Crowdsensing networks for the sole purpose of performing spectrum measurements have resulted in prior initiatives that have failed primarily due to their costs for maintenance. In this paper, we take a different view and propose ORAN-Sense, a novel architecture of Internet of Things (IoT) spectrum crowd-sensing devices integrated into the Next Generation of cellular networks. We use this framework to extend the capabilities of 5G networks and localize a transmitter that does not collaborate in the process of positioning. While 5G signals can not be applied to this scenario as the transmitter does not participate in the localization process through dedicated pilot symbols and data, we show how to use Time Difference of Arrival-based positioning using low-cost spectrum sensors, minimizing hardware impairments of low-cost spectrum receivers, introducing methods to address errors caused by over-the-air signal propagation, and proposing a low-cost synchronization technique. We have deployed our localization network in two major cities in Europe. Our experimental results indicate that signal localization of non-collaborative transmitters is feasible even using low-cost radio receivers with median accuracies of tens of meters with just a few sensors spanning cities, which makes it suitable for its integration in the Next Generation of cellular networks.

Positioning is a key focus in 5G standardization, starting with 3GPP Release 16. However, most of the effort from the research community and work presented in the technical standardization has been limited mainly to simulation studies. This paper explores the use of software-defined radios (SDRs) platforms for 5G positioning, presenting an overview of the current state-of-the-art and available open-source platforms. Utilizing an advanced SDR-based multi-gNodeBs (gNBs) synchronized testbed, the paper conducts a series of time-based, over-the-air measurements. Results offer insights into the impact of various real-world system parameters, such as the number of gNBs, transmission bandwidth, signal processing techniques, and localization algorithms on positioning accuracy and Time to First Fix (TTFF). These findings provide a pathway for the cost-effective and efficient implementation of high-precision 5G localization systems. The paper contributes to advancing both theoretical understanding and practical applications, serving as a guide for the development of 5G positioning technology.

Positioning based on aircraft signals has been proposed as an alternative to satellite-based positioning systems (e.g. GPS). However, so far, no deployment of this technique exists, and the real-world performance remains unclear. This paper contributes at better understanding the performance tradeoffs under realistic conditions. We implement SkyPos, a localization system for GPS-denied areas or location integrity that opportunistically uses the large availability of aircraft signals to self-localize receivers. We analyze SkyPos with data collected from hundreds of sensors and thousands of aircraft around Europe. Our results show that we can achieve median accuracy down to 10m in seconds, enabling almost real-time positioning or location verification using aircraft signals at scale.

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.

Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-place foraging and stigmergy-based navigation, a swarm of robots is able to improve the efficiency and autonomy of the urban waste management system in a realistic scenario. To achieve this, a diverse set of simulation experiments was conducted using real-world GIS data and implementing different garbage collection scenarios driven by robot swarms. Results presented in this research show that the proposed system outperforms current approaches. Moreover, results not only show the efficiency of our solution, but also give insights about how to design and customize these systems.