An Energy-Aware Hybrid Approach for Wireless Sensor Networks Using Re-clustering-Based Multi-hop Routing

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. For providing optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. Also, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. Finally, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

     

Pushing PbS/Metal‐Halide‐Perovskite Core/Epitaxial‐Ligand‐Shell Nanocrystal Photodetectors beyond 3 µm Wavelength

PbS nanocrystals have been proven to be highly suitable for photodetector fabrication by facile solution processing, and have been successfully tested as photosensitive material in imaging devices. So far, their spectral response has been blue‐shifted with respect to that of commercial bulk PbS detectors, due to quantum confinement in nanostructures smaller than the exciton Bohr radius. Here, a PbS nanocrystal synthesis approach is introduced, allowing to surpass this limit, and thus to push the cut‐off wavelength to the value of the bulk material. To avoid self‐absorbance from ligands within the spectral range of the photoconducting signal, an all inorganic metal‐halide‐perovskite is applied to form a semiconducting ligand shell. The photoconductors, which are provided from a single drop, do not only show a record in long wavelength operation for PbS nanocrystal detectors but also a room temperature detectivity > 10¹⁰ Jones, which is on par with that of commercial bulk PbS detectors. Combining these properties might find application in future low‐cost infrared imagers, which are currently still elusive due to their high prices.     

A New Filter Bank Multicarrier (FBMC) Based Cognitive Radio for 5G Networks Using Optimization Techniques

Ultra high definition radio, low latency, high bandwidth, machine to machine communication and several emergence application like e-health, IOT etc. will be dominated by 5G in future networks. Therefore there is dire need of more capacity which in turn will demand more efficient spectrum sensing. In this paper we propose a new technique of FBMC spectrum sensing cognitive radio technique in future networks. The proposed technique provides programmability that allows dynamic and real time configuration of the operating channel. The simulation results have been verified in terms of various parameters and found satisfactory results.

     

NIR Light-Triggered Shape Memory Polymers Based on Mussel-Inspired Iron–Catechol Complexes

Shape memory polymers (SMPs) with the permanent shape reconfiguration capability have received much research interest because they are capable of diversified tasks and the ability to work in conditions that required complex geometry. However, most of such SMPs are thermally triggered, which limits their applications. Inspired by reversible mussel adhesive protein chemistry, NIR light-triggered SMPs with the permanent shape reconfiguration capability are prepared. The polymer networks are constructed using biocompatible polyethylene glycol, which is crosslinked based on catechol–Fe³⁺ coordination. The polymer networks have a uniform network structure and exhibit a considerable one-way shape memory effect (1W-SME) as well as a good two-way shape memory effect (2W-SME) under stress conditions. Taking advantage of the dynamic nature of the catechol–Fe³⁺ coordination, the permanent shape of the polymers could be reconfigured. Moreover, the catechol–Fe³⁺ complexes have a broad absorption in the NIR window, which bestows the polymers with excellent NIR light-triggered SME. Further, the great potential of the obtained polymers in biomedical and electronic applications is presented. Owing to the NIR-triggered 1W-SME and the permanent shape reconfiguration capability, the polymer could be used as a personalizing vascular stent. Additionally, the polymer could be applied in light-driven switches based on the NIR light-triggered 2W-SME.     

Measuring global prosperity using data envelopment analysis and OWA operator

Prosperity is one of the key economic indicators of a nation's success. The measure of a country's true prosperity is best achieved by considering a set of criteria and identifying the optimal weights associated with each criterion. This study introduces a novel method for measuring global prosperity by employing a combination of variables that characterize economic wealth and social wellbeing using data envelopment analysis (DEA) and ordered weighted averaging (OWA) operator. It extends the existing global prosperity assessment approach proposed by the Legatum Institute, an international organization that produces a global prosperity index every year. The current Legatum Prosperity Index is obtained by averaging a set of distinct variables, but it fails to identify the optimal variable weights for each country. This is a significant drawback that we address in this study. Using DEA, each country can freely assign optimal weights that are most favorable to achieving maximum prosperity. It provides a flexible and competitive environment in which all countries can present their strengths, thereby creating a level playing field. This study also uses multilevel DEA efficiency frontiers for classifying countries into different groups based on their levels of prosperity score. Additionally, we apply the OWA operator to distinguish further between the countries within each cluster.     

Machine Learning Approach for COVID-19 Detection on Twitter

Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this purpose, we propose herein an intelligent model using traditional machine learning-based approaches, such as support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and decision tree (DT) with the help of the term frequency inverse document frequency (TF-IDF) to detect the COVID-19 pandemic in Twitter messages. The proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories, namely, confirmed deaths, recovered, and suspected. For the experimental analysis, the tweet data on the COVID-19 pandemic are analyzed to evaluate the results of traditional machine learning approaches. A benchmark dataset for COVID-19 on Twitter messages is developed and can be used for future research studies. The experiments show that the results of the proposed approach are promising in detecting the COVID-19 pandemic in Twitter messages with overall accuracy, precision, recall, and F1 score between 70% and 80% and the confusion matrix for machine learning approaches (i.e., SVM, NB, LR, RF, and DT) with the TF-IDF feature extraction technique.     

CNR: A Cluster-Based Solution for Connectivity Restoration for Mobile WSNs

Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving the challenges associated with connectivity restoration in sensor networks. Since energy is a scarce resource in sensor networks, it becomes the focus of research, and researchers strive to propose new solutions that are energy efficient. The common issue that is well studied and considered is how to increase the network’s life span by solving the node failure problem and achieving efficient energy utilization. This paper introduces a Cluster-based Node Recovery (CNR) connectivity restoration mechanism based on the concept of clustering. Clustering is a well-known mechanism in sensor networks, and it is known for its energy-efficient operation and scalability. The proposed technique utilizes a distributed cluster-based approach to identify the failed nodes, while Cluster Heads (CHs) play a significant role in the restoration of connectivity. Extensive simulations were conducted to evaluate the performance of the proposed technique and compare it with the existing techniques. The simulation results show that the proposed technique efficiently addresses node failure and restores connectivity by moving fewer nodes than other existing connectivity restoration mechanisms. The proposed mechanism also yields an improved field coverage as well as a lesser number of packets exchanged as compared to existing state-of-the-art mechanisms.     

Glucose‐Responsive Composite Microneedle Patch for Hypoglycemia‐Triggered Delivery of Native Glucagon

Insulin‐dependent patients with diabetes mellitus require multiple daily injections of exogenous insulin to combat hyperglycemia. However, administration of excess insulin can lead to hypoglycemia, a life‐threatening condition characterized by abnormally low blood glucose levels (BGLs). To prevent hypoglycemia associated with intensive insulin therapy, a “smart” composite microneedle (cMN) patch is developed, which releases native glucagon at low glucose levels. The cMN patch is composed of a photo‐crosslinked methacrylated hyaluronic acid (MeHA) microneedle array with embedded multifunctional microgels. The microgels incorporate zwitterionic moieties that stabilize loaded glucagon and phenylboronic acid moieties that provide glucose‐dependent volume change to facilitate glucagon release. Hypoglycemia‐triggered release of structurally unchanged glucagon from the cMN patch is demonstrated in vitro and in a rat model of type 1 diabetes (T1D). Transdermal application of the patch prevented insulin‐induced hypoglycemia in the diabetic rats. This work is the first demonstration of a glucose‐responsive glucagon‐delivery MN patch for the prevention of hypoglycemia, which has a tremendous potential to reduce the dangers of intensive insulin therapy and improve the quality of life of patients with diabetes and their caregivers.     

面向网络教学的小规模视频会议系统的设计与实现

为了对网络教学提供技术支持,设计和实现了面向网络教学的视频会议系统。系统主要由组播协议处理模块、用户管理模块、流媒体数据处理模块组成。其中,为了有效降低中心服务器的压力,均衡网络负载,提出一种基于度约束的组播树构造算法,进行节点的管理和优化;为了提高视频传输的稳定性和流畅性,提出采用回调机制进行视频捕获,采用块匹配法进行视频压缩处理,并运用完成端口模型进行多线程处理。实验结果表明,提出的组播树算法性能优于传统算法,具有高吞吐量的特点,实现的系统在局域网环境下表现良好,视频基本无停滞和跳动现象。     

A new model for discovering process trees from event logs

Process mining techniques aim at extracting knowledge from event logs. One of the most important tasks in process mining is process model discovery. In discovering process models, an algorithm is designed to build a process model from a given event log. In this paper, a new model to discover process models has been proposed. A combination of Genetic Algorithm and Simulated Annealing has been used in this model. Genetic Algorithms has previously been used in this context. Previous approaches had drawbacks in fitness evaluation that misguided the algorithm. Another problem was that the quality of the candidates, in the population, was low such that it reduced the chance of finding a perfect answer. In this paper, a new fitness measure has been proposed to evaluate process models based on event logs. Moreover SA has been used to improve the quality of candidates in the population. It has been demonstrated that the proposed model outperformed in terms of rediscovering process models, compared to other approaches which are proposed in the literature, which was the result of better fitness evaluation and increased quality of individuals,. It came to conclusion that using GA and SA in combination with each other can be effective in this context.