Collaborating Authors


The Intersection of the Internet of Things with AI (AIoT)


AI and IoT are both emerging innovative technologies with a lot of potentials. Traditional business procedures in numerous industries are being disrupted by advances in the internet of things (IoT) and artificial intelligence (AI). The IoT is a technology that is assisting us in reimagining modern routines, but AI is the true driving factor behind the IoT's maximum capabilities. As a result, combining these two will assist both individuals and authorities. AI technologies can do and will be used to improve IoT-related functions in order to provide deeply significant experiences for users and/or consumers.

Top 10 Smart Home Technologies to Make Life Smarter in 2021


The smart connected home is the next step in our houses' growth and how we interact with them. The various systems in our houses are developing as technology improves, much as lighting has progressed from candles to gas to electricity. The smart home is rapidly expanding. While all of these new smart home technologies may appear intimidating and difficult at first, the introduction of artificially intelligent assistants and voice control has made it much easier to accept. Here is a list of the essential smart home devices which work on smart home technologies.

AI and IoT Continue to Increase Interoperability Demands in 2021 - IntelligentHQ


We've been experiencing tremendous changes in our lives with the advent of IoT-based devices and equipment. So, the demand for IoT development services and the guidance of DPO services has increased in various industries, including healthcare, logistics, education, retail, and others. Still, the future of IoT is in its win-win combination with artificial intelligence. For the last few years, growing demand for AI and IoT interoperability is observed in almost every sector where IoT has proved its efficiency. Let's take a closer look at how we can leverage the two technologies combined.

An Internet of Things Service Roadmap

Communications of the ACM

The Internet of things (IoT) is taking the world by storm, thanks to the proliferation of sensors and actuators embedded in everyday things, coupled with the wide availability of high-speed Internet50 and evolution of the 5th-generation (5G) networks.34 IoT devices are increasingly supplying information about the physical environment (for example, infrastructure, assets, homes, and cars). The advent of IoT is enabling not only the connection and integration of devices that monitor physical world phenomena (for example, temperature, pollution, energy consumption, human activities, and movement), but also data-driven and AI-augmented intelligence. At all levels, synergies from advances in IoT, data analytics, and artificial intelligence (AI) are firmly recognized as strategic priorities for digital transformation.10,41,50 IoT poses two key challenges:36 Communication with things and management of things.41 The service paradigm is a key mechanism to overcome these challenges by transforming IoT devices into IoT services, where they will be treated as first-class objects through the prism of services.9 In a nutshell, services are at a higher level of abstraction than data. Services descriptions consist of two parts: functional and non-functional, such as, Quality of Service (QoS) attributes.27 Services often transform data into an actionable knowledge or achieve physical state changes in the operating context.9 As a result, the service paradigm is the perfect basis for understanding the transformation of data into actionable knowledge, that is, making it useful. Despite the increasing uptake of IoT services, most organizations have not yet mastered the requisite knowledge, skills, or understanding to craft a successful IoT strategy.

Digital Twins and Self-Sovereign Identity: Build the next generation of Simulation with privacy preservation The IoT Portal Platform


The rise in the use of advanced analytics, machine learning (ML) and Artificial Intelligence (AI) and the Internet of Things (IoT) today have driven the technology of simulation into the concept of the digital twin. Digital twins are generally defined as a virtual digital model of a physical system that is used to make better decisions about the real world physical system. Digital twins are usually intertwined with sensors and include a two-way interaction between the physical and digital twin. The growth of "smart" IoT devices – devices with an increasing amount of processing or computational capability – results in more use cases where devices perform independent functions or complex system behaviors. These devices must both provide cryptographic trust to participate in network data exchange and be assured they can trust the network and nodes to which they communicate.

IoT Trends and Beyond for 2021


From healthcare and retail to automotive and manufacturing, every industry is getting smarter with technologies like IoT. Failing to stay competitive in this space can result in significant losses. The global pandemic was a significant roadblock for IoT growth in 2020. Although a November 2019 forecast predicted that IoT spending would grow 14.9% in 2020, it could only grow 8.2%. Based on forecasting from the International Data Corporation, IoT will return in stride this year and achieve a growth rate of 11.3% from 2020 to 2024. Recently, a shortage in semiconductors and other IoT components poses questions for IoT's growth in 2021.

AoI-minimizing Scheduling in UAV-relayed IoT Networks Artificial Intelligence

Due to flexibility, autonomy and low operational cost, unmanned aerial vehicles (UAVs), as fixed aerial base stations, are increasingly being used as \textit{relays} to collect time-sensitive information (i.e., status updates) from IoT devices and deliver it to the nearby terrestrial base station (TBS), where the information gets processed. In order to ensure timely delivery of information to the TBS (from all IoT devices), optimal scheduling of time-sensitive information over two hop UAV-relayed IoT networks (i.e., IoT device to the UAV [hop 1], and UAV to the TBS [hop 2]) becomes a critical challenge. To address this, we propose scheduling policies for Age of Information (AoI) minimization in such two-hop UAV-relayed IoT networks. To this end, we present a low-complexity MAF-MAD scheduler, that employs Maximum AoI First (MAF) policy for sampling of IoT devices at UAV (hop 1) and Maximum AoI Difference (MAD) policy for updating sampled packets from UAV to the TBS (hop 2). We show that MAF-MAD is the optimal scheduler under ideal conditions, i.e., error-free channels and generate-at-will traffic generation at IoT devices. On the contrary, for realistic conditions, we propose a Deep-Q-Networks (DQN) based scheduler. Our simulation results show that DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline schedulers, i.e., Maximal AoI First (MAF), Round Robin (RR) and Random, employed at both hops under general conditions when the network is small (with 10's of IoT devices). However, it does not scale well with network size whereas MAF-MAD outperforms all other schedulers under all considered scenarios for larger networks.

Secure solutions for Smart City Command Control Centre using AIOT Artificial Intelligence

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 It will bring more niche devices like Smart Home appliances, Smart Home Security, Digital Assistants and Home Robots from different providers.

Amazon says most Echo speakers will support the Matter smart home platform


Amazon's support for the Matter smart home platform is coming into focus. Previously known as Project Chip (Connected Home over IP), Matter comes from the Connectivity Standards Alliance, a group made up of device manufacturers like Amazon, Google, Apple and Samsung. It's meant to standardize voice assistant support across multiple devices, as well as to make it easier to connect smart home gadgets to your home network. During its Alexa Live developer presentation, Amazon said that practically every plug-in Echo speaker will support Matter, save for the first-generation Echo, Echo Dot and Echo Tap, The Verge reports. It's unclear when the Echo support will actually arrive, but at this point we're expecting Matter devices to launch later this year. Google has already declared strong commitment for the platform -- so much so that we've speculated it could help unite the fragmented smart home ecosystem.

Framework for an Intelligent Affect Aware Smart Home Environment for Elderly People Artificial Intelligence

The population of elderly people has been increasing at a rapid rate over the last few decades and their population is expected to further increase in the upcoming future. Their increasing population is associated with their increasing needs due to problems like physical disabilities, cognitive issues, weakened memory and disorganized behavior, that elderly people face with increasing age. To reduce their financial burden on the world economy and to enhance their quality of life, it is essential to develop technology-based solutions that are adaptive, assistive and intelligent in nature. Intelligent Affect Aware Systems that can not only analyze but also predict the behavior of elderly people in the context of their day to day interactions with technology in an IoT-based environment, holds immense potential for serving as a long-term solution for improving the user experience of elderly in smart homes. This work therefore proposes the framework for an Intelligent Affect Aware environment for elderly people that can not only analyze the affective components of their interactions but also predict their likely user experience even before they start engaging in any activity in the given smart home environment. This forecasting of user experience would provide scope for enhancing the same, thereby increasing the assistive and adaptive nature of such intelligent systems. To uphold the efficacy of this proposed framework for improving the quality of life of elderly people in smart homes, it has been tested on three datasets and the results are presented and discussed.