The exponential growth of IoT devices presents critical challenges in bandwidth-constrained wireless networks, particularly regarding efficient data transmission and privacy preservation. This paper presents a novel federated semantic communication (SC) framework that enables collaborative training of bandwidth-efficient models for image reconstruction across heterogeneous IoT devices. By leveraging SC principles to transmit only semantic features, our approach dramatically reduces communication overhead while preserving reconstruction quality. We address the fundamental challenge of client selection in federated learning environments where devices exhibit significant disparities in dataset sizes and data distributions. Our framework implements three distinct client selection strategies that explore different trade-offs between system performance and fairness in resource allocation. The system employs an end-to-end SC architecture with semantic bottlenecks, coupled with a loss-based aggregation mechanism that naturally adapts to client heterogeneity. Experimental evaluation on image data demonstrates that while Utilitarian selection achieves the highest reconstruction quality, Proportional Fairness maintains competitive performance while significantly reducing participation inequality and improving computational efficiency. These results establish that federated SC can successfully balance reconstruction quality, resource efficiency, and fairness in heterogeneous IoT deployments, paving the way for sustainable and privacy-preserving edge intelligence applications.

This article was written by Roger Strukhoff and Sophie Turol. The Internet of Things (IoT) is covering the gamut of industries: healthcare, aviation, automative industry, predictive maintenance, and many more. "IoT helps cities to predict accidents and crime as well as gives doctors real-time insight into information from pacemakers or biochips," said Ahmed Banafa of San Jose State University at a recent webinar. "IoT optimizes productivity across industries through on equipment and machinery, creates truly smart homes with connected appliances, and provides critical communication between self-driving cars." Most enterprises seem to be able to think of something uniquely valuable to them, as it looks like the IoT will soon be adopted by a majority of companies.

At its core, an IoT network is a group of connected devices that communicate without human involvement. Popular examples include autonomous cars, smart appliances, and wearable tech. The global IoT market -- already worth more than $700 billion -- is expected to grow rapidly in the years to come. What is often ignored, however, is the immense volume of information that must be facilitated across these devices on a continuous basis. By building IoT networks with Syntropy Stack, businesses, developers, and end users all reap the rewards.

The IoT is getting smarter. Companies are incorporating artificial intelligence--in particular, machine learning--into their Internet of Things applications and seeing capabilities grow, including improving operational efficiency and helping avoid unplanned downtime. WITH a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT). Companies crafting an IoT strategy, evaluating a potential new IoT project, or seeking to get more value from an existing IoT deployment may want to explore a role for AI. Artificial intelligence is playing a growing role in IoT applications and deployments, a shift apparent in the behavior of companies operating in this area.

With digital transformation in full swing, the number of connected devices is increasing at a fast pace. IDC Data predicts 152,200 connected IoT devices every minute by the year 2025. While this translates to more data and, subsequently, more avenues to improve efficiency, a robust network is necessary for this data exchange. The fifth-generation wireless technology has features that will not only support high-speed mobile communication but also make IoT data transfer more efficient. Let's look at these features in contrast with the existing 4G network: All these features make the 5G network adaptable to the external environment, unlike its predecessors, which has limited network flexibilities.

The oil and gas industry is massive and highly-diversified in its operational characteristics between the upstream, mid-stream and downstream sectors of the industry. Even within each sector, there are distinct differences; offshore gas/oil rigs have a completely different set of requirements to onshore well pads in the fracking industry. However, every sector is susceptible to the boom and bust cycles that have traditionally characterised the oil and gas industry. All of this makes oil and gas ideal for adopting IOT technologies to address a whole range of problems and risks, and to smooth out the ups and downs of the business cycle. Where are oil and gas companies today with edge computing adoption?

IoT and SD-WAN might not sound like they belong together, but ask VMware's VeloCloud or managed service provider Apcela and you might be surprised by what they have to say. The two companies see SD-WAN as the key to making large IoT deployments manageable at a human scale. Sanjay Uppal, who co-founded VeloCloud and now serves as the head of VMware's SD-WAN division, said the expanding scope of SD-WAN has opened the door to several applications that the technology wouldn't normally be associated with, and IoT is one of them. "You think of IoT, it's not just IoT running on a cellular network or IoT running on Bluetooth, you could absolutely run IoT on your enterprise SD-WAN," Uppal said in an earlier interview. "Just think of that IoT traffic as a new data type that you will steer across the WAN and you can add services to it as it is steered." While IoT may not be a new concept, with some companies having been in the business for decades, the rise of IoT to the mainstream is forcing networks to change, said Apcela CEO Mark Casey.

Dreams of a future built on 5G networks and powered by IoT dominated the conversation at conferences in 2019. But for all these grand visions there's a problem: How do you manage networks with millions of cell sites connecting billions of IoT devices? According to some the answer is better visibility enabled by artificial intelligence (AI). While most of these technologies are still years from reaching maturity, that's not stopping companies in the performance analytics space like EXFO and Vitria from investing big in machine learning (ML) and AI. According to Ken Gold, director of test, monitoring, and analytics solutions at EXFO, the implicit complexity associated with massive 5G IoT deployments is only going to make identifying and resolving network anomalies all the more challenging.

We may begin to hear the term itself used less frequently – but that's because it's moving out of the hype phase and quickly becoming a part of everyday life. Soon, it will be taken for granted that pretty much any device we own – cars, TVs, watches, kitchen appliances can go online and communicate with each other. In industry too, tools and machinery are increasingly intelligent and connected, generating data that drives efficiency and enables new paradigms such as predictive maintenance to become a reality, rather than a pipe-dream. In fact, it is predicted that by the end of 2019 there will be 26 billion connected devices around the world. Here are five predictions about how this is likely to play out over the next 12 months as we become increasingly used to the fact that the internet isn't just something we connect to using computers and smartphones, but virtually anything we can think of: According to research by Forrester, businesses will lead the surge in IoT adoption in 2019, with 85% of companies implementing or planning IoT deployments this year.

We may begin to hear the term itself used less frequently – but that's because it's moving out of the hype phase and quickly becoming a part of everyday life. Soon, it will be taken for granted that pretty much any device we own – cars, TVs, watches, kitchen appliances can go online and communicate with each other. In industry too, tools and machinery are increasingly intelligent and connected, generating data that drives efficiency and enables new paradigms such as predictive maintenance to become a reality, rather than a pipe-dream. In fact, it is predicted that by the end of 2019 there will be 26 billion connected devices around the world. Here are five predictions about how this is likely to play out over the next 12 months as we become increasingly used to the fact that the internet isn't just something we connect to using computers and smartphones, but virtually anything we can think of: According to research by Forrester, businesses will lead the surge in IoT adoption in 2019, with 85% of companies implementing or planning IoT deployments this year.