Search Results (16)

The Internet has become the primary vehicle for doing almost everything online, and smartphones are needed for almost everyone to live their daily lives. As a result, cybersecurity is a top priority in today’s world. As Internet usage has grown exponentially with billions of users and the proliferation of Internet of Things (IoT) devices, cybersecurity has become a cat-and-mouse game between attackers and defenders. Cyberattacks on systems are commonplace, and defense mechanisms are continually updated to prevent them. Based on a literature review of cybersecurity vulnerabilities, attacks, and preventive measures, we find that cybersecurity problems are rooted in computer system architectures, operating systems, network protocols, design options, heterogeneity, complexity, evolution, open systems, open-source software vulnerabilities, user convenience, ease of Internet access, global users, advertisements, business needs, and the global market. We investigate common cybersecurity vulnerabilities and find that the bare machine computing (BMC) paradigm is a possible solution to address and eliminate their root causes at many levels. We study 22 common cyberattacks, identify their root causes, and investigate preventive mechanisms currently used to address them. We compare conventional and bare machine characteristics and evaluate the BMC paradigm and its applications with respect to these attacks. Our study finds that BMC applications are resilient to most cyberattacks, except for a few physical attacks. We also find that BMC applications have inherent security at all computer and information system levels. Further research is needed to validate the security strengths of BMC systems and applications. Full article

Urban resilience provides the foundation and guarantee for the tourism economy, and the development of the tourism industry provides new opportunities and impetus for urban resilience. The coordinated development of urban resilience (UR) and the tourism economy (TE) contributes to the high-quality development of the regional economy. This study takes 27 cities in the Yangtze River Delta (YRD) urban agglomeration as an example. Various analytical techniques, including the entropy method, coupling coordination degree model (CCDM), kernel density estimation, Theil index, and obstacle degree model, are employed to investigate the spatiotemporal evolution patterns and influencing factors that affect the coupling coordination degree (CCD) between UR and TE. The findings indicate that: (1) The urban resilience and tourism economy exhibited an increasing trend denoted by “N” and “M”, respectively. (2) The coupling coordination level has undergone a development phase of “Moderate disorder–Bare coordination–Moderate disorder”. (3) The level of coordination has been enhanced, with intra-regional differences identified as the primary source of variation. (4) The number of Internet users, the number of students in institutions of higher learning, per capita public financial expenditure, science and technology expenditures as a share of fiscal expenditures, urban per capita disposable income, foreign exchange earnings from tourism, and the number of inbound tourists is the main factors affecting the CCD of urban resilience and tourism economy. Full article

This study presents internet of things (IOT) and artificial intelligence technologies that are critical in reducing the harmful effects of this illness and assisting its recovery. It explores COVID-19’s economic impacts before learning about new technologies and potential solutions. The research objective was to propose a solution for self-diagnosis, self-monitoring, and self-management of COVID-19 with personal mobiles and personal data using cloud solutions and mobile applications with the help of an intelligent IoT system, artificial intelligence, machine learning, and 5G technologies. The proposed solution based on self-diagnosis without any security risk for users’ data with low cost of cloud-based data analytics by using handsets only is an innovative approach. Since the COVID-19 outbreak, the global social, economic, religious, and cultural frameworks and schedules have been affected adversely. The fear and panic associated with the new disease, which the world barely knew anything about, amplified the situation. Scientists and epidemiologists have traced the first outbreak of COVID-19 at Wuhan, China. A close examination of the genetic makeup of the virus showed that the virus is zoonotic, meaning that the virus changed hosts from animals to humans. The uncertainty associated with the above features and characteristics of the virus, as well as the high mortality rates witnessed in many parts of the globe, significantly contributed to the widespread global panic that brought the world to a standstill. Different authorities and agencies associated with securing the public have implemented different means and methods to try and mitigate the transmission of the infection as scientists and medical practitioners work on remedies to curb the spread of COVID-19. Owing to different demographics, different parts of the globe have attempted to effectively implement locally available resources to efficiently fight and mitigate the adverse effects of the COVID-19 pandemic. The general framework provided by the World Health Organization (WHO) has been implemented or enhanced in different parts of the globe by locally available resources and expertise to effectively mitigate the impact of COVID-19. There is currently no effective vaccine for COVID-19, but new technology can be available within weeks to reduce the spread of the disease; current approaches such as contact tracing and testing are not secure, and the cost of testing is high for end users. The proposed solution based on self-diagnosis without any security risk for users’ data with low cost of cloud-based data analytics functions by using an intelligent internet of things (IOT) system for collecting sensors data and processing them with artificial intelligence to improve efficiency and reduce the spread of COVID-19. Full article

Most existing approaches to image classification neglect the concept of semantics, resulting in two major shortcomings. Firstly, categories are treated as independent even when they have a strong semantic overlap. Secondly, the features used to classify images into different categories can be the same. It has been demonstrated that the integration of ontologies and semantic relationships greatly improves image classification accuracy. In this study, a hybrid ontological bagging algorithm and an ensemble technique of convolutional neural network (CNN) models have been developed to improve forest image classification accuracy. The ontological bagging approach learns discriminative weak attributes over multiple learning instances, and the bagging concept is adopted to minimize the error propagation of the classifiers. An ensemble of ResNet50, VGG16, and Xception models is used to generate a set of features for the classifiers trained through an ontology to perform the image classification process. To the authors’ best knowledge, there are no publicly available datasets for forest-type images; hence, the images used in this study were obtained from the internet. Obtained images were put into eight categories, namely: orchards, bare land, grassland, woodland, sea, buildings, shrubs, and logged forest. Each category comprised 100 images for training and 19 images for testing; thus, in total, the dataset contained 800 images for training and 152 images for testing. Our ensemble deep learning approach with an ontology model was successfully used to classify forest images into their respective categories. The classification was based on the semantic relationship between image categories. The experimental results show that our proposed model with ontology outperformed other baseline classifiers without ontology with 96% accuracy and the lowest root-mean-square error (RMSE) of 0.532 compared to 88.8%, 86.2%, 81.6%, 64.5%, and 63.8% accuracy and 1.048, 1.094, 1.530, 1.678, and 2.090 RMSE for support-vector machines, random forest, k-nearest neighbours, Gaussian naive Bayes, and decision trees, respectively. Full article

The rise of gas-sensing applications and markets has led to microwave sensors associated to polymer-based sensitive materials gaining a lot of attention, as they offer the possibility to target a large variety of gases (as polymers can be easily functionalised) at ultra-low power and wirelessly (which is a major concern in the Internet of Things). A two-channel microstrip sensor with one resonator coated with 1,2 epoxybutane-functionalised poly(ethyleneimine) (EB-PEI) and the other left bare was designed and fabricated for humidity sensing. The sensor, characterised under controlled laboratory conditions, showed exponential response to RH between 0 and 100%, which is approximated to −1.88 MHz/RH% (−0.03 dB/RH%) and −8.24 MHz/RH% (−0.171 dB/RH%) in the RH ranges of 30–80% and 80–100%, respectively. This is the first reported use of EB-PEI for humidity sensing, and performances, especially at high humidity level (RH > 80%), as compared with transducer working frequencies, are better than the state of the art. When further tested in real outdoor conditions, the sensor shows satisfying performances, with 4.2 %RH mean absolute error. Most importantly, we demonstrate that the sensor is selective to relative humidity alone, irrespective of the other environmental variables acquired during the campaign (O₃, NO, NO₂, CO, CO₂, and Temperature). The sensitivities obtained outdoors in the ranges of 50–70% and 70–100% RH (−0.61 MHz/%RH and −3.68 MHz/%RH, respectively) were close to lab results (−0.95 MHz/%RH and −3.51 MHz/%RH, respectively). Full article

The Internet of Things includes all connected objects from small embedded systems with low computational power and storage capacities to efficient ones, as well as moving objects like drones and autonomous vehicles. The concept of Internet of Everything expands upon this idea by adding people, data and processing. The adoption of such systems is exploding and becoming ever more significant, bringing with it questions related to the security and the privacy of these objects. A natural solution to data integrity, confidentiality and single point of failure vulnerability is the use of blockchains. Blockchains can be used as an immutable data layer for storing information, avoiding single point of failure vulnerability via decentralization and providing strong security and cryptographic tools for IoE. However, the adoption of blockchain technology in such heterogeneous systems containing light devices presents several challenges and practical issues that need to be overcome. Indeed, most of the solutions proposed to adapt blockchains to devices with low resources confront difficulty in maintaining decentralization or security. The most interesting are probably the Layer 2 solutions, which build offchain systems strongly connected to the blockchain. Among these, zk-rollup is a promising new generation of Layer 2/off-chain schemes that can remove the last obstacles to blockchain adoption in IoT, or more generally, in IoE. By increasing the scalability and enabling rule customization while preserving the same security as the Layer 1 blockchain, zk-rollups overcome restrictions on the use of blockchains for IoE. Despite their promises illustrated by recent systems proposed by startups and private companies, very few scientific publications explaining or applying this barely-known technology have been published, especially for non-financial systems. In this context, the objective of our paper is to fill this gap for IoE systems in two steps. We first propose a synthetic review of recent proposals to improve scalability including onchain (consensus, blockchain organization, …) and offchain (sidechain, rollups) solutions and we demonstrate that zk-rollups are the most promising ones. In a second step, we focus on IoE by describing several interesting features (scalability, dynamicity, data management, …) that are illustrated with various general IoE use cases. Full article

Nowadays Internet of Things is gaining more and more focus all over the world. As a concept it gives many opportunities for applications for society and it is expected that the number of software services deployed in this area will still grow fast. Especially important in this context are properties connected with deployment such as portability, scalability and balance between software requirements and hardware capabilities. In this article, we present results of practical tests with multiple clients representing sensors sending notifications to an IoT middleware—DeviceHive. Firstly, we investigate performance using two deployment configurations—containerized and bare-metal showing small overhead of the former under different loads by various numbers of IoT clients. We present scaling of the middleware on the server side using various numbers of cores as well as HyperThreading for all aforementioned configurations. Furthermore, we also investigated how containarization affects performance when the system is scaled with various numbers of nodes each using a predefined number of cores, considering memory usage of various configurations. The latter could be found useful when assigning cores to Docker nodes in cloud environments. Full article

Devices in wireless sensor networks are typically powered by batteries, which must last as long as possible to reduce both the total cost of ownership and potentially pollutant wastes when disposed of. By lowering the duty cycle to the bare minimum, time slotted channel hopping manages to achieve very low power consumption, which makes it a very interesting option for saving energy, e.g., at the perception layer of the Internet of Things. In this paper, a mechanism based on probabilistic blacklisting is proposed for such networks, which permits to lower power consumption further. In particular, channels suffering from non-negligible disturbance may be skipped based on the perceived quality of communication so as to increase reliability and decrease the likelihood that retransmissions have to be performed. The only downside of this approach is that the transmission latency may grow, but this is mostly irrelevant for systems where the sampling rates are low enough. Full article

One of the causes of mortality in bees is varroosis, a bee disease caused by the Varroa destructor mite. Varroa destructor mites may occur suddenly in beehives, spread across them, and impair bee colonies, which finally die. Edge IoT (Internet of Things) devices capable of processing video streams in real-time, such as the one we propose, may allow for the monitoring of beehives for the presence of Varroa destructor. Additionally, centralization of monitoring in the Cloud data center enables the prevention of the spread of this disease and reduces bee mortality through monitoring entire apiaries. Although there are various IoT or non-IoT systems for bee-related issues, such comprehensive and technically advanced solutions for beekeeping and Varroa detection barely exist or perform mite detection after sending the data to the data center. The latter, in turn, increases communication and storage needs, which we try to limit in our approach. In the paper, we show an innovative Edge-based IoT solution for Varroa destructor detection. The solution relies on Tensor Processing Unit (TPU) acceleration for machine learning-based models pre-trained in the hybrid Cloud environment for bee identification and Varroa destructor infection detection. Our experiments were performed in order to investigate the effectiveness and the time performance of both steps, and the study of the impact of the image resolution on the quality of detection and classification processes prove that we can effectively detect the presence of varroosis in beehives in real-time with the use of Edge artificial intelligence invoked for the analysis of video streams. Full article

Animal welfare labeling schemes have been developed to respond to consumers’ expectations regarding farm animal welfare. They are designed to certify that labeled products comply with certain animal welfare standards. In this study, 12 pig welfare labeling schemes were reviewed, and their criteria related to pig welfare were compared. Information regarding farrowing criteria, space allowance, outdoor access, mutilations, and provision of enrichments and bedding material were gathered from the labels’ internet pages and documentation. The results indicated a substantial variation between the labels in terms of the level of animal welfare they ensure. While certain schemes barely exceeded the minimum standards for the protection of pigs in the European Union, more demanding tiers of the multitier schemes had the potential to improve animal welfare substantially. The most ambitious tiers of multistage schemes were often comparable to organic standards providing outdoor facilities and additional space. The heterogeneity of the labels’ standards complicates the comparison of labels. Full article

Smartphones are becoming increasingly versatile thanks to the wide variety of sensor and actuator systems packed in them. Mobile devices today go well beyond their original purpose as communication devices, and this enables important new applications, ranging from augmented reality to the Internet of Things. Personalized diagnostics is one of the areas where mobile devices can have the greatest impact. Hitherto, the camera and communication abilities of these devices have been barely exploited for point of care (POC) purposes. This short review covers the recent evolution of mobile devices in the area of POC diagnostics and puts forward some ideas that may facilitate the development of more advanced applications and devices in the area of personalized diagnostics. With this purpose, the potential exploitation of wireless power and actuation of sensors and biosensors using near field communication (NFC), the use of the screen as a light source for actuation and spectroscopic analysis, using the haptic module to enhance mass transport in micro volumes, and the use of magnetic sensors are discussed. Full article

Internet of Things (IoT) has been deployed in a vast number of smart applications with the aim to bring ease and comfort into our lives. However, with the expansion of IoT applications, the number of security and privacy breaches has also increased, which brings into question the resilience of existing security and trust mechanisms. Furthermore, the contemporaneous centralized technology is posing significant challenges viz scalability, transparency and efficiency to wide range of IoT applications such as smart logistics, where millions of IoT devices need to be connected simultaneously. Alternatively, IOTA is a distributed ledger technology that offers resilient security and trust mechanisms and a decentralized architecture to overcome IoT impediments. IOTA has already been implemented in many applications and has clearly demonstrated its significance in real-world applications. Like any other technology, IOTA unfortunately also encounters security vulnerabilities. The purpose of this study is to explore and highlight security vulnerabilities of IOTA and simultaneously demonstrate the value of threat modeling in evaluating security vulnerabilities of distributed ledger technology. IOTA vulnerabilities are scrutinized in terms of feasibility and impact and we have also presented prevention techniques where applicable. To identify IOTA vulnerabilities, we have examined existing literature and online blogs. Literature available on this topic is very limited so far. As far as we know IOTA has barely been addressed in the traditional journals, conferences and books. In total we have identified six vulnerabilities. We used Common Vulnerability Scoring System (CVSS v3.0) to further categorize these vulnerabilities on the basis of their feasibility and impact. Full article

The container-based cloud is used in various service infrastructures as it is lighter and more portable than a virtual machine (VM)-based infrastructure and is configurable in both bare-metal and VM environments. The Internet-of-Things (IoT) cloud-computing infrastructure is also evolving from a VM-based to a container-based infrastructure. In IoT clouds, the service availability of the cloud infrastructure is more important for mission-critical IoT services, such as real-time health monitoring, vehicle-to-vehicle (V2V) communication, and industrial IoT, than for general computing services. However, in the container environment that runs on a VM, the current fault detection method only considers the container’s infra, thus limiting the level of availability necessary for the performance of mission-critical IoT cloud services. Therefore, in a container environment running on a VM, fault detection and recovery methods that consider both the VM and container levels are necessary. In this study, we analyze the fault-detection architecture in a container environment and designed and implemented a Fast Fault Detection Manager (FFDM) architecture using OpenStack and Kubernetes for realizing fast fault detection. Through performance measurements, we verified that the FFDM can improve the fault detection time by more than three times over the existing method. Full article

The Manifesto for a European research network into Problematic Usage of the Internet was published in May 2018. It was written from the perspective of the COST Action Network, a programme of the European Cooperation in Science and Technology CA16207 and is expected to have significant influence on research funding priorities over the next decade. The Manifesto identified nine key research priorities to advance understanding in the field. Our analysis shows that while at the most general level it identified problematic usage of pornography (PUP) as a key research priority, it then barely mentioned it again within the body of the report. This paper uses the Manifesto’s framework to suggest research areas into the problematic usage of pornography which are of particular relevance to clinicians and other professionals working in the field who want to develop approaches to assist individuals and target groups affected by PUP. It also looks at potential research opportunities inspired by the lived-experience of users withdrawing from PUP. A large number of opportunities are identified for new work on PUP across all nine key research areas of the Manifesto. Full article

Addressing environmental governance conflicts requires the adoption of a complexity approach to carry out an adaptive process of collective learning, exploration, and experimentation. In this article, we hypothesize that by integrating community-based participatory mapping processes with internet-based collaborative digital mapping technologies, it is possible to create tools and spaces for knowledge co-production and collective learning. We also argue that providing a collaborative web platform enables these projects to become a repository of activist knowledge and practices that are often poorly stored and barely shared across communities and organizations. The collaborative Webmap of Water Conflicts in Andalusia, Spain, is used to show the benefits and potential of mapping processes of this type. The article sets out the steps and methods used to develop this experience: (i) background check; (ii) team discussion and draft proposal; (iii) in-depth interviews, and (iv) integrated participative and collaborative mapping approach. The main challenge that had to be addressed during this process was to co-create a tool able to combine the two perspectives that construct the identity of integrated mapping: a data-information-knowledge co-production process that is useful for the social agents—the environmental activists—while also sufficiently categorizable and precise to enable the competent administrations to steer their water management. Full article