http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

In Distributed Algorithms, Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. She directs her book at a wide audience, including students, programmers, system designers, and researchers.



Distributed Algorithms contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others.



The material is organized according to the system model-first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference.



The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms-to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.


Table of Contents

1 Introduction 
2 Modelling I; Synchronous Network Model 
3 Leader Election in a Synchronous Ring 
4 Algorithms in General Synchronous Networks 
5 Distributed Consensus with Link Failures 
6 Distributed Consensus with Process Failures 
7 More Consensus Problems 
8 Modelling II: Asynchronous System Model 
9 Modelling III: Asynchronous Shared Memory Model 
10 Mutual Exclusion 
11 Resource Allocation 
12 Consensus 
13 Atomic Objects 
14 Modelling IV: Asynchronous Network Model 
15 Basic Asynchronous Network Algorithms 
16 Synchronizers 
17 Shared Memory versus Networks 
18 Logical Time 
19 Global Snapshots and Stable Properties 
20 Network Resource Allocation 
21 Asynchronous Networks with Process Failures 
22 Data Link Protocols 
23 Partially Synchronous System Models 
24 Mutual Exclusion with Partial Synchrony 
25 Consensus with Partial Synchrony

Distributed algorithms

Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.

Internet of Things in Industries: A Survey

It has been next to impossible in the past months not to come across the term ‘‘Internet of Things’’ (IoT) one way or another. Especially the past year has seen a tremendous surge of interest in the Internet of Things. Consortia have been formed to define frameworks and standards for the IoT. Companies have started to introduce numerous IoTbased products and services. And a number of IoT-related acquisitions have been making the headlines, including, e.g., the prominent takeover of Nest by Google for $3.2 billion and the subsequent acquisitions of Dropcam by Nest and of SmartThings by Samsung. Politicians as well as practitioners increasingly acknowledge the Internet of Things as a real business opportunity, and estimates currently suggest that the IoT could grow into a market worth $7.1 trillion by 2020 (IDC 2014). While the term Internet of Things is now more and more broadly used, there is no common definition or understanding today of what the IoT actually encompasses. The origins of the term date back more than 15 years and have been attributed to the work of the Auto-ID Labs at the Massachusetts Institute of Technology (MIT) on networked radio-frequency identification (RFID) infrastructures (Atzori et al. 2010; Mattern and Floerkemeier 2010). Since then, visions for the Internet of Things have been further developed and extended beyond the scope of RFID technologies. The International Telecommunication Union (ITU) for instance now defines the Internet of Things as ‘‘a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies’’ (ITU 2012). At the same time, a multitude of alternative definitions has been proposed. Some of these definitions exhibit an emphasis on the things which become connected in the IoT. Other definitions focus on Internet-related aspects of the IoT, such as Internet protocols and network technology. And a third type centers on semantic challenges in the IoT relating to, e.g., the storage, search and organization of large volumes of information (Atzori et al. 2010). The fields of application for IoT technologies are as numerous as they are diverse, as IoT solutions are increasingly extending to virtually all areas of everyday. The most prominent areas of application include, e.g., the smart industry, where the development of intelligent production systems and connected production sites is often discussed under the heading of Industry 4.0. In the smart home or building area, intelligent thermostats and security systems are receiving a lot of attention, while smart energy applications focus on smart electricity, gas and water meters. Smart transport solutions include, e.g., vehicle fleet tracking and mobile ticketing, while in the smart health area, topics such as patients’ surveillance and chronic disease management are being addressed. And in the context of Accepted after one revision by Prof. Dr. Sinz.

/pdf/internet-of-things-technology-and-value-added-24gs2175e1.pdf

Internet of Things - Technology and Value Added

In wireless sensor networks, many applications rely on the ability of sensor nodes to estimate their physical location and time with respect to a common reference scale. This necessity is reflected by the development of a significant number of algorithms for localization and time synchronization for sensor networks in the recent past. However, research on location estimation on the one hand and on time synchronization on the other hand has been largely separated. Despite this, models, requirements, techniques, and algorithms of the two domains are rather similar and in some respects closely related. The purpose of this paper is to make this affinity explicit, with the hope of stimulating a mutual fertilization and enabling a better understanding of both domains.

/pdf/towards-a-unified-view-on-space-and-time-in-sensor-networks-4l28h6aqmf.pdf

Towards a unified view on space and time in sensor networks

The internet of things: First international conference : proceedings

With smart electricity meters being widely deployed, data on residential energy usage is increasingly becoming available. To make sensible use of these data, we investigated the suitability of mobile phones as an interface to provide feedback on overall and device-related energy consumption. Based on the results of a user survey, we implemented the most highly valued feedback features on an iPhone that communicates with a smart meter. In a follow-up user study, we evaluated how users perceive the experience of such energy consumption feedback and how they rate the importance of different functionalities. Our work confirms the suitability of energy feedback delivered on a mobile phone. It outlines that a clear and easy to explain use case scenario is key and that knowledge-increasing functionalities as well as those functionalities from which monetary savings can be directly implied are perceived as most important. To address technophobe users, action-guiding feedback that goes beyond displaying aggregated information is required.

/pdf/evaluating-mobile-phones-as-energy-consumption-feedback-b334tsz40w.pdf

Evaluating Mobile Phones as Energy Consumption Feedback Devices

When it comes to conserving electricity, it is crucial for users to know how much energy is consumed by individual appliances. However, the technical feedback provided by existing energy consumption feedback systems in the form of dry numbers and intangible units is not appropriate for most users. To address this shortcoming, we developed PowerPedia, a system that provides behavior-influencing feedback over and above pure consumption values. By integrating a community platform--a Wikipedia for electrical appliances--PowerPedia enables users to identify and compare the consumption of their domestic appliances with that of others. It thus helps users to better understand their electricity consumption and take effective action to save electricity.

PowerPedia: changing energy usage with the help of a community-based smartphone application

Over the last 30 years, we have seen the power of microprocessors double about every 18 months. An equally rapid increase applies to some other technological parameters such as storage capacity and communications bandwidth. This continuing trend means that computers will become considerably smaller, cheaper, and more abundant – they are becoming ubiquitous, and are even finding their way into everyday objects. This is resulting in the creation of “smart” things that can access the Internet and its varied resources, and maybe even cooperate with each other. Mobile phones are a forerunner in this technological field – they are now true computers equipped with a whole range of functionality and may well develop into control centers for a multitude of other personal auxiliary services. In the long term, ubiquitous computing will take on great economic significance. Industrial products will become “smart” due to their integrated information processing capacity, or take on an electronic identity that can be remotely queried, or be equipped with sensors for detecting their environment, enabling innovative products and totally new services to be developed. However, an “informatized” world full of objects that can detect aspects of their environment and communicate with each other also has serious societal implications. The social and political challenges of the ubiquitous computing era will be characterized by an increasing dependence on technology, control over the information to which everyday objects are linked, and the protection of privacy.

/pdf/wireless-future-ubiquitous-computing-1bfyn9t4fk.pdf

Friedemann Mattern

Papers

Towards a unified view on space and time in sensor networks

The internet of things: First international conference : proceedings

Evaluating Mobile Phones as Energy Consumption Feedback Devices

PowerPedia: changing energy usage with the help of a community-based smartphone application

Wireless Future: Ubiquitous Computing