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.

Despite recent advancements, deep reinforcement learning (DRL) still struggles at learning sparse-reward goal-directed tasks. Classical planning excels at addressing hierarchical tasks by employing symbolic knowledge, yet most of the methods rely on assumptions about pre-defined subtasks. To bridge the best of both worlds, we propose a framework that integrates DRL with classical planning by automatically inducing task structures and substructures from a few demonstrations. Specifically, genetic programming is used for substructure induction where the program model reflects prior domain knowledge of effect rules. We compare the proposed framework to state-of-the-art DRL algorithms, imitation learning methods, and an exploration approach in various domains. Experimental results show that our proposed framework outperforms all the abovementioned algorithms in terms of sample efficiency and task performance. Moreover, our framework achieves strong generalization performance by effectively inducing new rules and composing task structures. Ablation studies justify the design of our induction module and the proposed genetic programming procedure.

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.

A multi-robot system has advantages in complex tasks, where formation control is one of the most critical and fundamental tasks. For small-sized, autonomous, and enduring robots, realizing high energy and area efficiency is extremely important. This paper presents a approach that combines swarm intelligence and reinforcement learning to realize accurate and reliable operations. An area-energy-efficient hardware architecture is proposed to perform formation control in a distributed robotic system. The proposed system demonstrates substantially lower cost and power consumption when compared with the state-of-the-art designs.

Summary Currently, wireless sensor networks (WSNs) have been widely developed in different domains. Generally, sensors work independently, so it is relatively easier for a WSN to add new nodes. Often, a WSN is constrained to low energy consumption. However, data encryption/decryption not only takes a long time but also consumes many network resources. Due to limited battery power and CPU capability, a sensor's encryption algorithm should not be complicated. However, a simple security algorithm will downgrade the system's security level. Hence, designing a secure wireless transmission system with high encryption/decryption performance, particularly for 5G networks, is an urgent task. Therefore, in this study, we propose an efficient and secure approach, named the Symmetric Key Matrix and Rectangular Frame Scheme (SKeMRF, for short), which involves security mechanisms to speed up encryption/decryption operations for a WSN. Further, the SKeMRF integrates global Symmetric Key Matrix (SKM, for short) and other security mechanisms to ensure the security of transmitted data and enable the system to effectively resist the forgery KDS attack, thus greatly enhancing security of the whole system. Theoretical analysis and simulation show that the SKeMRF is an efficient and highly secure WSN compared with those state‐of‐the‐art schemes.

Summary In recent years, by merit of convenient and unique features, bio‐authentication techniques have been applied to identify and authenticate a person based on his/her spoken words and/or sentences. Among these techniques, speaker recognition/identification is the most convenient one, providing a secure and strong authentication solution viable for a wide range of applications. In this paper, to safeguard real‐world objects, like buildings, we develop a speaker identification system named mel frequency cepstral coefficients (MFCC)‐based speaker identification system for access control (MSIAC for short), which identifies a speaker U by first collecting U's voice signals and converting the signals to frequency domain. An MFCC‐based human auditory filtering model is utilized to adjust the energy levels of different frequencies as U's voice quantified features. Next, a Gaussian mixture model is employed to represent the distribution of the logarithmic features as U's specific acoustic model. When a person, eg, x, would like to access a real‐world object protected by the MSIAC, x's acoustic model is compared with known‐people's acoustic models. Based on the identification result, the MSIAC will determine whether the access will be accepted or denied.

In order to improve accessibility and efficiency of a cloud system, this work proposed a mechanism to integrate Ceph, HDFS and Swift based on the OpenStack. We first build a heterogeneous storage environment including Ceph, HDFS and Swift based on the open source OpenStack and then measure their performances. To integrate storage services of Ceph, HDFS and Swift, we propose a proportion-based file distribution mechanism. The proportion for file partition is dependent on the remaining storage capacity so that we can distribute those sub files to different storage. This mechanism also enhances the file security. In addition, a high usability user interface is provided so as to make the proposed system more friendly. Experimental results show the efficiency of our system.

This work implement a cloud infrastructure that can monitor the status of OpenStack and monitor the real-time status of virtual machine on OpenStack then achieve to energy saving through live migration. The projects of monitoring include the utilization of CPU, load of memory, and power consumption. These data show in real-time, completely monitor the real-time status of physical machines and virtual machines. It also record the utilization and power consumption of physical machines then show on this cloud infrastructure, to provide experimental evidence for the user as a reference. Base on the power consumption we monitoring, we can automatically allocate virtual machines on every physical machines by live migration, to balance the power consumption of every physical machines. Its not only can avoid idle and waste of resources but also can avoid reducing machine life because of the physical machines always keep in high usage, and achieve to power saving.

In this paper, we propose an improved IP-based handover scheme, named the Heterogeneous Network Handover by using 3GPP ANDSF (HNH3A for short) for S-PMIPv6 where S-PMIPv6 is a MIPv6 family protocol developed in one of our previous studies, and ANDSF (standing for Access Network Discovery and Selection Function) is an entity within an evolved packet core (EPC) of the system architecture evolution (SAE) for 3GPP compliant mobile networks. The purpose of using the ANDSF is to assist user equipment (UE) to discover non-3GPP access networks, such as WiFi or WIMAX. The HNH3A can help the handover among mobile WiMAX (i.e., Worldwide Interoperability for Microwave Access), 3GPP (i.e., 3rd Generation Partnership Project) family (including 3GPP systems, 3G LTE and 4G LTE-A) and WiFi based on existing handover techniques among the three heterogeneous networks. The analytical results demonstrate that the HNH3A can effectively mitigate handover delays and handover signaling costs.

Today the quick development of computers and networks not only enriches our everyday life, but also provides us with a convenient living environment. However, hackers are everywhere in the world. Hence, our sensitive data need to be securely protected. Currently, AES and DES as the most widely used block ciphering mechanisms are popularly employed to encrypt important data. But DES was publicly cracked several years ago. On the other hand, due to the fast computer processing speed and processing power, currently no one dare to say that AES is still secure. It may be cracked someday. Therefore, in this paper, we propose a secure encryption method, called the Random Wrapping Encryption Method (RWEM for short), which is developed by employing information of current time as a part of the parameters to wrap up ciphertext in its encryption process. If a file is encrypted at different time points, the content and the length of the wrapped ciphertext file will not be the same. Thus, it is hard for hackers to analyze and crack the ciphertext. We also analyze the security of the DES, AES and RWEM, and explain why the RWEM can effectively protect some specific attacks, and why it is more secure than the DES and AES.

In this paper, we use a wildcard mask to implement the load balance method directly on switches or routers and add a user prediction mechanism to dynamically change the range of the wildcard mask, in this way, the load balance mechanism can be applied conforming to real service situations. In our experiment, we tested the accuracies of flow prediction with different prediction algorithms and compared the delay times and balance effects of the proposed method with other load balancers. With the popularity of cloud computing, the demand of cloud infrastructure also increases. As a result, we also applied our load balance mechanism on cloud services and have proven that the proposed method can be easily applied on varieties of service platforms.

The improvement of sanitary conditions and changes of life style making people's health awareness began to rise. The progress of medical and the promotion of medicine and health, the concept of preventive healthcare is widespread. People begin to concern about the quality of the environment where they stayed. The environmental comfort index include temperature, related humidity, illumination, noise and carbon dioxide. Among these, we monitor and record the values of temperature, related humidity and carbon dioxide. In this paper, we built an environment quality monitoring system, which can adjust the air quality in door and monitor the concentration of harmful gases like formaldehyde, volatile organic compounds and carbon monoxide. If the environment comfort value is not up to standard, the related equipment will be turned on. The system will alert if the content of harmful gases exceed the standard. We hope that based on these real-time data, the proposed system can help people make right and timely decisions, and act on time to maintain a beneficial environment in the monitored area.

Currently, most health care systems used among divisions in medical centers still adopt the Excel file format for a variety of scales statistics, such as the clinical self-care ability scale for Functional Independence Measure. Although people can further analyze Excel files using other statistical analysis software, such as SAS, SPSS, and STATA, they cannot effectively share the archived data in Excel among divisions. We propose to do format conversion on these data and store them in a database. As the collection of Excel files cannot be shared with ease, we plan to use HBase, a non-relational database, to further integrate data. The purpose of this paper is to construct complete import tools and solutions based on HBase to facilitate easy access of data in HBase. Besides, a visual interface is also used to manage HBase to implement user friendly client connection tools for the HBase database.

In this work, we particularly focus on the use of free open-source software, so that end users do not need to spend a huge amount of software license fees. For cloud computing, virtualization technology delivers numerous benefits in addition to being one of the basic roles to build a cloud environment. By virtualization, enterprises can maximize working efficiency without the need to install more facilities in the computer room. In this study, we implemented a virtualization environment and performed experiments on it. The main subject of it is how to use the Open Stack open-source software to build a cloud infrastructure with high availability and a dynamic resource allocation mechanism. It provides a private cloud solution for business and organizations. It belongs to Infrastructure as a Service (IaaS), one of the three service models in the cloud. For the part of the user interface, a web interface was used to reduce the complexity of access to cloud resources for users. We measured the performance of live migration of virtual machines with different specifications and analyzed the data. Also according to live migration modes, we wrote an algorithm to solve the traditional migration problem that needs manually determining whether the machine load is too heavy or not, as a result, the virtual machine load level is automatically detected, and the purpose of automatic dynamic migration to balance resources of servers is achieved.

Along with the improvement of sanitary conditions and changes of life style, people begin to pay attention to the modern concept of health promotion and preventive medicine. Therefore, we built an intelligent environment monitoring feedback system to collect data of physical conditions of employees and air conditions of the working environment, displayed the collected data with a real time interface, and sent out warming messages to prevent accidents. We hope that based on these real-time data, the proposed system can help people make right and timely decisions, and act on time to maintain a beneficial environment in the monitored area.

ABSTRACT In recent years, cloud computing has become popular and its applications widespread. Thus, there exists a common concern, that is, how to arrange and monitor various resources in the cloud computing environment. In the literature, Ganglia and Network Weather Service (NWS) were used to monitor and gather node status and network‐related data, respectively. With supports of Ganglia and NWS, one can effectively administer available resources in the cloud computing environment. In order to achieve high performance of cloud computing, comprehensive monitoring and efficient management are critical. Ganglia is often used to gather status data of resources, such as live states of hosts, CPU or memory utilizations, and surely Ganglia is also capable of monitoring network‐related information; however, instead of Ganglia, we used NWS services to gather network‐related information such as end‐to‐end transmission control protocol/Internet protocol performance data. Compared with Ganglia, NWS services offer more selections and flexibility for measurement schemes. Besides, NWS services could be deployed with nonintruding manner that makes it easier and faster in deploying services to cloud nodes. The network‐related information is acquired immediately after deployment. Although NWS services also provide measurements for CPU and memory utilizations, but less functionality is provided by them than Ganglia in these aspects. Therefore, we combine advantageous features of Ganglia and NWS to achieve the aims of effective monitoring and management of available resources in the cloud environment. Nevertheless, Ganglia and NWS services may not provide sufficient data in realistic situations due to diversified needs of users, especially application developers. For instance, users are not able to directly access utilizations or allocations of resources in the cloud environment via interfaces or channels of Ganglia or NWS. In addition, NWS services based on a domain‐based network information model could greatly decrease overheads caused by unnecessary measurements. Hence, we propose a heuristic QoS measurement approach based on the domain‐based information model. This measurement approach is capable of providing essential information to satisfy user requirements, and thus let users manage and monitor various resources in the cloud environment in a more efficient way. © 2013 Wiley Periodicals, Inc.

In recent years, the target of the denial-of-service attacks, such as the Http flooding attack, have been switched from the network layer to the application layer. Various attacking schemes are adopted by the attackers to break down the Web site server, some of them send tremendous amount of packets to break down the target host, others send specific request packets to keep the Web site server busy in performing great amount of computations or searches of the database. In order to develop a detection scheme to detect denial-of-service attacks, one needs log data for analysis of browsing behaviors of users. But suitable log data are not easy to obtain and some of available ones are out of date and are not appropriate for analyzing behaviors of present networks. So, we propose an access log generator to generate access logs with characteristics of browsing behaviors for the particular Web site under investigation. We also include malicious behaviors into the generated access log, which is combined with actual access log of the Web site for further tests and analyses.

For the current technique, the tuning latency ¿ on each transmitter is more than the packet transmission time, and it cannot be ignored in packet scheduling. So in order to reduce the serious affection of tuning operations, we propose the multi-hop model for the All-to-All Broadcast (AAB) problem in WDM optical star networks with N nodes and w available wavelengths, which the number of hop distance for packet transmissions is limited to a constant ¿. Our strategy can overlap the tuning latency in multi-hop scheduling, and reduce the number of tuning operations on each transmitter to 2¿(w - 1)/¿¿. If ¿ can be completely covered in scheduling, the schedule length is k(k - 1) + ¿ + 2¿(w-1)/¿¿[k2¿(¿+1)/2] time slots, where k = N/w. Otherwise, it is k(k - 1) + ¿ + 2¿(w-1)/¿¿[k¿(¿+1)/2 + ¿] slots. Our proposed method is very suitable for the scalable WDM networks if the number of wavelength is fixed, and its multi-hop schedule length is shorter than that of other scheduling approaches if k is small or ¿ is larger.

In WDM-based network, a single fiber abortion may cause many logical lightpaths failures such that embedded logical topology of a WDM network may become disconnected. And a hung amount of data of a single link carried is lost by this link errors. For this reason, we propose an efficient survivable routing approach, which is based on the technique of ear-decomposition, to create protected routing of the embedded logical topology that can withstand a physical link failure. Our approach divides the logical topology into several ears, and restricts the lightpaths of the same ear are routed by using disjoint physical links. To solve the survivable problem is NP-complete and we formulate the survivable routing problem as an ILP problem based on the results of ear decomposition. And our experiments has shown that the solution generated from our ILP achieves the following two results: (I) a high performance in terms of the survivable routing and (2) a better performance than previous research results for the balance of traffic loads.