AI is being infused into the Internet of Things, setting the stage for significant improvements in manufacturing productivity, improved uptime, and reduced costs -- regardless of market segment. The traditional approach to improving manufacturing equipment reliability and efficiency is regular scheduled maintenance. While that is an improvement over just fixing or replacing equipment when it breaks, it's far from optimal. Even with periodic maintenance, equipment can suddenly break down, idling workers, delaying shipments, and disappointing customers. This is where AI fits in.
The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security.
Technology is powering the rise of smart cities, transforming everything from traffic management to waste collection. We dig into the digital revolution giving rise to cities that are more connected, sustainable, and efficient -- and what the future of urbanization might look like. Cities are evolving at a rapid pace. Over half the world's population currently lives in urban areas. By 2050, that number is expected to jump to 70%. Along with a growing population, new challenges are emerging as cities look to improve everything from infrastructure to connectivity. Many see this as a viable business opportunity, developing technology to help cities efficiently provide proper foundation, energy, transportation, resources, jobs, and services to their residents. As a result, cities are undergoing a digital transformation -- that is, they are turning into "smart" cities. Get a data-driven look at the startups and industry players developing smart city technologies.