Object detection is an essential technology for surveillance systems, particularly in areas with a high risk of accidents such as railway level crossings. To prevent future collisions, the system must detect and track any object that passes through the monitored area with high accuracy, and this process must be performed fulfilling real-time specifications. In this work, an edge IoT HW platform implementation capable of detecting and tracking objects in a railway level crossing scenario is proposed. The response of the system has to be calculated and sent from the proposed IoT platform to the train, so as to trigger a warning action to avoid a possible collision. The system uses a low-resolution 3D 16-channel LIDAR as a sensor that provides an accurate point cloud map with a large amount of data. The element used to process the information is a custom embedded edge platform with low computing resources and low-power consumption. This processing element is located as close as possible to the sensor, where data is generated to improve latency, privacy, and avoid bandwidth limitations, compared to performing processing in the cloud. Additionally, lightweight object detection and tracking algorithm is proposed in this work to process a large amount of information provided by the LIDAR, allowing to reach real-time specifications. The proposed method is validated quantitatively by carrying out implementation on a car road, emulating a railway level crossing.

Wireless sensor networks have been a big promise during the last few years, but a lack of real applications makes difficult the establishment of this technology. In this paper a real monitoring application in an instant coffee factory is presented. This application belongs to the group of environmental solutions based on wireless sensor networks, and it is focused on the impact of the instant coffee production processes in one of the largest instant coffee factories in Europe. The paper includes the entire application scenario, from the hardware of the WSN nodes to the software that will evaluate the impact and will close the loop.

Research on post-quantum cryptography aims to solve the problematic of modern public-key cryptography being broken by attacks coming from quantum computers in the future and, moreover, by using classical electronics. This task is so critical that the National Institute of Standards and Technology (NIST) is in the final process of standardizing post-quantum schemes for the future protection of embedded applications. Though there are some research work done on embedded systems, it is important to study the impact of these proposals in realistic environments for the Internet of Things (IoT), where the limited computational resources and the strict requirements for power consumption can become incompatible with the usage of cryptographic schemes. In this work, the performance of one of the finalists of the standardization process called NTRU is studied and implemented in a custom wireless sensor node designed for applications in the extreme edge of the IoT. The cryptosystem is implemented and evaluated within the processes of the Contiki-NG operating system. Furthermore, additional experiments are performed to check if commonly integrated hardware peripherals for cryptography inside modern microcontrollers can be used to achieve better performance with NTRU, not only at the single node level but also at the network level, where the NTRU key encapsulation mechanism is tested in a real communication process. The results derived from these experiments show that NTRU is suitable for modern microcontrollers targeting wireless sensor networks design, while old devices present in popular platforms might not afford the cost of its implementation.

The explosion of the Internet of Things has dramatically increased the data load on networks that cannot indefinitely increment their capacity to support these new services. Edge computing is a viable approach to fuse and process data on sensor platforms so that information can be created locally. However, the integration of complex heterogeneous sensors producing a great amount of diverse data opens new challenges to be faced. Rather than generating usable data straight away, complex sensors demand prior calculations to supply meaningful information. In addition, the integration of complex sensors in real applications requires a coordinated development from hardware and software teams that need a common framework to reduce development times. In this work, we present an edge and fog computing platform capable of providing seamless integration of complex sensors, with the implementation of an efficient data fusion strategy. It uses a symbiotic hardware/software design approach based on a novel messaging system running on a modular hardware platform. We have applied this platform to integrate Bluetooth vehicle identifiers and radar counters in a specific mobility use case, which exhibits an effective end-to-end integration using the proposed solution.

The hardware of networked embedded sensor nodes is in continuous evolution, from those 8-bit MCUs-based platforms such as Mica, up to powerful Edge nodes that even include custom hardware devices, such as FPGAs in the Cookies platform. This evolution process comes up with issues related to the deployment of the Internet of Things, particularly in terms of performance and communication bottlenecks. Moreover, the associated integration process from the Edge up to the Cloud layer opens new security concerns that are key to assure the end-to-end trustability and interoperability. This work tackles these questions by proposing a novel embedded Edge platform based on an EFR32 SoC from Silicon Labs with Contiki-NG OS that includes an ARM Cortex M4 MCU and an IEEE 802.15.4 transceiver, used for resource-constrained low-power communication capabilities. This IoT Edge node integrates security by hardware, adding support for confidentiality, integrity and availability, making this Edge node ultra-secure for most of the common attacks in wireless sensor networks. Part of this security relies on an energy-efficient hardware accelerator that handles identity authentication, session key creation and management. Furthermore, the modular hardware platform aims at providing reliability and robustness in low-power distributed sensing application contexts on what is called the Extreme Edge, and for that purpose a lightweight multi-hop routing strategy for supporting dynamic discovery and interaction among participant devices is fully presented. This embedded algorithm has served as the baseline end-to-end communication capability to validate the IoT hardware platform through intensive experimental tests in a real deployment scenario.