Energy harvesting-aware design of wireless networks

Recent advances in low-power electronics and energy-harvesting (EH) technologies enable the design of self-sustained devices that collect part, or all, of the needed energy from the environment. Several systems can take advantage of EH, ranging from portable devices to wireless sensor networks (WSNs). While conventional design for battery-powered systems is mainly concerned with the battery lifetime, a key advantage of EH is that it enables potential perpetual operation of the devices, without requiring maintenance for battery substitutions. However, the inherent unpredictability regarding the amount of energy that can be collected from the environment might cause temporary energy shortages, which might prevent the devices to operate regularly. This uncertainty calls for the development of energy management techniques that are tailored to the EH dynamics. While most previous work on EH-capable systems has focused on energy management for single devices, the main contributions of this dissertation is the analysis and design of medium access control (MAC) protocols for WSNs operated by EH-capable devices. In particular, the dissertation first considers random access MAC protocols for single-hop EH networks, in which a fusion center collects data from a set of nodes distributed in its surrounding. MAC protocols commonly used in WSNs, such as time division multiple access (TDMA), framed-ALOHA (FA) and dynamic-FA (DFA) are investigated in the presence of EH-capable devices. A new ALOHA-based MAC protocol tailored to EH-networks, referred to as energy group-DFA (EG-DFA), is then proposed. In EG-DFA nodes with similar energy availability are grouped together and access the channel independently from other groups. It is shown that EG-DFA significantly outperforms the DFA protocol. Centralized scheduling-based MAC protocols for single-hop EH-networks with communication resource constraints are considered next. Two main scenarios are addressed, namely: i) nodes exclusively powered via EH; ii) nodes powered by a hybrid energy storage system, which is composed by a non-rechargeable battery and a capacitor charged via EH. For the former case the goal is the maximization of the network throughput, while in the latter the aim is maximizing the lifetime of the non-rechargeable batteries. For both scenarios optimal scheduling policies are derived by assuming different levels of information available at the fusion center about the energy availability at the nodes. When optimal policies are not derived explicitly, suboptimal policies are proposed and compared with performance upper bounds. Energy management policies for single devices have been investigated as well by focusing on radio frequency identification (RFID) systems, when the latter are operated by enhanced RFID tags with energy harvesting capabilities

Phenotyping spontaneous locomotor activity in inbred and outbred mouse strains by using Digital Ventilated Cages

Mouse strains differ markedly in all behaviors, independently of their genetic background. We undertook this study to disentangle the diurnal activity and feature key aspects of three non-genetically altered mouse strains widely used in research, C57BL/6NCrl (inbred), BALB/cAnNCrl (inbred) and CRL:CD1(ICR) (outbred). With this aim, we conducted a longitudinal analysis of the spontaneous locomotor activity of the mice during a 24-h period for 2 months, in two different periods of the year to reduce the seasonality effect. Mice (males and females) were group-housed in Digital Ventilated Cages (Tecniplast), mimicking standard housing conditions in research settings and avoiding the potential bias provided in terms of locomotor activity by single housing. The recorded locomotor activity was analyzed by relying on different and commonly used circadian metrics (i.e., day and night activity, diurnal activity, responses to lights-on and lights-off phases, acrophase and activity onset and regularity disruption index) to capture key behavioral responses for each strain. Our results clearly demonstrate significant differences in the circadian activity of the three selected strains, when comparing inbred versus outbred as well as inbred strains (C57BL/6NCrl versus BALB/cAnNCrl). Conversely, males and females of the same strain displayed similar motor phenotypes; significant differences were recorded only for C57BL/6NCrl and CRL:CD1(ICR) females, which displayed higher average locomotor activity from prepuberty to adulthood. All strain-specific differences were further confirmed by an unsupervised machine learning approach. Altogether, our data corroborate the concept that each strain behaves under characteristic patterns, which needs to be taken into consideration in the study design to ensure experimental reproducibility and comply with essential animal welfare principles

AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. We investigate the potential of artificial intelligence to predict the prognosis of such patients, distinguishing between severe and mild cases, thus offering a baseline reference for other researchers and practitioners. To this goal, we present three approaches that use features extracted from CXR images, either handcrafted or automatically by convolutional neuronal networks, which are then integrated with the clinical data. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources

Microglia modulates hippocampal synaptic transmission and sleep duration along the light/dark cycle

Microglia, the brain's resident macrophages, actively contributes to the homeostasis of cerebral parenchyma by sensing neuronal activity and supporting synaptic remodeling and plasticity. While several studies demonstrated different roles for astrocytes in sleep, the contribution of microglia in the regulation of sleep/wake cycle and in the modulation of synaptic activity in the different day phases has not been deeply investigated. Using light as a zeitgeber cue, we studied the effects of microglial depletion with the colony stimulating factor-1 receptor antagonist PLX5622 on the sleep/wake cycle and on hippocampal synaptic transmission in male mice. Our data demonstrate that almost complete microglial depletion increases the duration of NREM sleep and reduces the hippocampal excitatory neurotransmission. The fractalkine receptor CX3CR1 plays a relevant role in these effects, because cx3cr1GFP/GFP mice recapitulate what found in PLX5622-treated mice. Furthermore, during the light phase, microglia express lower levels of cx3cr1 and a reduction of cx3cr1 expression is also observed when cultured microglial cells are stimulated by ATP, a purinergic molecule released during sleep. Our findings suggest that microglia participate in the regulation of sleep, adapting their cx3cr1 expression in response to the light/dark phase, and modulating synaptic activity in a phase-dependent manner.Bordeaux Region Aquitaine Initiative for Neuroscienc

Medium access control protocols for wireless sensor networks with energy harvesting

The design of Medium Access Control (MAC) protocols for wireless sensor networks (WSNs) has been conventionally tackled by assuming battery-powered devices and by adopting the network lifetime as the main performance criterion. While WSNs operated by energy-harvesting (EH) devices are not limited by network lifetime, they pose new design challenges due to the uncertain amount of energy that can be harvested from the environment. Novel design criteria are thus required to capture the trade-offs between the potentially infinite network lifetime and the uncertain energy availability. This paper addresses the analysis and design of WSNs with EH devices by focusing on conventional MAC protocols, namely TDMA, framed-ALOHA (FA) and dynamic-FA (DFA), and by accounting for the performance trade-offs and design issues arising due to EH. A novel metric, referred to as delivery probability, is introduced to measure the capability of a MAC protocol to deliver the measurement of any sensor in the network to the intended destination (or fusion center, FC). The interplay between delivery efficiency and time efficiency (i.e., the data collection rate at the FC), is investigated analytically using Markov models. Numerical results validate the analysis and emphasize the critical importance of accounting for both delivery probability and time efficiency in the design of EH-WSNs