Index Air Quality Monitoring for Light and Active Mobility.

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment.

End-to-End Emulation of LoRaWAN Architecture and Infrastructure in Complex Smart City Scenarios Exploiting Containers.

In a LoRaWAN network, the backend is generally distributed as Software as a Service (SaaS) based on container technology, and recently, a containerized version of the LoRaWAN node stack is also available. Exploiting the disaggregation of LoRaWAN components, this paper focuses on the emulation of complex end-to-end architecture and infrastructures for smart city scenarios, leveraging on lightweight virtualization technology. The fundamental metrics to gain insights and evaluate the scaling complexity of the emulated scenario are defined. Then, the methodology is applied to use cases taken from a real LoRaWAN application in a smart city with hundreds of nodes. As a result, the proposed approach based on containers allows for the following: (i) deployments of functionalities on diverse distributed hosts; (ii) the use of the very same SW running on real nodes; (iii) the simple configuration and management of the emulation process; (iv) affordable costs. Both premise and cloud servers are considered as emulation platforms to evaluate the resource request and emulation cost of the proposed approach. For instance, emulating one hour of an entire LoRaWAN network with hundreds of nodes requires very affordable hardware that, if realized with a cloud-based computing platform, may cost less than USD 1.

Enhancing Safety on Construction Sites: A UWB-Based Proximity Warning System Ensuring GDPR Compliance to Prevent Collision Hazards.

Construction is known as one of the most dangerous industries in terms of worker safety. Collisions due the excessive proximity of workers to moving construction vehicles are one of the leading causes of fatal and non-fatal accidents on construction sites internationally. Proximity warning systems (PWS) have been proposed in the literature as a solution to detect the risk for collision and to alert workers and equipment operators in time to prevent collisions. Although the role of sensing technologies for situational awareness has been recognised in previous studies, several factors still need to be considered. This paper describes the design of a prototype sensor-based PWS, aimed mainly at small and medium-sized construction companies, to collect real-time data directly from construction sites and to warn workers of a potential risk of collision accidents. It considers, in an integrated manner, factors such as cost of deployment, the actual nature of a construction site as an operating environment and data protection. A low-cost, ultra-wideband (UWB)-based proximity detection system has been developed that can operate with or without fixed anchors. In addition, the PWS is compliant with the General Data Protection Regulation (GDPR) of the European Union. A privacy-by-design approach has been adopted and privacy mechanisms have been used for data protection. Future work could evaluate the PWS in real operational conditions and incorporate additional factors for its further development, such as studies on the timely interpretation of data.

Assessing a Methodology for Evaluating the Latency of IPv6 with SCHC Compression in LoRaWAN Deployments.

The Internet of Things (IoT) approach relies on the use of the Internet Protocol (IP) as a pervasive network protocol. IP acts as a "glue" for interconnecting end devices (on the field side) and end users, leveraging on very diverse lower-level and upper-level protocols. The need for scalability would suggest the adoption of IPv6, but the large overhead and payloads do not match with the constraints dictated by common wireless solutions. For this reason, compression strategies have been proposed to avoid redundant information in the IPv6 header and to provide fragmentation and reassembly of long messages. For example, the Static Context Header Compression (SCHC) protocol has been recently referenced by the LoRa Alliance as a standard IPv6 compression scheme for LoRaWAN-based applications. In this way, IoT end points can seamlessly share an end-to-end IP link. However, implementation details are out of the specifications' scope. For this reason, formal test procedures for comparing solutions from different providers are important. In this paper, a test method for assessing architectural delays of real-world deployments of SCHC-over-LoRaWAN implementations is presented. The original proposal includes a mapping phase, for identifying information flows, and a subsequent evaluation phase, in which flows are timestamped and time-related metrics are computed. The proposed strategy has been tested in different use cases involving LoRaWAN backends deployed all around the world. The feasibility of the proposed approach has been tested by measuring the end-to-end latency of IPv6 data in sample use cases, obtaining a delay of less than 1 s. However, the main result is the demonstration that the suggested methodology permits a comparison of the behavior of IPv6 with SCHC-over-LoRaWAN, allowing the optimization of choices and parameters during deployment and commissioning of both infrastructure components and software.

An IoT Based Architecture for Enhancing the Effectiveness of Prototype Medical Instruments Applied to Neurodegenerative Disease Diagnosis.

Human errors are probably the most critical cause of the large amount of medical accidents. Medical cyber-physical systems (MCPS) have been suggested as a possible approach for detecting and limiting the impact of errors and wrong procedures. However, during the initial development phase of medical instruments, regular MCPS systems are not a viable approach, because of the high costs of repeating complex validation procedures, due to modifications of the prototype instrument. In this work, a communication architecture, inspired by recent Internet of Things (IoT) advances, is proposed for connecting prototype instruments to the cloud, to allow direct and real-time interaction between developers and instrument operators. Without loss of generality, a real-world use case is addressed, dealing with the use of transcranial magnetic stimulation (TMS) for neurodegenerative disease diagnosis. The proposed infrastructure leverages on a message-oriented middleware, complemented by historical database for further data processing. Two of the most diffused protocols for cloud data exchange (MQTT and AMQP) have been investigated. The experimental setup has been focused on the real-time performance, which are the most challenging requirements. Time-related metrics confirm the feasibility of the proposed approach, resulting in an end-to-end delay on the order of few tens of milliseconds for local networks and up to few hundreds of milliseconds for geographical scale networks.

Diagnosis of Mild Cognitive Impairment Due to Alzheimer's Disease with Transcranial Magnetic Stimulation.

BACKGROUND: Considering the increasing evidence that disease-modifying treatments for Alzheimer's disease (AD) must be administered early in the disease course, the development of diagnostic tools capable of accurately identifying AD at early disease stages has become a crucial target. In this view, transcranial magnetic stimulation (TMS) has become an effective tool to discriminate between different forms of neurodegenerative dementia. OBJECTIVE: To determine whether a TMS multi-paradigm approach can be used to correctly identify mild cognitive impairment (MCI) due to AD (AD MCI). METHODS: A sample of 69 subjects with MCI were included and classified as AD MCI or MCI unlikely due to AD (non-AD MCI) based on 1) extensive neurological and neuropsychological evaluation, 2) MRI imaging, and 3) cerebrospinal fluid analysis or/and amyloid PET imaging. A paired-pulse TMS multi-paradigm approach assessing short interval intracortical inhibition-facilitation (SICI-ICF), dependent on GABAergic and glutamatergic intracortical circuits, respectively, and short latency afferent inhibition (SAI), dependent on cholinergic circuits, was performed. RESULTS: We observed a significant impairment of SAI and unimpaired SICI and ICF in AD MCI as compared to non-AD MCI. According to ROC curve analysis, the SICI-ICF / SAI index differentiated AD MCI from non-AD MCI with a specificity of 87.9% and a sensitivity of 94.4%. CONCLUSIONS: The assessment of intracortical connectivity with TMS could aid in the characterization of MCI subtypes, correctly identifying AD pathophysiology. TMS can be proposed as an adjunctive, non-invasive, inexpensive, and time-saving screening tool in MCI differential diagnosis.

White Rabbit Clock Synchronization: Ultimate Limits on Close-In Phase Noise and Short-Term Stability Due to FPGA Implementation.

This paper investigates the ultimate limits of White Rabbit (WR), an high-accuracy time distribution system based on field-programmable gate array (FPGA). The knowledge of such limits is essential for new emerging applications that are evaluating WR. In this paper, we identify and study the key elements in the WR synchronization: the digital dual mixer time difference phase detector and the Gigabit Ethernet transceiver. The benchmarks and experimental analysis of these key elements allow us to determine the WR switch (WRS) performance limits and evaluate their evolution with newer FPGAs. The identified performance limits are achievable by the present-day generation of WRS. The ultimate limits of short-term synchronization performance due to FPGA implementation have been derived through analysis and then demonstrated using the existing WRS enhanced with an additional daughterboard. This combination (WRS and daughterboard) achieves a tenfold improvement in terms of phase noise, jitter, and short-term stability with respect to the current WR performance. Both phase detectors and Gigabit transceivers have a similar phase noise contribution equal to a short-term stability of modified Allan deviation 4E-13 at $\tau = 1$ s (dominated by flicker phase modulation noise).

Transcranial magnetic stimulation distinguishes Alzheimer disease from frontotemporal dementia.

OBJECTIVE: To determine whether a transcranial magnetic stimulation (TMS) multiparadigm approach can be used to distinguish Alzheimer disease (AD) from frontotemporal dementia (FTD). METHODS: Paired-pulse TMS was used to investigate short-interval intracortical inhibition (SICI) and facilitation (ICF), long-interval intracortical inhibition, and short-latency afferent inhibition (SAI) to measure the activity of different intracortical circuits in patients with AD, patients with FTD, and healthy controls (HC). The primary outcome measures were sensitivity and specificity of TMS measures, derived from receiver operating curve analysis. RESULTS: A total of 175 participants met the inclusion criteria. We diagnosed 79 patients with AD, 64 patients with FTD, and 32 HC. We found that while patients with AD are characterized by a specific impairment of SAI, FTD shows a remarkable dysfunction of SICI-ICF intracortical circuits. With the use of the best indexes, TMS differentiated FTD from AD with a sensitivity of 91.8% and specificity of 88.6%, AD from HC with a sensitivity of 84.8% and specificity of 90.6%, and FTD from HC with a sensitivity of 90.2% and specificity of 78.1%. These results were confirmed in patients with mild disease. CONCLUSIONS: TMS is a noninvasive procedure that reliably distinguishes AD from FTD and HC and, if these findings are replicated in larger studies, could represent a useful additional diagnostic tool for clinical practice. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that TMS measures can distinguish patients with AD from those with FTD.