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Abstract This study aimed to assess the performance of an artificial intelligence (AI) model for predicting clinical pregnancy using enhanced inner cell mass (ICM) and trophectoderm (TE) images. In this retrospective study, we included static images of 2555 day-5-blastocysts from seven in vitro fertilization centers in South Korea. The main outcome of the study was the predictive capability of the model to detect clinical pregnancies (gestational sac). Compared with the original embryo images, the use of enhanced ICM and TE images improved the average area under the receiver operating characteristic curve for the AI model from 0.716 to 0.741. Additionally, a gradient-weighted class activation mapping analysis demonstrated that the enhanced image-trained AI model was able to extract features from crucial areas of the embryo in 99% (506/512) of the cases. Particularly, it could extract the ICM and TE. In contrast, the AI model trained on the original images focused on the main areas in only 86% (438/512) of the cases. Our results highlight the potential efficacy of using ICM- and TE-enhanced embryo images when training AI models to predict clinical pregnancy.

BackgroundCurrent embryo assessment methods for in vitro fertilization depend on subjective morphological assessments. Recently, artificial intelligence (AI) has emerged as a promising tool for embryo assessment; however, its clinical efficacy and trustworthiness remain unproven. Simulation studies may provide additional evidence, provided that they are meticulously designed to mitigate bias and variance. ObjectiveThe primary objective of this study was to evaluate the benefits of an AI model for predicting clinical pregnancy through well-designed simulations. The secondary objective was to identify the characteristics of and potential bias in the subgroups of embryologists with varying degrees of experience. MethodsThis simulation study involved a questionnaire-based survey conducted on 61 embryologists with varying levels of experience from 12 in vitro fertilization clinics. The survey was conducted via Google Forms (Google Inc) in three phases: (1) phase 1, an initial assessment (December 23, 2022, to January 22, 2023); (2) phase 2, a validation assessment (March 6, 2023, to April 5, 2023); and (3) phase 3 an AI-guided assessment (March 6, 2023, to April 5, 2023). Inter- and intraobserver assessments and the accuracy of embryo selection from 360 day-5 embryos before and after AI guidance were analyzed for all embryologists and subgroups of senior and junior embryologists. ResultsWith AI guidance, the interobserver agreement increased from 0.355 to 0.527 and from 0.440 to 0.524 for junior and senior embryologists, respectively, thus reaching similar levels of agreement. In a test of accurate embryo selection with 90 questions, the numbers of correct responses by the embryologists only, embryologists with AI guidance, and AI only were 34 (38%), 45 (50%), and 59 (66%), respectively. Without AI, the average score (accuracy) of the junior group was 33.516 (37%), while that of the senior group was 35.967 (40%), with P

Internet of Things (IoT) devices supporting intelligent cloud applications such as healthcare for hospitals rely on connecting with local base stations and access points to provide rich data analysis and real-time services to users. Devices authenticate with local base stations and perform handover operations to connect with access points with higher signal strength. Attackers disguise as valid base stations and access points using publicly accessible SSID information connect with local IoT devices during the handover process and give rise to data integrity and privacy concerns. This paper proposes a lightweight authentication scheme for private blockchain-based networks for securing devices from rogue base stations during the handover process. An authentication certificate is designed for base stations and machines in local clusters using SHA256 and modulo operations for enabling quick handover operations. The keys assigned to each device and base station joining the network are hashed, and their sizes are reduced using modulo operations. Furthermore, the compressed key size forms a certificate, which is used by the machines and the base stations to authenticate mutually. In comparison with existing studies, the performance analysis of the proposed scheme is based on the transmission of three messages required for completing the authentication process. Evaluation based on the Communication Overhead demonstrates a minimum improvement of 99.30% fewer bytes exchanged during the handover process and 89.58% reduced Storage Overhead compared with existing studies.

Background and Aims: While reports suggest that the Inner Cell Mass (ICM) ratio may impact pregnancy rates, the Gardner criteria for grading ICM does not specify the ICM size or ratio. This study investigates the potential impact of the ICM ratio on predicting ICM grades using artificial intelligence. Methods: The study analyzed 1,963 Day 5 blastocyst images obtained from seven IVF clinics between June 2011 and May 2022. The images were matched with metadata, including ICM grade and ICM ratio. ICM ratio was computed as the ratio of the ICM area to the embryo area. ICM was graded according to the Gardner criteria and only ICM A and C grade groups were used to avoid ambiguity. Two CNN (Convolutional Neural Network) models were built: one with embryo images including ICM ratio and one without. Results: The use of ICM ratio in conjunction with the original grading model resulted in an improved model performance. Logistic regression analysis revealed the ICM ratio as a significant variable impacting embryo grade, with a p-value of less than 0.001. The coefficient for ICM ratio demonstrated an 8-fold increase in the odds ratio for grade A embryos with every 1-unit increase. Conclusions: We demonstrated that the CNN model could predict ICM grades with fair accuracy and that the inclusion of the ICM ratio significantly improved its performance. While CNNs are widely used for image analysis by extracting features from patterns, they may not effectively learn the characteristics of an area. To capture the important area-related features, measured values should be considered along with the image. Furthermore, the ICM ratio appears to be a significant feature that embryologists consider when grading embryos in clinical settings, beyond the Gardner criteria. Additional studies are warranted to investigate other features for evaluating ICM.

The field of polymer‐based nanoscience has always been of significant interest in the search for polymer/carbon nanotube (CNT) nanocomposites with optimized material properties for new applications. Herein, it is demonstrated that data collected from the online literature can be used to develop an efficient deep learning model to design acrylonitrile–butadiene–styrene (ABS)/CNT binary composites. A dataset of 14 945 data points is constructed from 110 studies. The results demonstrate that compared with a vanilla deep regression model, the proposed model achieves a 26% lower average mean absolute error and an accuracy of 80.6%, which is 16.2% higher than the vanilla deep regression model in predicting the six electrical and mechanical properties of target ABS/CNT composites. In addition, a Monte Carlo simulation integrated with the developed deep neural network (DNN) model effectively screens input variables for users and thus appropriately guides them to manufacture a composite product with desired physical properties.

Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs; instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories.

Recently, many IoT applications, such as smart transportation, healthcare, and virtual and augmented reality experiences, have emerged with fifth-generation (5G) technology to enhance the Quality of Service (QoS) and user experience. The revolution of 5G-enabled IoT supports distinct attributes, including lower latency, higher system capacity, high data rate, and energy saving. However, such revolution also delivers considerable increment in data generation that further leads to a major requirement of intelligent and effective data analytic operation across the network. Furthermore, data growth gives rise to data security and privacy concerns, such as breach and loss of sensitive data. The conventional data analytic and security methods do not meet the requirement of 5G-enabled IoT including its unique characteristic of low latency and high throughput. In this paper, we propose a Deep Learning (DL) and blockchain-empowered security framework for intelligent 5G-enabled IoT that leverages DL competency for intelligent data analysis operation and blockchain for data security. The framework’s hierarchical architecture wherein DL and blockchain operations emerge across the four layers of cloud, fog, edge, and user is presented. The framework is simulated and analyzed, employing various standard measures of latency, accuracy, and security to demonstrate its validity in practical applications.

Abstract Background Macrophages play a crucial role in inflammation. Astilbe chinensis is one of perennial herbs belonging to the genus Astilbe. Plants in the genus have been used for pain, headaches, arthralgia, and chronic bronchitis. However, the effect of A.chinensis on inflammation remains unclear. To study the anti-inflammatory action of A.chinensis ethanol extract (ACE), we investigated the effect of ACE on the production of pro-inflammatory mediators and cytokines in macrophages. Methods We evaluated the effectiveness of ACE in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages and thioglycollate (TG)-elicited peritoneal macrophages from male C57BL/6 mice. We measured the levels of pro-inflammatory mediators and cytokines, and examined the anti-inflammatory actions of ACE on nuclear factor κB (NF-κB) pathway in the macrophages. Western blot analysis and immunofluorescence microscopy were used to determine protein level and translocation, respectively. Results ACE suppressed the output of nitric oxide (NO), prostaglandin E2 (PGE2), and pro-inflammatory cytokines in stimulated macrophages via inhibiting the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) proteins. ACE suppressed mRNA expression of pro-inflammatory cytokines such as interleukin (IL)-6 and tumor necrosis factor-alpha (TNF-α). We examined the efficacies of ACE on NF-κB activation by measuring the expressions including IκB kinase (IKK), inhibitor of κB (IκB), and nuclear p65 proteins. In addition, the inhibition of NF-κB p65’s translocation was determined with immunofluorescence assay. Conclusion Our findings manifested that ACE inhibited LPS or TG-induced inflammation by blocking the NF-κB signaling pathway in macrophages. It indicated that ACE is a potential therapeutic mean for inflammation and related diseases.

Although cervical spinal deformity (CSD) can have a profoundly negative impact on an individual’s quality of life and there have been many advances in surgical treatment of CSD in recent years, there exists no comprehensive classification system of surgical treatment that categorizes anterior and posterior surgery separately according to the grade of surgery. The objective of this study is to introduce the new classification system of various surgical treatments for CSD. We developed a new classification system (SOF system) for CSD surgery that describes the sequence of surgical approach (S), the grade of osteotomy (O), and the information of fixation (F) using alphanumeric codes. This new classification system can provide a consistent description of the various osteotomies performed in CSD surgery. Especially, regarding research, there has been a clear benefit to this classification. Having a standardized classification that allows for common frame for cervical deformity correction surgery, communication between surgeons and the evaluation of the CSD surgeries make it possible to conduct global comparative research about surgical outcome.

Craniovertebral junction (CVJ) deformity is a challenging pathology that can result in progressive deformity, myelopathy, severe neck pain, and functional disability, such as difficulty swallowing. Surgical management of CVJ deformity is complex for anatomical reasons; given the discreet relationships involved in the surrounding neurovascular structures and intricate biochemical issues, access to this region is relatively difficult. Evaluation of the reducibility, CVJ alignment, and direction of the mechanical compression may determine surgical strategy. If CVJ deformity is reducible, posterior in situ fixation may be a viable solution. If the deformity is rigid and the C1–2 facet is fixed, osteotomy may be necessary to make the C1–2 facet joint reducible. C1–2 facet release with vertical reduction technique could be useful, especially when the C1–2 facet joint is the primary pathology of CVJ kyphotic deformity or basilar invagination. The indications for transoral surgery are becoming as narrow as a treatment for CVJ deformity. In this article, we will discuss CVJ alignment and various strategies for the management of CVJ deformity and possible ways to prevent complications and improve surgical outcomes.

Abstract As a promising transparent semiconducting oxide (TSO) candidate, zinc-tin-oxide (ZTO) thin films were fabricated by combining solution coating and ultraviolet (UV) laser annealing. Instead of external heating, an intense UV laser was applied to transform sol-gel coatings via surface heating and photoexcited dissociation into oxide films. The laser-induced phase transformation was extensively investigated with synchrotron-based X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and electron energy loss spectroscopy (EELS). The chemical states and microscopic distributions of oxygen, zinc, and tin were significantly modified during the laser irradiation. Relative oxidation and surface migration between zinc and tin gradually evolved toward a combination of ZnO1−x host and SnO2−y grains. Our results present deeper insight into the use of UV laser annealing for developing a room temperature (RT) fabrication method of TSO thin films and other relevant solution coatings.

Internet of Things (IoT) technology provides the basic infrastructure for a hyper connected society where all things are connected and exchange information through the Internet. IoT technology is fused with 5G and artificial intelligence (AI) technologies for use various fields such as the smart city and smart factory. As the demand for IoT technology increases, security threats against IoT infrastructure, applications, and devices have also increased. A variety of studies have been conducted on the detection of IoT malware to avoid the threats posed by malicious code. While existing models may accurately detect malicious IoT code identified through static analysis, detecting the new and variant IoT malware quickly being generated may become challenging. This paper proposes a dynamic analysis for IoT malware detection (DAIMD) to reduce damage to IoT devices by detecting both well-known IoT malware and new and variant IoT malware evolved intelligently. The DAIMD scheme learns IoT malware using the convolution neural network (CNN) model and analyzes IoT malware dynamically in nested cloud environment. DAIMD performs dynamic analysis on IoT malware in a nested cloud environment to extract behaviors related to memory, network, virtual file system, process, and system call. By converting the extracted and analyzed behavior data into images, the behavior images of IoT malware are classified and trained in the Convolution Neural Network (CNN). DAIMD can minimize the infection damage of IoT devices from malware by visualizing and learning the vast amount of behavior data generated through dynamic analysis.

Recently, wearable technologies have evolved in the most unexpected field like a military force outside of healthcare, fitness, lifestyle and similar areas. The growing need for soldiers' coordination, training and health, the increase in asymmetric warfare, suspected geopolitical conflicts and soldiers' modernization programs, among others, are some of the factors that fuel the growth of the military wearables market. Wearable computation plays an important role in improving the capabilities of the soldier. Further, the 5G network promises a solution to the many network and performance challenges in order to adopt more sophisticated wearable technologies in defense automation. In this paper, we conduct a study to identify the role of wearable computing for the defence automation system. We present the taxonomy of wearable computing in defence automation system and explain the relationship of each attribute. In addition, we identify the raise issues and challenges in communication and cybersecurity when deploying the 5G network in defense automation. Furthermore, we propose the design of the wearable smartwatch architecture as a use case of healthcare transformation in defense automation in the 5G environment.

As IoT devices are always connected to mobile devices or other computing devices via the Internet, clever malwares targeting IoT devices or other computing devices connected to IoT devices are emerging. Therefore, effective IoT security research is needed to respond to hacking attacks by these kinds of malware. This paper studied the method of identifying and analyzing malware combined with social engineering from the perspective of digital forensics. The paper classified and analyzed intelligent malware characteristics and proposed a method of quickly identifying and analyzing the malware that secretly intruded into the devices installed with Android, Linux OS, using digital forensics techniques. Moreover, this paper proved its effectiveness by applying this investigation method to two actual malware cases. The research outcomes will be useful in responding to increasingly clever malware attacking IoT devices.

Nowadays, 5G network is considered to be one of the main pillars of various industries, including the Internet of Things (IoT), smart cities, virtual reality, and many more. Unlike previous network generations, 5G utilizes complex digital technologies such as massive Multiple Input Multiple Output (mMIMO) and runs over higher radio frequencies. The introduction of new technologies and advanced features in the 5G network raises new challenges for network operators, and merging Artificial Intelligence (AI) is one of the effective solutions to address these complexities. However, AI-enabled 5G network engenders security concerns and requires improvement to meet the standardization and qualification of the new network generation. To mitigate these dilemmas, Blockchain must be integrated. Blockchain, as a decentralized methodology provides a secure sharing of information and resources among various nodes of 5G environments. Blockchain can support other technologies, such as AI-based 5G, to create smarter, more efficient, and secure cellular networks. In this article, we present a comprehensive intelligence and secure data analytics framework for 5G networks based on the convergence of Blockchain and AI named “Block5GIntell”. We depict the applications of Blockchain and AI on 5G networks separately and we argue on the support that Blockchain can provide for AI to create smart and secure 5G networks relying on our proposed framework. To support our proposition, we present an energy-saving case study using Blockchain for AI-enabled 5G. The simulation shows an overall 20% decrease in energy consumption at the RAN level.

Introducción: la enfermedad inflamatoria intestinal (EII) es una enfermedad inmunomediada, cuya incidencia en Latinoamérica ha aumentado en los últimos años. Objetivo: analizar las características demográficas y clínicas de los pacientes con EII tratados en un hospital universitario y presentar los datos epidemiológicos con respecto a otros centros en Colombia. Pacientes y métodos: estudio descriptivo de pacientes con EII (1996-2019) en el Hospital Universitario Fundación Santa Fe de Bogotá. Análisis de datos de centros de Medellín, Cali, Bogotá y Cartagena. Resultados: de 386 pacientes, 277 presentaron colitis ulcerativa (CU), 102 enfermedad de Crohn (EC) y 7 colitis no clasificable. La EII fue más frecuente en mujeres (53 %). La mortalidad fue menor de 1 %. El compromiso de la CU fue principalmente la pancolitis (42,6 %). Entre mayor la extensión de la enfermedad, más alta fue la tasa de hospitalización y cirugías (OR 3,70; p < 0,01). El 13 % de los pacientes con CU recibió biológicos. El compromiso por la EC fue principalmente ileocolónico (43,6 %) e ileal (43,6 %). El patrón clínico predominante de la EC fue estenosante (50%). El 45 % recibió biológicos y 56% cirugía. La colangitis esclerosante primaria (CEP) se encontró en 4 % de los pacientes (n = 15). Dos pacientes con CEP desarrollaron cáncer colorrectal (OR 4,18; p 0,008), mientras que 13 pacientes con CU desarrollaron cáncer de colon y 7 cambios displásicos. 3 pacientes con EC desarrollaron cáncer de colon. Conclusiones: se compararon los resultados en relación con otros centros de referencia. Encontramos tendencias similares en el comportamiento clínico y en el tratamiento de la EII, con mayores tazas de hospitalizaciones y cirugías en nuestros casos.

Intertwining smart manufacturing and the Internet of Things (IoT) is known as the Industrial Internet of Things (IIoT). IIoT improves product quality and reliability and requires intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. Recently, it has been increasingly deployed; however, multi-party collaborative information processing is often required in heterogeneous IIoT. The security and efficiency requirements of each party interacting with other partners have become a significant challenge in information security. This paper proposes an automated smart manufacturing framework based on Digital Twin (DT) and Blockchain. The data used in the DT are all from the cluster generated after blockchain authentication. The processed data in the DT will only be accessed and visualized in the cloud when necessary. Therefore, all the data transmitted in the process are result reports, avoiding the frequent transmission of sensitive data. Simulation results show that the proposed authentication mode takes less time than the standard protocol. In addition, our DT framework for a smart factory deploys the PDQN DRL model, proving to have higher accuracy, stability, and reliability.

Carbon fiber-reinforced polymers (CFRPs) show excellent mechanical strength and lightweight. However, conventional CFRPs require high loading of carbon fibers (CFs) to improve their mechanical properties due to poor polymer–CF interfacial adhesion. We propose a dry and ecofriendly method to fabricate high-performance CFRPs by improving interfacial adhesion. The plasma-assisted mechanochemistry (PMC) process can create physical and chemical linkages between polymers and carbon fillers via application of a mechanical force and plasma treatment. We use carbon nanotubes as bridge materials to increase the effective interfacial area and promote mechanical interlocking. The PMC-treated CFRPs exhibit reduced C-factor and increased degree of entanglement, indicating efficient stress transfer the polymer to the filler. Additionally, the PMC process can provide the significantly improved mechanical properties of CFRPs even at low CF loading. Notably, the PMC-treated CFRPs showed continuously increasing tensile strength, Young's modulus, and elongation at break with increasing CF content up to 30 wt%, which has not been observed previously for CFRPs. As a result, the tensile strength, Young's modulus, and elongation at break of the PMC-treated composites with a CF content of 30 wt% increased by 47.1%, 43.0%, and 91.7%, respectively, compared to those of the conventional composites. We believe that this simple approach opens a new route to the fabrication of high-performance CFRPs exhibiting extraordinary mechanical properties.

Resource constraints in the Industrial Internet of Things (IIoT) result in brute-force attacks, transforming them into a botnet to launch Distributed Denial of Service Attacks. The delayed detection of botnet formation presents challenges in controlling the spread of malicious scripts in other devices and increases the probability of a high-volume cyberattack. In this paper, we propose a secure Blockchain-enabled Digital Framework for the early detection of Bot formation in a Smart Factory environment. A Digital Twin (DT) is designed for a group of devices on the edge layer to collect device data and inspect packet headers using Deep Learning for connections with external unique IP addresses with open connections. Data are synchronized between the DT and a Packet Auditor (PA) for detecting corrupt device data transmission. Smart Contracts authenticate the DT and PA, ensuring malicious nodes do not participate in data synchronization. Botnet spread is prevented using DT certificate revocation. A comparative analysis of the proposed framework with existing studies demonstrates that the synchronization of data between the DT and PA ensures data integrity for the Botnet detection model training. Data privacy is maintained by inspecting only Packet headers, thereby not requiring the decryption of encrypted data.

Medical supply chain communication networks engender critical information and data. Notably in the COVID era, inner personal and private information is being shared between healthcare providers regarding the medical supply chain. In recent years, multiple cyber-attacks have targeted medical supply chain communication networks due to their lack of security measures. In the era where cyber-attacks are cheaper and easier due to the computational power and various algorithms available for malicious uses, security, and data privacy requires intensive and higher measures. On the other hand, Information Hiding Techniques (IHT) compromise various advanced methods to hide sensitive information from being disclosed to malicious nodes. Moreover, with the support of Blockchain, IHT can bring higher security and the required privacy levels. In this paper, we propose the implementation of Blockchain and smart contract with the information hiding technique to enhance the security and privacy of data communication in critical systems, such as smart healthcare supply chain communication networks. Results show the feasibility of the framework using Hyperledger smart contract along with the desired security level.

The Internet of Things (IoT) leads to intelligent services by collecting information from tiny sensor devices. In recent years, storage-less sensing devices have been used to implement IoT services. They depend on delivered software from a network server to operate service functions, and IoT services are based on collected user information. Therefore, it is important to maintain trusted connections during software delivery or data transmission. If a network connection is untrustworthy, stable data transmission cannot be achieved. Untrustworthy data connections cause many problems in IoT services. Therefore, this paper proposes a software update method in trusted connection of IoT networking. The proposed method employs a low-power wide area network (LPWAN) as a long-range IoT networking technology and uses a mobile edge cloud to improve computing efficiency in an access network that consists of IoT devices with insufficient resources. In the proposed method, the mobile edge cloud is integrated into a gateway and processes sensing data and remote software updates of LPWAN. IoT devices can receive software functions from the mobile edge cloud. The proposed method analyzes statistical information about connections in an access network and determines the LPWAN trusted connections. Then, software updates can be performed over the trusted connection. Using trusted connections leads to an increased packet delivery rate and reduced transmission energy consumption. The proposed method is compared with currently available systems through computer simulation and the proposed method’s efficiency is validated.

The recent expansion of the Internet of Things (IoT) and the consequent explosion in the volume of data produced by smart devices have led to the outsourcing of data to designated data centers. However, to manage these huge data stores, centralized data centers, such as cloud storage cannot afford auspicious way. There are many challenges that must be addressed in the traditional network architecture due to the rapid growth in the diversity and number of devices connected to the internet, which is not designed to provide high availability, real-time data delivery, scalability, security, resilience, and low latency. To address these issues, this paper proposes a novel blockchain-based distributed cloud architecture with a software defined networking (SDN) enable controller fog nodes at the edge of the network to meet the required design principles. The proposed model is a distributed cloud architecture based on blockchain technology, which provides low-cost, secure, and on-demand access to the most competitive computing infrastructures in an IoT network. By creating a distributed cloud infrastructure, the proposed model enables cost-effective high-performance computing. Furthermore, to bring computing resources to the edge of the IoT network and allow low latency access to large amounts of data in a secure manner, we provide a secure distributed fog node architecture that uses SDN and blockchain techniques. Fog nodes are distributed fog computing entities that allow the deployment of fog services, and are formed by multiple computing resources at the edge of the IoT network. We evaluated the performance of our proposed architecture and compared it with the existing models using various performance measures. The results of our evaluation show that performance is improved by reducing the induced delay, reducing the response time, increasing throughput, and the ability to detect real-time attacks in the IoT network with low performance overheads.

The rapid development of information & communication technology has led to the wide popularity of mobile devices, which have helped to improve business efficiency and enabled simple mobility as small and light devices and convenience of being available anytime, anywhere for cyber-physical-social big data. There are many ongoing studies on mobile cloud computing (MCC) to overcome the limited computing capability and storage capacity and internal battery limitation by taking advantage of the popularity of mobile devices for the processing cyber-physicalsocial big data. MCC consists of service-oriented architecture, agent-client architecture, and collaborative architecture, with job splitting and allocation as the critical factor. As such, job allocation techniques considering the performance resources of mobile devices have been studied. Note, however, that there is a problem of job reallocation due to continuous battery consumption, since the studies consider only the performance resources of mobile devices at the time of job allocation or take into account the performance resources and remaining battery power only. This paper proposes the job allocation mechanism (JAM) for battery consumption minimization of cyber-physical-social big data processing in MCC, which continuously reflects the battery consumption rate to process jobs with mobile devices only without an external cloud server in a collaborative architecture-based MCC environment. JAM allocates jobs considering the periodic measurement of battery consumption and surplus resource to minimize the problem of job reallocation due to battery rundown of the mobile devices. This paper designs and implements a system for verifying JAM and demonstrated that the job processing speed increased in an MCC environment for cyber-physical-social big data.

Healthcare applications store private user data on cloud servers and perform computation operations that support several patient diagnoses. Growing cyber-attacks on hospital systems result in user data being held at ransom. Furthermore, mathematical operations on data stored in the Cloud are exposed to untrusted external entities that sell private data for financial gain. In this paper, we propose a privacy-preserving scheme using homomorphic encryption to secure medical plaintext data from being accessed by attackers. Secret sharing distributes computations to several virtual nodes on the edge and masks all arithmetic operations, preventing untrusted cloud servers from learning the tasks performed on the encrypted patient data. Virtual edge nodes benefit from cloud computing resources to accomplish computing-intensive mathematical functions and reduce latency in device–edge node data transmission. A comparative analysis with existing studies demonstrates that homomorphically encrypted data stored at the edge preserves data privacy and integrity. Furthermore, secret sharing-based multi-node computation using virtual nodes ensures data confidentiality from untrusted cloud networks.

Topology control is one of the significant research topics in traditional wireless networks. The primary purpose of topology control ensures the connectivity of wireless nodes participated in the network. Low-power Internet of Things communication networks look like wireless network environments in which the main communication devices are wireless devices with limited energy like battery. In this paper, we propose a distributed topology control algorithm by merging the combinatorial block design from a design theory with the multiples of 2. The proposed technique especially focuses on asynchronous and asymmetric neighbor discovery. The concept of block design is used to generate the neighbor discovery schedule when a target duty cycle is given. In addition, the multiples of 2 are applied to overcome the challenge of the block design and support asymmetric operation. We analyze the worst case discovery latency and energy consumption numerically by calculating the total number of slots and wake-up slots based on the given duty cycle. It shows that our proposed method has the smallest total number of slots and wake-up slots among existing representative neighbor discovery protocols. The numerical analysis represents the proposed technique find neighbors quickly with minimum battery power compared with other protocols for distributed topology control. For future research direction, we could perform a simulation study or real experiment to investigate the best parameter for choosing the multiple of a certain number.

Nowadays, data security is becoming an emerging and challenging issue due to the growth in web-connected devices and significant data generation from information and communication technology (ICT) platforms. Many existing types of research from industries and academic fields have presented their methodologies for supporting defense against security threats. However, these existing approaches have failed to deal with security challenges in next-generation ICT systems due to the changing behaviors of security threats and zero-day attacks, including advanced persistent threat (APT), ransomware, and supply chain attacks. The symmetry-adapted machine-learning approach can support an effective way to deal with the dynamic nature of security attacks by the extraction and analysis of data to identify hidden patterns of data. It offers the identification of unknown and new attack patterns by extracting hidden data patterns in next-generation ICT systems. Therefore, we accepted twelve articles for this Special Issue that explore the deployment of symmetry-adapted machine learning for information security in various application areas. These areas include malware classification, intrusion detection systems, image watermarking, color image watermarking, battlefield target aggregation behavior recognition models, Internet Protocol (IP) cameras, Internet of Things (IoT) security, service function chains, indoor positioning systems, and cryptoanalysis.

With the continuous development of wireless communication technology, the Internet of Things (IoT) is being used in a wide range of fields. The IoT collects and exchanges large amounts of data with objects, either tangible or intangible, such as sensors or physical devices, connected to the Internet. Wireless sensor networks (WSNs) are components of IoT systems. WSNs are used in various IoT systems, such as monitoring, tracking, and detection systems, to extract relevant information and deliver it to users. WSNs consist of sensor nodes with low power, low cost, and multiple functions. Because sensor nodes have limited resources, such as power and memory, a reduction in the energy efficiency of the sensor nodes in WSNs will lead to a decrease in wireless network performance and an increase in packet loss, which affects IoT system performance. Therefore, this study aimed to find an energy-efficient routing method that extends the lifetime of WSNs by minimizing the battery use of sensor nodes to improve the network performance of IoT systems. Conserving energy from sensor nodes and increasing network throughput in WSNs involves having protocols. The low-energy adaptive clustering hierarchy (LEACH ) protocol is a well-known hierarchical routing protocol in WSNs that constructs clusters and transmits data. LEACH increases energy efficiency by transmitting data from sensor nodes to the base station (BS) through the cluster head. It is widely adopted in the WSN network field, and many protocols are being studied to improve cluster header selection and data transmission to increase the energy efficiency of sensor nodes. In this study, we attempted to improve energy efficiency by removing unnecessary energy from LEACH. In LEACH, when the sensor node is located between the BS and the cluster head, the sensor node transmits data to the cluster head in the opposite direction of the BS. The data sent to the cluster head are transmitted in the direction of the BS. Thus, transmission in the opposite direction consumes unnecessary energy and affects the WSN performance of IoT systems. In this study, we propose a D-LEACH (direction-based LEACH) protocol based on the received signal strength indicator (RSSI) that improves the efficiency of transmission energy considering the data transmission direction of sensor nodes. D-LEACH aims to balance the energy of the sensor nodes and improve the performance of WSNs in the IoT system by reducing unnecessary energy consumption caused by reverse transmission considering the data transmission direction of the sensor nodes. In the course of the paper, we refer to the routing protocol of WSNs to improve network performance and describe LEACH. We also explain the D-LEACH protocol proposed in this paper and confirm the performance improvement of WSNs in an IoT system through simulation.

The service-based architecture of the Fifth Generation(5G) had combined the services and security architectures and enhanced the authentication process of services to expand the coverage of the network, including heterogeneous devices. This architecture uses the secondary authentication for mutual authentication between the User Equipment (UE) and the Data Network (DN) to authenticate devices and services. However, this authentication mechanism can cause a signaling storm in the Non-Access Stratum (NAS) because the end node needs to communicate with the authentication server of the NAS area. This problem could affect the availability of the network when the network is extended. This research proposes a mutual authentication framework that can efficiently perform a mutual authentication process through secondary authentication between UE and DN. The proposed framework uses newly devised network functions: Secondary Authentication Function (SAF) and the Authentication Data Management Function (ADMF). This framework proposes a methodology at the protocol level for efficient mutual authentication using the mobile edge computing architecture. We analyzed the proposed framework in the point of security considerations, and we evaluated the effect of the framework on the traffic of the NAS layer and user experience. Our simulation results show that the proposed framework can reduce the NAS traffic by 39% and total traffic of the overall network by 10%.

Abstract Multilayer aluminum oxide (Al2O3) resistive memory devices, exhibiting robust switching properties fabricated on a flexible polyethylene naphthalate substrate using only physical vapor deposition methods at room temperature, are reported here. Improved reliability and robust switching properties of the multilayer Al2O3 memory devices are observed compared to those of the single‐layer devices, with the assistance of a guide filament formed in the bottom Al2O3 resistive layer. The multilayer Al2O3 resistive memory devices exhibit nonvolatile and bipolar resistive switching properties with high ON/OFF ratio (>104), DC sweep endurance cycles (>700), statistical uniformity, and retention times (2 × 104 s). The role of the guide filament in the multilayer structure is identified in the non‐rewritable properties of the bottom layer device, increased reset current values, and spatial temperature distribution simulation. Thus, the formation sites of the switchable filament in the top Al2O3 layer can be specified by the guide filament, resulting in remarkable improvement of reproducibility with more robust metallic percolation paths. The reliable resistive switching properties in flexible conditions is also evaluated in a bent radius of 10 mm, and the suggested flexible multilayer memory devices can provide promising strategies to be utilized in future practical storage media.

Unlike CCTV, security video surveillance devices, which we have generally known about, IP cameras which are connected to a network either with or without wire, provide monitoring services through a built-in web-server. Due to the fact that IP cameras can use a network such as the Internet, multiple IP cameras can be installed at a long distance and each IP camera can utilize the function of a web server individually. Even though IP cameras have this kind of advantage, it has difficulties in access control management and weakness in user certification, too. Particularly, because the market of IP cameras did not begin to be realized a long while ago, systems which are systematized from the perspective of security have not been built up yet. Additionally, it contains severe weaknesses in terms of access authority to the IP camera web server, certification of users, and certification of IP cameras which are newly installed within a network, etc. This research grouped IP cameras hierarchically to manage them systematically, and provided access control and data confidentiality between groups by utilizing group keys. In addition, IP cameras and users are certified by using PKI-based certification, and weak points of security such as confidentiality and integrity, etc., are improved by encrypting passwords. Thus, this research presents specific protocols of the entire process and proved through experiments that this method can be actually applied.

Mobile users spend a tremendous amount of time surfing multimedia contents over the Internet to pursue their interests. A resource-constrained smart device demands more intensive computing tasks and lessens the battery life. To address the resource limitations (i.e., memory, lower maintenance cost, easier access, computing tasks) in mobile devices, mobile cloud computing is needed. Several approaches have been proposed to confront the challenges of mobile cloud computing, but difficulties still remain. However, in the coming years, context collecting, processing, and interchanging the results on a heavy network will cause vast computations and reduce the battery life in mobiles. In this paper, we propose a “context-based intelligent multimedia system” (CIMS) for ubiquitous cloud computing. The main goal of this research is to lessen the computing percentage, storage complexities, and battery life for mobile users by using pervasive cloud computing. Moreover, to reduce the computing and storage concerns in mobiles, the cloud server collects several groups of user profiles with similarities by executing K-means clustering on users’ data (context and multimedia contents). The distribution process conveys real-time notifications to smartphone users, according to what is stated in his/her profile. We considered a mobile cloud offloading system, which decides the offloading actions to/from cloud servers. Context-aware decision-making (CAD) customizes the mobile device performance with different specifications such as short response time and lesser energy consumption. The analysis says that our CIMS takes advantage of cost-effective features to produce high-quality information for mobile (or smart device) users in real time. Moreover, our CIMS lessens the computation and storage complexities for mobile users, as well as cloud servers. Simulation analysis suggests that our approach is more efficient than existing domains.

At the center of core technologies for a future cyber world, such as Internet of Things (IoT) or big data, is a context-rich system that offers services by using situational information. The field where context-rich systems were first introduced is near-field communication (NFC)-based electronic payments. Near-field Communication (NFC) integrated payment services collect the payment information of the credit card and the location information to generate patterns in the user’s consumption or movement through big data technology. Based on such pattern information, tailored services, such as advertisement, are offered to users. However, there is difficulty in controlling access to personal information, as there is a collaborative relationship focused on the trusted service manager (TSM) that is close knit to shared personal information. Moreover, in the case of Hadoop, among the many big data analytical technologies, it offers access control functions, but not a way to authorize the processing of personal information, making it impossible to grant authority between service providers to process information. As such, this paper proposes a key generation and distribution method, as well as a secure communication protocol. The analysis has shown that the efficiency was greater for security and performance compared to relation works.

Physical memory acquisition has been an import facet for digital forensics (DF) specialists due to its volatile characteristics. Nowadays, thousands of millions of global participants utilize online social networking (OSN) mechanisms to expand their social lives, ranging from business-oriented purposes to leisure motivations. Facebook (FB) is one of the most dominant social networking sites (SNS) available today. Unfortunately, it has been a major avenue for cybercriminals to commit illegal activities. Therefore, the digital traces of previous sessions of an FB user play an essential role as the first step for DF experts to pursue the disclosure of the identity of the suspect who was exploiting FB. In this research work, we provide a systematic methodology to reveal a previous session of an FB identity, as well as his/her partial social circle via collecting, analyzing, preserving and presenting the associated digital traces to obtain the online social snapshots of a specific FB user who was utilizing a computing device with Internet Explorer (IE) 10 without turning off the power of the gadget. This novel approach can be a paradigm for how DF specialists ponder the crime scene to conduct the first response in order to avoid the permanent loss of the precious digital evidence in previous FB sessions. The hash values of the image files of the random access memory (RAM) of the computing device have proven to be identical before and after forensics operations, which could be probative evidence in a court of law.

Internet Communication Technologies (ICTs) are making progress day by day, driven by the relentless need to utilize them for everything from leisure to business. This inevitable trend has dramatically changed contemporary digital behavior in all aspects. Undoubtedly, digital fingerprints will be at some point unwarily left on crime scenes creating digital information security incidents. On the other hand, corporates in the private sector or governments are on the edge of being exploited in terms of confidential digital information leakages. Some digital fingerprinting is volatile by its nature. Alternatively, once the power of computing devices is no longer sustainable, these digital traces could disappear forever. Due to the pervasive usage of Google Calendar and Safari browser among network communities, digital fingerprinting could be disclosed if forensics is carried out in a sound manner, which could be admitted in a court of law as probative evidences concerning certain cybercrime incidents.

In recent years, mobile and ubiquitous computing has emerged in our daily lives, and extensive studies have been conducted in various areas using smart devices, such as tablets, smartphones, smart TVs, smart refrigerators, and smart media devices, in order to realize this computing technology. Especially, the integration of mobile networking technology and intelligent mobile devices has made it possible to develop the advanced mobile distance learning system that supports portable smart devices such as smartphones and tablets for the future IT environment. We present a synchronous mobile learning system that enables both instructor and student to participate in distance learning with their tablets. When an instructor gives a lecture using a tablet with front-face camera by bringing up slides and making annotations on them, students in the distance can watch the instructor and those slides with annotation on their own tablets in real time. A student can also ask a question or have a discussion together using the text chat feature of the system during a learning session. We also show the user evaluation of the system. A user survey shows that about 67% are in favor of the prototype of the system.

Machine learning is a very important approach to pattern classification. This paper provides a better insight into Incremental Attribute Learning (IAL) with further analysis as to why it can exhibit better performance than conventional batch training. IAL is a novel supervised machine learning strategy, which gradually trains features in one or more chunks. Previous research showed that IAL can obtain lower classification error rates than a conventional batch training approach. Yet the reason for that is still not very clear. In this study, the feasibility of IAL is verified by mathematical approaches. Moreover, experimental results derived by IAL neural networks on benchmarks also confirm the mathematical validation.

Here we report on the issue of Advanced Persistent Threats (APT), which use malware for the purpose of leaking the data of large corporations and government agencies. APT attacks target systems continuously by utilizing intelligent and complex technologies. To overthrow the elaborate security network of target systems, it conducts an attack after undergoing a pre-reconnaissance phase. An APT attack causes financial loss, information leakage, etc. They can easily bypass the antivirus system of a target system. In this paper, we propose a Multi-Layer Defense System (MLDS) that can defend against APT. This system applies a reinforced defense system by collecting and analyzing log information and various information from devices, by installing the agent on the network appliance, server and end-user. It also discusses how to detect an APT attack when one cannot block the initial intrusion while continuing to conduct other activities. Thus, this system is able to minimize the possibility of initial intrusion and damages of the system by promptly responding through rapid detection of an attack when the target system is attacked.

Recently, intelligent transport systems have been applied to vehicle cloud environments. Such technology is especially useful for the systematic management of road traffic. Moreover, automobiles are increasingly equipped with a black box for accident prevention and preservation of evidence. Vehicle black boxes have become mandatory because black box images and voice data have served as forensic evidence in courts. However, the data from black boxes can be forged or modified by man-in-the-middle (MITM) attacks and message hijacking. In this paper, we propose a vehicle cloud computing-based black box service model that can provide integrity for black box data through digital signatures in vehicle cloud computing (VCC) environments. Our proposed model protects against MITM attacks and message hijacking using only a hash value and digital signature. Moreover, a mirroring technique (RAID 1) provides backup and recovery to protect the data from a traffic accident.

Device-to-device (D2D) communication is a direct means of communication between devices without an intermediate node, and it helps to expand cell coverage and to increase radio frequency reuse in a 5G network. Moreover, D2D communication is a core technology of 5G vehicle-to-everything (V2X) communication, which is an essential technology for autonomous driving. However, typical D2D communication in an 4G network which is typical telecommunication network has various security challenges including impersonation, eavesdropping, privacy sniffing, free-riding attack, etc. Moreover, when IoT technology emerges with 5G networks in massive machine type communication (mMTC) and ultra-reliable low latency communication (URLLC) application scenarios, these security challenges are more crucial and harder to mitigate because of the resource-constrained nature of IoT devices. To solve the security challenges in a 5G IoT environment, we need a lightweight and secure D2D communication system that can provide secure authentication, data confidentiality/integrity and anonymity. In this paper, we survey and analyze existing results about secure D2D communication systems in terms of their security considerations and limitations. Then, we lastly propose a secure D2D communication system to address the aforementioned security challenges and the limitations of the existing results. The proposed secure D2D communication was designed based on elliptic curve cryptography (ECC) and lightweight authenticated encryption with associated data (AEAD) ciphers to cover resource-constrained IoT devices.

Cloud computing services that provide computing resources to users through the Internet also provide computing resources in a virtual machine form based on virtualization techniques. In general, supercomputing and grid computing have mainly been used to process large-scale jobs occurring in scientific, technical, and engineering application domains. However, services that process large-scale jobs in parallel using idle virtual machines are not provided in cloud computing at present. Generally, users do not use virtual machines anymore, or they do not use them for a long period of time, because existing cloud computing assigns all of the use rights of virtual machines to users, resulting in the low use of computing resources. This study proposes a scheme to process large-scale jobs in parallel, using idle virtual machines and increasing the resource utilization of idle virtual machines. Idle virtual machines are basically identified through specific determination criteria out of virtual machines created using OpenStack, and then they are used in computing services. This is called the idle virtual machine−resource utilization (IVM−ReU), which is proposed in this study.

Blockchain is a technology that can ensure data integrity in a distributed network, and it is actively applied in various fields. Recently, blockchain is gaining attention due to combining with the Internet of Things (IoT) technology in the industrial field. Moreover, many researchers have proposed the Industrial IoT (IIoT) architecture with blockchain for data integrity and efficient management. The IIoT network consists of many heterogeneous devices (e.g., sensors, actuators, and programmable logic controllers (PLC)) with resources-constrained, and the availability of the network must be preferentially considered. Therefore, applying the existed blockchain technology is still challenging. There are some results about the technique of constructing blockchain lightly to solve this challenge. However, in these results, the analysis in perspective of cryptographic performance (area, throughput, and power consumption) has not been considered sufficiently, or only focused on the architecture of the blockchain network. The blockchain technology is based on cryptographic techniques, and the main part is a cryptographic hash function. Therefore, if we construct the blockchain-based IIoT architecture, we have to consider the performance of the hash function. Many lightweight hash functions have been proposed recently for the resource-constrained environment, and it can also be used to the blockchain. Therefore, in this paper, we analyze the considerations of lightweight blockchain for IIoT. Also, we conduct an analysis of lightweight hash for blockchain, and propose a new lightweight hash-based blockchain architecture that can change the hash algorithm used for mining adjust to network traffic.

Recently, the development of smart home technologies has played a crucial role in enhancing several real-life smart applications. They help improve the quality of life through systems designed to enhance convenience, comfort, entertainment, health of the householders, and security. Note, however, that malware attacks on smart home devices are increasing in frequency and volume. As people seek to improve and optimize comfort in their home and minimize their daily home responsibilities at the same time, this makes them attractive targets for a malware attack. Thus, attacks on smart home-based devices have emerged. The goals of this paper are to analyze the different aspects of cyber-physical threats on the smart home from a security perspective, discuss the types of attacks including advanced cyber-attacks and cyber-physical system attacks, and evaluate the impact on a smart home system in daily life. We have come up with a taxonomy focusing on cyber threat attacks that can also have potential impact on a smart home system and identify some key issues about VPNFilter malware that constitutes large-scale Internet of Things (IoT)-based botnet malware infection. We also discuss the defense mechanism against this threat and mention the most infected routers. The specific objective of this paper is to provide efficient task management and knowledge related to VPNFilter malware attack.

Pool hopping attack is the result of miners leaving the pool when it offers fewer financial rewards and joining back when the rewards of mining yield higher rewards in blockchain networks. This act of leaving and rejoining the pool only during the good times results in the miner receiving more rewards than the computational power they contribute. Miners exiting the pool deprive it of its collective hash power, which leaves the pool unable to mine the block successfully. This results in its competitors mining the block before they can finish mining. Existing research shows pool hopping resistant measures and detection strategies; however, they do not offer any robust preventive solution to discourage miners from leaving the mining pool. To prevent pool hopping attacks, a smart contract-based pool hopping attack prevention model is proposed. The main objective of our research is maintaining the symmetrical relationship between the miners by requiring them all to continually contribute their computational power to successfully mine a block. We implement a ledger containing records of all miners, in the form of a miner certificate, which tracks the history of the miner’s earlier behavior. The certificate enables a pool manager to better initiate terms of the smart contract, which safeguards the interests of existing mining pool members. The model prevents frequent mine hoppers from pool hopping as they submit coins in the form of an escrow and risk losing them if they abandon the pool before completing mining of the block. The key critical factors that every pool hopping attack prevention solution must address and a study of comparative analysis with existing solutions are presented in the paper.

For many years there has been a focus on individual welfare and societal advancement. In addition to the economic system, diverse experiences and the habitats of people are crucial factors that contribute to the well-being and progress of the nation. The predictor of quality of life called the Better Life Index (BLI) visualizes and compares key elements—environment, jobs, health, civic engagement, governance, education, access to services, housing, community, and income—that contribute to well-being in different countries. This paper presents a supervised machine-learning analytical model that predicts the life satisfaction score of any specific country based on these given parameters. This work is a stacked generalization based on a novel approach that combines different machine-learning approaches to generate a meta-machine-learning model that further aids in maximizing prediction accuracy. The work utilized an Organization for Economic Cooperation and Development (OECD) regional statistics dataset with four years of data, from 2014 to 2017. The novel model achieved a high root mean squared error (RMSE) value of 0.3 with 10-fold cross-validation on the balanced class data. Compared to base models, the ensemble model based on the stacked generalization framework was a significantly better predictor of the life satisfaction of a nation. It is clear from the results that the ensemble model presents more precise and consistent predictions in comparison to the base learners.

After the emergence of the Internet and mobile communication networks, the IoT has been considered as the third wave of information technology. The Industrial Internet of Things (IIoT) is the use of Internet of Things (IoT) technologies in manufacturing. IIoT incorporates machine learning and big data technology, sensor data, and machine-to-machine (M2M) communications that have existed in industrial areas for years. In the future, people and objects will be connected at any time, any place, with anything and anyone and will utilize any network and services. IIoT is creating a new world in which people and businesses can manage their assets in more informed ways and can make more opportune and better-informed decisions. Many advanced IIoT and 5G technologies have been successfully applied in everyday life, but there are still many practical problems tackled by traditional methods which are generally difficult to experimentally solve in the advanced Industrial Internet of Things. Therefore, in this special issue, we accepted five articles in three different dimensions: communication networks, optimized resource provisioning and data forwarding, privacy and security.

Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.

Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.

WSN (wireless sensor network) technology came to become fairly activated and as WSN is a part of the world's environment, research on it is considered important. WSN automated life and work; miniaturized wireless sensor nodes enable observational measurement in regions where access by humans is difficult or periodic sensing data is necessary. Thousands of such sensor nodes are needed. However, when they are wrongly placed in an observed area, sensor node batteries may run out fast or disconnection may result, which may trigger the loss of sensing data by sensor nodes and create an enormous cost. Moreover, even though sensor nodes have the same function, there are different kinds; therefore, it is difficult to select and place optimal sensor nodes. Accordingly, this paper proposed the WS3L (wireless sensor simulator using sensor log) as a simulator aimed at the composition of a more efficient sensing infrastructure and problem analysis for Internet of Things. This WS3L utilized sensor log data for precise prediction, not merely execution depending on simulators developed earlier. This WS3L read sensor log data of the observed area from the user and provided various analysis functions to examine problems.

A 49-year-old man was referred to our department with profuse serous fluid discharge from a Penrose drain after undergoing internal fixation with metal screws for multiple pelvic bone fractures. A definite ureteral penetration was identified that was orientated from the lateral to the medial aspect of the right distal ureter. The patient was surgically treated with excision of the 2-cm injured ureteral segment, end-to-end ureteroureterostomy, and double J ureteral stent placement. To our knowledge, a penetrating ureteral injury caused by bone drilling has not been reported previously in the published literature. This case shows that surgeons who do pelvic surgery, including orthopedic surgeons, should be familiar with the anatomical relationship of the ureter and its potential injuries.