Radio Frequency Fingerprint (RFF) is a unique characteristic of radio signals impacted by hardware imperfections of the device’s radio front-end. This characteristic can be used as a security measure to identify individual devices. There are various methods to extract RFF features, ranging from statistical analysis to deep learning methods, which have seen increasing adoption among researchers, given their automatic feature extraction capabilities and performance. However, the black-box nature of deep learning models is a significant drawback, as it raises concerns about the model’s reliability, especially in critical applications such as security. In this paper, we use In-phase and Quadrature (I/Q) samples to create spectrograms and train an EfficientNet model for device classification. The model is trained on a Wi-Fi dataset of 20 devices, achieving 99% accuracy on the test set. More importantly, to understand the model’s decision-making process, we apply the Gradient-weighted Class Activation Map (Grad-CAM) to visualize its attention. Grad-CAM highlights specific spectrogram regions that are linked to the device’s fingerprint signature, including power leakage, Direct Current (DC) components, and energy patterns.

Radio Frequency Fingerprint Identification (RF-FID) is a novel approach that aims to differentiate devices based on their unique signal transmissions, rather than their given identities. This approach has promising applications in wireless security, spectrum management, and sensing. Current RFFID research uses deep learning due to its success in various domains, which is largely attributed to the availability of massive labeled datasets for training. However, unlike other domains, labeled data in RFFID is limited, and the labeling process is expensive. In this paper, we propose a label-efficient learning approach for RFFID based on Contrastive Predictive Coding (CPC), a pre-training method that learns to predict future samples given the past without labels. Afterward, the model is fine-tuned to identify the device. We evaluate our approach on a fingerprint dataset of 20 devices. Our results show that CPC learns effective representations of RF signals and outperforms fully supervised learning in both classification performance and label efficiency, requiring up to 10 times fewer labels while maintaining competitive accuracy. Finally, we evaluate CPC's robustness against noise and observe competitive performance after fine-tuning.

This paper discusses the experiences gained from designing, deploying, and maintaining low-power Wireless Sensor Networks (WSN) in three geothermally active remote locations in Iceland. The network was deployed for environmental monitoring and real-time data collection to assist in investigating the impact of global warming on the (sub)Arctic climate and the resulting carbon release from the region. Functional networks with more than 50 sensor nodes from three sites with extreme weather conditions and hard-to-access terrain have been collecting data since 2021. The networks employ primary cell-powered wireless sensor nodes equipped with DASH7 Alliance Protocol (D7A) for low-power data transmission and solar-powered D7A-cellular gateways for the backend connection. The WSNs have so far achieved over three years of uptime with minimal maintenance required throughout this period. We present a detailed discussion of different network components, their architecture, and the networks' overall performance and reliability.

Emotions have a profound impact on our daily lives, influencing our thoughts, behaviors, and interactions, but also our physiological reactions. Recent advances in wearable technology have facilitated studying emotions through cardio-respiratory signals. Accelerometers offer a non-invasive, convenient, and costeffective method for capturing heart- and pulmonary-induced vibrations on the chest wall, specifically Seismocardiography (SCG) and Accelerometry-Derived Respiration (ADR). Their affordability, wide availability, and ability to provide rich contextual data make accelerometers ideal for everyday use. While accelerometers have been used as part of broader modality fusions for Emotion Recognition (ER), their stand-alone potential via SCG and ADR remains unexplored. Bridging this gap could significantly help the embedding of ER into real-world applications, minimizing the hardware, and increasing contextual integration potentials. To address this gap, we introduce SCG and ADR as novel modalities for ER and evaluate their performance using the EmoWear dataset. First, we replicate the single-trial emotion classification pipeline from the DEAP dataset study, achieving similar results. Then we use our validated pipeline to train models that predict affective valence-arousal states using SCG and compare them against established cardiac signals, Electrocardiography (ECG) and Blood Volume Pulse (BVP). Results show that SCG is a viable modality for ER, achieving similar performance to ECG and BVP. By combining ADR with SCG, we achieved a working ER framework that only requires a single chest-worn accelerometer. These findings pave the way for integrating ER into real-world, enabling seamless affective computing in everyday life.

The current satellite Positioning, Navigation and Timing (PNT) landscape is evolving rapidly. This is characterized by a shift from governmental initiatives to private enterprises. These “New Space” initiatives aim to improve PNT performance beyond the current state-of-the-art by implementing new technical advancements. This technical innovation and the resulting application potential are the central focus of this paper. In this paper, we compare the current “New Space” PNT initiatives and their progress toward creating an operational system. Furthermore, we analyze the proposed satellite constellations and their communication system. Focussing on, Pulsar, Centispace, Future Mobility, and Satellite Time and Location (STL). This technical analysis encompasses constellation design and resulting Dilution of Precision (DOP), frequency allocation, space segment lifetime, accuracy, availability, authentication, indoor coverage, and encryption. Afterward, their specifications are evaluated and compared against specific application needs. More specifically, focusing on the requirements of autonomous driving, pedestrian tracking, first responders, and the maritime sector. By reviewing the progress of these “New Space” initiatives in leveraging technological advancements to address application requirements, this study uncovers the evolving PNT landscape for diverse application domains.

Location data is an important piece of information in many Internet of Things (IoT) applications. Global Navigation Satellite Systems (GNSS) have been established as the standard for large-scale localization. However, the rapidly increasing need to locate IoT devices in recent years has exposed several shortcomings of traditional GNSS approaches. These limitations include the weak signal propagation in indoor and dense environments, the inability to calculate or obtain a location remotely, and a high energy consumption. Therefore, several industries have shown an increasing demand for alternative and innovative positioning solutions that are more suited in an IoT context. Hence, we conduct a survey on state-of-the-art, large-scale and energy-efficient positioning techniques for IoT applications. More specifically, we analyze the performance of terrestrial-based Low Power Wide Area Network (LPWAN) techniques, novel GNSS solutions, and innovative positioning techniques leveraging Low Earth Orbit (LEO) satellite constellations. A comparison is made in terms of 16 dimensions including energy consumption, positioning accuracy, coverage, and scalability. The analysis shows that interoperability between technologies is key to enable energy-efficient communication and positioning applications in the emerging market of satellite IoT.

Activity monitoring is crucial towards enabling smart metropolitan cities. It provides the city officials with the essential information to manage resources, plan events, and help detect and stop insider threats. An automatic activity monitoring methodology that can render real-time and stable estimates of the objects while respecting all privacy concerns in a given environment is therefore of significant interest. This paper investigates the feasibility and presents the promising results of using a sub-GHz passive radio frequency (RF) wireless sensor network (WSN) to monitor activity and determine the number of pedestrians, cars, and cyclists. In particular, we describe our device-free sensing experiment and measure the received signal strength (RSS) between transceiver nodes at an intersection within the smart zone of the city of Antwerp, Belgium, where targets to be monitored do not require carrying a device or a tag. Our results reveal a correlation between the time-averaged RSS measurements and the count of target objects, which indicates the feasibility of employing a sub-GHz WSN for activity monitoring. Solving traffic monitoring using this system will still require investigating the optimal necessary number of radio links and accurate counting models.

In this paper, we introduce a comprehensive angle of arrival (AoA) estimation solution for the long range (LoRa) communication network. Termed the LoRa array (LoRay), the proposed system constitutes hardware and software solutions to estimate the AoA of the received signals in real life urban environments. The hardware solution is based on converting multiple individual software defined radios (SDR) into a single SDR that consists of multiple RF-channels. The proposed hardware is cost effective, flexible and generic. The software solution, on the other hand, utilizes the space alternating generalized expectation-maximization (SAGE) algorithm to estimate the AoA of highly correlated received signals. The proposed software exploits few samples of the received signal to estimate the AoA of the direct and reflected paths in an intensive multipath environment. The LoRay system has been validated in outdoor urban environments. The experimental results show that the proposed system provides stable and accurate AoA estimates for both the line-of-sight (LoS) and the non-line-of-sight (NLoS) conditions. The AoA of 80% of the received signals have been estimated within an estimation error below 2° and 10° for the LoS and the NLoS locations, respectively.

5G has opened up possibilities of introducing new use cases and business models that could not be perceived before. In the context of public safety, 5G offers immense opportunities towards enhancing mission success and situation awareness during emergency management. This paper introduces Back-Situation Awareness (BSA) application enabling early warning/notification to vehicles of an approaching emergency vehicle indicating its presence and the time it will arrive. Such an application is expected to give drivers enough time to create a safety corridor for the emergency vehicle to pass through safely and unhindered. We provide details on the system and application design of the BSA application leveraging Multi-Access Edge Computing (MEC) systems that complement the 5G mobile communication system. An evaluation of the application is provided by using data measurements and indicating the accuracy of the computation and notification of the Estimated Time of Arrival (ETA) based on the ETSI C-ITS protocol messages.

The Internet of Things (IoT) has caused the modern society to connect everything in our environment to a network. In a myriad of IoT applications, smart devices need to be located. This can easily be done by satellite based receivers. However, there are more energy-efficient localization technologies, especially in Low Power Wide Area Networks (LPWAN). In this research, we discuss the accuracy of an outdoor fingerprinting technique using a large outdoor Sigfox dataset which is openly available. A kNN (k Nearest Neighbors) algorithm is applied to our fingerprinting database. 31 different distance functions and four RSS data representations are evaluated. Our analysis shows that a Sigfox transmitter can be located with a mean estimation error of 340 meters.

Automatic crowd density estimation can be very useful for a multitude of applications such as traffic control or crowd control systems during large-scale events. Classic camera-based setups have several shortcomings, the most notorious of which is the privacy issue. The use of a crowd estimator which makes use of a wireless sensor network (WSN) can provide a potential solution to this problem. We deployed a sub-GHz (433 MHz & 868 MHz) wireless sensor network in an indoor stage at a music festival. Visual validation was established by a team of volunteers who manually analyzed a large set of low-quality camera images which were taken during the event. Next, RSS measurements obtained by the network were classified into different density-based categories by a simple probabilistic neural network. Results indicate that the system is capable of estimating the crowd density with a high accuracy, proving the feasibility of using a WSN for such a task.

IEEE 802.11ah is the new sub-1 GHz Wi-Fi standard, targeting large-scale and dense deployments of low-power stations. One of its major improvements compared to previous 802.11 standards, is its ability to scale to thousands of stations per access point. Cost-effective evaluation at such a scale is only possible using simulation, which requires realistic path loss models and hardware parameters. In this paper, we evaluate seven path loss models, based on a large scale sub-urban measurement campaign, including macro line-of- sight (LoS), pico LoS, and pico non-LoS with different as well as equal antenna height deployments. For each of the four resulting scenarios, the most accurate model is determined and used in combination with radio transceiver parameters obtained from actual 802.11ah station hardware to determine MAC-layer throughput and packet loss as a function of distance. The standard promises a range of up to 1 km at 150 kbps. Our results paint a less optimistic picture. When using realistic hardware parameters ranges up to 450 and 130 m can be achieved for a near LoS macro and pico deployment scenario respectively. For the non-LoS pico scenario ranges of 80 and 150 m can be achieved for transmitter at height 12 m and transmitter at heights 1.5 m respectively. With an ideal hardware configuration that operates at the maximum allowed transmission power, this could ideally be increased to 1700, 490, 300 and 550 m respectively.

In the Internet of Things, location information is crucial for many applications. We want to obtain location information from a device by using its existing communication modality. DASH7 is designed for low power sensor and actuator communication on a medium range, using the license exempt radio frequency channels below one gigahertz. In this paper, we present a method to localize a DASH7 mobile node based on a single message and a deterministic propagation model. The propagation model is used to indicate the distance between sender and receiver so that single measurement localization approach is possible without maintaining a fingerprint database. We obtain a median location error of 3.9 m, where we still see room for improvement.

The industrial demands for accurate localization systems have been rapidly increasing after the introduction of the Internet of Things (IoT) concept. Self localization and tracking transmitting sources are considered essential parts of IoT applications. In this paper we studied the possibility of applying angle of arrival (AoA) estimations to localize an IoT transceiver device in an indoor environment. Furthermore, we propose an adaptive probabilistic model which works on top of the AoA estimation technique to improve the localization accuracy. The experimental results show the potential of using AoA-based localization for indoor environments. The results furthermore show that the proposed adaptive probabilistic model outperforms the traditional static probabilistic model in terms of the localization accuracy and the stability of the position estimate.

Unlike most currently available localization systems, tagless localization technologies do not require a target to wear a passive or active hardware device. Radio Tomographic Imaging (RTI) is one such technique, which operates based on the use of a tomographic radio frequency (RF) sensor network. The majority of RTI-systems communicate using a single frequency band: 2.4 GHz. The use of sub-1 GHz frequencies within RTI could potentially provide important benefits regarding energy efficiency, accuracy in complex indoor environments and size of the environments in which a system can be installed. We deployed a combined 433 MHz and 868 MHz RF sensor network in a complex indoor environment and performed localization when a human individual was present in the environment. Two different RTI-algorithms were investigated: a Bayesian-based method we developed earlier and an adaptation of an existing 2.4 GHz technique based on fade level. Both methods turned out to be capable of accurately locating individuals with a median error lower than 1 meter. This proves the feasibility of using a combination of sub-1 GHz frequencies in RTI for indoor localization in complex environments.

The accuracy of a positioning system is usually expressed as its average position error in an experiment. However, when the ground truth is no longer available, it would still be useful to know the reliability of a position estimate based on a single measurement. To obtain a reliability metric, we hypothesize that there is a relation between the uncertainty in a position's posterior probability distribution, expressed as its conditional entropy, and the position error of the position that is derived from this distribution. In this paper, we present the correlation between these two metrics as calculated for the UJIIndoorLoc Wi-Fi fingerprinting dataset, using a new probabilistic sensor model. We found that there is no significant correlation between the conditional entropy and the position error. However, we learned that our sensor model is usually very certain in the dataset, and saw that the suggestion of a correlation improves when we increase the uncertainty by selecting a fixed, larger variance. Interestingly, the position error results improve as well.

Conventional localization systems require the target to carry a tag, which can be highly impractical for some individuals, such as the elderly, or in some situations, such as in emergencies. This requirement can be alleviated by an emerging set of tagless or device-free localization systems, of which Radio Tomographic Imaging (RTI) is a common example. Most variations of this technique assume the use of radio frequency (RF) signals in the 2.4 GHz band, or combinations of lower frequencies with that band; however, using only lower frequencies might decrease the power consumption and the influence of the environment, and increase the range of the system. We tested the combination of 433 MHz and 868 MHz in a single RTI system. We studied two approaches to combine the RTI images generated by one frequency with the other: an approach based on literature and a newly developed approach based on probability theory. This paper compares the result of both approaches. We found that the result based on literature has a root-mean-square error (RMSE) of 1.09 m, while our approach has an RMSE of 0.54 m. While also improving the state-of-the-art fusion of two frequencies, we proved the feasibility of combining only frequencies under 1 GHz in an RTI system.

The performance of a localization algorithm is usually expressed as its mean error distance. We argue that this assumes a unimodal distribution of the localization posterior, which is not always appropriate. We propose to additionally quantify the localization posterior distribution by its conditional entropy. This informs us of the uncertainty over the position after a measurement, which must be processed by the localization algorithm. Our example measurement model was ranked in the Evaluating Ambient Assisted Living competition, for which we present the results. Furthermore, we discuss the conditional entropy of our measurement model and two additional measurement models, based on the absolute difference distance and the Pompeiu-Hausdorff distance. We compare these results by using the UJIIndoorLoc database that was also used for the competition.

Wi-Fi is a commonly available source of localization information in urban environments but is challenging to integrate into conventional mapping architectures. Current state of the art probabilistic Wi-Fi SLAM algorithms are limited by spatial resolution and an inability to remove the accumulation of rotational error, inherent limitations of the Wi-Fi architecture. In this paper we leverage the low quality sensory requirements and coarse metric properties of RatSLAM to localize using Wi-Fi fingerprints. To further improve performance, we present a novel sensor fusion technique that integrates camera and Wi-Fi to improve localization specificity, and use compass sensor data to remove orientation drift. We evaluate the algorithms in diverse real world indoor and outdoor environments, including an office floor, university campus and a visually aliased circular building loop. The algorithms produce topologically correct maps that are superior to those produced using only a single sensor modality.