Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation

The Internet of Things (IoT) has revolutionized the world, connecting billions of devices that offer assistance in various aspects of users' daily lives. Context-aware IoT applications exploit real-time environmental, user-specific, or situational data to dynamically adapt to users' needs, offering tailored experiences. In particular, Location-Based Services (LBS) exploit geographical information to adapt to environmental settings or provide recommendations based on users' and nodes' positions, thus delivering efficient and personalized services. To this end, there is growing interest in developing IoT localization systems within the scientific community. In addition, due to the sensitivity and privacy inherent to precise location information, LBS introduce new security challenges. To ensure a more secure and trustworthy system, researchers are studying how to prevent vulnerabilities and mitigate risks from the early design stages of LBS-empowered IoT applications. The goal of this study is to carry out an in-depth examination of localization techniques for IoT, with an emphasis on both the signal-processing design and security aspects. The investigation focuses primarily on active radio localization techniques, classifying them into range-based and range-free algorithms, while also exploring hybrid approaches. Next, security considerations are explored in depth, examining the main attacks for each localization technique and linking them to the most interesting solutions proposed in the literature. By highlighting advances, analyzing challenges, and providing solutions, the survey aims to guide researchers in navigating the complex IoT localization landscape.

Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device

Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for continuous monitoring using wearable devices. This paper proposes a single-microcontroller-based system designed for the automatic detection of MI based on the Edge Computing paradigm. Two solutions for MI detection are evaluated, based on Machine Learning (ML) and Deep Learning (DL) techniques. The developed algorithms are based on two different approaches currently available in the literature, and they are optimized for deployment on low-resource hardware. A feasibility assessment of their implementation on a single 32-bit microcontroller with an ARM Cortex-M4 core was examined, and a comparison in terms of accuracy, inference time, and memory usage was detailed. For ML techniques, significant data processing for feature extraction, coupled with a simpler Neural Network (NN) is involved. On the other hand, the second method, based on DL, employs a Spectrogram Analysis for feature extraction and a Convolutional Neural Network (CNN) with a longer inference time and higher memory utilization. Both methods employ the same low power hardware reaching an accuracy of 89.40% and 94.76%, respectively. The final prototype is an energy-efficient system capable of real-time detection of MI without the need to connect to remote servers or the cloud. All processing is performed at the edge, enabling NN inference on the same microcontroller.

Impact of High Light Intensity and Low Temperature on the Growth and Phenylpropanoid Profile of Azolla filiculoides

Exposure to high light intensity (HL) and cold treatment (CT) induces reddish pigmentation in Azolla filiculoides, an aquatic fern. Nevertheless, how these conditions, alone or in combination, influence Azolla growth and pigment synthesis remains to be fully elucidated. Likewise, the regulatory network underpinning the accumulation of flavonoids in ferns is still unclear. Here, we grew A. filiculoides under HL and/or CT conditions for 20 days and evaluated the biomass doubling time, relative growth rate, photosynthetic and non-photosynthetic pigment contents, and photosynthetic efficiency by chlorophyll fluorescence measurements. Furthermore, from the A. filiculoides genome, we mined the homologs of MYB, bHLH, and WDR genes, which form the MBW flavonoid regulatory complex in higher plants, to investigate their expression by qRT-PCR. We report that A. filiculoides optimizes photosynthesis at lower light intensities, regardless of the temperature. In addition, we show that CT does not severely hamper Azolla growth, although it causes the onset of photoinhibition. Coupling CT with HL stimulates the accumulation of flavonoids, which likely prevents irreversible photoinhibition-induced damage. Although our data do not support the formation of MBW complexes, we identified candidate MYB and bHLH regulators of flavonoids. Overall, the present findings are of fundamental and pragmatic relevance to Azolla's biology.