Abstract: In an era marked by the big data paradigm and ubiquitous computing systems, it is increasingly common to see devices embedded in any type of object with the aim of collecting and sharing owned or read data from its environment. This type of interconnection between devices is known as the Internet of Things (IoT). In particular in the logistics sector, vehicles are equipped with control units that are capable of monitoring a large number of parameters to ensure the correct operation of the vehicle. In addition, they are now able to share this data in near real time so that this information can be accessed and analysed at any time. However, due to the large amount of shared data, the frequency of data generation and delivery, and the high potential for growth in the number of devices, traditional technologies and architectures are not able to meet the performance demands of these real time decision making systems. In this article we describe and evaluate the benefits and potential trade-offs of implementing services based on a distributed and scalable architecture, called RAI4.0, in a truck fleet management company, which currently has 20,000 on-board devices and expects to grow to 80,000 devices in the next 2 years. With the change of architecture, the company expects to be able to implement near real-time services to monitor and notify its drivers of driving tips and diagnose possible vehicle failures in advance, among others.

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