Results 81 to 90 of about 149,022 (244)
A Microservice-based Middleware for the Digital Factory
, 2017 In recent years a considerable effort has been spent by research and industrial communities in the digitalization of production environments with the main objective of achieving a new automation paradigm, more flexible, responsive to changes, and safe ...
M. Ciavotta +4 more
semanticscholar +1 more source
Flexible IoT security middleware for end-to-end cloud-fog communication
Future generations computer systems, 2018 IoT (Internet of Things) based smart devices such as sensors have been actively used in edge clouds i.e., ‘fogs’ along with public clouds. They provide critical data during scenarios ranging from e.g., disaster response to in-home healthcare.
B. Mukherjee +7 more
semanticscholar +1 more source
Journal of Product Innovation Management, EarlyView.ABSTRACT Despite their transformative potential, Industrial Internet of Things (IIoT) platforms often fail to evolve into scalable ecosystems. Research on IIoT platforms attributes failure to discrete factors such as governance misalignment or technological complexity and rarely considers how failure unfolds.
Philipp Kernstock +3 more
wiley +1 more source
A classification of emerging and traditional grid systems [PDF]
, 2008 The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data.
Al-Raweshidy, H, Kurdi, H, Li, M
core +1 more source
A Highly Accurate Deep Learning Based Approach for Developing Wireless Sensor Network Middleware
IEEE Access, 2018 Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, security problems associated with them have not been completely resolved. Middleware is generally introduced as an intermediate layer between WSNs and the end user
Remah Alshinina, K. Elleithy
semanticscholar +1 more source
An Ecosystem to Develop Multi‐Agent Systems in Real‐World IoT Applications
Software: Practice and Experience, Volume 56, Issue 4, Page 339-356, April 2026.ABSTRACT Software agents and multi‐agent systems are critical components in the development of distributed autonomous systems, as they exhibit a wide range of “intelligence,” from reactive behaviors to advanced reasoning and planning. Integrating these capabilities into multi‐agent systems enables distributed artificial intelligence, a programming ...
Davide Carnemolla +3 more
wiley +1 more source
Enabling quantitative data analysis through e-infrastructures [PDF]
, 2009 This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can ...
Bardsley, N. +26 more
core +3 more sources
International Journal of Laboratory Hematology, Volume 48, Issue 2, Page 353-364, April 2026.ABSTRACT Introduction Point‐of‐care testing (POCT) for hematology enables clinicians to access actionable results during patient visits, supporting timely decisions such as transfusions or chemotherapy adjustments. However, POCT analyzers must demonstrate clinical performance comparable to centralized laboratory systems. We conducted a proof‐of‐concept
Ryan C. Shean, Sterling T. Bennett
wiley +1 more source
, 2016 In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has
Akanbi A. K. +4 more
core +1 more source
An Overview of Deep Learning Techniques for Big Data IoT Applications
International Journal of Communication Systems, Volume 39, Issue 4, 10 March 2026.Reviews deep learning integration with cloud, fog, and edge computing in IoT architectures. Examines model suitability across IoT applications, key challenges, and emerging trends Provides a comparative analysis to guide future deep learning research in IoT environments.
Gagandeep Kaur +2 more
wiley +1 more source