IoT in Transportation and Logistics: Use Cases, Challenges, and Best Practices

December 13, 2022

While improving fleet management, asset tracking, etc., the IoT requires security, networking, and other measures in place.

The state of the IoT in transportation

Modern transportation and logistics are making the world interconnected, communicating, or, we may say, “smaller.” Over the last two decades, the Internet of Things (IoT) has further amplified this effect, providing the industry with networks of devices to meet both operational and analytical requirements. These include accurate insights into the supply chain, automation, cost efficiency, and enhanced decision-making, according to a 2021 report by Inmarsat, a global satellite communications provider.

The same study highlights that 54% of respondents considered the IoT important to address the challenges related to the COVID-19 pandemic. As pointed out by Deloitte, this technology has also come in handy to mitigate other factors putting a strain on the industry, such as e-commerce growth, low margins, and fragmented markets. The advantages described above are likely to further catalyze the adoption of the IoT in the transportation industry in the near future. According to Ericsson, for example, the number of IoT connections may grow from 100 million in 2020 to 292 million in 2030.

Adoption drivers for implementing the IoT (source: Inmarsat)

So, how does the IoT in the transportation industry work?

The Internet of Things in logistics relies on networks of smart devices to collect data from moving assets, be it vehicles or goods, and related infrastructures (such as terminals and warehouses). This information is processed by data analytics solutions and used to harmonize the supply chain, specifically in terms of route optimization, load capacity, maintenance, driver safety, etc.

Data collection and analysis require comprehensive IoT solutions, described by Deloitte as end-to-end systems that feature a multilayered architecture:

  • Sensors and devices to collect data about vehicles (speed, location, fuel, etc.), cargo (temperature, humidity, etc.), and drivers (shifts, driving patterns, etc.).
  • A network layer, including gateway devices and communication protocols, to transfer data via the Internet.
  • An integration layer to aggregate sensor data, bind it with other information from external sources (such as weather or traffic conditions), and store it into databases or similar systems. These can be on-premises or cloud-based, depending on the solution.
  • Data analytics software, often powered by machine learning (ML) algorithms, to process information and identify relationships, patterns, or anomalies. The resulting insights can be visualized via dashboards and graphics to foster data interpretability.

An example of an IoT architecture for transportation (source: Deloitte)

Use cases and real-life examples of the IoT in logistics

The advantages of the IoT in logistics can be leveraged in a variety of processes. Below follow common use cases reported by McKinsey and Deloitte, along with related examples of adopting the Internet of Things in transportation for real-life scenarios.

Fleet management

Coordinating vast fleets of vehicles requires huge organizational efforts. IoT systems help to streamline such workflows in several ways. Transportation providers can supervise demand and fleet availability to optimize vehicle allocation. They can also adapt routes based on location, traffic, weather, or road conditions, as well as send updates (including estimated arrival time) to drivers and partners via integrated communication systems.

For example, DHL implemented an IoT-based fleet management system named SmarTrucking to improve fleet scheduling and shipment visibility. According to the company, the solution may reduce transit times by up to 50%.

Tracking assets and goods

This use case of the IoT in logistics can involve both vehicles and the goods they transport, such as pallets and parcels. Asset tracking represents a key element to efficient management of the supply chain and workflows in warehouses, yards, and terminals. This includes operations such as outbound order shipping, loading in docking stations, truck queuing at ports, etc.

In this regard, Airbus implemented a GPS-based IoT system to track 15,000 connected packages, minimize asset misplacements, and send timely notifications in case of deviations. Another example would be a Norwegian software provider for transportation that partnered with Altoros to automate order planning. The developed platform enabled each of 100+ logistics companies using it to facilitate the delivery of 1,000 orders on a daily basis.

An example of an asset tracking system architecture (source: Microsoft)

Condition monitoring and maintenance

Combining IoT sensors and ML-based software, it is possible to collect vehicle health data, such as fuel consumption, tire pressure, and engine performance. This data is analyzed via ML algorithms to identify any deviations from standard conditions and predict future failures. Once the system detects an anomaly, it can send an alert and request maintenance.

A mining corporation Rio Tinto, for example, adopted a pilot IoT platform based on Azure to monitor trucks and other equipment. The initiative facilitated asset maintenance and streamlined the supply chain.

Driving optimization

IoT systems can act as drivers’ coaches and “guardian angels,” utilizing data to provide a variety of services. For example, an IoT platform may keep track of shifts and resting times to facilitate compliance with labor regulations. It is also possible to monitor and analyze driving behavior to offer insights that will help improve safety while reducing fuel consumption.

A similar solution for truck driver assistance and trailer monitoring has been developed by ZF Group, a German manufacturer of automotive components. The software aims at promoting safer and eco-friendly driving behavior, as well as cutting 6% of expenses by a mid-size truck transport company.

One step forward toward the future of the IoT in logistics will be a large-scale adoption of autonomous vehicles, where drivers would have a merely supervisory role. However, similar solutions are still not in commercial use, as highlighted by McKinsey.

Cold-chain delivery

Transporting temperature-sensitive products, such as medicine, vaccines, or food, can be particularly complex both in operational and regulatory terms. Cold-chain delivery is aimed at addressing this challenge, and the Internet of Things in transportation nterner can be one of its most valuable tools. Sensors are commonly used to analyze and ensure optimal cargo conditions, therefore, maintaining product quality and minimizing losses.

For instance, an air cargo carrier partnered with Altoros to develop an IoT system that can monitor temperature and humidity at up to 10,000 aircrafts, transporting pharmaceuticals. The testbed for the project comprised 3,000 sensors with a possibility to scale on demand.

Challenges and best practices for the IoT in transportation and logistics

Despite the advantages of IoT in logistics and supply chain, several companies get stuck in what McKinsey defines as a “pilot purgatory.” The Inmarsat’s report highlights common IoT adoption challenges, both from technical and business perspectives. The issues relate to connection, security, integration, budget, skill gaps, and more.

In this section, we’ll focus on the technical problems of adopting the IoT in this industry and the best practices to mitigate the disadvantages.

IoT adoption barriers in transportation and logistics (source: Inmarsat)


Integrating multiple components that comprise an IoT system can be rather complex. In logistics, IoT systems rely on thousands of sensors mounted on transportation means and assets to gather all sorts of data: location, speed, temperature, humidity, etc. To deliver meaningful insights and analytics, all this data has to be gathered and processed in real time.

Sensors mounted on a vehicle (source: Deloitte)

The thing is that sensors may come from different manufacturers and employ different networking protocols under the hood. So, in addition to integrating sheer volumes of real-time data, there is an issue with the compatibility of underlying protocols and data formats they use.

At the architecture level, serverless solutions bring quite a few perks to the table. In essence, the concept implies that all the trouble around allocating and managing computing resources is on the cloud provider’s side. According to DataStax, a serveless approach and IoT systems are a perfect match if your priorities are performance under high loads, availability during spikes, and scalability. Vendors like Amazon have various tools to build a serverless architecture that handles vehicle telemetry. For instance, AWS IoT Core, AWS Lambda, AWS Kinesis, AWS Functions, etc.

Together with Altoros, a global provider of connected solutions utilized a similar stack to enable real-time data processing of its fleet management platform. As a result, the company was able to aggregate 36,000 records per hour gathered from 5,300 devices.

When it comes to big data in the IoT, Amazon also suggests pairing traditional relational databases with NoSQL solutions. The latter store data distributedly as objects (documents or a key-value pair), which promotes performance, consistency, and flexibility. There are numerous options out there, including Couchbase Server, MongoDB, Cassandra, etc.

Moving onto the protocol compatibility, it’s worth considering the implementation of an enterprise service bus (ESB). There also exist specific tools to tackle the issue of heterogeneous protocols and data—e.g., AWS IoT Greengrass.

Network connection issues

While the IoT in manufacturing typically deals with stationary assets, the same can’t be said about transportation and logistics. This means that a truck may enter cellular network blind spots, such as tunnels or rural areas, resulting in high latency or lost connection between sensors and an IoT platform.

To address this disadvantage, HiveMQ recommends relying on a publish–subscribe pattern with IoT sensors loosely coupled to other platform components. When a connection is available, IoT devices deliver and receive data in real time. Otherwise, these devices memorize historical data with time stamps assigned to it and create an offline message queue. As soon as the connection is restored, all buffered information will be sent to the cloud, preventing any loss of messages. Such data points with associated time stamps are typically stored in time-series databases, as they’re optimized to handle this type of information.

When it comes to latency in transportation scenarios, another solution suggested by McKinsey is edge computing. The idea is to move part of the storage and computing resources from traditional data centers to the devices, where data is actually generated (vehicles or terminals). This minimizes the distance between data sources (IoT devices) and the processing power, reducing dependency on the network.

Device security

Wide networks of interconnected IoT devices can suffer from multiple points of vulnerability. This makes cybersecurity a top-tier parameter when building or choosing an IoT solution, as pointed out by McKinsey. Among the features to protect your IoT platform from data breaches and hacks, Amazon mentioned access management, device authentication and authorization, data encryption, event management, etc.

Further protection can be achieved by relying on cryptographic protocols, including Transport Layer Security (TLS), or technologies such as blockchain. The latter was mentioned by Deloitte as one of the innovations set to enhance the role of IoT in logistics. Specifically, blockchain-based architectures and algorithms make sure that information collected through IoT devices is not altered after being stored. In practical terms, this prevents carriers from modifying any relevant documentation, such as waybills.

An example of an IoT architecture with TLS (source: GSMA)

Battery durability

Batteries run out, and those powering IoT sensors are no exception. The European Commission reported that most devices have a 10-years operational life, while their batteries can last less than 2 years. Research in this field is still ongoing, focusing on the creation of batteries that can recharge themselves through heat, vibration, or lights (for example, via microsolar cells).

In the meantime, what transport providers can do is minimize consumption. Low-power protocols and technologies, such as LoRaWAN, can help in this regard. As for short-range connectivity, Inmarsat’s report mentioned Bluetooth Low Energy (BLE). Due to its intrinsic nature, however, BLE can be used just for specific logistics operations rather than long-range monitoring.

Using LoRaWAN gateways in the IoT architecture (source: Microsoft)

Load spikes

Holidays and rush hours can put a strain on logistics, as much as on IoT systems used in this industry, due to the consequent spikes in network traffic. A first solution to address this issue is proper network utilization via bandwidth-efficient and lightweight protocols, such as MQTT. In this regard, a test by HiveMQ revealed that sending 100 messages via HTTP required 554,600 bytes, while the same operation with MQTT took only 44,748 bytes.

Another option suggested by HiveMQ is to build a masterless cluster architecture. Basically, networks of cluster nodes (i.e., processing or storage units) without shared resources serve thousands of IoT devices. Depending on the network load, the amount of nodes can be scaled up or down. The devices connected to a certain node can simply switch to another one and resume the current session.

IoT device maintenance

An IoT sensor can monitor the status of assets and goods. But who monitors the sensor itself? In addition, how to upgrade software on the edge (e.g., install security patches)? Well, these tasks can be challenging, both for the incredible amount of devices involved and for their geographic distribution across vast logistics ecosystems. To ensure remote, ongoing supervision over each sensor, device management software may help (e.g., this one by Amazon).

Transportation companies may also need to implement functionality that allows for:

  • organizing devices into groups of hierarchies
  • sending firmware updates
  • detecting anomalous behaviors (typically via machine learning algorithms)
  • sending automatic alerts or rebooting the sensor to reduce the risk of future breakdown

The enablement of event stream processing will make it possible to track metrics related to the health status of IoT devices, detect patterns and anomalies, and respond to the changing conditions in real time. The instruments available on the market for this purpose are Apache Kafka, Apache Spark, AWS Kinesis, Apache Flink, Google Cloud Dataflow, etc.

Summing up

The IoT in transportation and logistics has proven invaluable for process optimization and data-driven decision making, amplifying the connecting role of this industry in the global market. On the other hand, the Internet of Things in logistics risks magnifying a potential disadvantage of the domain, namely strict interdependence, with single points of vulnerability affecting entire systems.

To draw a parallel, think of the single vessel that obstructed the Suez Canal in 2021 and caused massive supply chain disruptions across the globe. Well, something similar can happen when logistics companies relying on IoT networks face connectivity issues or cyberattacks that can disarrange their large-scale operations. However, integration with suitable protocols, reliable connectivity, and a focus on cybersecurity will minimize such issues, making sure that the aforementioned interdependence is an asset and not a problem.

This article was written by Andrea Di Stefano, Sophia Turol, and Alex Khizhniak.

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