Andrey Somov

Bio: Andrey Somov is an academic researcher from Skolkovo Institute of Science and Technology. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 22, co-authored 104 publications receiving 1735 citations. Previous affiliations of Andrey Somov include University of Exeter & University of Trento.

Enabling smart cities through a cognitive management framework for the internet of things

Abstract: The Internet of Things (IoT) is expected to substantially support sustainable development of future smart cities. This article identifies the main issues that may prevent IoT from playing this crucial role, such as the heterogeneity among connected objects and the unreliable nature of associated services. To solve these issues, a cognitive management framework for IoT is proposed, in which dynamically changing real-world objects are represented in a virtualized environment, and where cognition and proximity are used to select the most relevant objects for the purpose of an application in an intelligent and autonomic way. Part of the framework is instantiated in terms of building blocks and demonstrated through a smart city scenario that horizontally spans several application domains. This preliminary proof of concept reveals the high potential that self-reconfigurable IoT can achieve in the context of smart cities.

An energy aware framework for virtual machine placement in cloud federated data centres

Abstract: Data centres are powerful ICT facilities which constantly evolve in size, complexity, and power consumption. At the same time users' and operators' requirements become more and more complex. However, existing data centre frameworks do not typically take energy consumption into account as a key parameter of the data centre's configuration. To lower the power consumption while fulfilling performance requirements we propose a flexible and energy-aware framework for the (re)allocation of virtual machines in a data centre. The framework, being independent from the data centre management system, computes and enacts the best possible placement of virtual machines based on constraints expressed through service level agreements. The framework's flexibility is achieved by decoupling the expressed constraints from the algorithms using the Constraint Programming (CP) paradigm and programming language, basing ourselves on a cluster management library called Entropy. Finally, the experimental and simulation results demonstrate the effectiveness of this approach in achieving the pursued energy optimization goals.

Development of wireless sensor network for combustible gas monitoring

Abstract: a b s t r a c t This paper describes the development and the characterization of a wireless gas sensor network (WGSN) for the detection of combustible or explosive gases. The WGSN consists of a sensor node, a relay node, a network coordinator, and a wireless actuator. The sensor node attains early gas detection using an on board 2D semiconductor sensor. Because the sensor consumes a substantial amount of power, which negatively affects the node lifetime, we employ a pulse heating profile to achieve significant energy savings. The relay node receives and forwards traffic from sensor nodes towards the network coordinator and vice versa. When an emergency is detected, the network coordinator alarms an operator through the GSM/GPRS or Ethernet network, and may autonomously control the source of gas emission through the wireless actuator. Our experimental results demonstrate how to determine the optimal temperature of the sensor's sensitive layer for methane detection, show the response time of the sensor to various gases, and evaluate the power consumption of the sensor node. The demonstrated WGSN could be used for a wide range of gas monitoring applications.

Deployment and evaluation of a wireless sensor network for methane leak detection

Abstract: Wireless sensor networks (WSN) have been adopted in various monitoring applications. However, due to the high power consumption of catalytic gas sensors, which enable reliable gas detection, there is a lack of real WSN deployments aimed at the monitoring of combustible gases. This work reports on the evaluation of a WSN deployed in a real operational boiler facility. The WSN consists of nine battery-powered wireless sensor nodes (with an onboard catalytic sensor) controlled by a network coordinator. In this safety critical environment our objective is twofold: (i) guarantee precise and fast sensor response, and (ii) deliver the sensed data from the sensor nodes to the network coordinator safely in case of methane leakage. We first describe the deployment of the WSN and then evaluate the catalytic sensor response under various conditions. Besides, we evaluate the wireless links using the received signal strength indicator (RSSI) and link quality indicator (LQI) metrics. Finally, the experimental results demonstrate that during 5 months of deployment the sensor nodes have been discharged for 22–27%.

Virtualization and Cognitive Management of Real World Objects in the Internet of Things

Abstract: This paper presents a framework for the virtualization of real world objects and the cognitive management of their virtual counterparts. The framework consists of three levels of functionality and each level comprises cognitive entities that provide the means for self-management and learning, allowing for smart, flexible applications and objects. The presented framework enables the abstraction of the heterogeneity that derives from the vast amount of diverse objects/devices, while enhancing reliability and facilitates the consideration of the views of various users/stakeholders (owners of objects & communication means) for ensuring proper application provision, business integrity and, therefore, maximization of exploitation opportunities. The paper also presents a corresponding prototype that has been developed for the validation of the proposed approach, in a real-life fire detection scenario in a Smart Home.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

MDS Clinical Diagnostic Criteria for Parkinson's Disease (S19.001)

Abstract: Objective To present the International Parkinson and Movement Disorder Society (MDS) Clinical Diagnostic Criteria for Parkinson9s disease. Background Although several diagnostic criteria for Parkinson9s disease have been proposed, none have been officially adopted by an official Parkinson society. Moreover, the commonest-used criteria, the UK brain bank, were created more than 25 years ago. In recognition of the lack of standard criteria, the MDS initiated a task force to design new diagnostic criteria for clinical Parkinson9s disease. Methods/Results The MDS-PD Criteria are intended for use in clinical research, but may also be used to guide clinical diagnosis. The benchmark is expert clinical diagnosis; the criteria aim to systematize the diagnostic process, to make it reproducible across centers and applicable by clinicians with less expertise. Although motor abnormalities remain central, there is increasing recognition of non-motor manifestations; these are incorporated into both the current criteria and particularly into separate criteria for prodromal PD. Similar to previous criteria, the MDS-PD Criteria retain motor parkinsonism as the core disease feature, defined as bradykinesia plus rest tremor and/or rigidity. Explicit instructions for defining these cardinal features are included. After documentation of parkinsonism, determination of PD as the cause of parkinsonism relies upon three categories of diagnostic features; absolute exclusion criteria (which rule out PD), red flags (which must be counterbalanced by additional supportive criteria to allow diagnosis of PD), and supportive criteria (positive features that increase confidence of PD diagnosis). Two levels of certainty are delineated: Clinically-established PD (maximizing specificity at the expense of reduced sensitivity), and Probable PD (which balances sensitivity and specificity). Conclusion The MDS criteria retain elements proven valuable in previous criteria and omit aspects that are no longer justified, thereby encapsulating diagnosis according to current knowledge. As understanding of PD expands, criteria will need continuous revision to accommodate these advances. Disclosure: Dr. Postuma has received personal compensation for activities with Roche Diagnostics Corporation and Biotie Therapies. Dr. Berg has received research support from Michael J. Fox Foundation, the Bundesministerium fur Bildung und Forschung (BMBF), the German Parkinson Association and Novartis GmbH.

Literature review of Industry 4.0 and related technologies

Abstract: Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective

Abstract: Internet of Things (IoT), which will create a huge network of billions or trillions of “Things” communicating with one another, are facing many technical and application challenges. This paper introduces the status of IoT development in China, including policies, R&D plans, applications, and standardization. With China's perspective, this paper depicts such challenges on technologies, applications, and standardization, and also proposes an open and general IoT architecture consisting of three platforms to meet the architecture challenge. Finally, this paper discusses the opportunity and prospect of IoT.

Smart Textiles for Electricity Generation.

Abstract: Textiles have been concomitant of human civilization for thousands of years. With the advances in chemistry and materials, integrating textiles with energy harvesters will provide a sustainable, environmentally friendly, pervasive, and wearable energy solution for distributed on-body electronics in the era of Internet of Things. This article comprehensively and thoughtfully reviews research activities regarding the utilization of smart textiles for harvesting energy from renewable energy sources on the human body and its surroundings. Specifically, we start with a brief introduction to contextualize the significance of smart textiles in light of the emerging energy crisis, environmental pollution, and public health. Next, we systematically review smart textiles according to their abilities to harvest biomechanical energy, body heat energy, biochemical energy, solar energy as well as hybrid forms of energy. Finally, we provide a critical analysis of smart textiles and insights into remaining challenges and future directions. With worldwide efforts, innovations in chemistry and materials elaborated in this review will push forward the frontiers of smart textiles, which will soon revolutionize our lives in the era of Internet of Things.