Simulation support for internet-based energy services

The rapidly developing Internet broadband network offers new opportunities for deploying a range of energy, environment and health-related services for people in their homes and workplaces. Several of these services can be enabled or enhanced through the application of building simulation. This paper describes the infrastructure for e-services under test within a European research project and shows the potential for simulation support for these services

Logic-centred architecture for ubiquitous health monitoring

One of the key points to maintain and boost research and development in the area of smart wearable systems (SWS) is the development of integrated architectures for intelligent services, as well as wearable systems and devices for health and wellness management. This paper presents such a generic architecture for\ud multiparametric, intelligent and ubiquitous wireless sensing platforms. It is a transparent, smartphone-based sensing framework\ud with customizable wireless interfaces and plug‘n’play capability to easily interconnect third party sensor devices. It caters to wireless\ud body, personal, and near-me area networks. A pivotal part of the platform is the integrated inference engine/runtime environment\ud that allows the mobile device to serve as a user-adaptable personal health assistant. The novelty of this system lays in a rapid visual\ud development and remote deployment model. The complementary visual InferenceEngineEditor that comes with the package enables\ud artificial intelligence specialists, alongside with medical experts, to build data processing models by assembling different components\ud and instantly deploying them (remotely) on patient mobile devices. In this paper, the new logic-centered software architecture for ubiquitous health monitoring applications is described, followed by a\ud discussion as to how it helps to shift focus from software and hardware development, to medical and health process-centered design of new SWS applications

A Survey on Automation Challenges and Opportunities for IoT based Agriculture

Agriculture automation is a major concern and a contentious issue in every country. This study provides a comprehensive assessment of the obstacles and potential associated with automating agricultural practises using IoT (Internet of Things) technology. It begins with an introduction that highlights the prior work and discusses the proposed proposal, which is centred on IoT and machine learning applications and breakthroughs in irrigation systems. The report digs into several IoT applications in agriculture, including crop and soil management, drone field surveillance, cattle and resource management, and pesticide/fertilizer tracking. It delves into the breakthroughs made possible by IoT and machine learning, particularly in smart irrigation systems, livestock monitoring, drone technology, precision agriculture, and integrated pest management. The paper thoroughly examines the challenges associated with automating irrigation practises, such as interoperability, data storage, connectivity, hardware and software maintenance, security concerns, data collection, environmental variability, cost, infrastructure, privacy, and adoption by small-scale farmers. The survey finishes by synthesising the important findings and emphasising the crucial need of overcoming these problems in order to successfully adopt IoT-driven agriculture automation

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Progression and Challenges of IoT in Healthcare: A Short Review

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments.Comment: 7 page

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control

Utilizing Deep Learning for Automated Inspection and Damage Assessment in Civil Infrastructure Systems

The integrity of civil infrastructure systems, including bridges, roads, tunnels, and buildings, is critical for public safety and economic stability. Traditional methods of inspection and damage assessment often rely on manual visual inspections, which can be time-consuming, subjective, and prone to errors. With advancements in deep learning, there is an opportunity to revolutionize the inspection and damage assessment processes through automated systems that offer increased accuracy, efficiency, and scalability. This paper explores the application of deep learning for automated inspection and damage assessment in civil infrastructure systems. We analyze various deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and their roles in defect detection, damage classification, and structural health monitoring. We also discuss the challenges associated with implementing these technologies, such as data quality, model interpretability, and integration with existing infrastructure. By addressing these challenges, deep learning can significantly enhance the capabilities of automated inspection systems, leading to more reliable and timely assessments of infrastructure health

Revolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions

Food security, a global concern, necessitates precise and diverse data-driven solutions to address its multifaceted challenges. This paper explores the integration of AI foundation models across various food security applications, leveraging distinct data types, to overcome the limitations of current deep and machine learning methods. Specifically, we investigate their utilization in crop type mapping, cropland mapping, field delineation and crop yield prediction. By capitalizing on multispectral imagery, meteorological data, soil properties, historical records, and high-resolution satellite imagery, AI foundation models offer a versatile approach. The study demonstrates that AI foundation models enhance food security initiatives by providing accurate predictions, improving resource allocation, and supporting informed decision-making. These models serve as a transformative force in addressing global food security limitations, marking a significant leap toward a sustainable and secure food future