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Low-cost IoT-based solution for tracking and monitoring of freight

monitoring of freight

This project will develop, test and deliver a low-cost IoT-based solution for live tracking and condition monitoring of freight consignments across multiple freight carriers and transport modes.

The delivered solution will include:

  1. A low-cost IoT device equipped with sensors that can monitor the location and condition of the freight consignment
  2. Software embedded in the IoT devices for sending sensor data to the cloud; and
  3. Cloud-based sensor data analytics software that incorporates machine learning (ML) models for detecting and visualising transport and freight consignment-related events of interest.


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Project background

The freight and logistics industry is a major contributor to Australia’s economy, and it is expected to grow at a CAGR of more than 4.5% over the period of 2021-20261. The Covid-19 pandemic and growing e-commerce business models have created further demand for freight transport service.

However, the freight transport eco-system is quite fragmented, involving large number of operators such as transport companies, warehousing, parcel couriers, and intermediaries. Some of these players specialise in transporting large volumes of cargo over long-haul, while others focus on parcel delivery and short-haul distribution of goods.

A typical movement of freight involves multiple transport legs and storage, with each stage relying on unique information capturing systems developed by individual operators. Since these systems have been developed independently, they are not integrated, and therefore, unable to provide a clear picture of the entire freight journey on a real-time basis. Such lack of visibility could result in several uncertainty challenges for freight companies and cargo owners. For example, manufacturers that reply on just-in-time inventory practices, could face major production uncertainty due to absence of information on the status of their orders from suppliers.

Solutions to track individual consignment/parcels are still challenged by issues such as battery life, network coverage, form factor, cost, but also lack of advanced analytics support to detect and predict adverse supply chain events.

The emergence of Internet of Thing (IoT)-based tracking solutions

In 2020, the global IoT market was estimated around AU$424.69 billion, with projections to grow up to AU$2,400 billion by 2028 with a CAGR of 25.4%2. Several factors contribute to this rapid growth, including advancements in communication technology, cheaper and more powerful hardware, and recent developments in the Artificial Intelligence technology, which could further enhance the capability of IoT solutions. IoT devices can range from simple RFID-based devices to complex customised systems, all providing high data availability and granularity through communication networks.

In the context of supply chains and logistics, IoT devices can range from large-scale systems implemented on a fleet of vehicles or containers, down to item and package level trackers. Such solutions allow for enhanced visibility, which could improve supply chain productivity while enabling business to make decisions based on real data information.

IoT solutions empower supply chain management decision making by collecting and analysing data from various points along a logistics network, ultimately providing visibility and context to shippers and carriers. Through its sensing layers, an IoT-based solution can be used in a variety of manners to collect and report several conditional factors such as temperature, vibration, humidity, location, etc.

IoT-based tracking is an emerging solution that can provide real-time tracking and condition monitoring via the use of smart sensors, networks and data analytics. There are emerging use cases of global navigation satellite system (GNSS) receiver-based devices with narrow-band IoT connectivity for tracking locations using low-cost components. More recent solutions explore the use of 5G short wavelength telecommunications and machine learning.

Project objectives

This research project aims to devise an IoT-based solution for real-time tracking and condition monitoring of individual freight parcel/consignments while investigating specific requirements of form factor, battery life, cost, standards, and cloud-based data analytics.

Target parcels/consignments of this research have unique requirements, such as being time-sensitive (sometimes perishable) and have high economic value. The project outcome will achieve these aims by addressing the limitations of existing solutions.

To achieve these aims, the project will identify, assess, devise and trial appropriate IoT technologies for tracking and monitoring the condition of freight consignments in real-time.

The main milestones of the project are:

  1. Conduct consultations with an industry partner to identify the functional and technical (hardware and software) requirements for developing a low-cost IoT-based solution for live tracking and condition monitoring of freight consignments. The consultation will aim to elicit prioritised requirements from the industry partner and use these to assess the trade-off between costs and features of the IoT solution.
  2. Design the IoT-based solution for tracking and condition monitoring of freight consignments.
  3. Implement the IoT solution, including hardware, embedded software and cloud-based data analytics.
  4. Undertake field trials to assess the IoT solution against the requirements identified in (1).


  1. Australia Freight and Logistics Market – Growth, Trends, COVID-19 Impact, and Forecasts (2021 – 2026)
  2. Internet of Things (IoT) Market Size, Share, COVID-19 Impact Analysis by Component (Platform, Solution, and Services)by End-Use Industry (BFSI, Retail, Government, Healthcare, Manufacturing, Agriculture, Sustainable Energy, Transportation, IT & Telecom, Others) and Regional Forecast, 2021 – 2028

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