Low-cost light-scattering particulate matter sensors are often advocated for dense monitoring networks. Recent literature has focused on evaluating their performance. Nonetheless, low-cost sensors are also considered unreliable and imprecise. Consequently, exploring techniques for anomaly detection, resilient calibration, and improvement of data quality should be more discussed. In this study, we analyze a year-long acquisition campaign by positioning 56 low-cost light-scattering sensors near the inlet of an official particulate matter monitoring station. We use the collected measurements to design and test a data processing pipeline composed of different stages, including fault detection, filtering, outlier removal, and calibration. These can be used in large-scale deployment scenarios where the quantity of sensors data can be too high to be analyzed manually. Our framework also exploits sensor redundancy to improve reliability and accuracy. Our results show that the proposed data processing framework produces more reliable measurements, reduces errors, and increases the correlation with the official reference.

Quantum computing is one of the most promising technology advances of the latest years. Qubits are highly sensitive to noise, which can make the output useless. Lately, it has been shown that superconducting qubits are extremely susceptible to external sources of faults, such as ionizing radiation. When adopted in large scale, radiation-induced errors are expected to become a serious challenge for qubits reliability. We propose an evaluation of the impact of transient faults in the execution of quantum circuits on superconducting chips. Inspired by the Architectural and Program Vulnerability Factors, widely used for classical computation, we propose the Quantum Vulnerability Factor (QVF) to measure the impact of qubit corruption on the circuit output. We model faults, and design a fault injector, based on the latest studies on real machines and radiation experiments. We report the finding of more than 388,000,000 fault injections, considering single and double faults, on three algorithms, identifying the faults and qubits that are more likely to impact the output. We give guidelines on how to map the qubits in real devices to reduce the output error and to reduce the probability of having a radiation-induced corruption modifying the output. Finally, we compare simulations with experiments on physical quantum computers.

Quantum machines are among the most promising technologies expected to provide significant improvements in the following years. However, bridging the gap between real-world applications and their implementation on quantum hardware is still a complicated task. One of the main challenges is to represent through qubits (i.e., the basic units of quantum information) the problems of interest. According to the specific technology under-lying the quantum machine, it is necessary to implement a proper representation strategy, generally referred to as embedding. This paper introduces a neural-enhanced optimization framework to solve the constrained unit disk problem, which arises in the context of qubits positioning for neutral atoms-based quantum hardware. The proposed approach involves a modified autoencoder model, i.e., the Distances Encoder Network, and a custom loss, i.e., the Embedding Loss Function, respectively, to compute Euclidean distances and model the optimization constraints. The core idea behind this design relies on the capability of neural networks to approximate non-linear transformations to make the Distances Encoder Network learn the spatial transformation that maps initial non-feasible solutions of the constrained unit disk problem into feasible ones. The proposed approach outperforms classical solvers, given fixed comparable computation times, and paves the way to address other optimization problems through a similar strategy.

Quantum computing is an up-and-coming technology that is expected to revolutionize the computation paradigm in the next few years. Qubits, the primary computing elements of quantum circuits, exploit the quantum physics proprieties to increase the parallelism and speed of computation drastically. Unfortunately, besides being intrinsically noisy, qubits have also been shown to be highly susceptible to external sources of faults, such as ionizing radiation. The latest discoveries highlight a much higher radiation sensitivity of qubits than traditional transistors and identify a much more complex fault model than bit-flip.We propose a framework to identify the quantum circuits sensitivity to radiation-induced faults and the probability for a fault in a qubit to propagate to the output. Based on the latest studies and radiation experiments performed on real quantum machines, we model the transient faults in a qubit as a phase shift with a parametrized magnitude. Additionally, our framework can inject multiple qubit faults, tuning the phase shift magnitude based on the proximity of the qubit to the particle strike location. As we show in the paper, the proposed fault injector is highly flexible, and it can be used on both quantum circuit simulators and real quantum machines. We report the finding of more than 285, 249, 536 injections on the Qiskit simulator and 53, 248 injections on real IBM machines. We consider three quantum algorithms and identify the faults and qubits that are more likely to impact the output. We also consider the fault propagation dependence on the circuit scale, showing that the reliability profile for some quantum algorithms is scale-dependent, with increased impact from radiation-induced faults as we increase the number of qubits. Finally, we also consider multi qubits faults, showing that they are much more critical than single faults. The fault injector and the data presented in this paper are available in a public repository to allow further analysis.

Quantum Computing is a highly promising new computation paradigm. Unfortunately, quantum bits (qubits) are extremely fragile and their state can be gradually or suddenly modified by intrinsic noise or external perturbation. In this paper, we target the sensitivity of quantum circuits to radiation-induced transient faults. We consider quantum circuit cuts that split the circuit into smaller independent portions, and understand how faults propagate in each portion. As we show, the cuts have different vulnerabilities, and our methodology successfully identifies the circuit portion that is more likely to contribute to the overall circuit error rate. Our evaluation shows that a circuit cut can have a 4.6 x higher probability than the other cuts, when corrupted, to modify the circuit output. Our study, identifying the most critical cuts, moves towards the possibility of implementing a selective hardening for quantum circuits.

Particulate Matter (PM) air pollution has received growing attention in recent years due to the increased sensitivity to the problem and the spread of low-cost sensing devices. These low-cost devices are able to produce valuable data, but they come with certain hurdles which are engineering challenges to be overcome. These challenges are mainly: reduction of consumed energy; reduction of data logged and transmitted; aging of sensors. The aim of this paper is to understand if it is possible to reduce the duty-cycle of an air pollution monitoring sensor and still get meaningful data on the general behavior. This has advantages from all perspectives: it enables less logging of redundant information; it reduces the strain of the sensor to extend its lifespan and to reduce maintenance costs; it reduces the energy consumed by the sensor, essential in battery-powered devices.

Embedded systems are common in the Internet of Things domain: their integration in vehicles and mobile devices is being fostered in the Internet of vehicles (IoV). IoV has direct applications on intelligent transportation systems and smart cities. Besides basic requirements, such as ease of installation, cost-effectiveness, scalability, and flexibility, IOV applications need to guarantee energy-efficient and good-quality communication. In fact, IoV implementations are commonly based on wireless nodes, which rely on a limited energy source; therefore, an efficient communication among the nodes is desirable to prolong the lifetime of the devices. In particular, the alternation of active and sleep states and the regulation of the transmission power represent two common approaches to save energy. Based on this strategy, an effective fuzzy control system is presented in the paper to manage power consumption and quality of services of IoV applications. Two fuzzy controllers increase the battery life while keeping a good throughput to workload ratio. This technique has been simulated with two leading technologies in IoV: IEEE 802.11b/g/n and IEEE 802.11p. Experimental results show a network lifetime improvement ranging from 30% to 40%, according to the adopted medium access control protocol.

Air pollution, in particular due to particulate matter, is considered a critical issue, and it is receiving ever growing attention. Environmental monitoring is not limited to the outdoor case in crowded cities, but it is also usefully applied to indoor cases such as factories and offices, or in indoor/outdoor mixed case, such as construction sites or in general industrial plants. This paper describes the case study of an environmental monitoring system, which is compliant with Internet of Things domain. In particular it is suitable for indoor/outdoor industrial applications focused on Particulate Matter measurements. In order to create a reference model, a PM10/PM2.5 sensor not yet tested in literature is used, the Honeywell HPMA115S0-XXX. Four PM10/PM2.5 sensors are used together to test repeatability. This device turned out to be effective in esteeming the level of pollution using low cost sensors, as an alternative to expensive and not portable professional devices. The implementation proposed in this paper is able to give high R values of linear regression, guaranteeing at the same time low cost in terms of hardware and low power consumption.