With the growing volume of Internet of Things (IoT) network traffic, machine learning (ML)-based anomaly detection is more relevant than ever. Traditional batch learning models face challenges such as high maintenance and poor adaptability to rapid anomaly changes, known as concept drift. In contrast, streaming learning integrates online and incremental learning, enabling seamless updates and concept drift detection to improve robustness. This study investigates anomaly detection in streaming IoT traffic as binary classification, comparing batch and streaming learning approaches while assessing the limitations of current IoT traffic datasets. We simulated heterogeneous network data streams by carefully mixing existing datasets and streaming the samples one by one. Our results highlight the failure of batch models to handle concept drift, but also reveal persisting limitations of current datasets to expose model limitations due to low traffic heterogeneity. We also investigated the competitiveness of tree-based ML algorithms, well-known in batch anomaly detection, and compared it to non-tree-based ones, confirming the advantages of the former. Adaptive Random Forest achieved F1-score of 0.990 $\pm$ 0.006 at one-third the computational cost of its batch counterpart. Hoeffding Adaptive Tree reached F1-score of 0.910 $\pm$ 0.007, reducing computational cost by four times, making it a viable choice for online applications despite a slight trade-off in stability.

Forecasts and estimation of the number of IoT devices deployed globally continue to rise and rise again. Few industry analysts or researchers agree on what that number eventually might be but all accept that there will likely be upwards of 30 billion of them by 2030 and that, by that time, the market will be worth US$ 20 trillion or more. Such massive figures would have been dismissed as laughable when the notion of the Internet of Things first filtered into public consciousness back in 2010. However, rather than undergoing the sort of steady linear expansion that characterized the "one device, one user" epoch (i.e. one mobile handset per subscriber or one PC per owner) the growth of IoT has been explosive and close to the point of becoming exponential. The fact is that there are already many millions of IoT devices and sensors already out there - and more and more are coming online every day. The remarkable direct result of the mass deployment of IoT-connected devices is that the internet we know and use today is very much bigger and more expansive than it would ever, or could ever, have been without them.

If the industry needed more evidence that IoT devices and applications are taking over the world, Cisco this week said that by 2023 machine-to-machine communications will make up 50% or about 14.7 billion of all networked connections compared to 33% (6.1 billion) in 2018 and 3.1 percent in 2017. The M2M findings were just a part of Cisco's annual forecast of networking trends now called the Cisco Annual Internet Report. The report replaces the Visual Networking Index (VNI) Forecast and looks at everything from 5G and Wi-Fi growth to broadband trends collected from actual network traffic reports and independent analyst forecasts. On the M2M projections, Cisco stated the rapid growth is due to a variety of hot M2M applications, such as smart meters, video surveillance, healthcare monitoring, transportation and package tracking. Traffic is growing faster than the number of connections because the use of video applications on M2M connections is up, as well as other high-bandwidth, low-latency applications such as telemedicine and smart-car navigation systems.

If the industry needed more evidence that IoT devices and applications are taking over the world, Cisco this week said that by 2023 machine-to-machine communications will make up 50% or about 14.7 billion of all networked connections compared to 33% (6.1 billion) in 2018 and 3.1 percent in 2017. The M2M findings were just a part of Cisco's annual forecast of networking trends now called the Cisco Annual Internet Report. The report replaces the Visual Networking Index (VNI) Forecast and looks at everything from 5G and Wi-Fi growth to broadband trends collected from actual network traffic reports and independent analyst forecasts. On the M2M projections, Cisco stated the rapid growth is due to a variety of hot M2M applications, such as smart meters, video surveillance, healthcare monitoring, transportation and package tracking. Traffic is growing faster than the number of connections because the use of video applications on M2M connections is up, as well as other high-bandwidth, low-latency applications such as telemedicine and smart-car navigation systems. It will provide access connections for applications that require greater bandwidth and lower latencies, and that will nurture new innovations not previously possible, wrote Thomas Barnett, Director of Thought Leadership in Cisco Systems' worldwide service provider marketing group in a blog about the report.

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.

Modern mobile networks are facing unprecedented growth in demand due to a new class of traffic from Internet of Things (IoT) devices such as smart wearables and autonomous cars. Future networks must schedule delay-tolerant software updates, data backup, and other transfers from IoT devices while maintaining strict service guarantees for conventional real-time applications such as voice-calling and video. This problem is extremely challenging because conventional traffic is highly dynamic across space and time, so its performance is significantly impacted if all IoT traffic is scheduled immediately when it originates. In this paper, we present a reinforcement learning (RL) based scheduler that can dynamically adapt to traffic variation, and to various reward functions set by network operators, to optimally schedule IoT traffic. Using 4 weeks of real network data from downtown Melbourne, Australia spanning diverse traffic patterns, we demonstrate that our RL scheduler can enable mobile networks to carry 14.7% more data with minimal impact on existing traffic, and outpeforms heuristic schedulers by more than 2x. Our work is a valuable step towards designing autonomous, "self-driving" networks that learn to manage themselves from past data.