The development of data-driven smart cities has been primarily supported by the Internet of Things (IoT) paradigm, with sensors and actuators playing a critical role in a myriad of applications. However, as IoT Fragmentation becomes a reality due to diverse hardware and networking standard settings, interoperability among heterogeneous IoT devices remains a persistent challenge. This paper proposes a holistic approach for hardware interoperability on the edge layer, leveraging the W3C Web of Things (WoT) standard as a reference. A new system framework and a suite of software components for edge and end nodes enable operational services such as self-identification, dynamic over-the-air reconfiguration, and automatic device onboarding. By doing so, our approach facilitates seamless integration across heterogeneous hardware and communication protocols while maintaining semantic consistency through WoT-based data modeling, potentially contributing to the easier and faster development of smart city applications.

Wildfires in the Wildland-Urban Interface (WUI) pose a significant threat to public safety and property, indicating the need for advanced early detection systems that are not only cost-effective and low-maintenance but also capable of operating in remote locations. This study addresses this need by developing an embedded wildfire detection system optimized for the WUI, using Convolutional Neural Network (CNN) models deployed on resource-constrained hardware. The approach involves building a balanced image dataset encompassing diverse wildfire scenarios, training and evaluating multiple CNN models on this dataset, in order to select the most effective model that adheres to the memory constraints of an Arduino Nano 33 BLE Sense platform. The models were tested and compared using metrics such as global accuracy, true positive accuracy, the area under the ROC curve (AUC-ROC), and inference time. The MobileNetV2 model was selected, achieving a global accuracy of 91.4% and an AUC-ROC of 0.93 while maintaining an inference time of 992 ms. Additionally, a custom enclosure was designed to protect the hardware from environmental factors, thereby ensuring its functionality and durability in real-world deployment scenarios.

The persistence of wildfires in natural landscapes calls for innovative early detection methods that leverage cutting-edge technologies. Traditional approaches, which rely solely on visual sensors or isolated devices, while valuable, often fall short in terms of accuracy, cost, scalability, and contextual adaptability. In response to these challenges, this paper introduces a novel fire detection system that integrates a sensor-based model with a dynamically triggered visual analysis module at edge devices. Central to our approach is a multi-sensor monitoring architecture that employs a TinyML classifier to continuously monitor environmental conditions under strict energy constraints. Upon detecting potential fire indicators, the system promptly activates a visual sensor that uses a camera platform to adjust its orientation based on the target position, capturing and analyzing images through a lightweight Convolutional Neural Network (CNN). This proposed system achieves an accuracy of up to 92%, while the quantized CNN models deliver an 83% reduction in inference time and a 74% and 70% decrease in peak RAM and Flash usage, respectively. Simulations also demonstrated that the system reduced the false-positive rate with minimal power increase.

This study addresses the challenges of dependability in Wireless Sensor Networks by proposing a Machine Learning-based approach using Convolutional Neural Networks for network planning for smart cities. Simulated scenarios were used to train the model, which predicts sensor placement and communication configurations to optimize coverage and availability. Results show significant improvements, including an average of 10.7% increase in dependability index and a rise in area coverage from 59% to 73% in 7-node networks, while reducing path failure rates by 27.6%. The method proves effective for enhancing WSN performance and adaptability in safety-critical applications.

In this paper a compact and high throughput hardware structure is proposed allowing for the computation of the novel 128-bit CLEFIA encryption algorithm and its associated full key expansion. In the existing state of the art only the 128-bit key schedule is supported, given the needed modification to the CLEFIA Feistel network. This work shows that with a small area cost and with no performance impact, full key expansion can be supported. This is achieved by using addressable shift registers, available in modern FPGAs, and adaptable scheduling, allowing to compute the 4 and 8 branch CLEFIA Feistel network within the same structure. The obtained experimental results suggest that throughputs above 1 Gbps can be achieved with a low area cost, while achieving efficiency metrics above those of the restricted state of the art.