"Sample chapter "A Peek at the Future Internet" available at
http://www.springer.com/978-94-007-... more "Sample chapter "A Peek at the Future Internet" available at
http://www.springer.com/978-94-007-1472-4
<< Beautifully written, this book takes the reader on a compelling tour of the state of affairs in today’s Internet and the challenges it faces for supporting pervasive services of tomorrow. The approach adopted by the authors looks at the big picture, discussing the evolution of the Internet from a rigidly defined layered architecture to an interactive multi-faceted system providing, beyond connectivity, a more generative next-generation network infrastructure. In this context, the authors describe a selection of some of the prominent network mechanisms that may help in shaping the architecture of the future Internet. Overall, this book is informative, enjoyable, and an excellent reference source for every student, network professional, or researcher interested in the post-Internet era. >>
Prof. Raouf Boutaba, University of Waterloo (Canada)
Since its inception in the 1970s the Internet has become larger, faster and wireless. It is the biggest machine ever built, the “generative” engine of our digital society. However, the software that runs the global network has not seen any substantial upgrade since the early 1990s. It is now evident that the existing mechanisms that transport data around the Internet are no longer adequate for the new breed of Web applications. This book explains why the time is ripe for a complete overhaul in view of the Future Internet. Through a series of simple examples, the authors present a wealth of network mechanisms, starting from those that sustain the Web today. Readers will become familiar with a range of advanced protocols that will make the Internet more ubiquitous, reactive, proactive, information-driven, distribution-efficient and searchable. This book presents a selection of remarkable research ideas, making them accessible to the non-specialist reader.
TABLE OF CONTENTS
Foreword
Preface
Acknowledgements
1. On the Way to the Pervasive Web
1.1 The Net, a Tool for Everyone 1.2 The Inexorable Transformation of Internet applications 1.3 The Application’s Mutiny 1.4 Everything on the Move 1.5 New Interaction Paradigms Emerge 1.6 The Scent of Pervasive Applications 1.7 The Billion Dollar Question.- References.
2 The Network, as We Know It
2.1 The Multiple Facets of Networks 2.2 Networks from the Eyes of an Ordinary User 2.3 Invite a Programmer to Understand What’s in the Cloud 2.4 A Network Engineer to Turn a Switch into a Router 2.5 The Computer Science of a Router 2.6 Simple Math to Stabilize the Net 2.7 Life of a Commuter 2.8 The Three Fundamental Principles.- References.
3 Six Problems for the Service Provider
3.1 The Net has Ossified 3.2 Problem 1: Not Truly Ubiquitous 3.3 Problem 2: The Unresponsive Net 3.4 Problem 3: Too Much, Too Stale Signaling 3.5 Problem 4: Lack of Parallelism 3.6 Problem 5: Data Agnosticism 3.7 Problem 6: Inadequate Net-search Engine 3.8 Concluding Remarks.- References.
4 Spontaneous Networks
4.1 The Gift of Ubiquity 4.2 Spontaneous Connectivity 4.3 The Hidden-terminal Problem 4.4 The Exposed-terminal Problem 4.5 Preventive Measures to Avoid Collision 4.6 Path Discovery in a Volatile Network 4.7 The KISS Approach.- References.
5 Reactive Networks
5.1 Why Networks on Demand? 5.2 A Traffic-free Network 5.3 Our First Path 5.4 Path Management 5.5 Our Second Path 5.6 Global Synchronization 5.7 Error Management 5.8 Remarks on Reactive Networks.- References.
6 Proactive networks
6.1 From Reactive to Responsive 6.2 Keep the Network Ready 6.3 How do I Find My Multipoint Relay? 6.4 Life of an OLSR Node 6.5 The Node’s Information Repository 6.6 Shortest Path over the MPR Sub-topology 6.7 A Complete Example 6.8 How Proactive Can You Be? 6.9 The Power of Hybrid Protocols.- References.
7 Content-aware Networks
7.1 Routers Should Read the Content 7.2 A Network on Top of the Physical Network 7.3 Centralized Assignment of Node Identifiers 7.4 Centralized Entry-point Discovery 7.5 Multiple Bootstrap Servers 7.6 Decentralized Assignment of Node Identifiers 7.7 Entry Point Discovery via Underlying Links 7.8 Content is an Asset at the Edges.- References.
8 Distribution-efficient Networks
8.1 Publishing goes beyond Bootstrapping 8.2 The Two Flavors of Virtual Networking 8.3 Creating Unstructured Neighborhoods 8.4 Making Yourself Known in Unstructured Neighborhoods 8.5 Unstructured Resource Publishing 8.6 Secure a Role in Structure Worlds 8.7 Build Strict Formations 8.8 Place Links and Resources into a Structured Ring 8.9 Data-awareness via Protocol-agnosticism.- References.
9 Discovering Virtual Resources
9.1 Four Ways to Reach a Resource 9.2 Assessment of Discovery Mechanisms 9.3 Containing the Proliferation of Discovery Messages 9.4 Blind Discovery for Unstructured Networks 9.5 Informed Discovery in Unstructured Networks 9.6 Discovery in Loosely-Structured Networks 9.7 Deterministic Discovery in Structured Networks.- References.
10 A Peek at the Future Internet
10.1 The Fourth Networking Principle: Beyond Mere Connectivity 10.2 Internet of Things: Sense and Influence your Environment 10.3 Small, Large Networks 10.4 Manage the Autonomics 10.5 Dependable Networks 10.6 The Fine Line Between Freedom, Security and Privacy 10.7 Energy-efficient Networks 10.8 No Matter What, the Network will Remain Generative.- References.
Index"
—Wireless sensor networks have been widely used in many different applications and in the future ... more —Wireless sensor networks have been widely used in many different applications and in the future they will play an increasingly important role. Since these networks have no fixed infrastructure and are usually distributed over large areas, the use of routing protocols is indispensable. However, when the number of nodes within an area increases, the communication interferences and collisions increase significantly, thus reducing the network performance. In this paper, we first introduce a new measurable quantity, the " node concentration " , in contrast to the standard network density. Then, the performance of the AODV (Ad-hoc On-demand Distance Vector) routing protocol is evaluated with respect to the variation in node concentration. Finally, we propose an enhancement of AODV, called CG-AODV, by introducing a " node concentration-driven gossiping " approach for limiting the flooding of control packets. The simulation results demonstrate that CG-AODV provides significant improvements in terms of packet delivery ratio and path discovery delay.
The evaluation of mobile streaming services, particularly in terms of delivered Quality of Experi... more The evaluation of mobile streaming services, particularly in terms of delivered Quality of Experience (QoE), entails the use of automated methods (which excludes subjective QoE) that can be executed in real-time (i.e. without delaying the streaming process). This calls for lightweight algorithms that provide accurate results under considerable constraints. Starting from a low complexity no-reference objective algorithm for still images, in this work we contribute a new version that not only works for videos but, is general enough to adjust to a diverse range of video types while not significantly increasing the computational complexity. To achieve the necessary level of flexibility and computational efficiency, our method relies merely on information available at the client side and is equipped with a lightweight Artificial Neural Network which makes the algorithm independent from type of network or video. Its resource efficiency and generality make our method fit to be used in mobile streaming services. To prove the viability of our approach, we show a high level of correlation with the well-known full-reference method SSIM.
International Journal of Pervasive Computing and Communications, 2015
ABSTRACT Purpose – This paper aims to argue on the efficiency of Quality of Service (QoS)-based a... more ABSTRACT Purpose – This paper aims to argue on the efficiency of Quality of Service (QoS)-based adaptive streaming with regards to perceived quality Quality of Experience (QoE). Although QoS parameters are extensively used even by high-end adaptive streaming algorithms, achieved QoE fails to justify their use in real-time streaming videos with high motion. While subjective measurements of video quality are difficult to be applied at runtime, objective QoE assessment can be easier to automate. For end-to-end QoS optimization of live streaming of high-motion video, objective QoE is a more applicable approach. This paper contributes to the understanding of how specific QoS parameters affect objective QoE measurements on real-time high-motion video streaming. Design/methodology/approach – The paper approached the question through real-life and extensive experimentation using the Skype adaptive mechanisms. Two Skype terminals were connected through a QoS impairment box. A reference video was used as input to one Skype terminal and streamed on one direction. The impairment box was stressing the stream with different conditions. Received video was stored and compared against the reference video. Findings – After the experimental analysis, the paper concludes that adaptive mechanisms based on QoS-related heuristics fail to follow unexpected changes to stream requirements. High-motion videos are an example of this variability, which makes the perceived quality sensitive to jitter more than to packet loss. More specifically, Skype seems to use if-else heuristics to decide its behavior to QoS changes. The weaknesses to high-motion videos seem to lie on this rigidity. Research limitations/implications – Due to the testbed developed, the results may be different if experiments are run over networks with simultaneous streams and a variety of other traffic patterns. Finally, other streaming clients and algorithms would contribute to a more reliable generalization. Practical implications – The paper motivates video streaming engineers to emphasize their efforts toward QoE and end-to-end optimization. Originality/value – The paper identifies the need of a generic adaptive streaming algorithm able to accommodate a big range of video characteristics. The effect of QoS variability to high-motion video streaming helps in modeling and design.
Monitoring and controlling the user's perceived quality, in modern video services is a challe... more Monitoring and controlling the user's perceived quality, in modern video services is a challenging proposition, mainly due to the limitations of current Image Quality Assessment (IQA) algorithms. Subjective Quality of Experience (QoE) is widely used to get a right impression, but unfortunately this can not be used in real world scenarios. In general, objective QoE algorithms represent a good substitution for the subjective ones, and they are split in three main directions: Full Reference (FR), Reduced Reference (RR), and No Reference (NR). From these three, the RR IQA approach offers a practical solution to assess the quality of an impaired image due to the fact that just a small amount of information is needed from the original image. At the same time, keeping in mind that we need automated QoE algorithms which are context independent, in this paper we introduce a novel stochastic RR IQA metric to assess the quality of an image based on Deep Learning, namely Restricted Boltzman...
As the number of mobile devices increases, so do the complexity of wireless networks and the user... more As the number of mobile devices increases, so do the complexity of wireless networks and the user's requirements. This tendency makes necessary for Multimedia Services to take the needed actions to adapt to the upcoming technology. A prominent example of this type of services is HTTP Adaptive Video Streaming Applications. In this research, we have studied how the latest HTTP Adaptive Streaming techniques, mainly developed for standard computers, could be adapted and used in mobile wireless devices. Furthermore, inspired by these solutions, which usually make use of Reinforcement Learning (RL) algorithms to find the suitable streaming rate, we have conceived a novel smart video player client in Java for Android platform using the Dynamic Adaptive Streaming over HTTP (DASH) protocol. We have assessed the performance of our proposed solution in a self-developed wireless test-bed under different network conditions. Thus, we have seen that by including in the reward function contribu...
2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013
The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produ... more The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produce large amounts of sensor data. While capabilities of WSN motes increase, their resources, primarily energy, are still limited. Lightweight online on-mote processing may improve energy consumption by selecting only unexpected sensor data (anomalies) for transmission, which is commonly more energy consuming. We detect anomalies by analyzing sensor reading predictions from a linear model. We use Recursive Least Squares (RLS) to estimate the model parameters, because for large datasets the standard Linear Least Squares Estimation (LLSE) is not resource friendly.
2014 IEEE International Conference on Data Mining Workshop, 2014
Ever-more ubiquitous embedded systems provide us with large amounts of data. Performing analysis ... more Ever-more ubiquitous embedded systems provide us with large amounts of data. Performing analysis close to the data source allows for data reduction while giving information when unexpected behavior (i.e. anomalies in the system under observation) occurs. This work presents a novel approach to online anomaly detection, based on an ensemble of classifiers that can be executed on distributed embedded systems.
2013 IEEE 13th International Conference on Data Mining Workshops, 2013
Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless... more Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme Learning Machine (OS-ELM), an online learning algorithm for Single Layer Feedforward Neural Networks (SLFN). To overcome the stability issues introduced by the fixed precision, we apply correction mechanisms previously proposed for Recursive Least Squares (RLS).
10th International Conference on Network and Service Management (CNSM) and Workshop, 2014
Today the performance of network services and devices is mainly assessed using Quality of Service... more Today the performance of network services and devices is mainly assessed using Quality of Services (QoS) factors. These provide statistics about the quality of the network behavior but cannot accurately reflect how the unpredictable impairments which might occur in the network end up affecting the perception of the final beneficiary of these services, i.e. the user. This situation arises because QoS-based performance analysis does not capture the combined end-to-end properties of networks and applications. In this paper, we introduce a new network performance methodology based on Quality of Experience benchmarks, whereby we estimate the quality of the service as it is perceived by the user. We illustrate this approach in the context of video streaming services, showing how to evaluate quality degradation in Software Defined Networks. Our approach is better suited to the evaluation of dynamic networks and helps better pinpointing the critical factors that affect the applications the most.
2014 IEEE International Conference on Image Processing (ICIP), 2014
Improving the users' Quality of Experience (QoE) in modern 3D Multimedia Systems is a challenging... more Improving the users' Quality of Experience (QoE) in modern 3D Multimedia Systems is a challenging proposition, mainly due to our limited knowledge of 3D image Quality Assessment algorithms. While subjective QoE methods would better reflect the nature of human perception, these are not suitable in real-time automation cases. In this paper we tackle this issue from a new angle, using deep learning to make predictions on the user's QoE rather than trying to measure it through deterministic algorithms. We benchmark our method, dubbed Quality of Experience for 3D images through Factored Third Order Restricted Boltzmann Machine (Q3D-RBM), with subjective QoE methods, to determine its accuracy for different types of 3D images. The outcome is a Reduced Reference QoE assessment process for automatic image assessment and has significant potential to be extended to work on 3D video assessment.
"Sample chapter "A Peek at the Future Internet" available at
http://www.springer.com/978-94-007-... more "Sample chapter "A Peek at the Future Internet" available at
http://www.springer.com/978-94-007-1472-4
<< Beautifully written, this book takes the reader on a compelling tour of the state of affairs in today’s Internet and the challenges it faces for supporting pervasive services of tomorrow. The approach adopted by the authors looks at the big picture, discussing the evolution of the Internet from a rigidly defined layered architecture to an interactive multi-faceted system providing, beyond connectivity, a more generative next-generation network infrastructure. In this context, the authors describe a selection of some of the prominent network mechanisms that may help in shaping the architecture of the future Internet. Overall, this book is informative, enjoyable, and an excellent reference source for every student, network professional, or researcher interested in the post-Internet era. >>
Prof. Raouf Boutaba, University of Waterloo (Canada)
Since its inception in the 1970s the Internet has become larger, faster and wireless. It is the biggest machine ever built, the “generative” engine of our digital society. However, the software that runs the global network has not seen any substantial upgrade since the early 1990s. It is now evident that the existing mechanisms that transport data around the Internet are no longer adequate for the new breed of Web applications. This book explains why the time is ripe for a complete overhaul in view of the Future Internet. Through a series of simple examples, the authors present a wealth of network mechanisms, starting from those that sustain the Web today. Readers will become familiar with a range of advanced protocols that will make the Internet more ubiquitous, reactive, proactive, information-driven, distribution-efficient and searchable. This book presents a selection of remarkable research ideas, making them accessible to the non-specialist reader.
TABLE OF CONTENTS
Foreword
Preface
Acknowledgements
1. On the Way to the Pervasive Web
1.1 The Net, a Tool for Everyone 1.2 The Inexorable Transformation of Internet applications 1.3 The Application’s Mutiny 1.4 Everything on the Move 1.5 New Interaction Paradigms Emerge 1.6 The Scent of Pervasive Applications 1.7 The Billion Dollar Question.- References.
2 The Network, as We Know It
2.1 The Multiple Facets of Networks 2.2 Networks from the Eyes of an Ordinary User 2.3 Invite a Programmer to Understand What’s in the Cloud 2.4 A Network Engineer to Turn a Switch into a Router 2.5 The Computer Science of a Router 2.6 Simple Math to Stabilize the Net 2.7 Life of a Commuter 2.8 The Three Fundamental Principles.- References.
3 Six Problems for the Service Provider
3.1 The Net has Ossified 3.2 Problem 1: Not Truly Ubiquitous 3.3 Problem 2: The Unresponsive Net 3.4 Problem 3: Too Much, Too Stale Signaling 3.5 Problem 4: Lack of Parallelism 3.6 Problem 5: Data Agnosticism 3.7 Problem 6: Inadequate Net-search Engine 3.8 Concluding Remarks.- References.
4 Spontaneous Networks
4.1 The Gift of Ubiquity 4.2 Spontaneous Connectivity 4.3 The Hidden-terminal Problem 4.4 The Exposed-terminal Problem 4.5 Preventive Measures to Avoid Collision 4.6 Path Discovery in a Volatile Network 4.7 The KISS Approach.- References.
5 Reactive Networks
5.1 Why Networks on Demand? 5.2 A Traffic-free Network 5.3 Our First Path 5.4 Path Management 5.5 Our Second Path 5.6 Global Synchronization 5.7 Error Management 5.8 Remarks on Reactive Networks.- References.
6 Proactive networks
6.1 From Reactive to Responsive 6.2 Keep the Network Ready 6.3 How do I Find My Multipoint Relay? 6.4 Life of an OLSR Node 6.5 The Node’s Information Repository 6.6 Shortest Path over the MPR Sub-topology 6.7 A Complete Example 6.8 How Proactive Can You Be? 6.9 The Power of Hybrid Protocols.- References.
7 Content-aware Networks
7.1 Routers Should Read the Content 7.2 A Network on Top of the Physical Network 7.3 Centralized Assignment of Node Identifiers 7.4 Centralized Entry-point Discovery 7.5 Multiple Bootstrap Servers 7.6 Decentralized Assignment of Node Identifiers 7.7 Entry Point Discovery via Underlying Links 7.8 Content is an Asset at the Edges.- References.
8 Distribution-efficient Networks
8.1 Publishing goes beyond Bootstrapping 8.2 The Two Flavors of Virtual Networking 8.3 Creating Unstructured Neighborhoods 8.4 Making Yourself Known in Unstructured Neighborhoods 8.5 Unstructured Resource Publishing 8.6 Secure a Role in Structure Worlds 8.7 Build Strict Formations 8.8 Place Links and Resources into a Structured Ring 8.9 Data-awareness via Protocol-agnosticism.- References.
9 Discovering Virtual Resources
9.1 Four Ways to Reach a Resource 9.2 Assessment of Discovery Mechanisms 9.3 Containing the Proliferation of Discovery Messages 9.4 Blind Discovery for Unstructured Networks 9.5 Informed Discovery in Unstructured Networks 9.6 Discovery in Loosely-Structured Networks 9.7 Deterministic Discovery in Structured Networks.- References.
10 A Peek at the Future Internet
10.1 The Fourth Networking Principle: Beyond Mere Connectivity 10.2 Internet of Things: Sense and Influence your Environment 10.3 Small, Large Networks 10.4 Manage the Autonomics 10.5 Dependable Networks 10.6 The Fine Line Between Freedom, Security and Privacy 10.7 Energy-efficient Networks 10.8 No Matter What, the Network will Remain Generative.- References.
Index"
—Wireless sensor networks have been widely used in many different applications and in the future ... more —Wireless sensor networks have been widely used in many different applications and in the future they will play an increasingly important role. Since these networks have no fixed infrastructure and are usually distributed over large areas, the use of routing protocols is indispensable. However, when the number of nodes within an area increases, the communication interferences and collisions increase significantly, thus reducing the network performance. In this paper, we first introduce a new measurable quantity, the " node concentration " , in contrast to the standard network density. Then, the performance of the AODV (Ad-hoc On-demand Distance Vector) routing protocol is evaluated with respect to the variation in node concentration. Finally, we propose an enhancement of AODV, called CG-AODV, by introducing a " node concentration-driven gossiping " approach for limiting the flooding of control packets. The simulation results demonstrate that CG-AODV provides significant improvements in terms of packet delivery ratio and path discovery delay.
The evaluation of mobile streaming services, particularly in terms of delivered Quality of Experi... more The evaluation of mobile streaming services, particularly in terms of delivered Quality of Experience (QoE), entails the use of automated methods (which excludes subjective QoE) that can be executed in real-time (i.e. without delaying the streaming process). This calls for lightweight algorithms that provide accurate results under considerable constraints. Starting from a low complexity no-reference objective algorithm for still images, in this work we contribute a new version that not only works for videos but, is general enough to adjust to a diverse range of video types while not significantly increasing the computational complexity. To achieve the necessary level of flexibility and computational efficiency, our method relies merely on information available at the client side and is equipped with a lightweight Artificial Neural Network which makes the algorithm independent from type of network or video. Its resource efficiency and generality make our method fit to be used in mobile streaming services. To prove the viability of our approach, we show a high level of correlation with the well-known full-reference method SSIM.
International Journal of Pervasive Computing and Communications, 2015
ABSTRACT Purpose – This paper aims to argue on the efficiency of Quality of Service (QoS)-based a... more ABSTRACT Purpose – This paper aims to argue on the efficiency of Quality of Service (QoS)-based adaptive streaming with regards to perceived quality Quality of Experience (QoE). Although QoS parameters are extensively used even by high-end adaptive streaming algorithms, achieved QoE fails to justify their use in real-time streaming videos with high motion. While subjective measurements of video quality are difficult to be applied at runtime, objective QoE assessment can be easier to automate. For end-to-end QoS optimization of live streaming of high-motion video, objective QoE is a more applicable approach. This paper contributes to the understanding of how specific QoS parameters affect objective QoE measurements on real-time high-motion video streaming. Design/methodology/approach – The paper approached the question through real-life and extensive experimentation using the Skype adaptive mechanisms. Two Skype terminals were connected through a QoS impairment box. A reference video was used as input to one Skype terminal and streamed on one direction. The impairment box was stressing the stream with different conditions. Received video was stored and compared against the reference video. Findings – After the experimental analysis, the paper concludes that adaptive mechanisms based on QoS-related heuristics fail to follow unexpected changes to stream requirements. High-motion videos are an example of this variability, which makes the perceived quality sensitive to jitter more than to packet loss. More specifically, Skype seems to use if-else heuristics to decide its behavior to QoS changes. The weaknesses to high-motion videos seem to lie on this rigidity. Research limitations/implications – Due to the testbed developed, the results may be different if experiments are run over networks with simultaneous streams and a variety of other traffic patterns. Finally, other streaming clients and algorithms would contribute to a more reliable generalization. Practical implications – The paper motivates video streaming engineers to emphasize their efforts toward QoE and end-to-end optimization. Originality/value – The paper identifies the need of a generic adaptive streaming algorithm able to accommodate a big range of video characteristics. The effect of QoS variability to high-motion video streaming helps in modeling and design.
Monitoring and controlling the user's perceived quality, in modern video services is a challe... more Monitoring and controlling the user's perceived quality, in modern video services is a challenging proposition, mainly due to the limitations of current Image Quality Assessment (IQA) algorithms. Subjective Quality of Experience (QoE) is widely used to get a right impression, but unfortunately this can not be used in real world scenarios. In general, objective QoE algorithms represent a good substitution for the subjective ones, and they are split in three main directions: Full Reference (FR), Reduced Reference (RR), and No Reference (NR). From these three, the RR IQA approach offers a practical solution to assess the quality of an impaired image due to the fact that just a small amount of information is needed from the original image. At the same time, keeping in mind that we need automated QoE algorithms which are context independent, in this paper we introduce a novel stochastic RR IQA metric to assess the quality of an image based on Deep Learning, namely Restricted Boltzman...
As the number of mobile devices increases, so do the complexity of wireless networks and the user... more As the number of mobile devices increases, so do the complexity of wireless networks and the user's requirements. This tendency makes necessary for Multimedia Services to take the needed actions to adapt to the upcoming technology. A prominent example of this type of services is HTTP Adaptive Video Streaming Applications. In this research, we have studied how the latest HTTP Adaptive Streaming techniques, mainly developed for standard computers, could be adapted and used in mobile wireless devices. Furthermore, inspired by these solutions, which usually make use of Reinforcement Learning (RL) algorithms to find the suitable streaming rate, we have conceived a novel smart video player client in Java for Android platform using the Dynamic Adaptive Streaming over HTTP (DASH) protocol. We have assessed the performance of our proposed solution in a self-developed wireless test-bed under different network conditions. Thus, we have seen that by including in the reward function contribu...
2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013
The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produ... more The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produce large amounts of sensor data. While capabilities of WSN motes increase, their resources, primarily energy, are still limited. Lightweight online on-mote processing may improve energy consumption by selecting only unexpected sensor data (anomalies) for transmission, which is commonly more energy consuming. We detect anomalies by analyzing sensor reading predictions from a linear model. We use Recursive Least Squares (RLS) to estimate the model parameters, because for large datasets the standard Linear Least Squares Estimation (LLSE) is not resource friendly.
2014 IEEE International Conference on Data Mining Workshop, 2014
Ever-more ubiquitous embedded systems provide us with large amounts of data. Performing analysis ... more Ever-more ubiquitous embedded systems provide us with large amounts of data. Performing analysis close to the data source allows for data reduction while giving information when unexpected behavior (i.e. anomalies in the system under observation) occurs. This work presents a novel approach to online anomaly detection, based on an ensemble of classifiers that can be executed on distributed embedded systems.
2013 IEEE 13th International Conference on Data Mining Workshops, 2013
Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless... more Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme Learning Machine (OS-ELM), an online learning algorithm for Single Layer Feedforward Neural Networks (SLFN). To overcome the stability issues introduced by the fixed precision, we apply correction mechanisms previously proposed for Recursive Least Squares (RLS).
10th International Conference on Network and Service Management (CNSM) and Workshop, 2014
Today the performance of network services and devices is mainly assessed using Quality of Service... more Today the performance of network services and devices is mainly assessed using Quality of Services (QoS) factors. These provide statistics about the quality of the network behavior but cannot accurately reflect how the unpredictable impairments which might occur in the network end up affecting the perception of the final beneficiary of these services, i.e. the user. This situation arises because QoS-based performance analysis does not capture the combined end-to-end properties of networks and applications. In this paper, we introduce a new network performance methodology based on Quality of Experience benchmarks, whereby we estimate the quality of the service as it is perceived by the user. We illustrate this approach in the context of video streaming services, showing how to evaluate quality degradation in Software Defined Networks. Our approach is better suited to the evaluation of dynamic networks and helps better pinpointing the critical factors that affect the applications the most.
2014 IEEE International Conference on Image Processing (ICIP), 2014
Improving the users' Quality of Experience (QoE) in modern 3D Multimedia Systems is a challenging... more Improving the users' Quality of Experience (QoE) in modern 3D Multimedia Systems is a challenging proposition, mainly due to our limited knowledge of 3D image Quality Assessment algorithms. While subjective QoE methods would better reflect the nature of human perception, these are not suitable in real-time automation cases. In this paper we tackle this issue from a new angle, using deep learning to make predictions on the user's QoE rather than trying to measure it through deterministic algorithms. We benchmark our method, dubbed Quality of Experience for 3D images through Factored Third Order Restricted Boltzmann Machine (Q3D-RBM), with subjective QoE methods, to determine its accuracy for different types of 3D images. The outcome is a Reduced Reference QoE assessment process for automatic image assessment and has significant potential to be extended to work on 3D video assessment.
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Books by Antonio Liotta
http://www.springer.com/978-94-007-1472-4
<< Beautifully written, this book takes the reader on a compelling tour of the state of affairs in today’s Internet and the challenges it faces for supporting pervasive services of tomorrow. The approach adopted by the authors looks at the big picture, discussing the evolution of the Internet from a rigidly defined layered architecture to an interactive multi-faceted system providing, beyond connectivity, a more generative next-generation network infrastructure. In this context, the authors describe a selection of some of the prominent network mechanisms that may help in shaping the architecture of the future Internet. Overall, this book is informative, enjoyable, and an excellent reference source for every student, network professional, or researcher interested in the post-Internet era. >>
Prof. Raouf Boutaba, University of Waterloo (Canada)
Since its inception in the 1970s the Internet has become larger, faster and wireless. It is the biggest machine ever built, the “generative” engine of our digital society. However, the software that runs the global network has not seen any substantial upgrade since the early 1990s. It is now evident that the existing mechanisms that transport data around the Internet are no longer adequate for the new breed of Web applications. This book explains why the time is ripe for a complete overhaul in view of the Future Internet. Through a series of simple examples, the authors present a wealth of network mechanisms, starting from those that sustain the Web today. Readers will become familiar with a range of advanced protocols that will make the Internet more ubiquitous, reactive, proactive, information-driven, distribution-efficient and searchable. This book presents a selection of remarkable research ideas, making them accessible to the non-specialist reader.
TABLE OF CONTENTS
Foreword
Preface
Acknowledgements
1. On the Way to the Pervasive Web
1.1 The Net, a Tool for Everyone 1.2 The Inexorable Transformation of Internet applications 1.3 The Application’s Mutiny 1.4 Everything on the Move 1.5 New Interaction Paradigms Emerge 1.6 The Scent of Pervasive Applications 1.7 The Billion Dollar Question.- References.
2 The Network, as We Know It
2.1 The Multiple Facets of Networks 2.2 Networks from the Eyes of an Ordinary User 2.3 Invite a Programmer to Understand What’s in the Cloud 2.4 A Network Engineer to Turn a Switch into a Router 2.5 The Computer Science of a Router 2.6 Simple Math to Stabilize the Net 2.7 Life of a Commuter 2.8 The Three Fundamental Principles.- References.
3 Six Problems for the Service Provider
3.1 The Net has Ossified 3.2 Problem 1: Not Truly Ubiquitous 3.3 Problem 2: The Unresponsive Net 3.4 Problem 3: Too Much, Too Stale Signaling 3.5 Problem 4: Lack of Parallelism 3.6 Problem 5: Data Agnosticism 3.7 Problem 6: Inadequate Net-search Engine 3.8 Concluding Remarks.- References.
4 Spontaneous Networks
4.1 The Gift of Ubiquity 4.2 Spontaneous Connectivity 4.3 The Hidden-terminal Problem 4.4 The Exposed-terminal Problem 4.5 Preventive Measures to Avoid Collision 4.6 Path Discovery in a Volatile Network 4.7 The KISS Approach.- References.
5 Reactive Networks
5.1 Why Networks on Demand? 5.2 A Traffic-free Network 5.3 Our First Path 5.4 Path Management 5.5 Our Second Path 5.6 Global Synchronization 5.7 Error Management 5.8 Remarks on Reactive Networks.- References.
6 Proactive networks
6.1 From Reactive to Responsive 6.2 Keep the Network Ready 6.3 How do I Find My Multipoint Relay? 6.4 Life of an OLSR Node 6.5 The Node’s Information Repository 6.6 Shortest Path over the MPR Sub-topology 6.7 A Complete Example 6.8 How Proactive Can You Be? 6.9 The Power of Hybrid Protocols.- References.
7 Content-aware Networks
7.1 Routers Should Read the Content 7.2 A Network on Top of the Physical Network 7.3 Centralized Assignment of Node Identifiers 7.4 Centralized Entry-point Discovery 7.5 Multiple Bootstrap Servers 7.6 Decentralized Assignment of Node Identifiers 7.7 Entry Point Discovery via Underlying Links 7.8 Content is an Asset at the Edges.- References.
8 Distribution-efficient Networks
8.1 Publishing goes beyond Bootstrapping 8.2 The Two Flavors of Virtual Networking 8.3 Creating Unstructured Neighborhoods 8.4 Making Yourself Known in Unstructured Neighborhoods 8.5 Unstructured Resource Publishing 8.6 Secure a Role in Structure Worlds 8.7 Build Strict Formations 8.8 Place Links and Resources into a Structured Ring 8.9 Data-awareness via Protocol-agnosticism.- References.
9 Discovering Virtual Resources
9.1 Four Ways to Reach a Resource 9.2 Assessment of Discovery Mechanisms 9.3 Containing the Proliferation of Discovery Messages 9.4 Blind Discovery for Unstructured Networks 9.5 Informed Discovery in Unstructured Networks 9.6 Discovery in Loosely-Structured Networks 9.7 Deterministic Discovery in Structured Networks.- References.
10 A Peek at the Future Internet
10.1 The Fourth Networking Principle: Beyond Mere Connectivity 10.2 Internet of Things: Sense and Influence your Environment 10.3 Small, Large Networks 10.4 Manage the Autonomics 10.5 Dependable Networks 10.6 The Fine Line Between Freedom, Security and Privacy 10.7 Energy-efficient Networks 10.8 No Matter What, the Network will Remain Generative.- References.
Index"
Papers by Antonio Liotta
http://www.springer.com/978-94-007-1472-4
<< Beautifully written, this book takes the reader on a compelling tour of the state of affairs in today’s Internet and the challenges it faces for supporting pervasive services of tomorrow. The approach adopted by the authors looks at the big picture, discussing the evolution of the Internet from a rigidly defined layered architecture to an interactive multi-faceted system providing, beyond connectivity, a more generative next-generation network infrastructure. In this context, the authors describe a selection of some of the prominent network mechanisms that may help in shaping the architecture of the future Internet. Overall, this book is informative, enjoyable, and an excellent reference source for every student, network professional, or researcher interested in the post-Internet era. >>
Prof. Raouf Boutaba, University of Waterloo (Canada)
Since its inception in the 1970s the Internet has become larger, faster and wireless. It is the biggest machine ever built, the “generative” engine of our digital society. However, the software that runs the global network has not seen any substantial upgrade since the early 1990s. It is now evident that the existing mechanisms that transport data around the Internet are no longer adequate for the new breed of Web applications. This book explains why the time is ripe for a complete overhaul in view of the Future Internet. Through a series of simple examples, the authors present a wealth of network mechanisms, starting from those that sustain the Web today. Readers will become familiar with a range of advanced protocols that will make the Internet more ubiquitous, reactive, proactive, information-driven, distribution-efficient and searchable. This book presents a selection of remarkable research ideas, making them accessible to the non-specialist reader.
TABLE OF CONTENTS
Foreword
Preface
Acknowledgements
1. On the Way to the Pervasive Web
1.1 The Net, a Tool for Everyone 1.2 The Inexorable Transformation of Internet applications 1.3 The Application’s Mutiny 1.4 Everything on the Move 1.5 New Interaction Paradigms Emerge 1.6 The Scent of Pervasive Applications 1.7 The Billion Dollar Question.- References.
2 The Network, as We Know It
2.1 The Multiple Facets of Networks 2.2 Networks from the Eyes of an Ordinary User 2.3 Invite a Programmer to Understand What’s in the Cloud 2.4 A Network Engineer to Turn a Switch into a Router 2.5 The Computer Science of a Router 2.6 Simple Math to Stabilize the Net 2.7 Life of a Commuter 2.8 The Three Fundamental Principles.- References.
3 Six Problems for the Service Provider
3.1 The Net has Ossified 3.2 Problem 1: Not Truly Ubiquitous 3.3 Problem 2: The Unresponsive Net 3.4 Problem 3: Too Much, Too Stale Signaling 3.5 Problem 4: Lack of Parallelism 3.6 Problem 5: Data Agnosticism 3.7 Problem 6: Inadequate Net-search Engine 3.8 Concluding Remarks.- References.
4 Spontaneous Networks
4.1 The Gift of Ubiquity 4.2 Spontaneous Connectivity 4.3 The Hidden-terminal Problem 4.4 The Exposed-terminal Problem 4.5 Preventive Measures to Avoid Collision 4.6 Path Discovery in a Volatile Network 4.7 The KISS Approach.- References.
5 Reactive Networks
5.1 Why Networks on Demand? 5.2 A Traffic-free Network 5.3 Our First Path 5.4 Path Management 5.5 Our Second Path 5.6 Global Synchronization 5.7 Error Management 5.8 Remarks on Reactive Networks.- References.
6 Proactive networks
6.1 From Reactive to Responsive 6.2 Keep the Network Ready 6.3 How do I Find My Multipoint Relay? 6.4 Life of an OLSR Node 6.5 The Node’s Information Repository 6.6 Shortest Path over the MPR Sub-topology 6.7 A Complete Example 6.8 How Proactive Can You Be? 6.9 The Power of Hybrid Protocols.- References.
7 Content-aware Networks
7.1 Routers Should Read the Content 7.2 A Network on Top of the Physical Network 7.3 Centralized Assignment of Node Identifiers 7.4 Centralized Entry-point Discovery 7.5 Multiple Bootstrap Servers 7.6 Decentralized Assignment of Node Identifiers 7.7 Entry Point Discovery via Underlying Links 7.8 Content is an Asset at the Edges.- References.
8 Distribution-efficient Networks
8.1 Publishing goes beyond Bootstrapping 8.2 The Two Flavors of Virtual Networking 8.3 Creating Unstructured Neighborhoods 8.4 Making Yourself Known in Unstructured Neighborhoods 8.5 Unstructured Resource Publishing 8.6 Secure a Role in Structure Worlds 8.7 Build Strict Formations 8.8 Place Links and Resources into a Structured Ring 8.9 Data-awareness via Protocol-agnosticism.- References.
9 Discovering Virtual Resources
9.1 Four Ways to Reach a Resource 9.2 Assessment of Discovery Mechanisms 9.3 Containing the Proliferation of Discovery Messages 9.4 Blind Discovery for Unstructured Networks 9.5 Informed Discovery in Unstructured Networks 9.6 Discovery in Loosely-Structured Networks 9.7 Deterministic Discovery in Structured Networks.- References.
10 A Peek at the Future Internet
10.1 The Fourth Networking Principle: Beyond Mere Connectivity 10.2 Internet of Things: Sense and Influence your Environment 10.3 Small, Large Networks 10.4 Manage the Autonomics 10.5 Dependable Networks 10.6 The Fine Line Between Freedom, Security and Privacy 10.7 Energy-efficient Networks 10.8 No Matter What, the Network will Remain Generative.- References.
Index"