Location awareness is vital for emerging Internet-of-Things applications and opens a new era for Localization-of-Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.

In the era of the Internet of Things (IoT), efficient localization is essential for emerging mass-market services and applications. IoT devices are heterogeneous in signaling, sensing, and mobility, and their resources for computation and communication are typically limited. Therefore, to enable location awareness in large-scale IoT networks, there is a need for efficient, scalable, and distributed multisensor fusion algorithms. This article presents a framework for designing network localization and navigation (NLN) for the IoT. Multisensor localization and operation algorithms developed within NLN can exploit spatiotemporal cooperation, are suitable for arbitrary, large network sizes, and only rely on an information exchange among neighboring devices. The advantages of NLN are evaluated in a large-scale IoT network with 500 agents. In particular, because of multisensor fusion and cooperation, the presented network localization and operation algorithms can provide attractive localization performance and reduce communication overhead and energy consumption.

The Third Generation Partnership Project (3GPP) has recently published a new set of specifications to enable advanced driving applications in fifth generation (5G) vehicle-to-everything (V2X) scenarios, with particular effort dedicated to the sidelink resource allocation in the autonomous mode, named Mode 2. In this paper, we conduct a comprehensive analysis of Mode 2 performance via an open-source system-level simulator, which implements the 5G New Radio (NR) flexible numerology and physical layer aspects together with the newly specified sidelink resource allocation modes for V2X communications and different data traffic patterns. Results collected through extensive simulation campaigns, under a wide variety of vehicle density, data transmission settings and traffic patterns, showcase the effects of the new 5G-V2X features on the sidelink resource allocation performance and provide some insights into possible ways to further improve Mode 2 performance.

Counting people and things (targets) in a monitored area, also known as crowd-counting, enables several applications in diverse scenarios, including smart building, intelligent transportation, and public safety. In many scenarios, device-free systems relying on the signal backscattering from targets are preferred to device-based systems relying on the communication with the targets via dedicated or personal devices. However, the use of conventional radar techniques (e.g., for multi-target detection) requires us to associate a different set of measured data with each detected target. Data association is a redundant operation for counting and results in high complexity even with few targets. The need of lower dimensionality and complexity calls for signal features to associate the measured signals directly with the number of targets. This paper proposes a mathematical framework for the design of device-free counting systems. First, a maximum a posteriori algorithm is developed for counting via wideband signal backscattering by relying on model order selection. Then, a method that relies on low-level features is proposed to lower the computational complexity. The proposed method is verified via sample-level simulations in realistic operating conditions and compared with current solutions.

Cellular-vehicle-to-everything (C-V2X) is gaining an increasing interest among the technologies under consideration for future connected and automated vehicles, especially for sidelink LTE-V2X Mode 4 and sidelink 5G-V2X Mode 2, where nodes autonomously perform resource allocations and transmissions, without relying on any infrastructure. In these cases, the allocation process has been designed based on the assumption of periodic packet generation constrained to a few possible allocation periods. Nevertheless, even assuming that the awareness messages are continuously exchanged, the packet generation might not always be exactly periodical or might come with a periodicity not constrained to the expected values, as for example with the Cooperative Intelligent Transport Systems (C-ITS) standard defined by the European Telecommunications Standards Institute (ETSI). The non-ideal periodicity of packet generation, if not properly taken into account, can significantly impact the operation and performance of C-V2X when vehicles autonomously select their radio resources. This paper provides an analysis of how a misalignment between packet generation and resource allocation affects the system performance and provides insights into how to design the parameter settings to improve the performance. A case study is provided, focusing on LTE-V2X Mode 4 and the cooperative awareness message (CAM) generation in agreement with the the ETSI specifications. Results show that the performance generally improves if the smallest allocation interval is adopted and if the reservation is maintained even when no packets are ready for transmission. It is also shown how the parameter controlling the maximum latency in LTE-V2X, jointly with the misalignment between the allocation and generation intervals, can further affect the packet reception rate.

Joint communication and sensing allows to share frequency spectrum, hardware, and signal processing blocks between communication and sensing, providing new radar functionalities through the exploitation of communication signals. This is becoming an important trend also in vehicular communications, where future standardized technologies for connected vehicles can be used to detect targets nearby without overloading the already scarce spectrum with dedicated radar signals. Objective of this paper is to characterize the performance of 5 G new radio (NR) vehicle-to-everything (V2X) sidelink, in terms of lower bounds for sensing purposes; specifically, we evaluate the impact of physical and radio access communication parameters on the sensing performance in terms of detection capability and parameter estimation quality. As a result, it is shown how the effect of the presence of multiple vehicles in the scenario, and the inherent resource allocation policies, have a non-negligible impact on the communication and sensing performance. In particular, it is shown that when considering the interference of multiple vehicles, the influence on sensing performance of the chosen radio parameters, such as bandwidth, modulation and coding scheme, and packet size is different compared to the inference free case.