Aug 2026· Internet Technology Letters· 0 citations· 17 references
TL;DR
Simulations indicate the proposed approach improves overall performance over existing load‐balancing approaches by reducing processing delays, minimizing network congestion, and minimizing resource utilization.
Abstract
It is vitally important for intelligent transportation systems (ITS) to make use of vehicular ad hoc networks (VANETs) to improve road safety, traffic management, and communication. The mobility of vehicles and dynamic traffic conditions continue to pose challenges to network congestion. In this paper, we suggest a load‐balancing strategy for reducing network congestion through the optimization of control packet overhead and the enhancement of data dissemination between roadside units (RSUs) and vehicles. As part of the proposed methodology, real‐time traffic conditions, predictive modeling, and intelligent routing algorithms are incorporated to achieve an efficient load distribution among vehicular nodes. Urban VANETs can use the framework to measure performance metrics like packet delivery ratios (PDRs) and energy consumption (EC). Simulations indicate the proposed approach improves overall performance over existing load‐balancing approaches by reducing processing delays, minimizing network congestion, and minimizing resource utilization. Network operations need to optimize control packet overhead to achieve a balance between communication efficiency and network stability.
To overcome the inherent compromises between proactive and reactive data transmission in Vehicular Ad-hoc Networks (VANETs), this research introduces a novel framework tailored for highly unstable vehicular topologies. The developed system, termed the Dynamic Hybrid Routing Protocol (DHRP), merges the Optimised Link State Routing (OLSR) and Ad-hoc On-Demand Distance Vector (AODV) algorithms. A core feature of this architecture is its cross-layer power management module, which dynamically recalibrates transmission strength and routing paths by analysing real-time vehicle clustering and speed metrics. Comprehensive evaluations conducted via NS-3 and SUMO indicate that the proposed DHRP significantly surpasses both contemporary benchmarks and standard baselines. Notably, the architecture achieves a Packet Delivery Ratio (PDR) exceeding 90%, limits communication latency to well below the critical 40 ms safety boundary, and slashes energy expenditure by up to 90%. By effectively solving the traditional routing dichotomy, DHRP offers a highly scalable and sustainable communication backbone vital for the reliable operation of future Intelligent Transportation Systems (ITS).
The results demonstrate that the proposed PP-SAPF is suitable for real-time deployment in intelligent transportation systems (ITS) and autonomous vehicles where low latency, reliable connectivity, and adaptive resource management is significant.
Irshad Khan, Neetha Papanna Umalakshmi, Somshekhar Durgaiah et al.· Bulletin of Electrical Engin...· 0 citations
The smooth and efficient movement of emergency vehicles in congested areas has always been a problem in intelligent transportation systems (ITS). The conventional approach to routing, typically through static shortest path calculations, often fails to respond to dynamically changing traffic conditions, leading to avoidable delays in critical situations. In this article, we propose a new approach to routing, termed RACER (Real-time Adaptive Congestion-aware Emergency Routing), which dynamically responds to changing traffic conditions without requiring additional traffic-signal-control infrastructure, relying instead on congestion information obtained through standard vehicle-to-infrastructure (V2I) telemetry such as roadside units or cellular reporting, which we model in SUMO via its Traffic Control Interface (TraCI). This is implemented through a combination of a proactive multi-edge look-ahead approach, a congestion-aware cost function, and a controlled approach to rerouting, ensuring stability during navigation. The proposed approach is evaluated using the SUMO microscopic traffic simulator on two large-scale real urban road networks (Bhubaneswar and Visakhapatnam), across five source and destination pairs, three congestion levels (light, moderate, severe), and 20 random seeds, for a total of 1,200 controlled runs. Because emergency-vehicle travel times are heavily right-skewed, we report the median as the primary metric alongside the mean. RACER attains the lowest median travel time across all routes and congestion levels, improving on the strongest baseline in every route, and its median travel time remains essentially flat as congestion increases (372/395/385 s for light/moderate/severe), in contrast to static Dijkstra, which degrades sharply (993/1993/3321 s). Paired statistical testing confirms the improvements over all baselines are significant ( $p\lt 10^{-29}$ ), and measured wall-clock runtime confirms the method operates in real time. We further make explicit the vehicle-to-infrastructure communication architecture on which the method operates, and show that its bounded, cooldown-gated rerouting keeps the control-plane signaling overhead low (on average fewer than three route updates per trip), making congestion-aware routing feasible over capacity-limited vehicular networks. These results demonstrate the effectiveness of incorporating congestion awareness into routing decisions, leading to faster and more reliable emergency response in such congested areas.
Harinath Ankarboina, Jasmini Kumari, Amit Kumar Singh et al.· IEEE Open Journal of the Com...· 0 citations
The Internet of Vehicles (IoV) is a decentralized network architecture that enables autonomous driving, real-time applications, infotainment services, and seamless vehicle-to-everything communication. While infotainment systems enhance the user experience by providing entertainment and navigation features, their high data demands can cause network congestion, potentially delaying mission-critical messages and compromising safety and reliability. Further, the increasing volume of connected devices and data traffic exacerbates these challenges, resulting in high latency, low throughput, and reduced network efficiency. To address this, we propose a novel lightweight adaptive network slicing strategy with four dedicated slices and priority-weighted dynamic bandwidth allocation for IoV networks to mitigate congestion and ensure the availability of the required bandwidth for critical communications. The proposed mechanism defines four distinct network slices, allocating resources to balance infotainment and mission-critical needs. Simulation results demonstrate that our approach achieves ultra-low latency (<5 ms), near-zero packet loss (<0.5%), and high throughput (53 Mbps for infotainment), significantly outperforming existing methods, and ensuring reliable communication for safety-critical tasks while improving spectrum utilization. Findings validate implementing network slicing in IoV environments, paving the way for efficient, congestion-free, and high-performance vehicular networks.
Arbab Waheed Ahmad, Raja Sana Gul, M. Derawi· Italian National Conference...· 0 citations
In Vehicular Ad-Hoc Networks (VANETs), each vehicle continuously broadcasts real-time information messages, such as speed, location, acceleration, and atmospheric conditions, to surrounding vehicles. However, in the current scenario, vehicle density increases continually on the road day by day, which is responsible for excessive amount of messages generation, leads to message congestion on channel and degrades the performance of VANETs. To mitigate this issue, proposed work investigates a novel technique named as Congestion Control with Enhancing Vehicle Connectivity (CC-EVC), empowered by machine learning (ML). The CC-EVC technique works with the principle of K-Mean algorithm for grouping the neighbouring vehicles to enhance connectivity. It restricts the communication of safety messages to non-emergency (outlier) vehicles to reduce the channel congestion and monitors the real-time channel load. The proposed technique also manages the channel congestion by grouping the neighbouring vehicles according to transmission range and using adaptive message transmission rate. The performance of the proposed CC-EVC technique is measured on SUMO tool by designing a dense vehicular network. The K-Mean technique is implemented using MATLAB, and performance parameters such as Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Throughput, and End-to-End (E2E) delay are evaluated using the NS2 simulator. Simulation results demonstrate that the proposed CC-EVC technique improves the communication among vehicles by reducing channel congestion in a significant way compared to the Decentralized Congestion Control (DCC) technique. Proposed CC-EVC technique achieves 89.7298% PDR, 111.73ms E2E delay, and 0.32 NRL with Ad-Hoc On-Demand Distance Vector (AODV) routing protocol and 82.447% PDR, 93.45ms E2E delay, and 0.67 NRL with Destination-Sequenced Distance Vector (DSDV) routing protocol at 500mWatt Transmission Power (TP) and 100bytes Packet Size (PS). Further, operational efficiency of the proposed work is compared with the existing AODV and DSDV protocols with varying TP and PS. It is demonstrated that the proposed technique results in enhanced throughput and PDR with AODV protocol whereas lowering the E2E delay and NRL with DSDV protocols.
Bhupendra Dhakad, Rachit Jain, S. Ojha et al.· Scientific Reports· 0 citations
Increases in the use of UAVs for communication have led to the widespread emergence of Flying Ad hoc Networks (FANET). Conversely, UAV’s mobility and environmental obstacles affect communication links, resulting in link unreliability and inefficient routing. To combat these challenges, an Energy and Mobility-Aware Stable and Safe clustering (EMASS) protocol has been developed, which prevents obstacles in the routing path and minimizes the influence of high mobility on data transfer. However, it does not address the congestion issue in FANET routing, which degrades the data transfer in delay-constrained applications. Hence, this manuscript proposes a new Enhanced Intelligent-based Energy and Mobility, and Obstacle-aware Clustering (EIEMOC) protocol to control the network congestion while meeting End-to-End Delay (E2D) constraints in delay-constrained FANET applications. The main optimization objectives of this protocol are the cumulative rates over the connections and various factors that influence the E2D for 1-hop communication. First, a dispersed delay-aware congestion control scheme is developed that integrates a 1-hop delay constraint to obtain the best solution. Then, a delay support factor is introduced for every connection, and the 1-hop delay constraint is updated by conjointly merging the cumulative arriving flow and the probability of data being rejected at a specific connection. Thus, this protocol maximizes the system reliability and reduces the E2D in a dispersed manner. Finally, extensive simulations establish that the EIEMOC achieves higher network performance compared to the classical protocols in FANETs.
J. Rajeswari, R. Kousalya· International Journal of Ele...· 0 citations