LoRa is a widely adopted physical layer technology for low-power wide-area IoT networks, yet its performance under concurrent LoRa transmissions is not fully understood. Most existing analytical models focus on payload interference and overlook the preamble, which is essential for LoRa nodes synchronization, as a potential contributor to interference effects. In this paper, we analyze the LoRa preamble as a source of interference by applying existing Symbol Error Rate (SER) models to the specific case of preamble transmissions. Building on this analysis, we derive a preamble-aware Frame Error Rate (FER) model that jointly accounts for preamble-based detection and payload symbol decoding. Interestingly, our analysis demonstrates that preamble interference has a greater impact than payload interference, a result validated through controlled experiments conducted using commercial LoRa transceivers. This asymmetry indicates that, in time-slotted LoRa networks, interference can be mitigated by controlling transmitter–receiver timing to avoid preamble–preamble overlap, leading to the experimentally validated concept of Pair Synchronization.

This paper presents a comprehensive mathematical model to characterize the energy dynamics of batteryless IoT sensor nodes powered entirely by ambient energy harvesting. The model captures both the energy harvesting and consumption phases, explicitly incorporating power management tasks to enable precise estimation of device behavior across diverse environmental conditions. The proposed model is applicable to a wide range of IoT devices and supports intelligent power management units designed to maximize harvested energy under fluctuating environmental conditions. We validated our model against a prototype batteryless IoT node, conducting experiments under three distinct illumination scenarios. Results show a strong correlation between analytical and measured supercapacitor voltage profiles, confirming the proposed model’s accuracy.

This paper presents a novel multi-attribute decision-making approach for managing handovers in Optical Wireless Communication/Radio Frequency Heterogeneous Networks (OWC/RF HetNets). The proposed method systematically evaluates potential Access Points (APs) using multiple criteria, including fixed and variable handover costs, the ratio of the node's data rate demand to the achievable rate from APs, and the ratio of the utilized capacity of APs. Using local parameters, nodes assess these attributes over a finite future horizon and construct a decision matrix. The VIKOR method is then applied to decide whether to initiate a handover to a new AP or remain connected to the current one. Simulation results show that the proposed decentralized scheme effectively manages handovers, leading to improved load balancing and reduced handover latency in OWC/RF HetNets.

With the surge in IoT devices ranging from wearables to smart homes, prompt transmission is crucial. The Age of Information (AoI) emerges as a critical metric in this context, representing the freshness of the information transmitted across the network. This paper studies hybrid IoT networks that employ Optical Communication (OC) as a reinforcement medium to Radio Frequency (RF). We formulate a quadratic convex optimization that adopts a Pareto optimization strategy to dynamically schedule the communication between devices and select their corresponding communication technology, aiming to balance the maximization of network throughput with the minimization of energy usage and the frequency of switching between technologies. To mitigate the impact of dominant sub-objectives and their scale disparity, the designed approach employs a regularization method that approximates adequate Pareto coefficients. Simulation results show that the OC supplementary integration alongside RF enhances the network’s overall performances and significantly reduces the Mean AoI and Peak AoI, allowing the collection of the freshest possible data using the best available communication technology.

The sustainable design of Internet of Things (IoT) networks encompasses considerations related to energy efficiency and autonomy as well as considerations related to reliable communications, ensuring no energy is wasted on undelivered data. Under these considerations, this work proposes the design and implementation of energy-efficient Bluetooth Low Energy (BLE) and Light-based IoT (LIoT) batteryless IoT sensor nodes powered by an indoor light Energy Harvesting Unit (EHU). Our design intends to integrate these nodes into a sensing network to improve its reliability by combining both technologies and taking advantage of their features. The nodes incorporate state-of-theart components, such as low-power sensors and efficient System-on-Chips (SoCs). Moreover, we design a strategy for adaptive switching between active and sleep cycles as a function of the available energy, allowing the IoT nodes to continuously operate without batteries. Our results show that by adapting the duty cycle of the BLE and LIoT nodes depending on the environment’s light intensity, we can ensure a continuous and reliable node operation. In particular, measurements show that our proposed BLE and LIoT node designs are able to communicate with an IoT gateway in a bidirectional way, every 19.3 and 624.6 seconds, respectively, in an energy-autonomous and reliable manner.

In this paper, we introduce a runtime-efficient radio frequency (RF) map prediction method based on UNet convolutional neural networks (CNNs), trained on a large-scale 3D maps dataset. The proposed method calculates the line-of-sight maps and feeds them as input for the UNet CNN. A special Kullback–Leibler divergence loss function is adopted, enabling the proposed method to minimize both error’s mean and variance. The performance of our model is evaluated in the context of the 2023 IEEE ICASSP Signal Processing Grand Challenge, namely, the First Pathloss Radio Map Prediction Challenge. The evaluation results demonstrate that the proposed method achieves an average normalized root-mean-square error (RMSE) of 0.045 with an average of 14 milliseconds (ms) runtime.

Accurate network synchronization is a key enabler for services such as coherent transmission, cooperative decoding, and localization in distributed and cell-free networks. Unlike centralized networks, where synchronization is generally needed between a user and a base station, synchronization in distributed networks needs to be maintained between several cooperative devices, which is an inherently challenging task due to hardware imperfections and environmental influences on the clock, such as temperature. As a result, distributed networks have to be frequently synchronized, introducing a significant synchronization overhead. In this paper, we propose an online-LSTM-based model for clock skew and drift compensation, to elongate the period at which synchronization signals are needed, decreasing the synchronization overhead. We conducted comprehensive experimental results to assess the performance of the proposed model. Our measurement-based results show that the proposed model reduces the need for re-synchronization between devices by an order of magnitude, keeping devices synchronized with a precision of at least 10 microseconds with a probability 90%.

Given the availability of lightweight radio and processing technology, it becomes feasible to imagine spectrum sensing systems using weather balloons. Such balloons navigate the airspace up to 40 km, and can provide a bird's eye and clear view of terrestrial, as well as aerial spectrum use. In this paper, we present SkySense, which is an extension of the Electrosense sensing framework with mobile GPS-located sensors and local data logging. In addition, we present 6 different sensing campaigns, targeting multiple terrestrial or aerial technologies such as ADS-B, AIS or LTE. For instance, for ADS-B, we can clearly conclude that the number of airplanes that are detected is the same for each balloon altitude, but the message reception rate decreases strongly with altitude because of collisions. For each sensing campaign, the dataset is described, and some example spectrum analysis results are presented. In addition, we analyse and quantify important trends visible when sensing from the sky, such as temperature and hardware variations, increased ambient interference levels, as well as hardware limitations of the lightweight system. A key challenge is the automatic gain control and dynamic range of the system, as a radio navigating over 30km, sees a very wide range of possible signal levels. All data is publicly available through the Electrosense framework, to encourage the spectrum sensing community to further analyse the data or motivate further measurement campaigns using weather balloons.

Localization in long-range Internet of Things networks is a challenging task, mainly due to the long distances and low bandwidth used. Moreover, the cost, power, and size limitations restrict the integration of a GPS receiver in each device. In this article, we introduce a novel received signal strength indicator (RSSI)-based localization solution for ultra narrow band (UNB) long-range IoT networks such as Sigfox. The essence of our approach is to leverage the existence of a few GPS-enabled sensors nodes (GSNs) in the network to split the wide coverage into classes, enabling RSSI-based fingerprinting of other sensors nodes (SNs). By using machine learning algorithms at the network backed-end, the proposed approach does not impose extra power, payload, or hardware requirements. To comprehensively validate the performance of the proposed method, a measurement-based dataset that has been collected in the city of Antwerp is used. We show that a location classification accuracy of 80% is achieved by virtually splitting a city with a radius of 2.5 km into seven classes. Moreover, separating classes, by increasing the spacing between them, brings the classification accuracy up-to 92% based on our measurements. Furthermore, when the density of GSN nodes is high enough to enable device-to-device communication, using multilateration, we improve the probability of localizing SNs with an error lower than 20 m by 40% in our measurement scenario.

The use of aerial anchors for localizing terrestrial nodes has recently been recognized as a cost-effective, swift and flexible solution for better localization accuracy, providing localization services when the GPS is jammed or satellite reception is not possible. In this paper, the localization of terrestrial nodes when using mobile unmanned aerial vehicles (UAVs) as aerial anchors is presented. We propose a novel framework to derive localization error in urban areas. In contrast to the existing works, our framework includes height-dependent UAV to ground channel characteristics and a highly detailed UAV energy consumption model. This enables us to explore different trade-offs and optimize UAV trajectory for minimum localization error. In particular, we investigate the impact of UAV altitude, hovering time, number of waypoints and path length through formulating an energy-constrained optimization problem. Our results show that increasing the hovering time decreases the localization error considerably at the cost of a higher energy consumption. To keep the localization error below 100 m, shorter hovering is only possible when the path altitude and radius are optimized. For a constant hovering time of 5 seconds, tuning both parameters to their optimal values brings the localization error from 150m down to 65m with a power saving around 25% based on our simulation results.

Internet of things wireless networking with long-range, low power and low throughput is raising as a new paradigm enabling to connect trillions of devices efficiently. In such networks with low power and bandwidth devices, localization becomes more challenging. In this work we take a closer look at the underlying aspects of received signal strength indicator (RSSI) based localization in UNB long-range IoT networks such as Sigfox. Firstly, the RSSI has been used for fingerprinting localization where RSSI measurements of GPS anchor nodes have been used as landmarks to classify other nodes into one of the GPS nodes classes. Through measurements we show that a location classification accuracy of 100% is achieved when the classes of nodes are isolated. When classes are approaching each other, our measurements show that we can still achieve an accuracy of 85%. Furthermore, when the density of the GPS nodes is increasing, we can rely on peer-to-peer triangulation and thus improve the possibility of localizing nodes with an error less than 20m from 20% to more than 60% of the nodes in our measurement scenario. 90% of the nodes is localized with an error of less than 50m in our experiment with non-optimized anchor node locations.