Efficient long-range communication is critical for environmental monitoring, especially when dealing with large data transfers in remote areas. We present an AI-driven dynamic RF configuration mode, which combines the advanced capabilities of a novel AI*LoRa model with an enhanced RF configuration request mechanism. By leveraging TinyML, AI*LoRa dynamically adjusts key parameters of the physical layer, based on real-time environmental data, ensuring robust and energy-efficient communication. We conducted extensive real-world testing across distances ranging from a few meters to over 100 kilometers to collect the dataset necessary for training our model. The results demonstrate that our approach achieved over 90% accuracy in predicting optimal settings, leading to an average improvement of over 170% in communication efficiency. These findings underscore AI*LoRa's significant potential to enhance long-range IoT deployments.

Cellular communication standards have been established to ensure connectivity across most urban environments, complemented by deployment hardware and facilities tailored for city life. At the same time, numerous initiatives seek to broaden connectivity to rural and developing areas. However, with nearly half the global population still offline, there is an urgent need to drive research toward enhancing connectivity in areas and conditions that deviate from the norm. This article delves into innovative communication solutions not only for hard-to-reach and extreme environments but also introduces “hard-to-serve” areas as a crucial, yet underexplored, category within the broader spectrum of connectivity challenges.We explore the latest advancements in communication systems designed for environments subject to extreme temperatures, harsh weather, excessive dust, or even disasters such as fires. Our exploration spans the entire communication stack, covering communications on isolated islands, sparsely populated regions, mountainous terrains, and even underwater and underground settings. We highlight system architectures, hardware, materials, algorithms, and other pivotal technologies that promise to connect these challenging areas. Through case studies, we explore the application of 5G for innovative research, long range (LoRa) for audio messages and emails, LoRa wireless connections, free-space optics, communications in underwater and underground scenarios, delay-tolerant networks, satellite links, and the strategic use of shared spectrum and TV white space (TVWS) to improve mobile connectivity in secluded and remote regions. These studies also touch on prevalent challenges such as power outages, regulatory gaps, technological availability, and human resource constraints, where we introduce the concept of peri-urban hard-to-serve areas where populations might struggle with affordability or lack the skills for traditional connectivity solutions. This article provides an exhaustive summary of our research, showcasing how 6G and future networks will play a crucial role in delivering connectivity to areas that are hard-to-reach, hard-to-serve, or subject to extreme conditions (ECs).

LoRaWAN has proven helpful in many application areas, such as meteorological monitoring in sparsely populated areas. LoRaWAN End Nodes send data to a LoRaWAN Gateway, conveying it to network and application servers using an IP connection. In remote areas, connectivity is often unavailable, but these areas can provide a wealth of data valid for disaster prevention, wild animal tracking, climate assessment, and modeling, among others. We present a LoRaWAN Range Extender, which extends the coverage of existing gateways without requiring any modification to the existing LoRaWAN infrastructure. This device could double the achievable range of a standard LoRaWAN link or enable nodes to be serviced behind obstacles that prevent direct communication with the Gateway. The Range Extender must be within the communication reach of an existing LoRaWAN Gateway while also being reachable by the End Nodes of interest. It can be located in places without cellular coverage, and since it is cheaper to operate and install, it can lower both capital and operational expenses.

Over the last few years we have witnessed an exponential growth in the adoption of LoRaWAN as LPWAN technology for IoT. While LoRaWAN offers many advantages, one of its limitations is the paltry data rate. Most IoT applications don't require a high throughput but there are some that would benefit from a higher data rate. In this paper, we present TurboLoRa, a system that combines the strengths of LoRaWAN while providing a higher data rate by synchronizing the transmission of multiple LoRaWAN devices. Our proposal allows to combine cheap devices making it a frugal solution to this kind of problems. We present some preliminary results obtained using a real prototype of TurboLoRa.

Wireless links extending beyond the horizon at frequencies of 868 MHz cannot be attributed to ionospheric reflections, since those only happen at much lower frequencies. For very long links the propagation can be attributed to the bending of the radio waves due to anomalies in the atmospheric refractivity index. These anomalies are caused by abnormal variations in temperature and humidity versus elevation that result in tropospheric ducts that can reach thousands of kilometers. Leveraging available data from radiosondes that are periodically launched worldwide, it is possible to determine the refractivity index profile and from this the conditions for the existence of a tropospheric duct. We developed a series of Python tools to analyze such links and applied them to assess the propagation mechanism in three cases, reported by other users, that overcome the earth’s curvature obstruction. These tools can be used to determine the presence of ducting conditions at any place in the vicinity of a radiosonde launching site and results are valid at other frequencies as well. They can also be used by people not versed in Python by using the Jupyter Notebook hosted in Google Colab.

Traffic congestion is a major problem in many cities. It happens due to the demand-supply imbalance in the transportation network and poor management. Traffic flow slows down when the number of vehicles that travels on the road increases or the roadway capacity decreases due to various reasons. In order to solve this problem, different solutions are proposed to provide reliable, real-time transport management services in an Intelligent Transportation System (ITS). In this paper, we propose a novel real-time video analytics using low-cost IoT devices and LoRaWAN networks to realize new services and applications that include traffic management through IoT edge computing. The use of LoRaWAN for such application is our main contribution. We retrain YOLO v3 object detection machine learning model (transfer learning) for vehicle detection and counting, to make it lightweight and fast enough to be able to run on a Raspberry Pi, a single-board computer with limited RAM. The edge node, with low-cost smart camera and connectivity through LoRaWAN networks counts the number of vehicles using real-time video analytic and report only traffic count to the server. This experimental work provides insight into the applicability of a low-cost IoT system to traffic management with a resource-constrained environment. Real-world video analysis of vehicle detection and counting show the effectiveness of the designed solution. The results demonstrate the effectiveness of the proposed approach.

Connectivity is the essential requirement for a smart city, and traditional techniques are too costly to connect a great number of devices which might be served with lower data throughput, but require longer ranges to accommodate the requirements of IoT. Although cellular technologies and Wi-Fi have proved an enormous success in connecting billions of users to the Internet, they are not the best to connect objects with limited throughput requirements, limited processing power and low power consumption requirements. These needs are being addressed by Low Power Wide Area Networks (LPWAN) that use unlicensed frequencies and by newer cellular protocols aimed at lowering the cost of the devices by limiting their bandwidth capabilities in the licensed bands. This paper discusses these technologies and the choice made to provide an IoT infrastructure for the city of Maputo in Mozambique.

Despite successful pilots in developing countries, TVWS (Television White Spaces, unused portions of television broadcasting spectrum) has not gained the amount of attention that it deserves. One of the reasons is the lack of spectrum measurements that would convince the regulators about the abundance of idle spectrum. Spectrum measurements traditionally require expensive instruments and considerable operator's expertise. This has changed with the emergence of low cost spectrum analyzers and smartphones. In this paper we present RFTrack, a software suite that consists of an Android application and a TVWS analysis server. Together, they measure spectrum using a low-cost spectrum analyzer and geotag the data using the phone's internal GPS. The Android application is very easy to use and campaigns can be carried out by non-technical users. Once an Internet connection is available, the program sends data to the server that performs the required processes to present the results in an easy-to-understand way, also allowing for some user customization. The system has been used in eleven countries. We present the results from a measurement campaign in Costa Rica. We believe that this is a useful tool to demonstrate the existence of underutilized spectrum, especially in rural areas of developing countries.

This demo paper presents Cloudy, a Debian-based distribution to build and deploy clouds in community networks. The demonstration covers the following aspects: Installation of Cloudy, the Cloudy GUI for usage and administration by end users, demonstration of Cloudy nodes and services deployed in the Guifi community network.

This paper describes an operational geographically distributed and heterogeneous cloud infrastructure with services and applications deployed in the Guifi community network. The presented cloud is a particular case of a community cloud, developed according to the specific needs and conditions of community networks. We describe the concept of this community cloud, explain our technical choices for building it, and our experience with the deployment of this cloud. We review our solutions and experience on offering the different service models of cloud computing (IaaS, PaaS and SaaS) in community networks. The deployed cloud infrastructure aims to provide stable and attractive cloud services in order to encourage community network user to use, keep and extend it with new services and applications.

TV White Spaces (TVWS) technology and regulation has the potential to make connectivity both technically and economically feasible in rural Africa where affordable access remains a challenge. The superior propagation characteristics of TVWS technology make it particularly well suited to connecting remote communities. Evidence collected from our measurements in Malawi and Zambia suggest that most UHF spectrum is already available both in urban as well as in rural areas and could be used to provide Internet connectivity. In this paper we present the findings of a TVWS spectrum measurement initiative in Malawi and Zambia. We introduce an open hardware device that geo-tags spectrum measurements and saves the results on a micro SD card. The device can also be used to record the use of spectrum over long periods of time.

It is widely recognized that white spaces identification is an important milestone for the wide deployment of next generation cognitive wireless networks. However, spectrum holes detection tools used for white spaces discovery are still either in the infancy stage or too expensive to enable massive white spaces exploitation. Building upon cheap hardware equipment, this paper presents experiments conducted in the town of Trieste in Italy to sense the environment and find out which frequencies are not being used in a particular place and time-of-the-day. As a a step towards white spaces exploitation, we believe that our experimental frequency exploration is an important milestone upon which white spaces patterns recognition will be built with the aim of using these patterns in wireless network planning and management.