Bluetooth Localisation for Large Industrial Areas with Limited Infrastructure

The recent study Bluetooth Low Energy Indoor Localization for Large Industrial Areas and Limited Infrastructure discusses the use of Bluetooth Low Energy (BLE) in industrial applications, particularly in Smart Factory and Smart Farming settings. Bluetooth systems are beneficial for their low-power operation and are widely used for asset monitoring, management, tracking and localisation. The focus of this paper is on BLE-based localisation systems, which typically use radio propagation models and multi-lateration, or radio fingerprinting, to achieve high accuracy and precision. These methods rely on the received signal strength indicator (RSSI) measurements and its dependency on the distance between the transmitter and the receiver.

However, the paper highlights the challenges in achieving high localisation accuracy due to the inaccuracy of RSSI measurements and susceptibility to radio propagation phenomena. In industrial environments, where radio propagation is complex and the number of anchors (fixed reference points) is limited, achieving high accuracy is difficult. The paper proposes a set of localisation algorithms that require limited infrastructure, have low complexity, and can provide valuable location information at low costs. These algorithms were tested in a Smart Farming application for monitoring the well-being of farm animals, demonstrating reliable operation despite system-level constraints and varying propagation conditions.

The proposed algorithms are based on signal strength measurement. They allow for localising animals in a cowshed of 1600m² using only 10 anchors with an average positioning error below 8 meters.

The paper also discusses the applicability of RSSI-based localisation to different radio technologies and the limitations of these methods. The proposed approaches are designed to enable location-based services in existing systems at minimal additional costs, benefiting from the already available infrastructure, mechanisms and procedures.

Monitoring Sheep Location Using Bluetooth Beacons

There’s new research from Scotland, UK on Calibration of a novel Bluetooth Low Energy (BLE) monitoring device in a sheep grazing environment. Knowing an animal’s location and proximity can offer insights into landscape use, animal performance, behaviour and social contacts. However, the technologies currently used to collect these data are costly and challenging to implement, particularly due to the low value of individual animals and typically large flock sizes.

A device was specifically designed for a study to assess the relationship between the Received Signal Strength Indicator (RSSI) of a BLE beacon and BLE reader and to develop a distance prediction model. This model was then applied in a static situation and on-sheep studies, using a multi-lateration approach to determine a beacon’s location within a field setting. A purpose-built Wearable Integrated Sensor Platform (WISP) was developed for the study, featuring a BLE reader and other sensors. It was designed to report the identity and RSSI of the 16 ‘closest’ beacons seen for each duty cycle.

The findings revealed that the height of the device had an impact, with fewer beacons reported at a shorter distance in WISPs at the lower height of 0.3 m. RSSI can vary greatly based on factors like transmission power, device orientation, enclosure and the operating environment.

Using the distance prediction and adjusted distance prediction, beacon locations could be estimated for most of the beacons. Not all beacons could be located due to issues such as being reported by too few WISPs or the resulting multi-lateration circles not intersecting.

The study suggests that BLE can potentially be used for sheep localisation in outdoor environments. The multi-lateration approach is dependent on receiving RSSI readings from multiple readers at a similar timepoint, it could offer more information about localisation and movement than simple proximity ranges or presence/absence. Locating a sheep to within about 30 m in a field environment represents a significant step forward.

Using Beacons for Activity-Based Costing in Farm Management

The Department of Agricultural and Food Sciences, University of Bologna, Italy has been looking into CANBUS-enabled activity-based costing for leveraging farm management. They have created a methodology that helps farmers to make operational decisions and create data that can be used for input into farm management information systems (FMIS).

As an example, a study showing a tractor active for 59% of the time was idling for 25% of the time. Fuel and labour costs represented from 63% to 71% of the total cost per hectare. The tractor was equipped with a CANBUS logger and Bluetooth beacons on several tractor implements to automatically recognise agricultural operations. The data was processed to classify jobs by position (field, farm, or road) and operating state (moving, fieldwork, or idling).

With such systems, farmers can determine the economic impact of farm activities and compare historical data with previous farm practices or competitors’ activities. This helps farmers make more intelligent decisions regarding crop, land and farm operations management.

Bluetooth Sensing for Agriculture

There’s recent research into Development of Sensors-Based Agri-Food Traceability System Remotely Managed by a Software Platform for Optimized Farm Management.

The paper describes an IoT-based smart traceability and farm management system that calibrates irrigation and fertilisation based on crop typology, growth phase, soil and environment parameters and weather information.

The system uses a custom built Bluetooth LE sensor tag that’s fixed to containers agri-food products collected from the fields, being processed or stores. The sensor monitors gas, temperature and humidity that indicates whether the foods are spoiling over time or along the supply chain. The tag also supports traceability. A mobile application monitors the tracked information and condition of the agri-food products.

The sensor tag uses a HM-10 BLE module and CCS811 (gas) and BMP280 (temperature and humidity) sensors that were chosen to provide reliable markers of the freshness status of vegetables, meat and fish. The gas sensor measures Total Volatile Organic Compounds (TVOCs) and equivalent CO2 (eCO2) concentrations for air quality monitoring applications.

The developed system schedules irrigation and fertilisation according to the crops’ need reducing under or over watering thus minimising plants’ stress.

Smart farming allows farmers to control, in real time, the growth and harvest of cultivated crops. It allows agricultural companies to increase yields, nutritional quality and improve safety. It reduces production costs and the negative impacts on the environment for economic, environmental and social sustainability.
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Identifying Dairy Cattle Activity and Behaviour Using Beacons

The Department of Biosystems Engineering, of the Poznan University of Life Sciences in Poland has new research on Beacon in Information System as Way of Supporting Identification of Cattle Behavior.

Researchers identified the behaviour and physiological state of milk cattle using beacons and combined this with data from weather forecast stations.

Changes of motor activity of cows were recorded on the 24hr characteristics and registered during the period of cattle heat. Motorola smartphones were used as base stations to collect and process the data.

The researchers succeeded in collecting and processing data from beacon devices that provided an alterative to traditional pedometer-based solutions.

Using iBeacon for Sensing in Viticulture

There’s new research by Sotirios Kontogiannis and Christodoulos Asiminidis of University of Ioannina, Greece on A Proposed Low-Cost Viticulture Stress Framework for Table Grape Varieties.

The system automatically monitors vine stress to provide real-time surveillance and alerts. It identifies specific areas for irrigation, thereby saving water, energy and time.

The Bluetooth iBeacon protocol is used to relay temperature, humidity, UV levels and soil moisture levels. The authors modified the standard iBeacon protocol, using the existing iBeacon minor and major fields to encode the telemetry data.

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Farm Management with Bluetooth Sensors

There’s new research by the Universities of Salento Italy and Panamericana Mexico on the Development of Sensors-Based Agri-Food Traceability System Remotely Managed by a Software Platform for Optimized Farm Management.

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
  • Increased safety
  • Reduction in production costs
  • Reduction in negative impacts on the health and environment
  • Smart traceability

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.

Read about Beacon Proximity and Sensing for the Internet of Things (IoT)

Smart Farming with Bluetooth

Smart Farming, also sometimes known as the ‘Third Green Revolution’, is the use of IT to improve profitability, gain efficiency, reduce costs through making better, more optimal decisions and better management control.

Kristoffer Rist Skøien Senior R&D Engineer at Nordic Semiconductor, the making of the SoC in most beacons, has a recent blog post on Feeding the World with Connected Crops. He explains how farmers need to move beyond current precision agriculture (PA) and site specific crop management (SSCM) into more advanced realtime sensing of things such as weather conditions in a specific spot, soil humidity, soil acidity, growth progress and other productivity metrics.

IoT can improve the productivity of farming to improve yields, aid with cost management, waste reduction and automation to create a more efficient and productive business. In the future, this might even combine with autonomous vehicles for watering, fertilising and pesticide application.

There’s need to process significant amounts of data using game-changing business analytics at scale and at speed. Simple feedback mechanisms as well as complex deep learning algorithms can be combined with other, external data sources such as weather, market data or data from other farms. Techniques include benchmarking, analytics, predictive modelling and prognostics to provide models to manage crop failure risk.

A relatively recent paper, Big Data in Smart Farming – A review, covers the issues of extracting meaningful data from farm sensors. It explains the process from sensing and monitoring, through analysis and decision making to intervention with the implications on the efficiency of the entire supply chain. The paper covers the whole ecosystem that extends far from the farm itself:

Smart Farming is still in an early development stage. Current implementations are custom, proprietary solutions that can are experimental and expensive. Solutions need to be affordable, especially for developing countries. However, as with Industry, the early innovators will inevitably gain the early benefits while the laggards will get further left behind.

One way to reduce costs is using standard and open technologies. Bluetooth and more specifically, Bluetooth mesh, offers a low power, low cost, standard way of collecting sensor data. Open software platforms such as OpenATK and initiatives such as FIspace should make solutions more accessible.

Read about Beacon Proximity and Sensing for the Internet of Things (IoT)