Jul 2026· International Research Journal of Multidisciplinary Technovation· pp. 91-109· 0 citations· 27 references
TL;DR
Overall, IPL offers an efficient and scalable recovery solution for dynamic SDWSNs, particularly in environments with moderate failure rates, while highlighting opportunities for future enhancement through adaptive relay allocation and improved mobility-aware prediction.
Abstract
Software-defined wireless sensor networks (SDWSNs) improve network programmability and centralized control, but maintaining connectivity under node and link failures remains difficult because of node mobility, limited energy, and dynamic topology changes. This study proposes IPL, an Intelligent, Predefined, and Lightweight recovery framework for topology-aware SDWSNs. The framework integrates three coordinated mechanisms: predictive link-lifetime estimation using energy and mobility parameters, energy-aware target positioning through a weighted midpoint strategy, and ring-based coordination among mobile IPL relay nodes for deterministic and low-overhead recovery. IPL is designed to handle both isolated and multiple concurrent failures while reducing controller burden and avoiding expensive global recomputation. The method was evaluated in a Mininet/Floodlight-based SDWSN environment with 150 nodes under identical settings against four benchmark schemes: IFT, Fed-TSN, P4Neighbor, and LCD. Across varying failure conditions, IPL consistently achieved faster recovery and better communication reliability. Relative to the baselines, the proposed method reduced recovery time by up to 26%, lowered latency by up to 27%, decreased energy consumption by up to 18%, improved packet delivery ratio by up to 19%, increased recovery success rate by up to 17%, and extended network lifetime by up to 19%. These gains arise from proactive link monitoring, rapid relay repositioning, and structured recovery coordination. Overall, IPL offers an efficient and scalable recovery solution for dynamic SDWSNs, particularly in environments with moderate failure rates, while highlighting opportunities for future enhancement through adaptive relay allocation and improved mobility-aware prediction.
The integration of communication networks into smart grids introduces stringent requirements for reliability, low latency,
scalability, and energy efficiency. Existing routing protocols — the Routing Protocol for Low-Power and Lossy Networks
(RPL) and Greedy Perimeter Stateless Routing (GPSR) exhibit complementary strengths and weaknesses across varying
network conditions. This paper proposes an Adaptive Hybrid Routing Framework (AHRF) that integrates RPL and GPSR
under a machine learning (ML)-driven decision engine. The system dynamically selects the most suitable protocol
based on real-time network features including link quality, node degree, residual energy, queue occupancy, and traffic
load. We present rigorous mathematical models of both protocols, formulate a composite utility function capturing
trade-offs among packet delivery ratio (PDR), end-to-end delay, throughput, and energy consumption, and integrate a
Random Forest classifier for adaptive protocol selection. The framework is validated through a custom discrete-event
packet-level simulator implementing log-distance path loss with shadowing over a 500×500 m wireless mesh with up
to 200 randomly deployed nodes across four operational scenarios. Results demonstrate that the proposed Hybrid-ML
framework achieves PDR improvements of up to 8.6% over standalone RPL in the density scenario and up to 110.7%
over GPSR under node failure conditions, while achieving 27.4% lower energy consumption per packet than GPSR in
dense deployments. The Random Forest classifier achieves 95.6% cross-validation accuracy. Feature importance analysis
reveals that average SNR (29.9%), SNR standard deviation (20.8%), and path diversity (15.9%) are the dominant
predictors of optimal protocol selection, providing interpretability to the ML component. The findings demonstrate that
ML-based hybridization of complementary routing protocols offers a resilient and energy-efficient routing solution for
next-generation smart grid neighborhood area networks.
Teslim Komolafe, Enoch Owoeye, Samuel A. Adegbola et al.· Energy Science, Engineering,...· 0 citations
Wireless Sensor Networks (WSNs) play a critical role in various applications, including environmental monitoring, healthcare, and industrial automation. However, these networks face significant challenges related to energy efficiency, fault tolerance, and reliable data transmission, particularly in dynamic environments. Existing clustering and routing techniques often fail to ensure seamless fault tolerance and energy optimization simultaneously. Many traditional approaches lack robust mechanisms to handle Cluster Head (CH) failures, resulting in reduced network stability and shorter operational lifetimes. To address these limitations, this study proposes a Fault-Tolerant Backup Cluster Head with Ant Colony Optimization (FT-BKCH-ACO) approach that enhances energy efficiency and network resilience. The methodology involves optimized CH and Backup CH (BKCH) selection, considering parameters such as residual energy, distance to the base station, and network density. Additionally, Ant Colony Optimization (ACO) is employed to dynamically adjust pheromone levels for energy-efficient routing, ensuring reliable intra-cluster and inter-cluster communication. Simulation results demonstrate that the FT-BKCH-ACO approach significantly improves energy consumption by 23.2%, packet delivery ratio by 10.5% and end-to-end delay by 17.8% compared to existing models. The inclusion of backup CHs ensures seamless communication even in the event of node failures, making this method highly suitable for IoT-enabled WSN applications. The proposed approach bridges the gap between fault-tolerant clustering and adaptive routing, offering a scalable and energy-efficient solution for large-scale sensor networks.
Manisha Chandrakar, A. Hasan· International Journal of Wir...· 0 citations
A reproducible pre-deployment analytics framework for physical/MAC-layer assessment of RF900, G3-PLC, GPRS CS2–CS3, hybrid PLC–GPRS, and renewable-event communication profiles is presented.
A. Villarroel, Milton Ruiz· Electronics· 0 citations
Multiprotocol Label Switching (MPLS) plays a critical role in the backbone networks of Internet Service Providers (ISPs), ensuring robust and scalable network operation. By employing label-based switching instead of destination-based IP forwarding, MPLS significantly reduces forwarding complexity and supports advanced traffic engineering mechanisms. Within MPLS, the Label Distribution Protocol (LDP) is typically used for label distribution, relying on routing information from the Interior Gateway Protocol (IGP). When a link or node failure occurs, LDP is forced to wait for the IGP to re-calculate and update the new paths before it can assign labels for Forwarding Equivalence Classes (FECs). This dependency significantly increases service disruption time due to the combined IGP convergence time and the subsequent label replacement mechanism (withdrawing old labels and installing new ones). Therefore, this paper proposes a novel scheme to mitigate LDP’s dependence on the IGP, thereby minimizing network restoration time during network topology changes. The method utilizes an event-driven signaling mechanism to instantly announce link and node outages across the network. Furthermore, it establishes and installs both primary and backup Label Switched Paths (LSPs) during the initial setup phase of the router. Experimental results demonstrate that the proposed method significantly outperforms LDP, achieving a convergence time up to four times faster in large-scale network scenarios and proving superior scalability.
Trung Van Vu, La Van Thien, Quyet Hoang Dinh et al.· International Conference on...· 0 citations
Central governance and flexible network administration are made possible by Software Defined Networking (SDN); still, this architectural benefit also makes the control plane vulnerable to Distributed Denial of Service (DDoS) attacks. An extreme number of flow requests and packet-in events can significantly reduce controller effectiveness and interfere with network functions in the context of such attacks. In this work, we change and estimate an adaptive DDoS prevention framework based on knowledge gained from SDN emulation tests. Relatively than relying on predetermined mitigation thresholds, the framework dynamically adjusts mitigation strategies based on the attack's severity and the controller's present load. The proposed approach reduces unnecessary interactions in the control plane while maintaining service quality for authorized traffic by incorporating controller-aware decision-making. The adaptive outline lessens controller CPU utilization, speeds up mitigation response times, lowers end-to-end latency, and keeps higher throughput when compared to static mitigation procedures, according to experimental evaluations carried out in a precise SDN emulation environment
Nirzari Patel, H. Patel· International journal of com...· 0 citations
Mobile Ad Hoc Networks (MANETs) are expected to support highly dynamic and decentralized communication scenarios in future 6G-oriented wireless systems. However, routing remains challenging because of mobility, topology variability, and resource constraints. Reinforcement learning (RL) offers a promising alternative by enabling adaptive routing decisions based on observed network conditions. This paper presents QL-6GRP (Q-Learning for 6G Routing Protocol), a lightweight Q-learning-based routing protocol designed for fully distributed MANET environments. The protocol enables each node to learn next-hop forwarding decisions using local observations, including link quality, residual energy, hop progress, and neighborhood density. A complete implementation of QL-6GRP was developed within the NS-3 simulator, supporting online learning through hop-level feedback signaling and bounded-memory operation. The protocol was evaluated under multiple parameter settings and network sizes using Random Waypoint mobility and UDP constant-bit-rate traffic to examine both routing performance and computational behavior. The experimental results demonstrate the feasibility of adaptive routing with moderate signaling overhead under carefully tuned moderate-scale scenarios while revealing key trade-offs between feedback frequency, routing quality, and computational scalability. Moderate periodic feedback provides the most favorable balance, whereas excessive feedback increases overhead without improving performance. In addition, reinforcement-learning operations incur substantial computational costs, with the wall-clock runtime increasing by approximately 13 times when the network size increases from 50 to 100 nodes. These findings reveal the operating limits and practical design trade-offs of lightweight tabular RL-based MANET routing and provide useful guidelines for future scalable learning-driven protocols in dynamic wireless environments.
