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.
An Adaptive Hybrid Routing Framework that integrates RPL and GPSR under a machine learning (ML)-driven decision engine that offers a resilient and energy-efficient routing solution for next-generation smart grid neighborhood area networks is proposed.
Teslim Komolafe, Enoch Owoeye, Samuel A. Adegbola et al.· Energy Science, Engineering,...· 0 citations
This study proposes a Fault-Tolerant Backup Cluster Head with Ant Colony Optimization (FT-BKCH-ACO) approach, 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
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.
Samer Bali· IEEE Access· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
