Pay in installments of $59.05 with
,
and
Shipping Estimate
USA
- USA
- CAN
- USA
- CAN
Ships within 48 hours · Estimated delivery Aug 23 - Aug 28
Promo Codes Available:
For Your Every Summer RSVP, with Code: SUMMER15
Description
Lantronix SGX 5150 Wireless IoT Device Gateway, Dual Band 5G 802.11ac and 80211 b/g/n, USB Host and Device Modes, a single 10/100 Ethernet port, US ModelThe Lantronix SGX 5150 is a high performance wireless IoT device gateway designed to securely connect critical assets to the enterprise network. Built for reliability in demanding deployment environments, this US model gateway combines robust dual band WiFi, flexible USB options, and a dedicated Ethernet port to deliver seamless, scalable connectivity for a wide range of industrial and commercial applications. Whether you are deploying smart sensors,
The Lantronix SGX 5150 is a high-performance wireless IoT device gateway designed to securely connect critical assets to the enterprise network. Built for reliability in demanding deployment environments, this US-model gateway combines robust dual-band Wi‑Fi, flexible USB options, and a dedicated Ethernet port to deliver seamless, scalable connectivity for a wide range of industrial and commercial applications. Whether you are deploying smart sensors, asset trackers, or edge devices, the SGX 5150 provides a stable, secure bridge between your wireless edge and your enterprise infrastructure.
- High-Performance Dual-Band Wireless — The SGX 5150 supports simultaneous dual-band operation with 5 GHz 802.11ac and 2.4 GHz 802.11b/g/n. This architectural choice enables your network to handle busy IoT environments with reduced interference and improved throughput, ensuring that critical devices stay connected even in crowded environments. By leveraging the 5 GHz band for high-throughput workloads and the 2.4 GHz band for broader coverage, you gain both speed and reach. This dual-band capability is ideal for deployments where multiple devices demand reliable wireless access without sacrificing performance on legacy equipment.
- Enterprise-Grade Reliability and Security — Designed to serve business-critical assets, the SGX 5150 emphasizes a trustworthy wireless connection to your enterprise network. Its architecture prioritizes stability, robust session persistence, and dependable connectivity for IoT devices and gateways. In enterprise contexts, consistent uptime translates into fewer maintenance windows and more predictable data flows, which is essential for monitoring, analytics, and real-time decision-making. The gateway is built to integrate with existing security measures and network policies, helping you maintain control over access, authentication, and data protection across your IoT fleet.
- USB Host and USB Device Modes for Maximum Flexibility — The SGX 5150 offers USB Host and USB Device mode support, enabling flexible integration with a broad set of peripherals and devices. Use USB as a convenient bridge to local sensors, storage, or development tools, or connect as a USB device to the gateway for streamlined data exchange with host systems. This versatility simplifies firmware updates, local data buffering, and rapid prototyping, while also supporting a range of deployment models—from standalone edge devices to fleet-scale installations.
- One Dedicated Ethernet Port for Reliable Wired Connectivity — In addition to wireless connectivity, the SGX 5150 provides a single 10/100 Ethernet port for stable wired communication to your enterprise LAN. This port enables secure, predictable uplink and integration with existing network infrastructure, ensuring your IoT gateway can operate in environments with strict network topologies or where wireless coverage may be limited. The combination of wired and wireless interfaces gives you flexible options for topology design, redundancy planning, and traffic management across edge deployments.
- North American Focus with US Model Suitability — The SGX 5150 is a US-model gateway crafted for North American deployments. This makes it easier to align with regional compliance requirements, IT policies, and procurement standards while delivering the features you expect from an enterprise IoT gateway. With its US-model orientation, you gain straightforward compatibility with local networks, support channels, and service ecosystems designed to meet regional business needs.
Technical Details of Lantronix SGX 5150
- Product name: Lantronix SGX 5150 Wireless IoT Device Gateway
- Wireless standards: Dual-band 5 GHz 802.11ac and 2.4 GHz 802.11b/g/n
- USB options: USB Host and USB Device modes
- Ethernet: 1 x 10/100 Ethernet port
- Model: US Model (North America design and form factor)
How to Install Lantronix SGX 5150
Installing the SGX 5150 is designed to be straightforward, allowing IT teams to quickly bring edge devices online and securely connected to the enterprise network.
- Prepare the gateway by locating a stable power source and a suitable mounting or placement location that provides clear line-of-sight for wireless coverage and convenient access to the Ethernet port and USB interfaces.
- Connect the SGX 5150 to your local network using the 10/100 Ethernet port. If you plan to rely primarily on wireless connectivity, you can omit the Ethernet connection, but a wired uplink is recommended for initial provisioning and management.
- Power on the device and allow the boot sequence to complete. Access to the device management interface is typically provided through a secure browser session or an approved management tool aligned with your IT policy.
- Configure wireless settings: set up the 2.4 GHz and 5 GHz networks, enable or adjust security policies, and apply any necessary enterprise access controls to ensure only authorized devices can connect to the gateway.
- Attach your IoT devices or peripherals via USB in Host or Device mode as needed. Validate data flow from the connected assets to your enterprise network, and verify that the gateway properly bridges wireless assets to the wired LAN.
- Apply firmware updates and security patches as they become available, and enable routine monitoring to detect any unusual activity or connectivity issues. Document the deployment for future maintenance and scale-out planning.
- Test failover and performance by simulating typical workload scenarios. Confirm stable operation under expected load, with attention to latency, throughput, and reliability of IoT data streams.
Frequently Asked Questions
- What is the Lantronix SGX 5150? The SGX 5150 is a Wireless IoT Device Gateway designed to securely connect business-critical assets to the enterprise network, delivering high-performance dual-band Wi‑Fi, USB flexibility, and a dedicated Ethernet port for robust edge connectivity.
- Which wireless standards does it support? It supports dual-band operation with 5 GHz 802.11ac and 2.4 GHz 802.11b/g/n, enabling efficient and flexible wireless deployments for a range of IoT devices.
- How many Ethernet ports does it have? It includes a single 10/100 Ethernet port for wired connectivity to the enterprise LAN.
- Does it support USB Host and USB Device modes? Yes. The gateway offers USB Host and USB Device modes to maximize integration options with peripherals, sensors, and development tools.
- Is this gateway specifically a US model? Yes. The SGX 5150 is presented as a US Model designed for North American deployments and compatibility with regional networks and standards.
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
- Standard Shipping : 3-10 business days
- If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
- Final sale items are not eligible for returns or exchanges.
- To process your return/exchange, please contact us at [email protected]
- Please click here for more details>>> Return & Exchange Policy
4.8 ★★★★★
Based on 19 reviews
Sort
Product Reviews
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents.
The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!!
The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!!
I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge.
Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents.
Personally I was disappointed by lack of any example on time series.
Must have for ML practitioner as a reference and guide.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book.
For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch.
For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min).
From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures.
I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications.
Several textbook editions later, what is different about this new edition?
First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills.
Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like.
Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026