It demonstrates the use of IoT to revolutionise farming. A system was implemented to provide for:
Optimum water and fertiliser use
Better quality and yield of crops
Reduction in production costs
Reduction in negative impacts on the health and environment
Sensors allow calibration of irrigation and fertilisation based on crop type, growth phase, soil and environmental conditions. The traceability allows monitoring of the movements of food products from the field, through storage to end consumers.
Bluetooth LE sensor tags are used for monitoring conditions during storage and transportation so as to assess freshness, integrity, as well as to provide for traceability.
The system enables enables management strategies that anticipate or delay crop collection, fine tuning the irrigation/fertilisation timing based on customers’ requests. This allows farmers to achieve economic benefits and reduce agri-food waste.
It never been easier to collect Bluetooth sensor information and store it in the cloud. The INGICS gateways come with step-by-step instructions how to set up AWS IoT Core, Azure IoT Hub and Google IoT Core.
In a previous post we asked ‘What is Productivity?’ and shared how the first wave of IT productivity related to cloud computing, customer relationship management (CRM) systems and enterprise resource planning (ERP) was only taken up by the top 5% frontier companies.
We explained how IoT, 4IR and AI machine learning will improve productivity but again, likely only for frontier companies. The difference this time is that the newer technologies will have more far reaching consequences. The frontier companies will further extend their reach over the laggards. The majority of the 5% are large companies with large budgets who are able to engage consultances such as IBM, Deloitte, Atos, PwC, WiPro, Accenture and KPMG. But what of the small to medium enterprises (SMEs)? Can they compete?
In most countries, a large proportion of companies are small to medium size. For example, in the UK, the Office for National Statistics says 98.6% of manufacturers are (SMEs). These organisations are more price sensitive and usually don’t have the luxury of significant financial resources for engaging the top consultancies and implementing their expensive solutions. Small and medium sized organisations have previously found it difficult to digitise due to the lack of availability of reasonably priced solutions.
However, solutions doesn’t have to be expensive. Low cost sensors such as Bluetoooth beacons, motion cameras, consumer AR can be combined with affordable cloud services to create solutions on a ‘shoestring’ budget. This is the aim of the University of Cambridge and University of Nottingham’s ‘Digital Manufacturing on a Shoestring’ initiative. The Institute for Manufacturing (IfM) is helping manufacturers benefit from digitalisation without excessive cost and risk. View the project’s latest news and communicate with them via Twitter.
“The manufacturing industry is absolutely ripe for potential with Bluetooth mesh”
“Industrial sensors and smart buildings among other use cases, are expected to outpace the overall Bluetooth LE market by 3X through 2022”
Research and Markets
The article mentions preventive maintenance, air quality sensing, asset tracking, robot control systems and traditional air conditioning as possible applications for Bluetooth Mesh. However, a key insight is that once a mesh network is in place it can be used for applications beyond those originally envisaged.
“The Priority Matrix shows that many IoT technologies are 5 years from mainstream adoption. However only one innovation profile will reach maturity in 2 years, indoor location for assets.
So why is ‘indoor location for assets’ more likely to achieve mainstream adoption sooner than other technologies? It’s because there are clear benefits for most companies and off-the-shelf software such as our BeaconRTLS™ is already available.
Our work with companies shows they are nevertheless cautious. Companies are taking time to understand the competing asset tracking technologies and are performing, sometimes lengthy, trials to determine how new systems will integrate with existing systems. They are considering the implications of SAAS vs on-premise solutions, the availability of second-sourced beacon hardware and the compromises of accuracy vs system complexity and cost.
Bluetooth® and LoRa™ are often said to be competing wireless technologies when, in fact, they work very well together. Both work with small quantities of data and both are optimised for powering via batteries. Bluetooth is good for collecting data up to 100m away while LoRa is good for relaying that data up to 15km or more depending on geographic topology.
LoRa has traditionally been used for outside tracking, alarms systems, smart signs and utility metering. Using sensor beacons with LoRa enables sensing of things such as location, movement, temperature, humidity, air pressure, light, magnetism (hall effect), proximity (short range and human), fall detection, smoke, gas and water leak. This brings new opportunities for use of LoRa in retail, industry, life sciences, health, hospitality, visitor spaces, transportation and education.
SensorLoRa™ is our new solution component that allows sensor beacon data to be sent over LoRa. We have developed a SensorLoRa™ detector that sees sensor beacons and sends sensor data, via LoRa, to a SensorLoRa™ gateway. The gateway sends sensor data on to your server via HTTP(S). Alternatively, it can be sent to BeaconServer™ for storage or to BeaconRTLS™ for showing location and sensor information on plans or maps.
We have a new fact sheet that explains more about SensorLoRa™:
The iGS04 is a new keyring/keyfob style beacon only 6mm thin. It advertises continuously and the button is used to change a value in the advertising data.
The iGS01 is similar to our other iGS beacons except it has no sensors other than the being able to detect the button press.
These Bluetooth beacons are not iBeacon nor Eddystone beacons. The advertising data is instead wholly used for sensor data. You will need an (your own) app or gateway to scan and obtain the advertising data.
The traditional IoT strategy of sending all data up to the cloud for analysis doesn’t work well for some sensing scenarios. The combination of lots of sensors and/or frequent updates leads to lots of data being sent to the server, sometimes needlessly. The server and onward systems usually only need to now about abnormal situations. The data burden manifests itself as lots of traffic, lots of stored data, lots of complex processing and significant, unnecessary costs.
The processing of data and creating of ongoing alerts by a server can also imply longer delays that can be too long or unreliable for some time-critical scenarios. The opposite, doing all or the majority of processing near the sensing is called ‘Edge’ computing. Some people think that edge computing might one day become more normal as it’s realised that the cloud paradigm doesn’t scale technically or financially. We have been working with edge devices for a while now and can now formally announce a new edge device with some unique features.
Another problem with IoT is every scenario is different, with different inputs and outputs. Most organisations start by looking for a packaged, ready-made solution to their IoT problem that usually doesn’t exist. They tend to end up creating a custom coded solution. Instead, with SensorCognition™ we use pre-created modules that we ‘wire’ together, using data, to create your solution. We configure rather than code. This speeds up solution creation, providing greater adaptability to requirements changes and ultimately allows us to spend more time on your solution and less time solving programming problems.
However, the main reason for creating SensorCognition™ has been to provide for easier machine learning of sensor data. Machine learning is a two stage process. First data is collected, cleaned and fed into the ‘learning’ stage to create models. Crudely speaking, these models represent patterns that have been detected in the data to DETECT, CLASSIFY, PREDICT. During the production or ‘inference’ stage, new data is fed through the models to gain real-time insights. It’s important to clean the new data in exactly the same way as was done with the learning stage otherwise the models don’t work. The traditional method of data scientists manually cleaning data prior to creating models isn’t easily transferable to using those same models in production. SensorCognition™ provides a way of collecting sensor data for learning and inference with a common way of cleaning it, all without using a cloud server.
With SensorCognition™ we have created a general purpose device that can process sensor data using machine learning to provide for business-changing Internet of Things (IoT) and ‘Industry 4.0’ machine learning applications. This technology is available as a component of BeaconZone Solutions.