Internet of Things最新文献

Our study recognized the crucial role of early diagnosis of pulmonary radiological abnormalities such as pneumothorax, effusion, pneumonia, cardiomegaly, and COVID-19. We proposed FedXNet, which is a collaborative deep learning model based on federated learning (FL) exploiting edge computing resources efficiently to accurately deal with them and ensure privacy. Our developed model is notable for its integration of Multi-Headed Self-Attention, a complex technique that allows the model to focus on several parts of the input data at once. This improves the model’s capacity to uncover complex patterns and correlations within the medical images. This multi-class CNN system uses a thorough four-pronged approach: (1) facilitating cross-institutional, federated training without sacrificing the integrity of individual data, (2) image preprocessing to achieve robust model accuracy, (3) efficient Feature extraction using pre-trained models and our dedicated FedXNet architecture, as well as (4) a variety of classifiers tailored to each disease, resulting in impressive diagnostic performance for a range of thoracic diseases, including COVID-19. This model paves the way for a future where timely diagnosis and better patient outcomes become a reality, empowered by the collaborative spirit of FL exploiting edge computing resources of IoT for implementing robust deep learning models.

Since sensor networks are deployed in harsh and unattended environments, and the nodes are not tamper-resistant, security is crucial for all the impending sensor network applications. When physically compromised, confidential credential of sensor nodes may be revealed to adversaries. Upon obtaining the credentials, an adversary can produce a number of replicas of a compromised legitimate node. These replicas interfere with or prevent the normal operation of the network through attacks called $replica$ or $imposter$ $attacks$. Hence detection, identification and quarantining of replicas attacks are crucial. Existing schemes mostly focus on static sensor networks while solutions for MWSN’s are also available. Upon discovering that an id is used by multiple nodes, these schemes quarantine all nodes with the same id without distinguishing the legitimate node from its replicas. In this paper, we propose a novel distributed light-weight and memory efficient signature-based protocol for quarantining malicious replicas in the presence of Byzantine replicas in MWSN’s. This protocol quarantines only the replicas that present wrong sensed data in the presence of Byzantine replicas that modify and communicate its data and send false claims. The proposed protocol uses a signature generation, exchange and verification scheme to detect, uniquely identify and distinguish replicas with the same id among multiple replicas for a single legitimate node. The protocol quarantines only the malicious replicas that present wrong sensed data among all the detected replicas of each legitimate node by uniquely identifying the malicious replicas using their unique signatures.

Advances in Internet of Things and Artificial Intelligence have transformed society with the potential to foster the prosperity of human beings and improving societal welfare. However, at the same time, there are concerns about their potential negative impact. In this regard, it is of utmost importance to establish a regulatory framework for the Artificial Intelligence and the Internet of Things that will ensure that human rights and fundamental freedoms are respected, promoted, and protected.

In this sense, the goal of this article is to propose the Artificial Intelligence and the Internet of Things Governance model. To achieve a better regulation concept, for the Regulatory Impact Assessment we used the Analytical Hierarchy Process method.

Black carbon (BC) has been under the spotlight of research during the last few years due to its non-regulation, its role in air pollution, and its hazardous effects. Given the high cost of the instrumentation needed to measure BC concentrations, data-driven techniques have been adopted to implement proxies that provide BC measurements from other sensor measurements. These sensors may present data quality issues due to maintenance actions, loss of data, or relocation, among others. In this paper, we propose a data-driven proxy model for BC estimation that is powered by a hybrid sensor array, including physical and virtual sensors created from machine learning techniques and governmental air quality monitoring networks. Therefore, the proposed method provides an accurate alternative to traditional data-driven BC proxies in scenarios where some physical sensors are unavailable. The results show how a BC proxy can be partially implemented using virtual sensors, obtaining only an increase in the estimation error of around 4%, allowing the estimation of BC levels even when some physical sensors are absent.

In the era of the IoT revolution, applications are becoming ever more sophisticated and accompanied by diverse functional and non-functional requirements, including those related to computing resources and performance levels. Such requirements make the development and implementation of these applications complex and challenging. Computing models, such as cloud computing, can provide applications with on-demand computation and storage resources to meet their needs. Although cloud computing is a great enabler for IoT and endpoint devices, its limitations make it unsuitable to fulfill all design goals of novel applications and use cases. Instead of only relying on cloud computing, leveraging and integrating resources at different layers (like IoT, edge, and cloud) is necessary to form and utilize a computing continuum. The layers’ integration in the computing continuum offers a wide range of innovative services, but it introduces new challenges (e.g., monitoring performance and ensuring security) that need to be investigated. A better grasp and more profound understanding of the computing continuum can guide researchers and developers in tackling and overcoming such challenges. Thus, this paper provides a comprehensive and unified view of the computing continuum. The paper discusses computing models in general with a focus on cloud computing, the computing models that emerged beyond the cloud, and the communication technologies that enable computing in the continuum. In addition, two novel reference architectures are presented in this work: one for edge–cloud computing models and the other for edge–cloud communication technologies. We demonstrate real use cases from different application domains (like industry and science) to validate the proposed reference architectures, and we show how these use cases map onto the reference architectures. Finally, the paper highlights key points that express the authors’ vision about efficiently enabling and utilizing the computing continuum in the future.

Linear wireless Sensor networks (LWSNs) are extensively utilized to monitor linear infrastructure such as railways, bridges, mines, and borders. In the large-scale deployment of sensor networks, extending network life and improving network energy balance remains a significant challenge. The existing routing protocols adopt the path planning method based on the routing information database, which can effectively transmit data packets. However, the methods consume additional energy when establishing and maintaining routing tables, significantly reducing the nodes’ lifespan and leading to unstable data transmission quality. This paper proposes a dynamic random multipath routing method (DRMRM) for LWSNs. The technique combines the node depth and residual energy models to select the optimal next-hop relay node without relying on the transmission routing table. At the same time, we designed a data loss retransmission mechanism and a data loop retreat mechanism to prevent data packets from reaching a dead end. The experimental results demonstrate that our routing method is superior to existing energy consumption balance and network lifespan protocols.

The Internet of Mobile Things (IoMT) is an emerging paradigm of Internet of Things (IoT) with special focus on enabling mobility to the ‘things’. Several IoMT applications such as group of robots or drones performing collaborative search and rescue operation, identification of mines, warehouse management, goods delivery, etc can be considered as examples of IoMT systems. In the applications mentioned above, the nodes may send the information in a multi-hop manner to the root or coordinator node which may be static or mobile. While the Routing Protocol for Low Power and Lossy Networks (RPL) is extensively utilized in static IoT networks, it encounters significant limitations in handling mobility and providing resilience against routing attacks in mobile IoT networks. In this work, we propose a modified RPL, RPL* which is robust to handling mobility in nodes and is resilient towards routing attacks. In RPL*, any deviation from the normal behaviors of the network are identified as anomalies using an unsupervised Explainable Artificial Intelligence (XAI) strategy. In RPL*, we propose a novel mobility detection mechanism that will identify the mobility in the network in an energy efficient manner without incurring additional communication overhead. To maintain the connectivity with parent node, we propose a novel proactive connectivity management mechanism in RPL* which will ensure a smooth transition from one parent to another if required, thus avoiding the network partitioning due to mobility. The performance analysis of the system has demonstrated an improvement in packet delivery ratio of the mobile nodes by 40% due to the proposed RPL* when compared to RPL. Also, the proposed XAI strategy provided an F1-score of over 95% for the detection of sink hole and black hole attacks in the tested IoMT network scenarios. It was observed that RPL* improves the performance of the IoMT network when compared to RPL. However it may be noted that the mechanisms introduced to support mobility does not lead to a drop in PDR or increase in control packet overhead for static networks. Hence, RPL* can be considered as an alternative to RPL for IoT as well as IoMT networks.

The field of construction engineering and management (CEM) faces multiple challenges such as improving efficiency and ensuring quality, and virtual reality (VR) offers new possibilities to solve these problems. This paper summarizes the research of VR in construction engineering and management (VRCEM) from the perspective of user experience. This paper analyzes 196 relevant literatures in the past five years, and discusses the application of VR in architectural design, building construction and building maintenance. Based on the summary of existing research, this paper proposes the gaps and requirements of current research, including immersion and realism; interactivity and ease of use; data visualization and analysis; design and simulation; efficiency and cost. For future research directions, it suggests focusing on the application of VR in building design, construction and prefabrication, building management, and maintenance, with an emphasis on leveraging IoT for advanced monitoring and management. This research helps deepen the understanding of VRCEM, promotes the digitalization and sustainability of the industry, and highlights the potential of IoT in transforming construction practices.

EU citizenship is strongly linked to digital transformations, as digitalisation redefines how citizens engage, communicate and participate within the EU. Digital advancements have transformed the European public sphere and are radically changing democratic deliberation by enhancing access to information, facilitating transnational dialogue and fostering new forms of civic engagement. E-governance and digital public services streamline administrative processes, making it more convenient for citizens to access various services. Digitalisation of interaction among EUs’ citizens can support their mobility and connectivity. However, rapid digital advancement also brings challenges of evolving multifaced EU identity in fast paced, post-truth, increasingly complex social system like the EU.

The main goal of this article is to show the relevance of postmodern theories for the creation of a theoretical framework for digital European citizenship and the digital European public sphere. By embracing complexity, encouraging critical reflexion and prioritizing adaptability, a postmodern theoretical framework can enhance our understanding of the challenges and opportunities presented by digital technologies in the EU context, ultimately contributing to more inclusive, democratic and responsive forms of digital citizenship and public discourse.

However, as this article shows, the synthesis of a postmodern perspective and digital technologies can also bring its own challenges, such as fragmentation, epistemological uncertainty or ethical dilemmas. Addressing these challenges requires critical engagement with both postmodern theories and digital technologies, acknowledging their complexities and contradictions while striving to promote ethical and inclusive uses of digital technology that empower EU citizens.

This paper aims to highlight the potential of smart university solutions in contributing to the advancement of the United Nations Sustainable Development Goals (SDGs). We surveyed solutions tailored to address each of the seventeen SDGs with an emphasis on identifying those adaptable within university settings. Our research reveals a diverse array of solutions within the university environment that align with the SDGs. Remarkably, we have identified initiatives relevant even to goals seemingly peripheral to the university’s primary mission, such as those addressing poverty eradication. On a further step, we have established a correlation between the smart university concept and the SDGs, effectively showcasing the practicality of utilizing existing solutions to enhance and accomplish all goals. We also propose definitions and a well-structured taxonomy for smart university and smart campus to support our study and guide future research.