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Programming in Plain English



Star Trek had really smart computers, that you could simply tell what you wanted to do and they did it. The [Rzeppa] family has started a plain English compiler. It runs under Windows and appears to be fairly capable.

Plain language programming isn’t exactly a new idea. COBOL was supposed to mimic natural language with statements like:


You could argue this didn’t go over very well, but there is still a whole lot of COBOL doing a whole lot of things in the business world. Today computers have more memory and speed, so programmers have been getting more and more verbose for decades. No more variable names such asX1 and fprdx. Maybe this will catch on.

A function to clear the screen starts out with a list of phrases you might say to call the routine. This is similar to the type of personal assistant logic in which you can speak natural language, but in doing so you had better say something that matches its known template. Here’s the function:

To erase the screen;
To blank out the screen;
To wipe off the screen;
To clear the screen:
Unmask everything.
Draw the screen’s box with the black color and the black color.
Refresh the screen.
Put the screen’s box into the context’s box.

This will work if you say “erase the screen” or “blank out the screen” but it won’t work if you say “blank the screen.” The hello world program shown in the accompanying graphic looks like this:

To run:
Start up.
Clear the screen.
Use medium letters. Use the fat pen.
Pick a really dark color.
Start in the center of the screen.
Turn left 1/32 of the way.
Turn right. Move 2 inches. Turn left.
Refresh the screen.
Lighten the current color about 20 percent.
Add 1 to a count. If the count is 32, break.
Wait for the escape key.
Shut down.

We were interested that some of the primitives let you insert machine code. For example:

To add a number to another number:
Intel $8B85080000008B008B9D0C0000000103.

That means you could do some interesting extensions if you were to take an interest. A cursory attempt shows it does work — at least somewhat — under Wine, if you want to try it out.

The post focuses on using the language with students, but we aren’t sure these are good habits for future programmers to develop unless it is the leading edge of a trend. We could make the same argument about Scratch and other visual development tools, too, though.



5 Reasons to Use IoT Mesh Networks



IoT Mesh Networks
Illustration: © IoT For All

The mesh network has become more common in IoT parlance over the last few years, especially in the smart home space, to make Wi-Fi more usable and effective. And lately, IoT companies have been looking for ways to improve their own networks, and implementing a mesh topology is one great way to begin.  

What Is IoT Mesh?

Before we get into the why, let’s peek into what, exactly, it means when we talk about IoT Mesh Networking. An IoT mesh network links all the devices on the network together in descending branches (sometimes called nodes). The branches interconnect in such a way as to route data more efficiently between the edge devices, or endpoints, and the cloud or server processors. The result is more consistent and reliable connectivity and data transfer rates because there are always many routes from end to home and back. 

Why Mesh?

Well, the first reason is the easiest one to see: reliability. The redundant pathways and multiple nodes on the network mean that an IoT mesh network just works reliably and is more resilient than other topologies to outages of nodes or connections. 

The second advantage of mesh networking is coverage. With this type of architecture, you can rest assured that dead spots, when they occur, are unlikely to interfere with operations because the data can always be routed along another path from the edge to the node, be it at the cloud or an on-premise server. 

Scalability is a key feature in IoT mesh. Because a mesh-style topography can automatically extend itself to include an almost unlimited number of endpoints and nodes, it can get as large as you need it to be. Mesh networks also benefit from increased range, so physical scaling is easier since signals can travel over great distances and experience fewer dead spots. 

A major benefit for any business looking to implement IoT is efficiency, and mesh networks can decrease power usage and costs compared to similar sizes of traditional networks. Each node on a mesh network requires less power than conventional IoT networking because the devices don’t need to put out signals strong enough to reach the central server or cloud, only strong enough to reach the next node. This results in cost savings for battery changes, power consumption, and device longevity. 

The last advantage we’ll discuss today is a big one, especially for IoT companies: security. If there is an attack or a breach of the network, the compromised node or branch can be shut down and segregated from the rest of the mesh, protecting critical data assets while not interrupting operations as the data is rerouted via other paths through the mesh. This kind of resiliency and protection in the face of security threats is tough to overcome for bad actors. 

So, when you’re looking to implement or upgrade your IoT network, perhaps it’s time to consider IoT Mesh. 

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6 Best Raspberry Pi Alternatives For IoT Development



Raspberry Pi is a good starting point whenever you want to build an app, device, or project for the IoT marketplace,. This credit card-sized device has changed the very concept of personal computing, and supports prototyping of every kind of new developer idea. It does have a few limitations though.

Despite the excellent specs of Raspberry Pi 4, the single-board computer lacks important capabilities as an embedded engineering device. If you want higher performance, you need a device with far superior specs that won’t suffer Pi’s problems of overheating, lower memory, and unsuitability for industrial applications.

Since so much of DIY syllabus is oriented towards Raspberry Pi, you should seek a close enough alternative when it’s time for you to “scale up”. We have listed some of these best alternatives which will give you a familiar feel to Raspberry Pi but with higher performance and more ruggedness.

1. SiLabs ThunderBoard EFR32BG22

For industrial uses, one of the disadvantages of Raspberry Pi is that the cost per unit becomes much higher. If your project has high-volume needs, Silicon Labs offers SLTB010A EFR32BG22 Thunderboard Kit which is scalable for any level of quantitative applications for the mass consumer market.

Best Raspberry Pi Altenatives Silabs Efr32bg22
SiLabs Thunderboard EFR32BG22

The Thunderboard BG22 supports dual Wi-Fi and Bluetooth connectivity at a low cost of less than $20. It has an array of embedded sensors including humidity and temperature, index and ambient light, six-axis motion, and hall effect sensors. Similar to older Raspberry Pi variants, BG22 has a 26-pin GPIO layout, and a simple CR2032 coin battery. You can also use a micro-USB cable The Thunderboard mobile app is available with both Apple Store and Google Play. To work with Thunderboard BG22 device, you need access to Simplicity Studio software program.

Unlike the Raspberry Pi, the BG22 device won’t be suitable for real computing needs as it only comes with 512 kB flash and 32 kB RAM. But for those looking to build their own battery-operated end user gadgets, Thunderboard BG22 is an excellent option.

2. ASUS Tinkerboard S

From the cheapest to the most expensive, we recommend Asus Tinkerboard S for those applications which require greater durability. It comes with power-packed features such as an ARM-based processor, 16 GB eMMC storage, HD audio quality, HDMI-CEC ready video entertainment, and Mali™-T760 MP4 GPU. The Tinkerboard device offers excellent compatibility with Raspberry Pi starting from form factor right to the placement of its ports. Unlike Thunderboard BG22, it has a 40-pin GPIO layout similar to latest version of Raspberry Pi.

Asus Tinkerboard View
ASUS Tinkerboard S

Coming from a reputed company like Asus, the Tinkerboard range of device support IDLE/Python, the Android operating system, and a range of third-party applications such as HiFiBerry, Flint OS, Kodi, Fuze Studio, and RetroPie. It has also been designed for AIoT applications which makes it futuristic for all your needs. Despite a hefty price tag of $150 and above, Tinkerboard S has the DNA of building superior devices with excellent clockwork and performance.

3. Odroid XU4

Odroid-XU4 is a mid-range SBC which comes with powerful specs such as high data transfer speeds (over USB 3.0), 2 GB DDR3 RAM (same as Asus Tinkerboard S), and Samsung’s octa-core processor. It contains a cooling fan which acts as a built-in heat sink which can handle your industrial needs when CPU load is higher.

Odroid Xu4.jpg

The XU4 is also proven to work great as a media/NAS server, handling large amounts of storage and various streaming services like Plex. The product supports video applications on account of its HDMI 1.4a support. It further supports a variety of Android and Linux platforms, and can be added as an Arduino library.


A powerful x86 device, UDOO NEO offers a variety of connectivity options for diverse uses. These include Wi-Fi, Bluetooth 4.0 LE, and fast ethernet. It comes with integrated sensors such as 9-axis motion sensors and a micro-HDMI interface. Despite being an SBC, the device is fully Arduino-compatible.

Best Raspberry Pi Altenatives Udoo Neo

The 9-axis motion sensor renders itself for any project involving drones, mobile vehicles, and robots. There’s a snap-in connector which helps the board interact with other external sensors and actuators. Described as Arduino, Android, and Linux in one’s pocket, UDOO NEO has a powerful 1 GHz Cortex-A9 processor, while being equipped with an NXP i.MX 6 SoloX application processor.

5. BeagleBoard Black

Beagleboard Black is a low cost community SBC alternative for rugged industrial applications. Costing less than $25, it beats Raspberry pi with specs such as 4 GB of eMMC flash storage, compatibility not only with Debian but also Android, Ubuntu and Cloud 9, Blynk, and Oracle/JAVA platform. Despite being a pocket-sized SBC device, Beagleboard Black supports AI features and Arduino libraries.

Best Raspberry Pi Altenatives Beaglebone Black Sbc
BeagleBone Black

6. STM32 Nucleo

Rounding off our list we have STMicroelectronics STM32 Nucleo device which is named as such because it resembles a nuclear reactor. Like SiLabs Thunderboard EFR32BG22, it has been designed for industrial use with media online compilers which assembles any design in minutes. It also supports Arduino connectivity and supports a wide range of integrated development environments (IDE).

Best Raspberry Pi Altenatives Stm32 Nucleo
STMicroelectronics STM32 Nucleo

STMicroelectronics is a pioneer in factory-scale industrial applications. STEVAL-IHM038V1, one of its fan controller boards has been deployed to over 1 million units of a smart fan. Therefore with its latest version STM32 Nucleo, STMicroelectronics boards are recommended for high volume, large-scale applications.

The above Raspberry Pi alternatives are for the serious developer who wants to take their Raspberry Pi-based projects to the next level.


Sayak Boral Sayak Boral

IoT-addicted since early 2016. Love to explore the challenges, opportunities and trend insights into what is becoming the third wave of Internet.

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7 Ways to Improve Field Service with IoT Asset Visibility



Field Service
Illustration: © IoT For All

Before the arrival of IoT-enabled asset monitoring, field service technicians would struggle with delays from unidentified malfunctions or inaccurate diagnosis resulting in high intervention bills, unnecessary truck rolls, and a bad customer service reputation.

Businesses introducing new use cases based on connected assets as part of their IoT strategy are incentivized by the opportunity to boost efficiency and reduce the costs of their field service organization. But what is the readiness level of your field service in real life, and how can you push to a higher level of ROI maturity?

IoT asset visibility can drastically improve field service efficiency if you manage these 7 key indicators of your field service success.

1. Avoid Truck Rolls

Giving customer service departments a real-time view of the actual state of assets, asset alarms, and asset environmental conditions is extremely powerful. This can be complemented by the execution of diagnostics tests to assess further and confirm the asset’s health. The cheapest truck roll is not having a truck roll at all. Remote problem resolution also minimizes health risks during the current COVID-19 pandemic.

2. Increase First-Time-Right Repairs

Before the field service intervention, execute diagnostic tests based on digital twin asset data to identify the root cause of an issue. This ensures that field technicians have the right spare parts with them, increasing the first time right repair and avoiding follow-up technician visits.

3. Decrease Repair Time & Increase Interventions Per Day

Knowing the root cause helps to solve the problem faster. Giving a field service technician access to historical asset data while onsite helps diagnose and resolve faster problems. Increasing the first-time repair rate and decreasing the average repair time naturally leads to higher productivity and the ability to schedule more interventions daily.

4. Avoid an Overload of Senior Field Service Technicians

More and more experienced field service technicians are retiring with an aging population, and their work needs to be taken over by either more junior colleagues or outsourced personnel. Having the appropriate level of preprocessing of IoT data and root cause analysis can help lower the threshold to get problems resolved and reduce the number of tickets and interventions requiring a visit from the (more expensive) ‘expert team.’

5. Improve SLA Compliance and Increase Customer Satisfaction

Real-time IoT asset visibility helps increase the predictability of field service interventions and the number of interventions that can be done in time, compliant with the customer SLA and increasing overall customer satisfaction and customer service reputation.

6. Evolve From Time-Based to Condition-Based Maintenance

Current field service interventions are still mainly planned according to a fixed schedule of minor or major maintenance interventions. Data on actual usage and performance of assets allows doing this more economically based on actual usage data.

7. Transform From Break-and-Fix to Outcome-Based Service Contracts

The traditional approach of the one-off sale of assets comes with an associated break-and-fix tactic where the customer is charged for the required field service interventions. From a customer perspective, any breakdown of equipment or interruption can lead to unplanned downtime and revenue loss. Hence, the world is moving to outcome-based service contracts with an associated guaranteed performance level. Real-time asset visibility with condition-based and predictive maintenance are key components in this servitization of business models.

Asset Visibility

In brief, IoT asset visibility helps businesses to reach a new level of field service maturity. Asset visibility empowers domain experts to manage their asset portfolio better, improve interventions, or avoid them altogether. Service departments and call center agents can play a key role and initiate asset telemetry diagnosis before field service needs to be involved. Field service engineers and technicians will grow in more interesting roles based on improved asset insights and better intervention planning. Thus, IoT asset visibility will help to increase profitability, will improve customer service, and bring a significant boost to field service operations efficiency.‍

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No Reinvention Required: The Power of Lean and IIoT



Lean IIoT
Illustration: © IoT For All

While Industry 4.0 continues to gather pace, replacing legacy technology and processes, not everything is grist for the mill. Indeed, principles that have stood the test of time, such as Lean – are core to smart manufacturing success. 

Lean manufacturing principles are far from a new idea or concept, but they have maintained relevance astonishingly well. They originally found wide recognition after being detailed in a 1991 book, The Machine That Changed the World, a study on the future of the automobile by Womack and Jones from MIT. The Lean principles were designed to improve manufacturing efficiency, although they have since been adapted to suit most industry verticals. They constitute five core principle steps: defining value, mapping the value stream, creating flow, using a pull system, and pursuing perfection.  

Lean Application Digital Challenges

Applying Lean principles to Industry 4.0-era digital processes can prove challenging, not just due to the potential complexity of the decision-making process but also because of the vast amount of data that should be considered. Even gathering the data at the beginning of the Lean process can prove a barrier for some companies, which is why there has been a steady growth in third-party data gathering experts. However, there are also a wide variety of tools available for internal stakeholders. 

However, even having completed data acquisition from a wide variety of internal sources, the key challenge is in interrogating and disseminating the ongoing waves of data effectively, without being distracted by volume and blinded by irrelevant data points. Just as with data, keeping approaches and methods to a minimum is also good practice – from Agile to 6-Sigma and Theory of Constraints to Kata and Kaizen, there are a huge variety of efficiency methodologies, but consistency is vital. 

IIoT and VSM

Applying IIoT technologies to a value stream mapping (VSM) process can create entirely new methods of removing waste from a process, both ‘non-value added but necessary’ and ‘non-value-added & unnecessary.’ The former can be reduced as much as possible with IIoT, while the latter should ideally be isolated and removed effectively without inhibiting business goals. 

A good example here is predictive maintenance, which attacks both Lean waste categories at multiple points, cutting actual maintenance costs and improving the effectiveness of maintenance procedures, reducing the requirement for spare parts inventory, etc. IIoT technologies not only remove wastage initially but also build a platform for continuous improvement.

While Lean and IIoT are excellent partners in data gathering and uncovering new efficiencies, other parallels can be drawn. Avoiding the latest ‘fad’ favoring thoughtful and careful integration is a key component of success in both areas. Indeed, many experts in Lean firmly advocate applying Lean principles to a process before leveraging any new technology at all, as simply replacing an inefficient legacy method or process with a technology solution can trap waste and make rolling back and removing that waste much more complex and potentially impossible. 

Pull-Based Systems

One of the core Lean principles is step four – using a pull system – and this certainly rings true for IIoT deployments. The central aim of a pull-based system is to limit inventory and work-in-process (WIP) items while ensuring that the required materials and/or information are available for the process at hand. Most famously in the automotive industry, where Toyota’s ‘Just in Time’ strategy has been widely replicated, the pull-based system ensures the end customer’s needs are fulfilled in the most efficient manner possible. 

An interesting parallel here is in IIoT pull systems, which ensure that data quality is maintained compared to push, or broadcast-style systems. The issue with the broadcast model – often the default for IIoT sensors – is that if incorrect or faulty data is transmitted, it can pollute the data record and prove difficult to remove successfully. 

When considering highly regulated industries such as food or healthcare, this could be a serious problem, so a pull-based approach is preferable, where a data point is specifically requested from a sensor, such as a temperature sensor in a food transport freezer, for example, which then responds with that specific data. This creates a ‘clean’ digital record of events that can be trusted but still enables sharing of other telemetric or maintenance data, for example. 

Technology Evolution

Although Lean principles have seen many variations and methods overlaid over the years, the underlying drive for efficiency has inspired many innovations and has become one of the most powerful uniting use cases for enterprise IIoT deployment, while the supporting technology might be evolving at an astonishing rate, some things stay the same: the need for accurate measurement, analysis, improvement, control, and wider enterprise education on emerging standards and use cases. Those IIoT use cases can then be promoted to those involved in VSM kaizen projects at the right time. It may be an increasingly mature market, but the history of Lean and IIoT has only just begun.

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