Exploring the benefits and challenges of Internet of Things (IoT) during Covid-19: a case study of Bangladesh

The Internet of Things (IoT) is expected to have a huge impact, especially during the pandemic period. The study reveals that people are using the IoT mostly for education purposes (as students and educators), office work, banks and medical purposes during the pandemic. The topmost benefit of using IoT services experienced by people during pandemic situations is that it helps to strictly maintain physical distance. However, the greatest challenge faced by people is that the use of the IoT increases social distancing and reduces personal communication. Data were collected through a questionnaire distributed online and using a convenient random sampling method. A total of 260 participants’ properly completed responses were analyzed after conducting Three-fold validation. Research method was quantitative and empirical. Although, some studies have been found about IoT prospects in Bangladesh, no study has specifically explored the benefits and challenges of IoT services in diverse fields of Bangladesh during this new normal COVID-19 situation. The results can be beneficial to academic scholars, business professionals and organizations in different sectors and any other parties interested in determining the impact of IoT services on pandemic.

Social behavior prediction with graph U-Net+

We focus on the problem of predicting social media user’s future behavior and consider it as a graph node binary classification task. Existing works use graph representation learning methods to give each node an embedding vector, then update the node representations by designing different information passing and aggregation mechanisms, like GCN or GAT methods. In this paper, we follow the fact that social media users have influence on their neighbor area, and extract subgraph structures from real-world social networks. We propose an encoder–decoder architecture based on graph U-Net, known as the graph U-Net+. In order to improve the feature extraction capability in convolutional process and eliminate the effect of over-smoothing problem, we introduce the bilinear information aggregator and NodeNorm normalization approaches into both encoding and decoding blocks. We reuse four datasets from DeepInf and extensive experimental results demonstrate that our methods achieve better performance than previous models.

Hybrid high-order semantic graph representation learning for recommendations

The amount of Internet data is increasing day by day with the rapid development of information technology. To process massive amounts of data and solve information overload, researchers proposed recommender systems. Traditional recommendation methods are mainly based on collaborative filtering algorithms, which have data sparsity problems. At present, most model-based collaborative filtering recommendation algorithms can only capture first-order semantic information and cannot process high-order semantic information. To solve the above issues, in this paper, we propose a hybrid graph neural network model based on heterogeneous graphs with high-order semantic information extraction, which models users and items respectively by learning low-dimensional representations for them. We introduced an attention mechanism to allow the model to understand the corresponding edge weights adaptively. Simultaneously, the model also integrates social information in the data to learn more abundant information. We performed some experiments on related datasets. Our method achieved better results than some current advanced models, which verified the proposed model’s effectiveness.

Social media analytics of the Internet of Things

The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This research illuminates the public view of the Internet of Things through a content-based and network analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current COVID-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry.

Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

With the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

Correction to: Bayesian Topology Learning and noise removal from network data

A correction to this paper has been published: https://doi.org/10.1007/s43926-021-00013-8

Drones as internet of video things front-end sensors: challenges and opportunities

Internet of Video Things (IoVT) has become an emerging class of IoT systems that are equipped with visual sensors at the front end. Most of such visual sensors are fixed one whereas the drones are considered flying IoT nodes capable of capturing visual data continuously while flying over the targets of interest. With such a dynamic operational mode, we can imagine significant technical challenges in sensor data acquisition, information transmission, and knowledge extraction. This paper will begin with an analysis on some unique characteristics of IoVT systems with drones as its front end sensors. We shall then discuss several inherent technical challenges for designing drone-based IoVT systems. Furthermore, we will present major opportunities to adopt drone-based IoVT in several contemporary applications. Finally, we conclude this paper with a summary and an outlook for future research directions.

Particle swarm optimization in image processing of power flow learning distribution

In order to realize the coordination and integration optimization of the power system itself, this paper constructed the mathematical model of the hybrid power system and solved the multi-objective optimization problem of the heating system through the optimized particle swarm optimization algorithm. Based on the back-to-back VSC-HVDC grid-connected composite system, this paper studied the integrated control strategy of the device to achieve the simultaneous parallel and tie line currents. At the same time, this paper simplified and improved the proposed disassembly criteria for grid-connected composite devices and integrated them into the grid-connected composite device. In addition, on this basis, the integrated control of the three functions of de-listing, juxtaposition and tie line power adjustment of the same device was further studied. Simulation studies show that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research.

Bayesian Topology Learning and noise removal from network data

Learning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph.

Study and design of a retrofitted smart water meter solution with energy harvesting integration

The reduction of water resources due to climate change and the increasing demand associated with population growth is a renewed concern. Water distribution monitoring and smart metering are essential tools to improve distribution efficiency. This paper reports on the study, design, and implementation of a smart water meter (SWM) prototype, designed for mechanical water meters that need to undergo a retrofitting process to enable automatic metering readings. Metering data is transmitted through innovative narrowband internet of things (NB-IoT) technology with low power, long-range, and effective penetration. A flexible power management design allows the introduction of an energy harvester that recovers energy from the surrounding environment and charges the internal battery. The energy harvesting feasibility was demonstrated with two proof-of-concept configurations, light and water-turbine based. The details on the performance of the proposed solution are presented, including the output voltages and harvested power. Although the energy harvesting technologies have not been integrated yet in commercial SWM applications, the results show that the integration is feasible and, once employed in a controlled environment, it can create business advantages by reducing the size and capacity of the internal batteries, enabling one to reduce the operation cost and mitigate long-term ecological problems associated with the use and disposal of batteries.