Adaptive Algorithms for Batteryless LoRa-Based Sensors

Ambient energy-powered sensors are becoming increasingly crucial for the sustainability of the Internet-of-Things (IoT). In particular, batteryless sensors are a cost-effective solution that require no battery maintenance, last longer and have greater weatherproofing properties due to the lack of a battery access panel. In this work, we study adaptive transmission algorithms to improve the performance of batteryless IoT sensors based on the LoRa protocol. First, we characterize the device power consumption during sensor measurement and/or transmission events. Then, we consider different scenarios and dynamically tune the most critical network parameters, such as inter-packet transmission time, data redundancy and packet size, to optimize the operation of the device. We design appropriate capacity-based storage, considering a renewable energy source (e.g., photovoltaic panel), and we analyze the probability of energy failures by exploiting both theoretical models and real energy traces. The results can be used as feedback to re-design the device to have an appropriate amount energy storage and meet certain reliability constraints. Finally, a cost analysis is also provided for the energy characteristics of our system, taking into account the dimensioning of both the capacitor and solar panel

Accelerating network resource allocation in LoRaWAN via distributed Big Data computing

LoRaWAN is a Low Power infrastructure for the Internet of Things (IoT) with a centralized architecture where a single node, the network server, handles all data collection and network management decisions. Given the proliferation and widespread adoption of IoT devices, it becomes essential to incorporate Big Data paradigms at the network server to efficiently manage the enormous volumes of data. In this paper, we introduce a distributed and high-performance methodology for resource allocation in dense LoRaWAN networks, addressing the scalability issues that arise when processing large amounts of information from IoT devices, such as radio link quality. Our contributions establish the groundwork for a distributed implementation of the EXPLORA-C allocation strategy, capable of efficiently operating in large-scale networks. We present two approaches for implementing this distributed scheme: the Multi-Thread (MT) scheme and the Fully-Distributed (FD) scheme. Furthermore, we demonstrate the feasibility of this distributed implementation on top of the NebulaStream stream-based end-to-end data management platform. To validate the proposed approach, we exploit our co-simulation framework, EXPLoSIM, where the distributed implementation is fed with data from a simulated LoRaWAN network. This validation shows significant savings in execution time, latency, and scalability. Additionally, we generalize the concept by decomposing a centralized data aggregation scheme into a chain of stream-processing operators, which can be dynamically allocated across device, Edge, and Cloud levels. In the best scenario, our approach improves metrics such as execution time and data reduction by over 90% when compared to its centralized operation

A unified radio control architecture for prototyping adaptive wireless protocols

Experimental optimization of wireless protocols and validation of novel solutions is often problematic, due to limited configuration space present in commercial wireless interfaces as well as complexity of monolithic driver implementation on SDR-based experimentation platforms. To overcome these limitations a novel software architecture is proposed, called WiSHFUL, devised to allow: i) maximal exploitation of radio functionalities available in current radio chips, and ii) clean separation between the logic for optimizing the radio protocols (i.e. radio control) and the definition of these protocols

A cultural heritage experience for visually impaired people

In recent years, we have assisted to an impressive advance of computer vision algorithms, based on image processing and artificial intelligence. Among the many applications of computer vision, in this paper we investigate on the potential impact for enhancing the cultural and physical accessibility of cultural heritage sites. By using a common smartphone as a mediation instrument with the environment, we demonstrate how convolutional networks can be trained for recognizing monuments in the surroundings of the users, thus enabling the possibility of accessing contents associated to the monument itself, or new forms of fruition for visually impaired people. Moreover, computer vision can also support autonomous mobility of people with visual disabilities, for identifying pre-defined paths in the cultural heritage sites, and reducing the distance between digital and real world

A Navigation and Augmented Reality System for Visually Impaired People

In recent years, we have assisted with an impressive advance in augmented reality systems and computer vision algorithms, based on image processing and artificial intelligence. Thanks to these technologies, mainstream smartphones are able to estimate their own motion in 3D space with high accuracy. In this paper, we exploit such technologies to support the autonomous mobility of people with visual disabilities, identifying pre-defined virtual paths and providing context information, reducing the distance between the digital and real worlds. In particular, we present ARIANNA+, an extension of ARIANNA, a system explicitly designed for visually impaired people for indoor and outdoor localization and navigation. While ARIANNA is based on the assumption that landmarks, such as QR codes, and physical paths (composed of colored tapes, painted lines, or tactile pavings) are deployed in the environment and recognized by the camera of a common smartphone, ARIANNA+ eliminates the need for any physical support thanks to the ARKit library, which we exploit to build a completely virtual path. Moreover, ARIANNA+ adds the possibility for the users to have enhanced interactions with the surrounding environment, through convolutional neural networks (CNNs) trained to recognize objects or buildings and enabling the possibility of accessing contents associated with them. By using a common smartphone as a mediation instrument with the environment, ARIANNA+ leverages augmented reality and machine learning for enhancing physical accessibility. The proposed system allows visually impaired people to easily navigate in indoor and outdoor scenarios simply by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback

Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation

Precise estimations of actual evapotranspiration (ETa) are essential for various environmental issues, including those related to agricultural ecosystem sustainability and water management. Indeed, the increasing demands of agricultural production, coupled with increasingly frequent drought events in many parts of the world, necessitate a more careful evaluation of crop water requirements. Artificial Intelligence-based models represent a promising alternative to the most common measurement techniques, e.g. using expensive Eddy Covariance (EC) towers. In this context, the main challenges are choosing the best possible model and selecting the most representative features. The objective of this research is to evaluate two different machine learning algorithms, namely Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict daily actual evapotranspiration (ETa) in a citrus orchard typical of the Mediterranean ecosystem using different feature combinations. With many features available coming from various infield sensors, a thorough analysis was performed to measure feature importance, scatter matrix observations, and Pearson's correlation coefficient calculation, which resulted in the selection of 12 promising feature combinations. The models were calibrated under regulated deficit irrigation (RDI) conditions to estimate ETa and save irrigation water. On average up to 38.5% water savings were obtained, compared to full irrigation. Moreover, among the different input variables adopted, the soil water content (SWC) feature appears to have a prominent role in the prediction of ETa. Indeed, the presented results show that by choosing the appropriate input features, the accuracy of the proposed machine learning models remains acceptable even when the number of features is reduced to only 4. The best performance was achieved by the Random Forest method, with seven input features, obtaining a root mean square error (RMSE) and a coefficient of determination (R2) of 0.39 mm/day and 0.84, respectively. Finally, the results show that the joint use of SWC, weather and satellite data significantly improves the performance of evapotranspiration forecasts compared to models using only meteorological variables

The Z-cad dual fluorescent sensor detects dynamic changes between the epithelial and mesenchymal cellular states

Z-cad sensor loses GFP expression early and gains RFP expression later upon miR-200c induction. All collected time points for time-lapse microscopy (from Fig. 1c) of identical grids within cell culture plate are shown for each treatment group. A) –DOX control. B) 2 μg/mL DOX treatment to induce miR-200c. All time points after DOX treatment are indicated. Scale bars = 50 μm. (TIF 9490 kb

A Flexible and Reconfigurable 5G Networking Architecture Based on Context and Content Information

The need for massive content delivery is a consolidated trend in mobile communications, and will even increase for next years. Moreover, while 4G maturity and evolution is driven by video contents, next generation (5G) networks will be dominated by heterogeneous data and additional massive diffusion of Internet of Things (IoT). The current network architecture is not sufficient to cope with such traffic, which is heterogeneous in terms of latency and QoS requirements, and variable in space and time. This paper proposes architectural advances to endow the network with the necessary flexibility helping to adapt to these varying traffic needs by providing content and communication services where and when actually needed. Our functional hardware/software (HW/SW) architecture aims at influencing future system standardization and leverage the benefits of some key 5G networking enablers described in the paper. Preliminary results demonstrate the potential of these key technologies to support the evolution toward content-centric and context-aware 5G systems

p130Cas is an essential transducer element in ErbB2 transformation

The ErbB2 oncogene is often overexpressed in breast tumors and associated with poor clinical outcome. p130Cas represents a nodal scaffold protein regulating cell survival, migration, and proliferation in normal and pathological cells. The functional role of p130Cas in ErbB2-dependent breast tumorigenesis was assessed by its silencing in breast cancer cells derived from mouse mammary tumors overexpressing ErbB2 (N202-1A cells), and by its reexpression in ErbB2-transformed p130Cas-null mouse embryonic fibroblasts. We demonstrate that p130Cas is necessary for ErbB2-dependent foci formation, anchorage-independent growth, and in vivo growth of orthotopic N202-1A tumors. Moreover, intranipple injection of p130Cas-stabilized siRNAs in the mammary gland of Balbc-NeuT mice decreases the growth of spontaneous tumors. In ErbB2-transformed cells, p130Cas is a crucial component of a functional molecular complex consisting of ErbB2, c-Src, and Fak. In human mammary cells, MCF10A.B2, the concomitant activation of ErbB2, and p130Cas overexpression sustain and strengthen signaling, leading to Rac1 activation and MMP9 secretion, thus providing invasive properties. Consistently, p130Cas drives N202-1A cell in vivo lung metastases colonization. These results demonstrate that p130Cas is an essential transducer in ErbB2 transformation and highlight its potential use as a novel therapeutic target in ErbB2 positive human breast cancers.-Cabodi, S., Tinnirello, A., Bisaro, B., Tornillo, G., Camacho-Leal, M. P., Forni, G., Cojoca, R., Iezzi, M., Amici, A., Montani, M., Eva, A., Di Stefano, P., Muthuswamy, S. K., Tarone, G., Turco, E., Defilippi, P. p130Cas is an essential transducer element in ErbB2 transformation