Identifying pavement damage is a crucial component in road maintenance and infrastructure management. Prompt detection and corrective action of pavement defects can prevent severe deterioration, maintain safety, and prolong the useful life of road infrastructure. Computer vision is widely applied to vehicle applications, such as driver assistance and self-driving, and that is a good platform to equip the pavement damage recognition service. Therefore, this work applies computer vision to develop the pavement damage detection service named high-performance pavement health recognition (HPPH) to detect common pavement damages, including longitudinal cracks, transverse cracks, alligator cracks, and potholes. The proposed HPPH can be deployed on a vehicle for regular road detection. To fit the requirements of vehicle applications, HPPH considers state-of-the-art techniques to optimize the performance of the recognition service on vehicles, e.g., DeepStream is applied to increase the inference performance. At the same time, YOLOv8s (you only look once, YOLO) provides real-time inference. The experimental results reveal that HPPH provides an accuracy of 90.3% and an average precision 0.5 of 74.3%. Moreover, the optimized HPPH provides three times better than the pure YOLOv8s in terms of the inference speed. In summary, the proposed HPPH provides high recognition accuracy with high efficiency and is feasible to be applied to vehicle applications to realize distributed road maintenance.

Location-based Systems (LBS) are popular for delivering customized information. However, some issues, such as place credibility, the efficiency of position calculations, and communication latency, pose challenges for indoor LBS. This work proposes the High-performance Perspective Platform (H3P) to help network managers understand network users’ information. The H3P provides indoor positioning services based on Wi-Fi 6 (IEEE 802.11ax) to stabilize service quality and ensure high computation efficiency for rapid service response. It utilizes Apache Kafka and Apache Zookeeper clusters on Kubernetes to handle large amounts of data. Wi-Fi usage data is transmitted to Kafka’s distributed real-time data streaming to enhance position credibility and the immediacy of position calculations. The data structure is also optimized to improve computation efficiency. Experimental results show that H3P improves data latency by up to 69% and data insertion latency by up to 73%. Additionally, H3P offers more stability and efficiency than Chang et al., 2012 in terms of data transmission, with an improvement of approximately 16.15%. This allows administrators to manage the network with user-friendly interfaces and a smooth user experience.

Wireless network stability is critical for organizations. Most companies rely on a solid Internet connection for at least part of their day-to-day activities. It is essential to show the fast and high capacity of Wi-Fi. Wireless network management also significantly supplies the user with quality service and helps the system administrator manage and track the network infrastructure. The user must be able to stay connected. The Wi-Fi connection engine was applied in this article using the analysis and locations engine (ALE). ALE provides the information, including MAC address, location, floor information, and the building where the device belongs. From this data, we presented the Web visualization of the Wi-Fi network monitoring system on the map using Cesium. Except for the common information about Wi-Fi users, this work presents the vertical position visualization in the buildings to provide more details about user usage. This work analyzes the user experience of using the proposed system, and latency is the appropriate metric to measure the user experience. The experiment results show that the optimized number of cluster partitions is four, and the latency is less than 1.3 s for 4-MB data. Therefore, the system provides a visualization platform with acceptable system response, and Internet managers would consider the proposed system as the management tool.

Traffic conditions are among the issues most concerned with the general public, and the freeway is a large-scale Internet-of-Things application. In addition to obtaining real-time road usage information, analysis of local road usage habits is crucial in evaluations of government policy implementation. Using road usage data provided by the electronic toll collection (ETC) system, we investigated the data on road usage history on the freeways. The ELK stack was employed to construct a platform for visualizing real-time road usage information and history in this article; the platform is named the local transportation knowledge (LTK) platform. By analyzing more than 500 million pieces of data, the LTK platform proposed in this article efficiently visualized road usage data and facilitated the acquirement of local road usage knowledge. We verified that residents of other counties and cities commuted to Taichung each day. We also discovered that a considerable number of Taichung city residents were employed in Hsinchu Science Park and commuted between the two places. The LTK platform can present real-time freeway traffic conditions, facilitate in-depth analysis of local road usage data, and provide data to verify the relevant information.

In this paper, we focus on finding out the parameter setting of the non-decreasing sponsored search auction (NDSSA) such that the produced outcome is satisfied by both the search engine provider (SEP) and advertisers. Six satisfaction factors are included in our problem formulation. The outcome satisfactions of the SEP and advertisers are modeled as the multi-objective optimization problem based on the satisfaction factors. The weighted versions of L2-norm, cosine similarity, and Euclidean distance are proposed to solve the multi-objective problem. We observe the outcome without bias for both participants in our simulation. Both cosine similarity and Euclidean distance show that SEP and advertisers prefer fixed-MIP to AIMD. Moreover, the initial MIP value should be set as small enough such that each advertiser has at least one opportunity to update his/her bid price.

Determining winners in combinatorial auctions is an NP-complete problem. Based on the idea of searching Nash Equilibria (NE), this paper presents a local search procedure to determine winners. To improve the solution quality calculated by the local search, we propose Nash Equilibrium Search Approach (NESA) to probe various NE solutions. According to the simulation results, auctioneer¡¦s revenue in NESA is at least 1.08% and 9.10% more than that in a general GA and Casanova, respectively. A stable solution based on NESA will be obtained within 400 seconds. Moreover, simulation results show that the solution quality of NESA is near-optimal.