The goal of this article is to investigate ultra wideband localization with time difference of arrival processing at the anchors. We consider scenarios where the anchors are placed very close to each other and the target to be localized is around the group of anchors. All target–anchor communications are assumed to be in line-of-sight. Since our analysis shows that symmetries in anchors’ placement, with respect to the target position, degrade the positioning accuracy of standard algorithms, we propose to use a subset selection strategy, where position estimates obtained with properly selected subsets of asymmetric anchors are fused together to get the final localization output. Our results show improved localization accuracy with respect to the use of all anchors, especially in estimating the angle of arrival. Finally, we analyze the impact of an inaccurate time synchronization among the anchors, deriving guidelines for hardware implementation.

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

The last years have been characterized by strong market exploitation of the Internet of Things (IoT) technologies in different application domains, such as Industry 4.0, smart cities, and eHealth. All the relevant solutions should properly address the security issues to ensure that sensor data and actuators are not under the control of malicious entities. Additionally, many applications should at the same time provide low-latency communications, as in the case for instance of remote control of industrial robots. Low latency and security are two of the most important challenges to be addressed for the successful deployment of IoT applications. These issues have been analyzed by several scientific papers and surveys that appeared in the last decade. However, few of them consider the two challenges jointly. Moreover, the security aspects are primarily investigated only in specific application domains or protocol levels and the latency issues are typically investigated only at low layers (e.g., physical, access). This paper addresses this shortcoming and provides a systematic review of state-of-the-art solutions for providing fast and secure IoT communications. Although the two requirements may appear to be in contrast to each other, we investigate possible integrated solutions that minimize device connection and service provisioning. We follow an approach where the proposals are reviewed by grouping them based on the reference architectural layer, i.e., access, network, and application layers. We also review the works that propose promising solutions that rely on the exploitation of the QUIC protocol at the higher levels of the protocol stack.

We evaluate the performance of realistic wireless sensor networks in indoor scenarios. Most of the considered networks are formed by nodes using the Zigbee communication protocol. For comparison, we also analyze networks based on the proprietary standard Z-Wave. Two main groups of network scenarios are proposed: (i) scenarios with direct transmissions between the remote nodes and the network coordinator, and (ii) scenarios with routers, which relay the packets between the remote nodes and the coordinator. The sensor networks of interest are evaluated considering different performance metrics. In particular, we show how the received signal strength indication (RSSI) behaves in the considered scenarios. Then, the network behavior is characterized in terms of end-to-end delay and throughput. In order to confirm the experiments, analytical and simulation results are also derived.

We investigate decentralized detection in clustered sensor networks with hierarchical multi-level fusion. We focus on simple majority-like fusion strategies, leading to closed-form analytical performance evaluation. The sensor nodes observe a binary phenomenon and transmit their own data to an access point (AP), possibly through intermediate fusion centers (FCs). We investigate the impact of uniform and nonuniform clustering on the system performance, evaluated in terms of probability of decision error on the phenomenon status at the AP. Our results show that, under a majority-like fusion rule, uniform clustering leads to the minimum performance degradation, which depends only on the number of decision levels rather than on the specific clustered topology. We then extend our approach, taking into account the impact of spatial variations of the phenomenon, noisy communication links, and weighed fusion rules. Finally the proposed distributed detection schemes are characterized with simulation and experimental results (relative to IEEE 802.15.4-based networks), which confirm the analytical predictions.