoS prediction layer, multi-objective utility function, Pareto filtering, and
a hysteresis-based switching controller. Three tabular prediction methods—linear regression, random forest, and gradient
boosting—are compared. A synthetic dataset of 1,800 observations is generated from six candidate model profiles, five network
conditions, three hardware conditions, and repeated measurements. The synthetic evaluation indicates that nonlinear predictors
outperform the linear baseline and that QoS-aware selection can reduce mean response time by approximately 31.8–42.1%
relative to an accuracy-only baseline in the modeled scenarios. Pareto and ablation analyses further show that latency and
resource terms materially influence the selected model. Because no real AV testbed experiment has yet been conducted, these
numerical results are explicitly treated as synthetic validation rather than empirical evidence. The paper therefore provides a
reproducible research design and a publication-oriented methodology whose final claims should be revalidated with measured
AV executions.
S. Singh, A. Mishra· International Journal for Re...· 0 citations
Simulation results indicate that HOA-MEPFL-CLCT-RP outperforms existing models in terms of Packet Delivery Ratio (PDR), energy efficiency, End-to-End Delay (E2D), and routing overhead.
Shaleena H, Sumangala K· International journal of com...· 0 citations
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