In a paper published in the journal Scientific Reports, researchers discussed the growing interest in intelligent transportation systems (ITS) and the challenges posed by vehicular communication technology, particularly in India's fluctuating traffic conditions. A solution involving the modified Manhattan grid topology was proposed to improve road navigation and reduce accidents.
Study: Traffic Coordination in VANETs with Probabilistic Manhattan Grid Topology. Image Credit: Felix Mizioznikov/Shutterstock
The study incorporated the fuzzy c-means (FCM) algorithm for detecting potential jamming attackers, the modified fisheye state routing (MFSR) algorithm to minimize data exchange among vehicles, and the particle swarm optimization (PSO) algorithm to enhance attacker localization accuracy. Results showed a significant reduction in attackers and improved accuracy, demonstrating the effectiveness of the proposed approach.
Related Work
Past work in vehicular ad-hoc networks (VANETs) has extensively covered various aspects, including stability of cluster members, security analysis, energy-efficient routing techniques, and congestion control algorithms. Many solutions have been utilized, including cluster duration evaluation for reliability assessment, FCM clustering for message reduction, and threshold-based techniques for traffic behavior differentiation.
Other contributions include the vehicle-consensus routing management scheme (VCRMS) for equitable roadside assistance and systems like collaborative information sharing for vehicular ad hoc networks with predictive congestion control (CoNeCT) for predictive congestion control through collaborative information sharing. Additionally, many analysts have developed congestion control algorithms based on transmission power and false message detection methods leveraging Bayesian principles to enhance VANET efficiency and accuracy in traffic management.
Efficient Traffic Management
The proposed methodology aims to minimize injuries from vehicle interactions in mixed traffic conditions on undivided roads. The team achieved this by implementing the modified Manhattan grid topology, which guides drivers on the correct path while navigating undivided roads. The FCM algorithm also identifies potential jamming attackers, and the modified fisheye algorithm reduces information exchange volume. Subsequently, the PSO algorithm calculates more accurate coordinates for jamming attackers within each cluster.
The modified VANET architecture integrates data service, information connector, single and multihop, and application protocol using modified fisheye blocks. The external information connector collects location-specific data through global positioning services (GPS), while data and control packet transmission occur through the physical layer. Clustering plays a pivotal role, with the FCM algorithm forecasting vehicle movements and reducing the count of potential attackers.
The signal flow coordination system synchronizes intersection traffic with the VANET coordination system. The modified Manhattan grid topology alters the traditional model, enhancing vehicle flow efficiency and adherence to geographical constraints. The probabilistic approach determines vehicle movements at intersections, optimizing traffic flow direction.
The MFSR algorithm facilitates the coordination beam, which oversees traffic flow in horizontal and vertical directions. This algorithm reduces the information needed to represent graphical data effectively, optimizing routing efficiency. Routing signals are depicted for vertical and horizontal flows, enhancing traffic management and congestion control.
Enhancing VANET Efficiency
The modified fisheye protocol and the Manhattan topology outperform existing algorithms in VANETs. In the demonstration, PSO iteratively optimizes problem-solving, focusing on a specific distance for the coordination beam's movement. In comparison, the PSO algorithm detects fewer attackers within a 300 km road distance than other algorithms. The efficiency of the modified fisheye routing algorithm, particularly for longer road distances of 1000 km and 5000 km, is highlighted.
The comparative analysis of attackers across different scenarios reveals the superiority of the proposed MFSR algorithm. In scenarios with road distances of 300 km, 1000 km, and 5000 km, the MFSR algorithm consistently identifies fewer attackers compared to existing PSO and FSR algorithms. The reduction in attacker count is particularly notable when employing the MFSR algorithm with a Manhattan grid topology.
The effectiveness of the MFSR algorithm in reducing the number of attackers underscores its potential for enhancing VANETs' security and efficiency. Despite its promising performance, challenges related to security and privacy remain pertinent concerns. Additionally, integrating such systems into smart city frameworks necessitates addressing specific data latency and throughput requirements to ensure seamless operation in real-world traffic environments.
A case study focusing on traffic management at the Kelambakkam intersection in Chennai exemplifies the practical implications of intersection analysis for urban traffic planning. Traffic volume count surveys and observations elucidate congestion patterns at the intersection, highlighting the imperative of implementing effective traffic management strategies to alleviate congestion and ensure smooth vehicular and pedestrian flow.
Conclusion
In summary, the team focused on enhancing road safety through innovative techniques like the modified Manhattan grid topology and fisheye algorithm. These methods aim to guide drivers effectively and detect potential jamming attackers while minimizing vehicle data exchange.
The PSO algorithm enhanced precision in identifying attackers. Results showed a 30% decrease in attacker count and 70% accuracy in challenging traffic scenarios. Future endeavors included extending traffic prediction using artificial intelligence (AI) and simulating realistic transportation scenarios integrated with smart cities' vehicle-to-everything (V2X) communication.
Journal reference:
- Santhi, G. B., Jet al. (2024). Traffic coordination by reducing jamming attackers in VANET using probabilistic Manhattan Grid Topology for automobile applications. Scientific Reports, 14:1, 8365. DOI:10.1038/s41598-024-58240-2, https://www.nature.com/articles/s41598-024-58240-2.
Article Revisions
- Jul 4 2024 - Fixed broken journal link.