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Cyber-threat protection is today’s one of the most challenging research branches of information technology, while the exponentially increasing number of tiny, connected devices able to push personal data to the Internet is doing nothing but exacerbating the battle between the involved parties. Thus, this protection becomes crucial with a typical Internet-of-Things (IoT) setup, as it usually involves several IoT-based data sources interacting with the physical world within various application domains, such as agriculture, health care, home automation, critical industrial processes, etc. Unfortunately, contemporary IoT devices often offer very limited security features, laying themselves open to always new and more sophisticated attacks and also inhibiting the expected global adoption of IoT technologies, not to mention millions of IoT devices already deployed without any hardware security support. In this context, it is crucial to develop tools able to detect such cyber threats. In this article, we present Passban, an intelligent intrusion detection system (IDS) able to protect the IoT devices that are directly connected to it. The peculiarity of the proposed solution is that it can be deployed directly on very cheap IoT gateways (e.g., single-board PCs currently costing few tens of U.S. dollars), hence taking full advantage of the edge computing paradigm to detect cyber threats as close as possible to the corresponding data sources. We will demonstrate that Passban is able to detect various types of malicious traffic, including Port Scanning, HTTP and SSH Brute Force, and SYN Flood attacks with very low false positive rates and satisfactory accuracies.

We consider the problem of computing the Credit Value Adjustment ({CVA}) of a European option in presence of the Wrong Way Risk ({WWR}) in a default intensity setting. Namely we model the asset price evolution as solution to a linear equation that might depend on different stochastic factors and we provide an approximate evaluation of the option's price, by exploiting a correlation expansion approach, introduced in \cite{AS}. We compare the numerical performance of such a method with that recently proposed by Brigo et al. (\cite{BR18}, \cite{BRH18}) in the case of a call option driven by a GBM correlated with the CIR default intensity. We additionally report some numerical evaluations obtained by other methods., Comment: 21 pages

This work aims to provide an efficient method to evaluate the Credit Value Adjustment (CVA) for a vulnerable European option, which is an option subject to some default event concerning the issuer solvability. Financial options traded in OTC markets are of this type. In particular, we compute the CVA in some popular stochastic volatility models such as SABR, Hull et al., which have proven to fit quite well market derivatives prices, admitting correlation with the default event. This choice covers the relevant case of Wrong Way Risk (WWR) when a credit deterioration determines an increase in the claim value. Contrary to the structural modeling adopted in [G. Wang, X. Wang & K. Zhu (2017) Pricing vulnerable options with stochastic volatility, Physica A 485, 91–103; C. Ma, S. Yue & Y. Ma (2020) Pricing vulnerable options with Stochastic volatility and Stochastic interest rate, Computational Economics 56, 391–429], we use the reduced-form intensity-based approach to provide an explicit representation formula for the vulnerable option price and related CVA. Later, we specialize the evaluation formula and construct its approximation for the three models mentioned above. Assuming a CIR model for the default intensity process, we run a numerical study to test our approximation, comparing it with Monte Carlo simulations. The results show that for moderate values of the correlation and maturities not exceeding one year, the approximation is very satisfactory as of accuracy and computational time.

The demand for modular, robust, and uninterrupted weather stations has increased as climate change is strongly affecting agriculture. Internet of Things (IoT) based weather stations is a promising solution to achieve uninterrupted operation of the weather station which is affected by constrained energy availability. This research provides ideas to overcome the energy challenge of a weather station and also the insights to develop an energy-neutral weather station. The primary requirements of a weather station for precision agriculture are explored and each component level challenges are presented. A novel development life cycle of an IoT based energy-neutral weather station is also presented for the developers. Finally, the best practices for developing an energy-autonomous weather station are presented.

Within the Internet of Things (IoT) and blockchain research, there is a growing interest in decentralizing health monitoring systems, to provide improved privacy to patients, without relying on trusted third parties for handling patients&rsquo, sensitive health data. With public blockchain deployments being severely limited in their scalability, and inherently having latency in transaction processing, there is room for researching and developing new techniques to leverage the security features of blockchains within healthcare applications. This paper presents a solution for patients to share their biomedical data with their doctors without their data being handled by trusted third party entities. The solution is built on the Ethereum blockchain as a medium for negotiating and record-keeping, along with Tor for delivering data from patients to doctors. To highlight the applicability of the solution in various health monitoring scenarios, we have considered three use-cases, namely cardiac monitoring, sleep apnoea testing, and EEG following epileptic seizures. Following the discussion about the use cases, the paper outlines a security analysis performed on the proposed solution, based on multiple attack scenarios. Finally, the paper presents and discusses a performance evaluation in terms of data delivery time in comparison to existing centralized and decentralized solutions.

In this paper we present a simple, but new, approximation methodology for pricing a call option in a Black \& Scholes market characterized by stochastic interest rates. The method, based on a straightforward Gaussian moment matching technique applied to a conditional Black \& Scholes formula, is quite general and it applies to various models, whether affine or not. To check its accuracy and computational time, we implement it for the CIR interest rate model correlated with the underlying, using the Monte Carlo simulations as a benchmark. The method's performance turns out to be quite remarkable, even when compared with analogous results obtained by the affine approximation technique presented in Grzelak and Oosterlee (2011) and by the expansion formula introduced in Kim and Kunimoto (1999), as we show in the last section., 19 pages, 3 figures

The extent of epistemic uncertainty in modeling and analysis of complex systems is ever growing, mainly due to increasing levels of the openness, heterogeneity and versatility in cloud-based applications that are being adopted in critical sectors, like banking and finance. State-of-the-art approaches for model-based performance assessment do not embed such uncertainty in analytic models, hence the predicted results do not account for the parametric uncertainty. In this paper, we develop a method for incorporating epistemic uncertainty of the input parameters (i.e., the arrival rate λ and the service rate μ ) to the M/M/1 queueing models, that are commonly used to analyze system performance. We consider two steady state and average output measures: the number of entities in the system and the response time. We start with closed-form solutions for these measures that enable us to study the propagation of epistemic uncertainty in input parameters to these output measures. We demonstrate the suitability of our method for the performance analysis of a cloud-based system, where the epistemic uncertainty comes from continuous re-deployment of applications across servers of different computational capabilities. System simulation results validate the ability of our models to produce satisfactorily accurate predictions of system performance indices under epistemic uncertainty.

For a better and more sustainable use of irrigation water in this article we present a system that incentivises and rewards virtuous behaviour in agricultural practices in a given multi-actor ecosystem while maintaining yield and improving quality of crops. The ongoing experimentation illustrated here takes place in the Northern Alps region of Italy and is made of IoT monitoring and actuating devices that, with the support of blockchain technologies and smart contracts enable the release of value (monetary or through value-add services) to the contributing actors based on monitored behaviours. Besides the specifics of the particular application the article illustrates how the presented framework could also be the common base for similar type of precision agriculture applications where widespread distributed IoT monitoring technologies and ledgers can be used to incentivise virtuous behaviours and fulfill given stakeholders interests.

The recent history has witnessed disruptive advances in disciplines related to information and communication technologies that have laid a rich technological ecosystem for the growth and maturity of latent paradigms in this domain. Among them, sensor networks have evolved from the originally conceived set-up where hundreds of nodes with sensing and actuating functionalities were deployed to capture information from their environment and act accordingly (coining the so-called wireless sensor network concept) to the provision of such functionalities embedded in quotidian objects that communicate and work together to collaboratively accomplish complex tasks based on the information they acquire by sensing the environment. This is nowadays a reality, embracing the original idea of an Internet of things (IoT) forged in the late twentieth century, yet featuring unprecedented scales, capabilities and applications ignited by new radio interfaces, communication protocols and intelligent data-based models. This chapter examines the latest findings reported in the literature around these topics, with a clear focus on IoT communications, protocols and platforms, towards ultimately identifying opportunities and trends that will be at the forefront of IoT-related research in the near future.

Soon after realizing that Cloud Computing could indeed help several industries overcome classical product-centric approaches in favor of more affordable service-oriented business models, we are witnessing the rise of a new disruptive computing paradigm, namely Fog Computing. Essentially, Fog Computing can be considered as an evolution of Cloud Computing, in the sense that the former extends the latter to the edge of the network (that is, where the connected devices -- the things -- are) without discontinuity, realizing the so-called "cloud-to-thing continuum". Since its infancy, Fog Computing has been considered as a necessity within several Internet of Things (IoT) domains (one for all: Industrial IoT) and, more generally, wherever embedded artificial intelligence and/or more advanced distributed capabilities were required. Fog Computing cannot be considered only a fancy buzzword: according to separate, authoritative analyses its global market will reach $18 billion by 2022, while nearly 45% of the world's data will be moved to the network edge by 2025. In this paper, we take stock of the situation, summarizing the most modern and mature Fog Computing initiatives from standardization, commercial, and open-source communities perspectives., Accepted for Publication in IEEE Internet of Things Magazine

In the recent years, a rapid growth of IoT devices has been observed, which in turn results in a huge amount of data produced from multiple sources towards the most disparate cloud platforms or the Internet in general. In a typical cloud-centric approach, the data produced by these devices is simply transmitted over the Internet, for consumption and/or storage. However, with the exponential growth in data production rates, the available network resources are becoming the actual bottleneck of this huge data flowing. Therefore, several challenges are appearing in the coming years, which are mainly related to data transmission, processing, and storage along the so-calledcloud-to-thing continuum. In fact, one of the most critical requirements of several IoT applications is low latency, which often hinders raw data consumption to happen at the opposite endpoint with respect to its production. In the context of IoT data stream analytics, for instance, the detection of anomalies or rare-events is one of the most demanding tasks, as it needs prompt detection to increase its significance. In this respect, Fog and Edge Computing seem to be the correct paradigms to alleviate these stringent demands in terms of latency and bandwidth as, by leveraging on re-configurable IoT gateways and smart devices able to support the distribution of the overall computational task, they envisage to liquefy data processing along the way from the sensing device to a cloud endpoint. In this paper, we will present IRESE, that is a rare-event detection system able to apply unsupervised machine learning techniques on the incoming data, directly on affordable gateways located in the IoT edge. Notwithstanding the proposed approach enjoys the benefits of a fully unsupervised learning approach, such as the ability to learn from unlabeled data, it has been tested against various audio rare-event categories, such as gunshot, glass break, scream, and siren, achieving precision and recall measures above 90% in detecting such events.

The blockchain technology has revolutionized the digital currency space with the pioneering cryptocurrency platform named Bitcoin. From an abstract perspective, a blockchain is a distributed ledger capable of maintaining an immutable log of transactions happening in a network. In recent years, this technology has attracted significant scientific interest in research areas beyond the financial sector, one of them being the Internet of Things (IoT). In this context, the blockchain is seen as the missing link toward building a truly decentralized, trustless, and secure environment for the IoT and, in this survey, we aim to shape a coherent and comprehensive picture of the current state-of-the-art efforts in this direction. We start with fundamental working principles of blockchains and how blockchain-based systems achieve the characteristics of decentralization, security, and auditability. From there, we build our narrative on the challenges posed by the current centralized IoT models, followed by recent advances made both in industry and research to solve these challenges and effectively use blockchains to provide a decentralized, secure medium for the IoT.

Introduction Nowadays, air pollution still remains a critical issue. Global agreements have been signed by several countries to limit emissions of air pollutants [1], introducing increasingly stringent legislation. Moreover, national and supranational organizations, such as the UN and the European Union, publish updated reports on the situation on an annual basis. In recent years, research has focused not only on the use of renewable energy sources for sustainable development, but also on increasingly simple and reliable methods for detecting and mapping the concentration of toxic and polluting gases [2]. To achieve this goal, it is essential to develop sensors capable of easily acquiring and providing data in real time. In this work we present the results obtained in the project CHEARIA, a collaborative activity between Bruno Kessler Foundation (FBK) of Trento, the local environmental protection agency (APPA) and several high schools of the Trentino-Alto Adige Region, as part of the national training program "work-based learning". The program was mainly focused on the development of chemoresistive gas sensor arrays produced at FBK, and their use to monitor the city air quality. Data were collected by exploiting the Internet of Things (IoT). The obtained results were compared with those obtained with the certified systems of the APPA agency. Material and Method Different MOX nanopowder were prepared and tested: SnO2, ZnO nanograin, ZnO nanorods, WO3, TiO2 and solid solution of them. The nanostructured MOX were synthesized through sol-gel method in hydroalcoholic solutions and then calcined. The chemical composition, morphology and crystalline structure of the synthesized nanopowders were investigated. The sensing materials were then screen printed onto silicon microheaters, which were equipped with a Pt heater and interdigitated electrodes. The silicon substrates guarantee a sensor low-power consumption [3]. The gas sensors produced were first tested and calibrated in a sealed aluminum gas chamber, by using certified cylinders and mass flow controller to regulate the target gas concentration inside the test chamber. Gases analyzed in the laboratory were CO, NO2, NO, O3 and CH4, at different concentrations, based on the concentration limit defined by the EU law. The sensing responses were collected by thermo-activating the sensing materials at different temperatures. The on-field sensing measurements were carried out in the city of Trento by using two sensor arrays developed in FBK. Each FBK sensor arrays were composed of eight MOX gas sensors, chosen based on the results obtained in the laboratory measurements. The two systems were also equipped with a commercial pump, to inject a constant air flux inside the sensors chamber, and a Raspberry Pi 3 model B, to transmit data via wireless to a dedicated FBK cloud. The two gas sensor arrays were placed in the APPA monitoring stations, in order to use the same sampling air analyzed by the certified instruments of the environmental protection agency. Results and Conclusions The laboratory measurements were carried out to identify the most promising gas sensors to be used in the arrays for the on-field analysis. For this purpose, the different sensors were exposed to various concentrations of pollutant gases, in the presence of 30%-60% of RH%. Cross-selectivity of the sensors was also tested to get as close as possible to the real on-field conditions, by injecting several target gases into the gas chamber at the same time. In Figure 1a) is reported the cross-selectivity characterization carried out by first injecting CO into the measuring chamber, and then a mix of CO and NO2. Figure 1b) shows some representative responses obtained with WO3, STN and SnO2 gas sensors. It can be observed that the presence of NO2 did not affect the STN sensing response vs. CO, while WO3 and SnO2 gas sensors resulted to be more reactive vs. NO2 than CO. The laboratory calibration allowed to identify the four best sensors for our aim, i.e. WO3, ZnO nanograin, SnO2 and STN. In Figure 2a) and b) are reported the two gas sensor arrays developed. To verify the repeatability of the sensors’ behavior, it was decided to use 2 WO3, 2 ZnO, 2 SnO2 and 2 STN sensors for each gas sensor array. The two systems were placed in two different APPA monitoring stations in Trento, to measure both urban background and traffic-oriented locations. The on-field measurements, which began on 27 August 2019, are still ongoing. Figure 3 shows the sensor signals collected in a month at the monitoring station for the detection of urban background pollution. The calibrations of the sensors carried out in the laboratory have been expanded by a further calibration on-field, by combining the sensor signals and data from certified monitoring systems with machine learning. A preliminary result, presented in Figure 4, shows the comparison between the daily average CO concentration determined by the certified sensor and that measured by our array of sensors, over a period of 2 weeks. In the next few months, the validity of the two sensor arrays for the detection of the most common pollutants will be investigated. References [1] https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement [2] Liu, X., Cheng, S., Liu, H., Hu, S., Zhang, D., Ning, H. A survey on gas sensing technology (2012) Sensors (Switzerland), 12 (7), pp. 9635-9665. doi: 10.3390/s120709635 [3] Bagolini, A., Gaiardo, A., Crivellari, M., Demenev, E., Bartali, R., Picciotto, A., Valt, M., Ficorella, F., Guidi, V., Bellutti, P. Development of MEMS MOS gas sensors with CMOS compatible PECVD inter-metal passivation (2019) Sensors and Actuators, B: Chemical, 292, pp. 225-232. doi: 10.1016/j.snb.2019.04.116 Figure 1

Blockchain, the underlying technology of cryptocurrency networks like Bitcoin, can prove to be essentialtowards realizing the vision of a decentralized, secure, and open Internet of Things (IoT) revolution. There is a growing interest in many research groups towards leveraging blockchains to provide IoT data privacy without the need for a centralized data access model. This paper aims to propose a decentralized access model for IoT data, using a network architecture that we call a modular consortium architecture for IoT and blockchains. The proposed architecture facilitates IoT communications on top of a software stack of blockchains and peer-to-peer data storage mechanisms. The architecture is aimed to have privacy built into it, and to be adaptable for various IoT use cases. To understand the feasibility and deployment considerations for implementing the proposed architecture, we conduct performance analysis of existing blockchain development platforms, Ethereum and Monax

In this paper, we consider a market model with prices and consumption following a jump-diffusion dynamics. In this setting, we first characterize the optimal consumption plan for an investor with recursive stochastic differential utility on the basis of his/her own beliefs, then we solve the inverse problem to find what beliefs make a given consumption plan optimal. The problem is viewed in general for a class of homogeneous recursive utility, and later we choose a logarithmic model for the utility aggregator as an explicitly computable example. When beliefs, represented via Girsanov’s theorem, get incorporated into the model, the change of measure gives rise, up to a transformation, to a backward stochastic differential equation whose generator exhibits a quadratic behavior in the Brownian component and a locally Lipschitz one in the jump component, which is solvable on the basis of some recent results.

Within many industry verticals, the Internet of Things provides the means to collect and analyze crucial data for optimizing system performance. The technical evolution of IoT is shifting its computational capabilities away from centralized cloud platforms to a decentralized IoT edge. The IoT edge faces challenges in providing fully decentralized security mechanisms, as well as built-in guarantees for user-data privacy. In recent years, blockchain has been viewed as a potential fabric for a secure, decentralized IoT edge due to its inherent features of fault tolerance, transparency, and enforcement of service level agreements through smart contracts. Despite their virtues, blockchains have their fair share of challenges in being integrated into IoT. In this article, we discuss these challenges, as well as candidate solutions for integrating blockchains into the IoT edge.

We introduce Cloud4IoT, a platform offering automatic deployment, orchestration and dynamic configuration of IoT support software components and data-intensive applications for data processing and analytics, thus enabling plug-and-play integration of new sensor objects and dynamic workload scalability. Cloud4IoT enables the concept of Infrastructure as Code in the IoT context: it empowers IoT operations with the flexibility and elasticity of Cloud services. Furthermore it shifts traditionally centralized Cloud architectures towards a more distributed and decentralized computation paradigm, as required by IoT technologies, bridging the gap between Cloud Computing and IoT ecosystems. Thus, Cloud4IoT is playing a role similar to the one covered by solutions like Fog Computing, Cloudlets or Mobile Edge Cloud. The hierarchical architecture of Cloud4IoThosts a central Cloud platform and multiple remote edge Cloud modules supporting dedicated devices, namely the IoT Gateways, through which new sensor objects are made accessible to the platform. Overall, the platform is designed in order to support systems where IoT-based and data intensive applications may pose specific requirements for low latency, restricted available bandwidth, or data locality. Cloud4IoT is built on several Open Source technologies for containerisation and implementations of standards, protocols and services for the IoT. We present the implementation of the platform and demonstrate it in two different use cases., Comment: The article has been accepted for publication by IEEE

We propose an approach for computing the arbitrage-free interval for the price of an American option in discrete incomplete market models via linear programming. The main idea is built replicating strategies that use both the basic asset and some European derivatives available on the market for trading. This method goes under the name of calibrated option pricing and it has given significant results for European options. Here, we extend the analysis to American options showing that the arbitrage-free interval can be characterized in terms of martingale measures and that it gets significantly reduced with respect to the non-calibrated case. © 2013 © 2013 Taylor & Francis.

BACKGROUND Right heart dysfunction (RHD) is related to adverse outcomes in acute pulmonary embolism (PE). AIM OF THE STUDY To evaluate the relation between RHD, pulmonary clots distribution and biomarkers and prognosis of patients with PE. METHODS We analysed echocardiographic data of 70 patients with diagnosis of PE confirmed by pulmonary computer tomography, hCT. We considered the enddiastolic right/left ventricles ratio > 1 as index of RHD; echocardiographic data were compared with clots distribution in pulmonary vascular tree such as hCT findings and biomarkers. For each patient we calculated the shock index (heart rate/systolic blood pressure ratio, shock defined as ratio ≥ 1). RESULTS Hospital mortality was 8.5%. Mean age of dead patients was significantly higher compared to alive (85.67 vs 71.57 years, p < 0.05). 41% of patients revealed unilateral PE, 59% had bilateral. In 10% of patients main pulmonary artery was interested by clot, 48% of patients had involved one of the main branches, 90% had involved at least one of the lobar branches, 59% one of segmental branches of pulmonary arteries. 52% of patients had RHD. Mortality in RHD patients was 14.8% vs 8% in no RHD, p < 0.05. Mean values of troponin I and D-dimer were significantly higher in RHD patients. Shock index was ≥ 1 in 37.5% of RHD and 20% in no RHD. RHD patients showed significantly higher involvement of main pulmonary artery and its branches and higher bilateral involvement. CONCLUSIONS RHD is related to proximal and bilateral pulmonary clots distribution and troponin I and D-dimer values and poorer prognosis.

Inspired by utility optimization problems in finance, in this paper we prove the existence of the solution of a class of BSDE’s driven by a Brownian motion and a jump process, whose generator shows quadratic growth in the Brownian component and exponential growth with respect to the jump term. Existence and uniqueness of the solution is established first for bounded terminal value, then we extend the existence result to the unbounded case, under appropriate hypotheses.

We introduce a natural generalization of the forward-starting options. The main feature of the contract presented here is that the strike-determination time is not fixed ex-ante, but allowed to be random, usually related to the occurrence of some event, either of financial nature or not. We will call these options random time forward-starting (RTFS). We show that, under an appropriate “martingale preserving” hypothesis, we can exhibit arbitrage free prices, which can be explicitly computed in many classical market models, at least under independence and in absence of simultaneous jumps between the random time and the assets' prices. Practical implementations of the pricing methodologies are also provided. Finally, a credit value adjustment (CVA) formula for these over the counter (OTC) options is computed for the unilateral counterparty credit risk.

Background Spontaneous intracerebral hemorrhage (sICH) represents the most feared subtype of stroke. Despite the intracranial bleeding risk associated to oral anticoagulant (OAT) with vitamin K antagonists (VKA) has remained constant, the wider use of VKA over the last 20 years has proven the increasing of OAT related ICH (OAT-sICH).

Efficient valuation of exchange options with random volatilities while challenging at analytical level, has strong practical implications: in this paper we present a new approach to the problem which allows for extensions of previous known results. We undertake a route based on a multi-asset generalization of a methodology developed in Antonelli and Scarlatti (Finan Stoch 13:269–303, 2009) to handle simple European one-asset derivatives with volatility paths described by Ito’s diffusive equations. Our method seems to adapt rather smoothly to the evaluation of Exchange options involving correlations among all the financial quantities that specify the model and it is based on expanding and approximating the theoretical evaluation formula with respect to correlation parameters. It applies to a whole range of models and does not require any particular distributional property. In order to test the quality of our approximation numerical simulations are provided in the last part of the paper.

The Hobson–Rogers model is used to price derivative securities under the no-arbitrage condition in a stochastic volatility setting, preserving the completeness of the market. Here we are studying the rate of convergence of the Euler/Monte Carlo approximations, when pricing European, Asian and digital type options. The aim of the present work is to express the approximation error in terms of the time step size, denoted by h, used for the Euler scheme. We recover an already known result, obtained by other authors using PDE approximations, for European options. Namely we show that for a Lipschitz coefficient of the driving equations for the asset price and Lipschitz payoffs, we obtain an error of the order of [Formula: see text]. Moreover, using Malliavin Calculus techniques, we show that with a regular coefficient we may attain an error of the order of h for regular payoffs and of the order of [Formula: see text] for non Lipschitz payoffs. Finally we show some numerical simulations supporting our theoretical results.