# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Infrastructure
#### 4.1.3 Road Sensor Network (RSN) Infrastructure  
A Wireless Sensor Network (WSN) consists of resource-constrained devices with limited computing power, memory, and energy designed to perform fine-grained sensing. Figure 10 shows a typical WSN. Each WSN node typically comprises a microcontroller, a power source, and a radio transceiver for communication [61]. These nodes are generally classified into four types to facilitate efficient resource management: sensor nodes (SNs) for environmental monitoring, actuator nodes (ACNs) for altering the monitored environment, relay nodes (RNs) for forwarding data, and a base station (BS) for linking the WSN to external networks. The collective name of SNs and ACNs is end nodes (ENs) [61]. The RNs transfer data between ENs and the BS, while the BS collects data from the SNs and relays instructions from the network administrator to the SNs or ACNs. Road sensor networks (RSNs), a specialized subset of WSNs designed for ITS, consist of stationary sensor nodes embedded in or alongside roadway infrastructure. These networks form a multihop architecture to facilitate the real-time acquisition, transmission, and processing of traffic-related data, including vehicle movements, road conditions, and environmental parameters, which it subsequently relays to a central management system through the BS [62; 63]. These networks play a crucial role in traffic management and Smart City applications, utilizing various sensor types such as in-pavement sensors, fiber optic sensors, and WSNs to collect data on traffic volume, vehicle speed, and road roughness [63; 64]. Due to the impracticality of deploying wired links to every roadside sensor, most on-road sensors communicate wirelessly with designated gateway nodes, often employing a cluster-based architecture where local cluster heads aggregate and transmit data via long-range communication methods such as cellular 4G/5G or optical fiber [62]. The topology of these networks is typically hybrid, combining a linear multihop structure along roadways with a star topology between cluster heads and the central system [62]. Beyond traffic monitoring, WSNs contribute to the advancement of autonomous vehicles, providing essential real-time information on road hazards and conditions to enhance safety and operational efficiency [63]. These networks can dynamically adapt to changing traffic patterns, enabling real-time updates and intelligent decision making for effective road traffic management. Their integration into Smart City infrastructure enhances urban mobility, resource allocation, and overall road safety [63]. Regarding the utilization of sensors, [PERSON] et al. [65] presented a parameterized spatio-textual publish/subscribe system for RSNs, focusing on ITS where large volumes of geo-textual data arrive continuously (e.g., sensor readings, events, or alerts). Unlike typical ITS middleware solutions that only support basic keyword or Euclidean distance filtering, this system includes actual road distances. It also allows subscribers to specify their interests with weighted keywords, specific locations, and thresholds for textual match and spatial proximity. The authors developed and compared two indexing architectures:  
Figure 10: A typical WSN. Basic Indexing Architecture (BIA) and Enhanced Indexing Architecture (EIA). The BIA employs a quadtree for spatially organizing road segments, augmented with inverted lists of subscription keywords and tables storing minimum/maximum distance bounds. The EIA improves BIA by adding \"early pruning\" techniques and calculating tighter distance thresholds based on partial keyword matches, aggressively ruling out subscriptions or road networks, thereby reducing the time spent on false matches. The authors simulated the two indexing methods on the San Francisco Bay road network. They found that EIA runs significantly faster, sometimes as low as 17.6% of BIA's computation time. However, the filtering process is computationally intensive, especially as the system is scaling up.