How smart are our environments? An updated look at the state of the art
TL;DR: A look at the state of the art in smart environments research is taken, motivated by the recent dramatic increase in activity, and summarizes work in a variety of supporting disciplines.
About: This article is published in Pervasive and Mobile Computing.The article was published on 2007-03-01. It has received 610 citations till now. The article focuses on the topics: Smart environment.
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TL;DR: This paper provides a survey of the technologies that comprise ambient intelligence and of the applications that are dramatically affected by it and specifically focuses on the research that makes AmI technologies ''intelligent''.
921 citations
Cites background from "How smart are our environments? An ..."
...Some examples of such devices are electrodomestics (e.g., cooker and fridge), household items (e.g., taps, bed and sofa) and temperature handling devices (e.g., air conditioning and radiators)....
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07 Jun 2015
TL;DR: This paper gives an introduction to industrial IoT systems, the related security and privacy challenges, and an outlook on possible solutions towards a holistic security framework for Industrial IoT systems.
Abstract: Today, embedded, mobile, and cyberphysical systems are ubiquitous and used in many applications, from industrial control systems, modern vehicles, to critical infrastructure. Current trends and initiatives, such as "Industrie 4.0" and Internet of Things (IoT), promise innovative business models and novel user experiences through strong connectivity and effective use of next generation of embedded devices. These systems generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. Cyberattacks on IoT systems are very critical since they may cause physical damage and even threaten human lives. The complexity of these systems and the potential impact of cyberattacks bring upon new threats. This paper gives an introduction to Industrial IoT systems, the related security and privacy challenges, and an outlook on possible solutions towards a holistic security framework for Industrial IoT systems.
761 citations
Cites background from "How smart are our environments? An ..."
...This network of ubiquitous smart objects is known as the Internet of Things (IoT) and enables novel applications and services, in particular in the industrial sector [9, 37, 34, 42, 69]....
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TL;DR: This paper presents a generic system architecture for the proposed knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes, and describes the underlying ontology-based recognition process.
Abstract: This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes The approach goes beyond the traditional data-centric methods for activity recognition in three ways First, it makes extensive use of domain knowledge in the life cycle of activity recognition Second, it uses ontologies for explicit context and activity modeling and representation Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios An average activity recognition rate of 9444 percent was achieved and the average recognition runtime per recognition operation was measured as 25 seconds
558 citations
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...An average activity recognition rate of 94.44 percent was achieved and the average recognition runtime per recognition operation was measured as 2.5 seconds....
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Book•
01 Jan 2009
TL;DR: This paper aims to provide a history of Ubiquitous Computing and its applications in the Virtual, Human and Physical World, as well as some examples of how these applications have changed over time.
Abstract: List of Figures List of Tables Preface Acknowledgements 1 Ubiquitous Computing: Basics and Vision 11 Living in a Digital World 12 Modelling the Key Ubiquitous Computing Properties 13 Ubiquitous System Environment Interaction 14 Architectural Design for UbiCom Systems: Smart DEI Model 15 Discussion Exercises References 2 Applications and Requirements 21 Introduction 22 Example Early UbiCom Research Projects 23 Everyday Applications in the Virtual, Human and Physical World 24 Discussion Exercises References 3 Smart Devices and Services 31 Introduction 32 Service Architecture Models 33 Service Provision Life-Cycle 34 Virtual Machines and Operating Systems Exercises References 4 Smart Mobiles, Cards and Device Networks 41 Introduction 42 Smart Mobile Devices, Users, Resources and Code 43 Operating Systems for Mobile Computers and Communicator Devices 44 Smart Card Devices 45 Device Networks Exercises References 5 Human-Computer Interaction 51 Introduction 52 User Interfaces and Interaction for Four Widely Used Devices 53 Hidden UI Via Basic Smart Devices 54 Hidden UI Via Wearable and Implanted Devices 55 Human-Centred Design (HCD) 56 User Models: Acquisition and Representation 57 iHCI Design Exercises References 6 Tagging, Sensing and Controlling 61 Introduction 62 Tagging the Physical World 63 Sensors and Sensor Networks 64 Micro Actuation and Sensing: MEMS 65 Embedded Systems and Real-Time Systems 66 Control Systems (for Physical World Tasks) 67 Robots Exercises References 7 Context-Aware Systems 71 Introduction 72 Modelling Context-Aware Systems 73 Mobility Awareness 74 Spatial Awareness 75 Temporal Awareness: Coordinating and Scheduling 76 ICT System Awareness Exercises References 8 Intelligent Systems (IS) 81 Introduction 82 Basic Concepts 83 IS Architectures 84 Semantic KB IS 85 Classical Logic IS 86 Soft Computing IS Models 87 IS System Operations Exercises References 9 Intelligent System Interaction 91 Introduction 92 Interaction Multiplicity 93 Is Interaction Design 94 Some Generic Intelligent Interaction Applications Exercises References 10 Autonomous Systems and Artificial Life 101 Introduction 102 Basic Autonomous Intra-Acting Systems 103 Reflective and Self-Aware Systems 104 Self-Management and Autonomic Computing 105 Complex Systems 106 Artificial Life Exercises References 11 Ubiquitous Communication 111 Introduction 112 Audio Networks 113 Data Networks 114 Wireless Data Networks 115 Universal and Transparent Audio, Video and Alphanumeric Data 116 Ubiquitous Networks 117 Further Network Design Issues Exercises References 12 Management of Smart Devices 121 Introduction 122 Managing Smart Devices in Virtual Environments 123 Managing Smart Devices in Human User-Centred Environments 124 Managing Smart Devices in Physical Environments Exercises References 13 Ubiquitous System: Challenges and Outlook 131 Introduction 132 Overview of Challenges 133 Smart Devices 134 Smart Interaction 135 Smart Physical Environment Device Interaction 136 Smart Human-Device Interaction 137 Human Intelligence Versus Machine Intelligence 138 Social Issues: Promise Versus Peril 139 Final Remarks Exercises References Index
501 citations
Cites background from "How smart are our environments? An ..."
...Cook and Das (2007) refer to a smart environment as ‘one that is able to acquire and apply knowledge about the environment and its inhabitants in order to improve their experience in that environment’....
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TL;DR: A comprehensive analysis of the nature and characteristics of situations is provided, the complexities of situation identification are discussed, and the techniques that are most popularly used in modelling and inferring situations from sensor data are reviewed.
450 citations
Cites background from "How smart are our environments? An ..."
...It has many potential applications, from intelligent workplaces and smart homes to healthcare, gaming, leisure systems and to public transportation [2]....
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...Sensors in pervasive computing can capture a broad range of information on the following aspects [2]:...
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References
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Book•
01 Jan 1991TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Abstract: Preface to the Second Edition. Preface to the First Edition. Acknowledgments for the Second Edition. Acknowledgments for the First Edition. 1. Introduction and Preview. 1.1 Preview of the Book. 2. Entropy, Relative Entropy, and Mutual Information. 2.1 Entropy. 2.2 Joint Entropy and Conditional Entropy. 2.3 Relative Entropy and Mutual Information. 2.4 Relationship Between Entropy and Mutual Information. 2.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information. 2.6 Jensen's Inequality and Its Consequences. 2.7 Log Sum Inequality and Its Applications. 2.8 Data-Processing Inequality. 2.9 Sufficient Statistics. 2.10 Fano's Inequality. Summary. Problems. Historical Notes. 3. Asymptotic Equipartition Property. 3.1 Asymptotic Equipartition Property Theorem. 3.2 Consequences of the AEP: Data Compression. 3.3 High-Probability Sets and the Typical Set. Summary. Problems. Historical Notes. 4. Entropy Rates of a Stochastic Process. 4.1 Markov Chains. 4.2 Entropy Rate. 4.3 Example: Entropy Rate of a Random Walk on a Weighted Graph. 4.4 Second Law of Thermodynamics. 4.5 Functions of Markov Chains. Summary. Problems. Historical Notes. 5. Data Compression. 5.1 Examples of Codes. 5.2 Kraft Inequality. 5.3 Optimal Codes. 5.4 Bounds on the Optimal Code Length. 5.5 Kraft Inequality for Uniquely Decodable Codes. 5.6 Huffman Codes. 5.7 Some Comments on Huffman Codes. 5.8 Optimality of Huffman Codes. 5.9 Shannon-Fano-Elias Coding. 5.10 Competitive Optimality of the Shannon Code. 5.11 Generation of Discrete Distributions from Fair Coins. Summary. Problems. Historical Notes. 6. Gambling and Data Compression. 6.1 The Horse Race. 6.2 Gambling and Side Information. 6.3 Dependent Horse Races and Entropy Rate. 6.4 The Entropy of English. 6.5 Data Compression and Gambling. 6.6 Gambling Estimate of the Entropy of English. Summary. Problems. Historical Notes. 7. Channel Capacity. 7.1 Examples of Channel Capacity. 7.2 Symmetric Channels. 7.3 Properties of Channel Capacity. 7.4 Preview of the Channel Coding Theorem. 7.5 Definitions. 7.6 Jointly Typical Sequences. 7.7 Channel Coding Theorem. 7.8 Zero-Error Codes. 7.9 Fano's Inequality and the Converse to the Coding Theorem. 7.10 Equality in the Converse to the Channel Coding Theorem. 7.11 Hamming Codes. 7.12 Feedback Capacity. 7.13 Source-Channel Separation Theorem. Summary. Problems. Historical Notes. 8. Differential Entropy. 8.1 Definitions. 8.2 AEP for Continuous Random Variables. 8.3 Relation of Differential Entropy to Discrete Entropy. 8.4 Joint and Conditional Differential Entropy. 8.5 Relative Entropy and Mutual Information. 8.6 Properties of Differential Entropy, Relative Entropy, and Mutual Information. Summary. Problems. Historical Notes. 9. Gaussian Channel. 9.1 Gaussian Channel: Definitions. 9.2 Converse to the Coding Theorem for Gaussian Channels. 9.3 Bandlimited Channels. 9.4 Parallel Gaussian Channels. 9.5 Channels with Colored Gaussian Noise. 9.6 Gaussian Channels with Feedback. Summary. Problems. Historical Notes. 10. Rate Distortion Theory. 10.1 Quantization. 10.2 Definitions. 10.3 Calculation of the Rate Distortion Function. 10.4 Converse to the Rate Distortion Theorem. 10.5 Achievability of the Rate Distortion Function. 10.6 Strongly Typical Sequences and Rate Distortion. 10.7 Characterization of the Rate Distortion Function. 10.8 Computation of Channel Capacity and the Rate Distortion Function. Summary. Problems. Historical Notes. 11. Information Theory and Statistics. 11.1 Method of Types. 11.2 Law of Large Numbers. 11.3 Universal Source Coding. 11.4 Large Deviation Theory. 11.5 Examples of Sanov's Theorem. 11.6 Conditional Limit Theorem. 11.7 Hypothesis Testing. 11.8 Chernoff-Stein Lemma. 11.9 Chernoff Information. 11.10 Fisher Information and the Cram-er-Rao Inequality. Summary. Problems. Historical Notes. 12. Maximum Entropy. 12.1 Maximum Entropy Distributions. 12.2 Examples. 12.3 Anomalous Maximum Entropy Problem. 12.4 Spectrum Estimation. 12.5 Entropy Rates of a Gaussian Process. 12.6 Burg's Maximum Entropy Theorem. Summary. Problems. Historical Notes. 13. Universal Source Coding. 13.1 Universal Codes and Channel Capacity. 13.2 Universal Coding for Binary Sequences. 13.3 Arithmetic Coding. 13.4 Lempel-Ziv Coding. 13.5 Optimality of Lempel-Ziv Algorithms. Compression. Summary. Problems. Historical Notes. 14. Kolmogorov Complexity. 14.1 Models of Computation. 14.2 Kolmogorov Complexity: Definitions and Examples. 14.3 Kolmogorov Complexity and Entropy. 14.4 Kolmogorov Complexity of Integers. 14.5 Algorithmically Random and Incompressible Sequences. 14.6 Universal Probability. 14.7 Kolmogorov complexity. 14.9 Universal Gambling. 14.10 Occam's Razor. 14.11 Kolmogorov Complexity and Universal Probability. 14.12 Kolmogorov Sufficient Statistic. 14.13 Minimum Description Length Principle. Summary. Problems. Historical Notes. 15. Network Information Theory. 15.1 Gaussian Multiple-User Channels. 15.2 Jointly Typical Sequences. 15.3 Multiple-Access Channel. 15.4 Encoding of Correlated Sources. 15.5 Duality Between Slepian-Wolf Encoding and Multiple-Access Channels. 15.6 Broadcast Channel. 15.7 Relay Channel. 15.8 Source Coding with Side Information. 15.9 Rate Distortion with Side Information. 15.10 General Multiterminal Networks. Summary. Problems. Historical Notes. 16. Information Theory and Portfolio Theory. 16.1 The Stock Market: Some Definitions. 16.2 Kuhn-Tucker Characterization of the Log-Optimal Portfolio. 16.3 Asymptotic Optimality of the Log-Optimal Portfolio. 16.4 Side Information and the Growth Rate. 16.5 Investment in Stationary Markets. 16.6 Competitive Optimality of the Log-Optimal Portfolio. 16.7 Universal Portfolios. 16.8 Shannon-McMillan-Breiman Theorem (General AEP). Summary. Problems. Historical Notes. 17. Inequalities in Information Theory. 17.1 Basic Inequalities of Information Theory. 17.2 Differential Entropy. 17.3 Bounds on Entropy and Relative Entropy. 17.4 Inequalities for Types. 17.5 Combinatorial Bounds on Entropy. 17.6 Entropy Rates of Subsets. 17.7 Entropy and Fisher Information. 17.8 Entropy Power Inequality and Brunn-Minkowski Inequality. 17.9 Inequalities for Determinants. 17.10 Inequalities for Ratios of Determinants. Summary. Problems. Historical Notes. Bibliography. List of Symbols. Index.
45,034 citations
TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
17,936 citations
TL;DR: The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections.
Abstract: The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments in this field.
14,048 citations
"How smart are our environments? An ..." refers background in this paper
...Indeed, wireless sensor networks have attracted a plethora of research efforts due to their vast potential applications, such as smart buildings, environment or habitat monitoring, utility plants, industry process control, homes, ships, telemedicine, crisis management, transportation systems, and so on [4,15,51]....
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TL;DR: Consider writing, perhaps the first information technology: The ability to capture a symbolic representation of spoken language for long-term storage freed information from the limits of individual memory.
Abstract: Specialized elements of hardware and software, connected by wires, radio waves and infrared, will soon be so ubiquitous that no-one will notice their presence.
9,073 citations
TL;DR: The proposed concept of compressibility is shown to play a role analogous to that of entropy in classical information theory where one deals with probabilistic ensembles of sequences rather than with individual sequences.
Abstract: Compressibility of individual sequences by the class of generalized finite-state information-lossless encoders is investigated. These encoders can operate in a variable-rate mode as well as a fixed-rate one, and they allow for any finite-state scheme of variable-length-to-variable-length coding. For every individual infinite sequence x a quantity \rho(x) is defined, called the compressibility of x , which is shown to be the asymptotically attainable lower bound on the compression ratio that can be achieved for x by any finite-state encoder. This is demonstrated by means of a constructive coding theorem and its converse that, apart from their asymptotic significance, also provide useful performance criteria for finite and practical data-compression tasks. The proposed concept of compressibility is also shown to play a role analogous to that of entropy in classical information theory where one deals with probabilistic ensembles of sequences rather than with individual sequences. While the definition of \rho(x) allows a different machine for each different sequence to be compressed, the constructive coding theorem leads to a universal algorithm that is asymptotically optimal for all sequences.
3,753 citations
"How smart are our environments? An ..." refers background in this paper
...From an information theoretic viewpoint, an inhabitant’s mobility and activity create an uncertainty of their locations and hence subsequent activities....
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