Artificial intelligence (AI) at the edge of the network is a cornerstone that will influence the future direction of the technology industry. If AI is an engine of change, then semiconductors are the oil driving the new age that is being defined by machine learning (ML), neural networks, 5G connectivity and the advent of blockchain, digital twins and the metaverse. Despite recent disruptions to the chip industry due to supply chain and more recently, macroeconomic factors, the confluence of AI – and the Internet of Things (IoT) known as AIoT– is poised to shift the world from cloud-centric intelligence to a more distributed intelligence architecture. It is expected that a staggering 73.1 zettabytes of data is expected to be generated by IoT devices, in 2025 according to IDC Research. As a result, endpoint data will increase at a CAGR of 85% from 2017 to 2025, driving intelligence from the cloud to the endpoint to run AI/ML workloads within tiny machines (TinyML).

Abstract: To build a robust secure solution for smart city's IOT network from any Cyber-attacks using Artificial Intelligence (AI). In Smart City's IOT network, data collected from different log collectors or direct sources from cloud or edge should harness the potential of AI. The smart city command and control center team will leverage these models and deploy it in different city's IOT network to help on intrusion prediction, network packet surge, potential botnet attacks from external network. Some of the vital use cases considered based on the users of command-and-control center. Keywords-Artificial Intelligence, Internet of Things, Smart City, IOT Security, Smart City command and control center I. INTRODUCTION The Internet of Things market will grow from 170 Billion devices (as on 2017) to 561 Billion devices by 2020 as reply.com It will bring more niche devices like Smart Home appliances, Smart Home Security, Digital Assistants and Home Robots from different providers.

FogHorn, a Silicon Valley-based startup, is one of the early movers in the IIoT and edge computing market. The company has raised a total of $47.5M in funding over four rounds. The latest funding came from a Series B round in October 2017 by Intel Capital and Saudi Aramco Energy Ventures. Founded in 2014, FogHorn has been squarely focused on edge analytics and edge intelligence. According to the company, its solution enables high-performance edge processing, optimized analytics, and heterogeneous applications to be hosted as close as possible to the control systems and physical sensor infrastructure that pervade the industrial world.

Computer vision at the intelligent edge is real and it is here! Access sample code to get started and watch a live demo on the Channel 9 IoT Show for inspiration. Thanks to a collaboration between NVIDIA and Microsoft, the NVIDIA Metropolis video analytics application framework, which runs on EGX, is now optimized to work with Microsoft Azure IoT Edge. The NVIDIA Metropolis framework includes the NVIDIA DeepStream software developer kit. With Azure IoT Edge and NVIDIA DeepStream, you can take a small, inexpensive NVIDIA Jetson Nano Developer Kit and analyze HD video streams in real-time.

Aerendir Mobile Inc. will merge its mathematical deep learning cores and AI infrastructure with innovative SiFive, Inc., RISC-V Core IP to enable a new low-cost board format for deep learning. This combined, unique approach will radically decrease the cost of true AI, allowing it to be enabled at the IoT Edge and End Point inside affordable devices. Aerendir and SiFive expect that the IoT market, bolstered by future 5G networks, will require the most cost-effective high-end distributed learning capabilities. As data collection continues to grow and outstrip the ability of datacenters to store, process and analyze new devices at the edge, end point can help to make accurate machine learning decisions. Local data analysis reduces network congestion and latency, improving local device performance and helping to send important data to the cloud for further analysis.

You can now train machine learning models with Azure ML once and deploy them in the Cloud (AKS/ACI) and on the edge (Azure IoT Edge) seamlessly thanks to ONNX Runtime inference engine. In this new episode of the IoT Show we introduce the ONNX Runtime, the Microsoft built inference engine for ONNX models - its cross platform, cross training frameworks and op-par or better performance than existing inference engines. We will show how to train and containerize a machine learning model using Azure Machine Learning then deploy the trained model to a container service in the cloud and to an Azure IoT Edge device with IoT Edge across different HW platform – Intel, NVIDIA and Qualcomm.

The fourth edition of the Internet of Things Solutions World Congress (IoTSWC), which took place in Barcelona earlier this month, signaled an increasing interest in the technology, with the number of attendees jumping by 25 percent year over year, to 16,250. The range of topics discussed shows that IoT is being embraced by companies in every sector, and that the technology has now passed from the development phase to the implementation of practical solutions whose results are increasingly evident. The 200 speeches and panels were divided into thematic areas (manufacturing, healthcare, connected transport, energy and utilities, buildings and infrastructures and open industry). Along with two related events, AI & Cognitive Systems Forum and Blockchain Solutions World, these included -- at the insistence of Richard Soley, Executive Director of the Industrial IoT Consortium -- presentations of concrete use cases. The Industrial IoT Consortium was co-organizer of the event together with Fira Barcelona.

Tensor Processing Unit (TPU), an application specific integrated circuit, designed by Google for accelerating machine learning workloads, is going to be available at the edge. These tailor-made chips complement Cloud TPUs by inferencing machine learning models deployed at the edge. Google has also announced Cloud IoT Edge, an edge computing platform that extends Google Cloud's data processing and machine learning to edge devices. Google is the latest entrant into the edge computing market. The key competitors of Google Cloud – Amazon and Microsoft – have a comprehensive edge computing strategy.

Smart Manufacturing envisions a future where factory equipment can make autonomous decisions based on what's happening on the factory floor. Businesses can more easily integrate all steps of the manufacturing process including design, manufacturing, supply chain and operation. Enabling this vision requires a combination of related technologies such as IoT, AI/machine learning, and Edge Computing. In this article, we will introduce Edge Computing and discuss its role in enabling Smart Manufacturing. Put simply, Edge Computing is about taking code that runs in the cloud and running it on local devices or close to it.

This story was delivered to Business Insider Intelligence IoT Briefing subscribers hours before it appeared on Business Insider. To be the first to know, please click here. Microsoft made a slew of announcements about its IoT business segment at its annual Build Developer Conference on Monday. Microsoft announced it's open-sourcing Azure IoT Edge, its product suite that allows companies to run AI and Azure services without connecting to the cloud. Microsoft first unveiled IoT Edge at last year's Build Conference to help it keep pace in the rapidly growing edge computing market for IoT devices.