The explosion of Internet of Things (IoT) devices and services worldwide has contributed to an explosion in data processing and interconnectivity. Simultaneously, this interconnection and resulting interdependence have amplified a range of cybersecurity risks to individuals’ data, company networks, critical infrastructure, and the internet ecosystem writ large. Governments, companies, and civil society have proposed and implemented a range of IoT cybersecurity initiatives to meet this challenge, ranging from introducing voluntary standards and best practices to mandating the use of cybersecurity certifications and labels. However, issues like fragmentation among and between approaches, complex certification schemes, and placing the burden on buyers have left much to be desired in bolstering IoT cybersecurity. Ugly knock-on effects to states, the private sector, and users bring risks to individual privacy, physical safety, other parts of the internet ecosystem, and broader economic and national security.

In light of this systemic risk, this report offers a multinational strategy to enhance the security of the IoT ecosystem. It provides a framework for a clearer understanding of the IoT security landscape and its needs—one that focuses on the entire IoT product lifecycle, looks to reduce fragmentation between policy approaches, and seeks to better situate technical and process guidance into cybersecurity policy. Principally, it analyzes and uses as case studies the United States, United Kingdom (UK), Australia, and Singapore, due to combinations of their IoT security maturity, overall cybersecurity capacity, and general influence on the global IoT and internet security conversation. It additionally examines three industry verticals, smart homes, networking and telecommunications, and consumer healthcare, which cover different products and serve as a useful proxy for understanding the broader IoT market because of their market size, their consumer reach, and their varying levels of security maturity. Read More

The digital twin is an exciting concept and undoubtedly one of the hottest tech trends right now. It fuses ideas including artificial intelligence (AI), the internet of things (IoT), metaverse, and virtual and augmented reality (VR/AR) to create digital models of real-world objects, systems, or processes. These models can then be used to tweak and adjust variables to study the effect on whatever is being twinned – at a fraction of the cost of carrying out experiments in the real world.

Businesses around the globe are looking to deploy Digital Twins across a broad range of applications, ranging from engineering design of complex equipment and 3D immersive environments to precision medicine and digital agriculture. However, to date, applications have been highly customized and only accessible for high value use-cases, such as the operations of jet engines, industrial facilities and power plants. Now leading technology companies like AWS are working hard to lower the costs and simplify the deployment of this technology, with AWS IoT TwinMaker, making it easier and more accessible for all kinds and sizes of companies to build their own Digital Twins. Read More

A couple of weeks ago, the tech industry group Consumer Technology Association (CTA) released a predictably cheery report on the state of consumer technology. It forecasted record-breaking revenues of $487 billion this year, with laptops, wireless earbuds, personal fitness devices, and 5G phones singled out for especially strong growth.

Yet tucked into that upbeat forecast was a spot of dreariness: Smart home devices, once hailed as tech’s next big computing platform, would experience flat revenues of $15 billion in 2021, with unit sales up 11%.

The CTA says its stagnant forecast is merely a function of competition, as an influx of device makers drive down the cost of hardware. But as someone who’s been living with various smart home gadgets for several years now, I have a different theory: They’re just not worth a big investment unless you have a limitless supply of time and patience. Read More

An IoT device is a piece of hardware, typically a sensor, that transmits data from one place to another over the internet. Types of IoT devices include simple (often wireless) sensors, actuators, as well as more sophisticated computerized devices.

A digital twin (DT) is the software representation of a physical object. At a bare minimum, a DT must include the unique identifier of the physical object it represents. However, it only starts fulfilling its purpose once additional information — such as sensory information (position, temperature, humidity, etc.) and/or its actuation capabilities (turn lamp on/off, etc.) — is added. The DT will often include additional auxiliary data, such as the device’s firmware version, configuration, calibration, and setpoint data.

When it comes to actuation, we often talk about the DT as a “shadow” of its physical representation in order to highlight the fact that actuations are always transactional. For instance, the DT’s intent to change its device state (turn it off or on) requires a particular command to be sent to the device, which after successful completion of the actuation needs to be communicated back to the caller (the DT).

A digital twin is sometimes referred to as the representation of an IoT device, which is not exactly the same. Let us take a closer look at the two categories, as the difference between them is actually bigger than one might think. Read More

The edge is an end point where data is generated through some type of interface, device or sensor. Keep in mind that the technology is nothing new. But in light of the rapid innovations in a myriad of categories, the edge has become a major growth business.

“The edge brings the intelligence as close as possible to the data source and the point of action,” said Teresa Tung, who is the Managing Director at Accenture Labs. “This is important because while centralized cloud computing makes it easier and cheaper to process data at scale, there are times when it doesn’t make sense to send data off to the cloud for processing.” Read More