The rapid development of artificial intelligence (AI) and breakthroughs in Internet of Things (IoT) technologies have driven the innovation of advanced autonomous driving systems (ADSs). Image classification deep learning (DL) algorithms immensely contribute to the decision-making process in ADSs, showcasing their capabilities in handling complex real-world driving scenarios, surpassing human driving intelligence. However, these algorithms are vulnerable to adversarial attacks, which aim to fool them in real-time decision-making and compromise the reliability of the autonomous driving functions. This systematic review offers a comprehensive overview of the most recent literature on adversarial attacks and countermeasures on image classification DL models in ADSs. The review highlights the current challenges in applying successful countermeasures to mitigating these vulnerabilities. We also introduce taxonomies for categorizing adversarial attacks and countermeasures and provide recommendations and guidelines to help researchers design and evaluate countermeasures. We suggest interesting future research directions to improve the robustness of image classification DL models against adversarial attacks in autonomous driving scenarios.

Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

Global climate change originates frequent floods that may cause severe damage, justifying the need for real-time remote monitoring and alerting systems. Several works deal with LoRa (Long Range) communications over land and in the presence of obstacles, but little is known about LoRa communication reliability over water, as it may happen in real flooding scenarios. One aspect that is known to influence the communication quality is the height at which nodes are placed. However, its impact in water environments is unknown. This is an important aspect that may influence the location of sensor nodes and the network topology. To fill this gap, we conducted several experiments using a real LoRa deployment to evaluate several features related to data communication. We considered two deployment scenarios corresponding to countryside and estuary environments. The nodes were placed at low heights, communicating, respectively, over the ground and over the water. Measurements for packet loss, received signal strength indicator (RSSI), signal-to-noise ratio (SNR) and round-trip time (RTT) were collected during a period of several weeks. Results for both scenarios are presented and compared in this paper. One important conclusion is that the communication distance and reliability are significantly affected by tides when the communication is done over the water and nodes are placed at low heights. Based on the RTT measurements and on the characteristics of the hardware, we also derive a battery lifetime estimation model that may be helpful for the definition of an adequate maintenance plan.

Due to the increasing demand for Internet of Things (IoT) applications with sensitive data, the use of encryption on constrained devices is crucial for protecting and ensuring privacy and security. The devices used in such applications have limited computational power, memory, and energy resources, making them vulnerable to attacks that exploit these limitations. Lightweight cryptography has been growing fast in recent years aiming to circumvent the lack of security in these types of devices. We propose a modular solution that capitalizes on the strengths of lightweight cryptography to offer an approach that can be easily adapted to meet the needs of private wireless sensor networks. The solution is designed to work in multi-hop communication networks, where Nodes out of range of the Gateway can be part of the network, offering the same security level that a Node in the communication range of the Gateway has. To further enhance the security and lifetime of the network, our solution incorporates a key renewal mechanism and supports the use of signature schemes for integrity. Additionally, it is designed to be distributed to achieve high levels of scalability. Experimental results show that using our solution on top of standard communication protocols does not introduce significant overhead in terms of performance.

Low-power wide-area networks are extending beyond the conventional terrestrial domain. Coastal zones, rivers, wetlands, among others, are nowadays common deployment settings for Internet-of-Things nodes where communication technologies such as LoRa are becoming popular. In this article, we investigate large-scale fading dynamics of LoRa line-of-sight links deployed over an estuary with characteristic intertidal zones, considering both shore-to-shore and shore-to-vessel communications. We propose a novel methodology for path loss prediction which captures i) spatial, ii) temporal and iii) physical features of the RF signal interaction with the environmental dynamics, integrating those features into the two-ray propagation model. To this purpose, we resort to precise hydrodynamic modeling of the estuary, including the specific terrain profile ( bathymetry ) at the reflection point. These aspects are key to accounting for a reflecting surface of varying altitude and permittivity as a function of the tide. Experimental measurements using LoRa devices operating in the 868 MHz band show major trends on the received signal power in agreement with the methodology’s predictions.