Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle.

In the context of construction and demolition waste exacerbating environmental pollution, the lack of recycling technology has hindered the green development of the industry. Previous studies have explored robot-based automated recycling methods, but their efficiency is limited by movement speed and detection range, so there is an urgent need to integrate drones into the recycling field to improve construction waste management efficiency. Preliminary investigations have shown that previous construction waste recognition techniques are ineffective when applied to UAVs and also lack a method to accurately convert waste locations in images to actual coordinates. Therefore, this study proposes a new method for autonomously labeling the location of construction waste using UAVs. Using images captured by UAVs, we compiled an image dataset and proposed a high-precision, long-range construction waste recognition algorithm. In addition, we proposed a method to convert the pixel positions of targets to actual positions. Finally, the study verified the effectiveness of the proposed method through experiments. Experimental results demonstrated that the approach proposed in this study enhanced the discernibility of computer vision algorithms towards small targets and high-frequency details within images. In a construction waste localization task using drones, involving high-resolution image recognition, the accuracy and recall were significantly improved by about 2% at speeds of up to 28 fps. The results of this study can guarantee the efficient application of drones to construction sites.

Dynamic Privacy-Preserving Anonymous Authentication Scheme for Condition-Matching in Fog-Cloud-Based VANETs.

Secure group communication in Vehicle Ad hoc Networks (VANETs) over open channels remains a challenging task. To enable secure group communications with conditional privacy, it is necessary to establish a secure session using Authenticated Key Agreement (AKA). However, existing AKAs suffer from problems such as cross-domain dynamic group session key negotiation and heavy computational burdens on the Trusted Authority (TA) and vehicles. To address these challenges, we propose a dynamic privacy-preserving anonymous authentication scheme for condition matching in fog-cloud-based VANETs. The scheme employs general Elliptic Curve Cryptosystem (ECC) technology and fog-cloud computing methods to decrease computational overhead for On-Board Units (OBUs) and supports multiple TAs for improved service quality and robustness. Furthermore, certificateless technology alleviates TAs of key management burdens. The security analysis indicates that our solution satisfies the communication security and privacy requirements. Experimental simulations verify that our method achieves optimal overall performance with lower computational costs and smaller communication overhead compared to state-of-the-art solutions.

FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms.

The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers' concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into modeling based on historical data or a combination of sparse measurements and interpolation/modeling. In pursuit of service response and conserving network bandwidth, platforms have progressively expanded from the traditional cloud to the edge. Compared to cloud platforms, serverless edge platforms often lead to more running overhead due to their limited resources, resulting in undesirable financial costs for developers when using the existing solutions. Meanwhile, it is extremely challenging to handle the heterogeneity of edge platforms, characterized by distinct pricing owing to their varying resource preferences. To tackle these challenges, we propose an adaptive and efficient approach called FireFace, consisting of prediction and decision modules. The prediction module extracts the internal features of all functions within the serverless application and uses this information to predict the execution time of the functions under specific configuration schemes. Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). Extensive experimental results show that our prediction model obtains optimal results under all three metrics, and the prediction error rate for real-world serverless applications is in the range of 4.25∼9.51%. Our approach can find the optimal resource configuration scheme for each application, which saves 7.2∼44.8% on average compared to other classic algorithms. Moreover, FireFace exhibits rapid adaptability, efficiently adjusting resource allocation schemes in response to dynamic environments.

A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks.

With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud center is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific form of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on workflow applications (WAs) in wireless metropolitan area networks (WMANs). We devise a cloudlet placement strategy based on a particle swarm optimization algorithm using genetic algorithm operators with the encoding library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlet placement scheme. Compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04-44.99%.