Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs Artificial Intelligence

An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines. Energy efficiency is key in such edge environments, since they are often based on an infrastructure that consists of wireless and battery-run devices, e.g., e-tractors, drones, Automated Guided Vehicle (AGV)s and robots. The total energy consumption draws contributions from multiple iIoTe technologies that enable edge computing and communication, distributed learning, as well as distributed ledgers and smart contracts. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption. Finally, the paper provides a vision for integrating these enabling technologies in ...

A Review on Edge Analytics: Issues, Challenges, Opportunities, Promises, Future Directions, and Applications Artificial Intelligence

Edge technology aims to bring Cloud resources (specifically, the compute, storage, and network) to the closed proximity of the Edge devices, i.e., smart devices where the data are produced and consumed. Embedding computing and application in Edge devices lead to emerging of two new concepts in Edge technology, namely, Edge computing and Edge analytics. Edge analytics uses some techniques or algorithms to analyze the data generated by the Edge devices. With the emerging of Edge analytics, the Edge devices have become a complete set. Currently, Edge analytics is unable to provide full support for the execution of the analytic techniques. The Edge devices cannot execute advanced and sophisticated analytic algorithms following various constraints such as limited power supply, small memory size, limited resources, etc. This article aims to provide a detailed discussion on Edge analytics. A clear explanation to distinguish between the three concepts of Edge technology, namely, Edge devices, Edge computing, and Edge analytics, along with their issues. Furthermore, the article discusses the implementation of Edge analytics to solve many problems in various areas such as retail, agriculture, industry, and healthcare. In addition, the research papers of the state-of-the-art edge analytics are rigorously reviewed in this article to explore the existing issues, emerging challenges, research opportunities and their directions, and applications.

The 20 technologies that defined the first 20 years of the 21st Century

The Independent - Tech

The early 2000s were not a good time for technology. After entering the new millennium amid the impotent panic of the Y2K bug, it wasn't long before the Dotcom Bubble was bursting all the hopes of a new internet-based era. Fortunately the recovery was swift and within a few years brand new technologies were emerging that would transform culture, politics and the economy. They have brought with them new ways of connecting, consuming and getting around, while also raising fresh Doomsday concerns. As we enter a new decade of the 21st Century, we've rounded up the best and worst of the technologies that have taken us here, while offering some clue of where we might be going. There was nothing much really new about the iPhone: there had been phones before, there had been computers before, there had been phones combined into computers before. There was also a lot that wasn't good about it: it was slow, its internet connection barely functioned, and it would be two years before it could even take a video.

Next Money Fintech Finals Hong Kong: January 19, 2017


Bankers are in a state of shock Not what they signed up for Buried in'busy' unable to cope with "change" Future is more frightening than the present The goal needs to be more than just great reports … need great experiences. Of our 10 Retail Banking Trends and Predictions, all are supported by data and analytics beyond what is used today. The consumer is in control. The ability to choose what, when, and how to transact. The mobile device makes location and context more important than ever. Banking no longer needs to be an independent activity, but can be integrated into everything we do in our daily lives.