A Monte Carlo Method for Assessing the Quality of Duplication-Aware Alignment Algorithms

The increasing availability of high throughput sequencing technologies poses several challenges concerning the analysis of genomic data. Within this context, duplication-aware sequence alignment taking into account complex mutation events is regarded as an important problem, particularly in light of recent evolutionary bioinformatics researches that highlighted the role of tandem duplications as one of the most important mutation events. Traditional sequence comparison algorithms do not take into account these events, resulting in poor alignments in terms of biological significance, mainly because of their assumption of statistical independence among contiguous residues. Several duplication-aware algorithms have been proposed in the last years which differ either for the type of duplications they consider or for the methods adopted to identify and compare them. However, there is no solution which clearly outperforms the others and no methods exist for assessing the reliability of the resulting alignments. This paper proposes a Monte Carlo method for assessing the quality of duplication-aware alignment algorithms and for driving the choice of the most appropriate alignment technique to be used in a specific context

A Lossy Compression Technique Enabling Duplication-Aware Sequence Alignment

In spite of the recognized importance of tandem duplications in genome evolution, commonly adopted sequence comparison algorithms do not take into account complex mutation events involving more than one residue at the time, since they are not compliant with the underlying assumption of statistical independence of adjacent residues. As a consequence, the presence of tandem repeats in sequences under comparison may impair the biological significance of the resulting alignment. Although solutions have been proposed, repeat-aware sequence alignment is still considered to be an open problem and new efficient and effective methods have been advocated. The present paper describes an alternative lossy compression scheme for genomic sequences which iteratively collapses repeats of increasing length. The resulting approximate representations do not contain tandem duplications, while retaining enough information for making their comparison even more significant than the edit distance between the original sequences. This allows us to exploit traditional alignment algorithms directly on the compressed sequences. Results confirm the validity of the proposed approach for the problem of duplication-aware sequence alignment

Supporting Preemptive Multitasking in Wireless Sensor Networks

Supporting the concurrent execution of multiple tasks on lightweight sensor nodes could enable the deployment of independent applications on a shared wireless sensor network, thus saving cost and time by exploiting infrastructures which are typically underutilized if dedicated to a single task. Existing approaches to wireless sensor network programming provide limited support to concurrency at the cost of reducing the generality and the expressiveness of the language adopted. This paper presents a java-compatible platform for wireless sensor networks which provides a thorough support to preemptive multitasking while allowing the programmers to write their applications in java. The proposed approach has been implemented and tested on top of VirtualSense, an ultra-low-power wireless sensor mote providing a java-compatible runtime environment. Performance and scalability of the solution are discussed in light of extensive experiments performed on representative benchmarks

A Review on Blockchain for the Internet of Medical Things: Definitions, Challenges, Applications, and Vision

none3noNowadays, there are a lot of new mobile devices that have the potential to assist healthcare professionals when working and help to increase the well-being of the people. These devices comprise the Internet of Medical Things, but it is generally difficult for healthcare institutions to meet compliance of their systems with new medical solutions efficiently. A technology that promises the sharing of data in a trust-less scenario is the Distributed Ledger Technology through its properties of decentralization, immutability, and transparency. The Blockchain and the Internet of Medical Things can be considered as at an early stage, and the implementations successfully applying the technology are not so many. Some aspects covered by these implementations are data sharing, interoperability of systems, security of devices, the opportunity of data monetization and data ownership that will be the focus of this review.openGioele Bigini;Valerio Freschi;Emanuele LattanziBigini, Gioele; Freschi, Valerio; Lattanzi, Emanuel

Exploring Artificial Neural Networks Efficiency in Tiny Wearable Devices for Human Activity Recognition

The increasing diffusion of tiny wearable devices and, at the same time, the advent of machine learning techniques that can perform sophisticated inference, represent a valuable opportunity for the development of pervasive computing applications. Moreover, pushing inference on edge devices can in principle improve application responsiveness, reduce energy consumption and mitigate privacy and security issues. However, devices with small size and low-power consumption and factor form, like those dedicated to wearable platforms, pose strict computational, memory, and energy requirements which result in challenging issues to be addressed by designers. The main purpose of this study is to empirically explore this trade-off through the characterization of memory usage, energy consumption, and execution time needed by different types of neural networks (namely multilayer and convolutional neural networks) trained for human activity recognition on board of a typical low-power wearable device. Through extensive experimental results, obtained on a public human activity recognition dataset, we derive Pareto curves that demonstrate the possibility of achieving a 4× reduction in memory usage and a 36× reduction in energy consumption, at fixed accuracy levels, for a multilayer Perceptron network with respect to more sophisticated convolution network model

Unstructured Handwashing Recognition using Smartwatch to Reduce Contact Transmission of Pathogens

Current guidelines from the World Health Organization indicate that the SARS-CoV-2 coronavirus, which results in the novel coronavirus disease (COVID-19), is transmitted through respiratory droplets or by contact. Contact transmission occurs when contaminated hands touch the mucous membrane of the mouth, nose, or eyes so hands hygiene is extremely important to prevent the spread of the SARSCoV-2 as well as of other pathogens. The vast proliferation of wearable devices, such as smartwatches, containing acceleration, rotation, magnetic field sensors, etc., together with the modern technologies of artificial intelligence, such as machine learning and more recently deep-learning, allow the development of accurate applications for recognition and classification of human activities such as: walking, climbing stairs, running, clapping, sitting, sleeping, etc. In this work, we evaluate the feasibility of a machine learning based system which, starting from inertial signals collected from wearable devices such as current smartwatches, recognizes when a subject is washing or rubbing its hands. Preliminary results, obtained over two different datasets, show a classification accuracy of about 95% and of about 94% for respectively deep and standard learning techniques

Bootstrap Based Uncertainty Propagation for Data Quality Estimation in Crowdsensing Systems

The diffusion of mobile devices equipped with sensing, computation, and communication capabilities is opening unprecedented possibilities for high-resolution, spatio-temporal mapping of several phenomena. This novel data generation, collection, and processing paradigm, termed crowdsensing, lays upon complex, distributed cyberphysical systems. Collective data gathering from heterogeneous, spatially distributed devices inherently raises the question of how to manage different quality levels of contributed data. In order to extract meaningful information, it is, therefore, desirable to the introduction of effective methods for evaluating the quality of data. In this paper, we propose an approach aimed at systematic accuracy estimation of quantities provided by end-user devices of a crowd-based sensing system. This is obtained thanks to the combination of statistical bootstrap with uncertainty propagation techniques, leading to a consistent and technically sound methodology. Uncertainty propagation provides a formal framework for combining uncertainties, resulting from different quantities influencing a given measurement activity. Statistical bootstrap enables the characterization of the sampling distribution of a given statistics without any prior assumption on the type of statistical distributions behind the data generation process. The proposed approach is evaluated on synthetic benchmarks and on a real world case study. Cross-validation experiments show that confidence intervals computed by means of the presented technique show a maximum 1.5% variation with respect to interval widths computed by means of controlled standard Monte Carlo methods, under a wide range of operating conditions. In general, experimental results confirm the suitability and validity of the introduced methodology

Decentralising the Internet of Medical Things with Distributed Ledger Technologies and Off-Chain Storages: a Proof of Concept

The privacy issue limits the Internet of Medical Things. Medical information would enhance new medical studies, formulate new treatments, and deliver new digital health technologies. Solving the sharing issue will have a triple impact: handling sensitive information easily, contributing to international medical advancements, and enabling personalised care. A possible solution could be to decentralise the notion of privacy, distributing it directly to users. Solutions enabling this vision are closely linked to Distributed Ledger Technologies. This technology would allow privacy-compliant solutions in contexts where privacy is the first need through its characteristics of immutability and transparency. This work lays the foundations for a system that can provide adequate security in terms of privacy, allowing the sharing of information between participants. We introduce an Internet of Medical Things application use case called “Balance”, networks of trusted peers to manage sensitive data access called “Halo”, and eventually leverage Smart Contracts to safeguard third party rights over data. This architecture should enable the theoretical vision of privacy-based healthcare solutions running in a decentralised manner

In-Band Controllable Radio Interference Generation for Wireless Sensor Networks

Interference signals negatively impact the performance of wireless embedded systems. The increased packet losses, delays, and energy consumption experienced by devices operating in environments subject to interference are particularly critical in constrained systems such as wireless sensor networks. The need to design and test systems for mitigating the effects of interference prompts for the capability of reproducing in a controllable way suitable interference signals. Solutions have been proposed recently, which tackle the problem by making use of 802.15.4 compliant radio transceivers, like those available on board of commonly used sensor nodes, thus paving the way for low cost and repeatable generation of interference which could reliably emulate real-world scenarios, for instance densely deployed networks. In this paper, we present an investigation regarding the emulation of interference sources by means of 802.15.4 radios on novel 32-bit wireless system-on-chip. The study is based on an extensive experimental evaluation, providing novel insights into the main features of the system. In particular, the effects of interference on the communication link, measured in terms of packet reception rate, are investigated for different parameters (namely, duty cycle and power of the interference signal, communication protocols, and payload size), and results are discussed concerning the feasibility of emulating background noise by means of the analyzed techniques

A Scalable Multitasking Wireless Sensor Network Testbed for Monitoring Indoor Human Comfort

Achieving occupants comfort in built environments is a major goal of modern building automation systems. Nonetheless, even a quantification of human comfort represents a significant challenge because of the number of physical quantities affecting it which, therefore, have to be tracked at suitable spatial and temporal resolution. Wireless sensor and actuator networks are increasingly considered an enabling technology for many monitoring and remote control tasks. Indeed, their reduced intrusiveness, low cost, and low power requirements represent attractive features for the design of monitoring and control infrastructures. In this paper we present a wireless sensor network testbed aimed at monitoring human comfort in a two-century-old building used as university campus. The proposed solution is based on sensor nodes with multitasking capabilities allowing concurrent execution of multiple tasks. Experimental evaluations highlight the flexibility and scalability of the adopted design which allows monitoring of heterogeneous parameters at different rates also permitting the coexistence of event driven and asynchronous operating modes