1,194 research outputs found
A survey of secure middleware for the Internet of Things
The rapid growth of small Internet connected devices, known as the Internet of Things (IoT), is creating a new set of challenges to create secure, private infrastructures. This paper reviews the current literature on the challenges and approaches to security and privacy in the Internet of Things, with a strong focus on how these aspects are handled in IoT middleware. We focus on IoT middleware because many systems are built from existing middleware and these inherit the underlying security properties of the middleware framework. The paper is composed of three main sections. Firstly, we propose a matrix of security and privacy threats for IoT. This matrix is used as the basis of a widespread literature review aimed at identifying requirements on IoT platforms and middleware. Secondly, we present a structured literature review of the available middleware and how security is handled in these middleware approaches. We utilise the requirements from the first phase to evaluate. Finally, we draw a set of conclusions and identify further work in this area
Federated Embedded Systems – a review of the literature in related fields
This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous
computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways
Smart City IoT Data Management with Proactive Middleware
With the increased emergence of cloud-based services, users are frequently perplexed as to which cloud service to use and whether it will be beneficial to them. The user must compare various services, which can be a time-consuming task if the user is unsure of what they might need for their application. This paper proposes a middleware solution for storing Internet of Things (IoT) data produced by various sensors, such as traffic, air quality, temperature, and so on, on multiple cloud service providers depending on the type of data. Standard cloud computing technologies become insufficient to handle the data as the volume of data generated by smart city devices grows. The middleware was created after a comparative study of various existing middleware. The middleware uses the concept of the federal cloud for the purpose of storing data. The middleware solution described in this paper makes it easier to distribute and classify IoT data to various cloud environments based on its type. The middleware was evaluated using a series of tests, which revealed its ability to properly manage smart city data across multiple cloud environments. Overall, this research contributes to the development of middleware solutions that can improve the management of IoT data in settings such as smart cities
Developed A Hybrid Optimal Feature Vector Selection with Blockchain Technology for Smart Healthcare 4.0
The health economy has been an innovative technology since time-honored. Preserving and maintaining patient data are essential in a routine life. Patient’s medical information is very important for every individual not only for patients but also for doctors who are examining them. Advances in sensing technology, processing of data, and communication protocols have transformed the healthcare industry. Patients, physicians, hospitals, and other stakeholder may keep vital data and medical records with the use of electronic healthcare records (EHR). The goal of research should be to develop a Hybrid Optimal Feature Vector Selection with Blockchain Technology (HOFVS-BT) for smart healthcare 4.0 to improve the secure transmission of data which is supported intelligent IoT and medical detection platform possible. For Feature vector selection, proposed an Orthogonal Wolf Optimization (OWO) algorithm. Furthermore, safeguarding private patient details is taken into account by establishing an upgraded Blockchain-based IoT data security solution that not only secures the data, but also fosters trust between patients/users and healthcare service providers
FRAMH: A Federated Learning Risk-Based Authorization Middleware for Healthcare
Modern healthcare systems operate in highly dy-
namic environments requiring adaptable access control mecha-
nisms. Access to sensitive data and medical equipment should
be granted or denied according to the current health situation
of the patient. To handle the need for adaptable access control
of healthcare scenarios, we propose a novel model that allows
dynamic access control decisions based on the context character-
izing the source, type of access request, patient, and estimated
risk corresponding to the conditions of the patient. Estimating
patient status risk requires analyzing vital physiological data
whose availability is growing thanks to the widespread diffusion
of the Internet of Medical Things (IoMT) devices. Inferring
the patient health status risk through Machine Learning (ML)
techniques is possible, but to achieve better accuracy, the training
phase requires the aggregation of vast amounts of data from
different sources. This aggregation could be difficult or even
impossible due to organization regulations and privacy laws.
To address these issues, this paper proposes a novel Federated
Learning Risk-based Authorization Middleware for Healthcare
(FRAMH) that supports risk-based access control to deal with
changing and unforeseen medical situations. Our solution infers
the risk of health status through a federated learning (FL)
approach enriched with blockchain to avoid the weaknesses
of centralized servers. The implemented prototype and a large
set of experimental results demonstrate the advantages of FL
in estimating the risk in healthcare scenarios. Through this
approach, even a medical institution with a limited dataset can
achieve a satisfying risk estimation and efficient access control
enforcement
Addressing the Challenges in Federating Edge Resources
This book chapter considers how Edge deployments can be brought to bear in a
global context by federating them across multiple geographic regions to create
a global Edge-based fabric that decentralizes data center computation. This is
currently impractical, not only because of technical challenges, but is also
shrouded by social, legal and geopolitical issues. In this chapter, we discuss
two key challenges - networking and management in federating Edge deployments.
Additionally, we consider resource and modeling challenges that will need to be
addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and
Paradigms; Editors Buyya, Sriram
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