Hypnos Board: A Low-Cost All-In-One Solution for Environment Sensor Power Management, Data Storage, and Task Scheduling

The recent and rapid development of the Internet of Things (IoT') leads to the need for embedded devices that comprise one or more resource-constrained computing elements and sensors. Energy Harvesting (EH) technology provides the ability for any low-power embedded device to operate thanks to the generation of electricity from the energy available in the device's immediate surroundings such as light or motion. Energy neutrality is the central requirement of autonomous real-time computing systems that should be designed to correctly function for long times with no possible manual intervention to charge or replace batteries. Unfortunately, most of environmental energy sources are fluctuating and not controllable. It means that a stable power supply cannot be relied upon that makes challenging the issue of compliance with hard real-time constraints. Specific power management and scheduling solutions have to be conceived in order to prevent energy starvation and guarantee real-time responsiveness. Task scheduling should take into account not only the timing parameters of the deadline constrained tasks such as worst-case execution times but also energy consumptions, profile of the energy source and capacity of the energy storage unit. The classical greedy scheduler Earliest Deadline First (EDF) or Rate Monotonic (RM) used in battery powered devices should be revisited for this novel operational context. This keynote discusses the state of the art as well as our findings in real-time scheduling and dynamic processor management for autonomous embedded systems. Short biography: Maryline Chetto is currently a full professor in computer engineering with Nantes Université, France and researcher with CNRS. She received the degree of Docteur de 3ème cycle in control engineering and the degree of Habilitee á Diriger des Recherches in Computer Science from the University of Nantes, France, in 1984 and 1993, respectively. From 1984 to 1985, she held the position of Assistant professor of Computer Science at the University of Rennes, while her research was with the Institut de Recherche en Informatique et Systemics Aléatoires (IRISA), Rennes. In 1986, she returned to Nantes and has been from 2002 a full professor with the University of Nantes. She is conducting her research at Laboratoire des Sciences du Numérique de Nantes (LS2N, UMR CNRS n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> 6004) in the Real Time System group.

The explosive growth of data produced and disseminated across various networks has contributed to an abundance of data, called Big Data. The diversity and the sheer amount of this data pose significant challenges in efficiently processing and handling it. Addressing these challenges has drawn much attention in recent years. Existing solutions for memory, data storage, and processing management cannot fulfill the needs of this enormous amount of data. On the other hand, given the dramatic growth of smart mobile devices in number, popularity, and processing power, Mobile Cloud Computing (MCC) has emerged as a viable extension of cloud computing concepts to mobile environments. MCC employs cloud computing principles and solutions to overcome problems such as efficiency, battery lifetime, bandwidth, memory, heterogeneity, availability, scalability and security. In this paper, a new concept of Big Data Mobile Cloud Computing (BDMCC) is introduced. After reviewing the open challenges of this notion, a general architecture is proposed for it. To address one of these challenges, the idea of location-based scheduling is proposed along with a list of possible scenarios for which this approach may be very effective. Experimental results confirm the efficacy of the proposed approach.

There is an increasing need for high-capacity, highdensity storage media that can retain data for a long time, due to the exponential development in the capacity of information generated. The durability and high information density of synthetic deoxyribonucleic acid (DNA) make it an attractive and promising medium for data storage. DNA data storage technology is expected to revolutionize data storage in the coming years, replacing various Big Data storage technologies. As a medium that addresses the need for high-latency, immutable information storage, DNA has several potential advantages. One of the key advantages of DNA storage is its extraordinary density. Theoretically, a gram of DNA can encode 455 exabytes, or 2 bits per nucleotide. Unlike other digital storage media, synthetic DNA enables large quantities of data to be stored in a biological medium. This reduces the need for traditional storage media such as hard disks, which consume energy and require materials such as plastic or metals, and also often leads to the generation of electronic waste when they become obsolete or damaged. Additionally, although DNA degrades over thousands of years under non-ideal conditions, it is generally readable. Furthermore, as DNA possesses natural reading and writing enzymes as part of its biological functions, it is expected to remain the standard for data retrieval in the foreseeable future. However, the high error rate poses a significant challenge for DNA-based information coding strategies. Currently, it is impossible to execute DNA strand synthesis, amplification, or sequencing errors-free. In order to utilize synthetic DNA as a storage medium for digital data, specialized systems and solutions for direct error detection and correction must be implemented. The goal of this paper is to introduce DNA storage technology, outline the benefits and added value of this approach, and present an experiment comparing the effectiveness of two error detection and correction codes (Hamming and CRC) used in the DNA data storage strategy.

Machine learning for the intelligent analysis of 3D printing conditions using environmental sensor data to support quality assurance

The age of information has placed unprecedented demands on data storage. In response, a rich hub of research activity has erupted in the search for new types of memory with improved capacity and data processing speed [1, 2]. There has even been progress in mimicking the subtleties of how the human neural system remembers and forgets with artificial synaptic devices [3, 4]. Yet many challenges remain. As researchers at Brigham Young University in the US point out, 'Current data storage technologies have limitations that prevent their use in archival data storage applications'. Anthony C Pearson and colleagues look at the potential role of graphene in data storage solutions. Graphene's wide range of extraordinary properties has recommended it for a number of applications. This issue reports how it can form the basis of nanofuses that may be used for permanent, write-once-read-many (WORM) data storage devices [5].Leon Chua's forecast of 'Memristor—the missing circuit element' [6] and the subsequent demonstration of memristance in nanoscale systems byresearchers at HP Labs [7] triggered a surge of studies investigating the application of memristor devices in non-volatile memory [8]. In fact thesestructures may have a still broader impact in nanotechnology. At the nanoscale statistical variations in defect concentrations give rise to vast differences in behaviour, and device variability and ageing can be profound. These characteristics led George Snider to observe, 'Nanodevices are crummy', when considering their application in conventional Boolean logic systems [9]. Yet Snider's grim assessment was merely a prologue to an insightful recount of simulations demonstrating how to side-step a number of nanotechnology's main challenges by using self-organizing networks based on memristive devices.Similar fault tolerance is demonstrated in the organic-nanoparticle transistors developed by researchers at the University of Lille, who demonstrate a weighting behaviour that mimics aspects of synaptic functions that may be key to implementing neuromorphic computing [10]. Developments in this field show increasingly more sophisticated imitation of human neural processing with analogue memory functionality [3] and circuits that both learn and forget [4]. Keep an eye out for the Nanotechnology special issue on synaptic electronics later this year with guest editors James Gimzewski from the University of California, Los Angeles, and Dominique Vuillaume from the University of Lille, for a one-stop update on the latest cutting edge developments in this field.Graphene has demonstrated excellent potential in a number of applications from supercapacitors [11] to photomechanical actuators [12]. However, so far its potential in memory has been notably less explored. In this issue Anthony C Pearson and colleagues at Brigham Young University report how they fabricate graphene 'bow-tie' structures for fuses and program them though thermal oxidation [5]. As the researchers point out graphene's low atomic mobility, high chemical resistance to oxidation and excellent electrical conductivity make it well suited as archival data storage fuse material. They also highlight a number of attractive attributes in the devices as a whole: 'graphene WORM devices can be read and written electronically, can potentially have the very high data densities of flash memory, appear to be highly stable in both the on and off states, have a high on/off current ratio, and can be programmed with low voltages and powers'.In fuses an electrical connection is destroyed by an applied voltage. The process sounds destructive yet fuses are incredibly useful. Edison once famously commented, 'Just because something does not do what you planned it to do does not mean it is useless' [13]. There are synergies of this philosophy throughout nanotechnology research where apparently awkward behaviour is put to good use. Adding the dynamical nature of nanodevices to the list of obstacles to scalability, Snider rounded up his assessment, 'So we are faced with the challenge of computing with devices that are not only crummy, but dynamical as well'. Yet it was that dynamic behaviour that he went on to exploit in his demonstration of robust self-organizing networks using memristor devices. Successful research is by nature continually revealing behaviour that is unexpected or unusual. Thework described here is just some of the many examples of how successful development in research does not tolerate the unexpected, but embraces it.

Background: With the development of information technology and the popularization of the Internet, a large amount of data has been generated and collected. These data typically have the characteristics of massive, diverse, and high-speed growth, and traditional data storage devices and technologies are no longer able to meet the storage and analysis needs of data. And people need more and more storage space to store personal files, photos, videos, and other data. Traditional personal computer hard drives have limited capacity and cannot meet the growing data demand. The emergence of cloud storage provides more flexible and efficient solutions for big data storage and analysis, as well as larger capacity and more convenient access methods, solving the challenges of personal storage and sharing. Method: Based on comparative analysis, explore the advantages and disadvantages of cloud storage in the fields of personal data storage and sharing, as well as big data storage and analysis. Result: Cloud storage has certain advantages and limitations in the fields of personal storage and sharing, as well as big data storage and analysis. Users need to consider these factors comprehensively based on their own needs and actual situation.

We designed and evaluated an assumption-free, deep learning-based methodology for animal health monitoring, specifically for the early detection of respiratory disease in growing pigs based on environmental sensor data. Two recurrent neural networks (RNNs), each comprising gated recurrent units (GRUs), were used to create an autoencoder (GRU-AE) into which environmental data, collected from a variety of sensors, was processed to detect anomalies. An autoencoder is a type of network trained to reconstruct the patterns it is fed as input. By training the GRU-AE using environmental data that did not lead to an occurrence of respiratory disease, data that did not fit the pattern of “healthy environmental data” had a greater reconstruction error. All reconstruction errors were labelled as either normal or anomalous using threshold-based anomaly detection optimised with particle swarm optimisation (PSO), from which alerts are raised. The results from the GRU-AE method outperformed state-of-the-art techniques, raising alerts when such predictions deviated from the actual observations. The results show that a change in the environment can result in occurrences of pigs showing symptoms of respiratory disease within 1–7 days, meaning that there is a period of time during which their keepers can act to mitigate the negative effect of respiratory diseases, such as porcine reproductive and respiratory syndrome (PRRS), a common and destructive disease endemic in pigs.

When looking at the history of humanity, continuous change and development have been observed that have been the new needs of people, and solutions can be found for these requirements. The Industrial Revolution is one of these developments and consumption type and limit was changed after this point. This revolution brought the significance of energy production and consumption. Since the construction industry is the field that consumes the most energy, it is very important to control energy in buildings. With this essential, smart building systems which are require sensor data and an internet connection. Thus, a new requirement has emerged regarding the security and storage of sensor data. Blockchain technology is considered a feasible option to address uncertainties related to process tracking, data storage and data transparency. In this paper, the keywords "blockchain technology" and "smart building" found in the literature are examined to discuss what can be used to process data in smart buildings and ensure the security of this data.

Introduction. The banking sector assigns high priority to data storage, as it is a critical aspect of business operations. The volume of data in this area is steadily growing. With the increasing volume of data that needs to be stored, processed and analyzed, it is critically important to select a suitable data storage solution and develop the required architecture. The presented research is aimed at filling the gap in the existing knowledge of the data base management system (DBMS) suitable for the banking sector, as well as to suggest ways for a fault-tolerant data storage cluster. The purpose of the work is to analyze the key DBMS for analytical queries, determine the priorities of the DBMS for the banking sector, and develop a fault-tolerant data storage cluster. To meet the performance and scalability requirements, a data storage solution with a fault-tolerant architecture that meets the requirements of the banking sector has been proposed.Materials and Methods. Domain analysis allowed us to create a set of characteristics that a DBMS for analytical queries (OnLine Analytical processing — OLAP) should correspond to, compare some popular DBMS OLAP, and offer a fault-tolerant cluster configuration written in xml, supported by the ClickHouse DBMS. Automation was done using Ansible Playbook. It was integrated with the Gitlab version control system and Jinja templates. Thus, rapid deployment of the configuration on all nodes of the cluster was achieved.Results. For OLAP databases, criteria were developed and several popular systems were compared. As a result, a reliable cluster configuration that met the requirements of analytical queries has been proposed for the banking industry. To increase the reliability and scalability of the DBMS, the deployment process was automated. Detailed diagrams of the cluster configuration were also provided.Discussion and Conclusions. The compiled criteria for the DBMS OLAP allowed us to determine the need for this solution in the organization. Comparison of popular DBMS can be used by organizations to minimize costs when selecting a solution. The proposed configuration of the data warehouse cluster for analytical queries in the banking sector will improve the reliability of the DBMS and meet the requirements for subsequent scalability. Automation of cluster deployment by the mechanism of templating configuration files in Ansible Playbook provides configuring a ready-made cluster on new servers in minutes.

Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection

Cyber-physical applications need to process a lot of sensor data, for example, to analyze traffic patterns and structural soundness of critical infrastructures. Although the amount of sensor data to process is increasing fast, system support to efficiently store and analyze an extensive amount of sensor data largely lags behind. To efficiently store, retrieve, and process massive sensor data, we are developing a sensor data center (SDC) that supports spatio-temporal sensor data structures and parallel sensor data processing using clustered computational nodes composed of commodity hardware. The SDC sharply contrasts to most existing data centers that do not support spatio-temporal sensor data storage, retrieval, and processing. In this paper, we especially focus on the problem of potential load imbalance due to data access skews that adversely affects the timeliness of parallel sensor data processing. Specifically, we present an adaptive data replication method to address access skews in a SDC. In our performance evaluation performed in a preliminary version of a SDC, our adaptive approach substantially outperforms a baseline that does not support adaptive data replication.

The Data Lake is emerging as a Big Data storage and management solution which can store any type of data at scale and execute data transformations for analysis. Higher flexibility in storage increases the risk of Data Lakes becoming data swamps. In this paper we show how provenance contributes to data management within a Data Lake infrastructure. We study provenance integration challenges and propose a reference architecture for provenance usage in a Data Lake. Finally we discuss the applicability of our tools in the proposed architecture.

The impact of next-generation sequencing on genomics

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The testing of vision sensor-based Advanced Driver Assistance (ADA) systems and Electronic Control Units (ECUs) is quickly becoming a major requirement in the automotive industry. ADA ECU development teams require a verification and validation (V&amp;amp;V) infrastructure early in the development cycle, and traditional approaches are not suitable for this purpose. This paper proposes the design of an integrated test suite for the functional and real-time testing of ADA algorithms and ECUs. The proposed test suite consists of a hardware bridge built using a Field Programmable Gate Array (FPGA), a Universal Serial Bus (USB) 3.0 interface, and a software application running on the Personal Computer (PC). By using an FPGA to build the hardware bridge, we get a compact, real-time, and reconfigurable platform to support the desired test requirements. The USB3.0 interface is specifically proposed to support the high data rates evident in ADA systems based on multiple vision sensor and sensor fusion technologies. The PC-based software application has a modular, extensible, and scalable architecture to support current and future test requirements, as well as several features that improve the end user's productivity. The proposed test suite can be used for: (a) acquisition and storage of sensor data (recording mode), (b) visualization of stored sensor data (replay mode), (c) offline evaluation and improvement of ADA algorithms (functional test mode), (d) online testing of advanced driver assistance system (ADAS) ECUs (real-time test mode). The major contribution of this paper is the design proposal for supporting the real-time test mode for laboratory testing of ADA ECUs, and it concludes with a qualitative discussion of potential enhancements.&lt;/div&gt;&lt;/div&gt;

Holographic data storage technology is a cost-effective solution for long-term archival data storage. However, the development of suitable holographic recording materials remains a challenge. Among these materials, phenanthraquinone-doped poly(methyl methacrylate) (PQ/PMMA) stands out due to its low cost and controllable thickness. Nevertheless, its limited photosensitivity and diffraction efficiency hinder its widespread application. In order to solve these problems, we put forward a kind of convenient and simple, low cost strategy, by adding plasticizer N,N-dimethylformamide (DMF) for preparation of DMF-PQ/PMMA photopolymer, avoid the use of complex compounds. The addition of DMF not only influences the thermal polymerization stage but also forms weak interactions with PQ during the photoreaction process, thereby enhancing the holographic performance of DMF-PQ/PMMA. Consequently, we achieved a remarkable 9.1-fold increase in photosensitivity (from ∼0.35 to 3.18 cm J-1), improved diffraction efficiency by 20% (from 65% to 80%), and reduced volume shrinkage by a factor of 8 (from 0.4% to 0.05%). Furthermore, utilizing a collinear holographic storage system with multiplexing shift at a scale of 5 μm resulted in an impressively low minimum bit error rate (BER) of only 0.36% (with an average BER of 1.4%), highlighting the fast processing capability and potential for low BER applications in holographic information storage using DMF-PQ/PMMA.