Recent Advances in Vehicular Communication Based on Wireless Sensor Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 487

Special Issue Editors

Department of Software & Computer Engineering, Ajou University, Suwon 16499, Republic of Korea
Interests: vehiclular networks; intelligent transportation systems; IoT systems

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16499, Republic of Korea
Interests: intelligent transportation systems; wireless networks IoT systems; vehicular ad Hoc networks (VANET); flying ad hoc networks (FANET)

Special Issue Information

Dear Colleagues,

As we transition toward fully autonomous transportation systems, the limitations of traditional vehicular ad hoc networks (VANETs), which focus primarily on node-to-node connectivity, are becoming apparent. To achieve Level 4 and 5 autonomies, vehicles require a “hyper-aware” environment where the infrastructure itself is intelligent. This Special Issue will focus on the integration of wireless sensor networks (WSNs) into the vehicular ecosystem. By embedding low-power, high-granularity sensors into roadside units (RSUs) and urban infrastructure, we can create a collaborative sensing environment that transcends the line-of-sight limitations of individual vehicle sensors. We invite original research and review articles on technical and application-oriented topics, including but not limited to the following:

  • Infrastructure-to-Vehicle (I2V) Sensing: Deploying WSNs in “smart roads” for real-time traffic monitoring, pavement health sensing, and detection of hazards (e.g., ice, oil spills).
  • In-Vehicle Sensor Networks (IVSNs): Wireless intra-vehicle communication for monitoring engine (or motor) health, tire pressure, and structural integrity, replacing heavy wired harnesses.
  • Protocol Design: Energy-efficient MAC and routing protocols specifically designed for the high-mobility and intermittent connectivity of vehicular environments.
  • Security and Privacy: Lightweight encryption and blockchain-based trust models for securing sensor data in autonomous driving systems.
  • Data Fusion and AI: Using machine learning to process extensive streams of heterogeneous data from both mobile (vehicle) and static (roadside) sensors for predictive traffic management.
  • Edge/Fog Computing: Architectures that process sensor data at the network edge to meet the ultra-low latency requirements of safety-critical vehicular applications.
  • Next-Generation Connectivity: The role of 6G and terahertz bands, as well as low earth orbit (LEO) satellite communications, in handling high-density sensor data, intent-based vehicular communications, and network management.
  • Satellite–Terrestrial Integration: Hybrid architectures where LEO satellites provide backhaul for remote roadside sensor clusters and fill terrestrial “blackspots”.
  • Non-Terrestrial Network (NTN) Protocols: Adaptation of WSN MAC and routing layers for high-latency, high-mobility satellite-to-vehicle (S2V) links.
  • Satellite-Assisted Edge Computing: Offloading computation-intensive sensing tasks (such as high-resolution LiDAR processing) from vehicles to LEO-based edge servers.
  • Seamless Handover Management: Algorithms for maintaining data continuity as vehicles transition between terrestrial RSUs and moving LEO satellite beams.
  • Integrated Communication, Navigation, and Sensing (ICNR): Leveraging LEO signals for high-precision positioning to supplement GNSS in “urban canyons” or remote tunnels.

This Special Issue will supplement existing research by addressing several “blind spots”. For example, much of the existing V2X literature focuses on communication (how to send data); this issue will shift the focus to sensing (how to generate and validate high-quality data from the physical environment). Additionally, standard VANET research often assumes the existence of high-power nodes. This Special Issue will explore how to integrate resource-constrained WSN nodes (with limited battery and computational power) into robust 5G/6G vehicular architectures. In addition, while many studies focus on vehicle-to-vehicle (V2V) autonomous logic, this Special Issue will highlight the “smart road” perspective, arguing that infrastructure-based sensing is a necessary redundancy for the safety of autonomous vehicles. Moreover, this issue will provide a platform for the burgeoning field of wireless harness replacement, a niche that is often overlooked in broader IoT or automotive electronics journals. By consolidating these themes, the Special Issue will serve as a specialized reference for researchers looking to transition from theoretical communication models to integrated, sensor-driven intelligent transportation systems (ITSs).

Dr. Yiwen Shen
Dr. Bien Aime Mugabarigira
Guest Editors

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Keywords

  • V2X (vehicle-to-everything) sensing
  • intelligent transportation systems
  • edge computing in VANETs
  • in-vehicle wireless sensor networks (IVWSNs)
  • heterogeneous network integration

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Published Papers (1 paper)

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20 pages, 1334 KB  
Article
CATS: Context-Aware Traffic Signal Control with Road Navigation Service for Connected and Automated Vehicles
by Yiwen Shen
Electronics 2026, 15(8), 1747; https://doi.org/10.3390/electronics15081747 - 20 Apr 2026
Viewed by 261
Abstract
Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, [...] Read more.
Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-Time Responsive (CTR) scheme through a two-step selection procedure: it first identifies the phase with the highest cumulative travel time (CTT) and then selects the compatible phase combination with the greatest group CTT, providing an explicit improvement over the single-combination evaluation of the original CTR that allows for a more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion-contribution step function, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. Under a 100% CAV penetration setting, SUMO-based simulations across moderate-to-heavy traffic conditions (vehicle inter-arrival times of 5 to 9 s) show that CATS reduces the mean end-to-end travel time by up to 23.72% and improves the throughput by up to 93.19% over three baselines (fixed-time navigation with enhanced signal control, congestion-aware navigation with original signal control, and fixed-time navigation with original signal control), confirming that the co-design of navigation and signal control produces complementary benefits. Full article
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