Many distributed applications, such as BitTorrent, need to know the distance between each pair of network hosts in order to optimize their performance. For small-scale systems, explicit measurements can be carried out to collect the distance information. For large-scale applications, this approach does not work due to the tremendous amount of measurements that have to be completed. To tackle the scalability problem, network coordinate system (NCS) was proposed to solve the scalability problem by using partial measurements to predict the unknown distances. However, the existing NCS schemes suffer seriously from either low prediction precision or unsatisfactory convergence speed. In this paper, we present a novel distributed network coordinate system (DISCS) that utilizes a limited set of distance measurements to achieve high-precision distance prediction at a fast convergence speed. Technically, DISCS employs the innovative robust nonnegative matrix completion method to improve the prediction accuracy. Through extensive experiments based on various publicly-available data sets, we found that DISCS outperforms the state-of-the-art NCS schemes in terms of prediction precision and convergence speed, which clearly shows the high usability of DISCS in real-life Internet applications.

In order to optimize energy efficiency, network operators try to switch off as many network devices as possible. Recently, there is a trend to introduce content caches as an inherent capacity of network equipment, with the objective of improving the efficiency of content distribution and reducing network congestion. In this work, we study the impact of using in-network caches and content delivery network (CDN) cooperation on an energy efficient routing. We formulate this problem as Energy Efficient Content Distribution; we propose an integer linear program and a heuristic algorithm to solve it. The objective of this problem is to find a feasible routing, so that the total energy consumption of the network is minimized while the constraints given by the demands and the link capacity are satisfied. We exhibit for which range of parameters (size of caches, popularity of content, demand intensity, etc.) it is useful to use caches. Experimental results show that by placing a cache on each backbone router to store the most popular content, along with choosing well the best content provider server for each demand to a CDN, we can save about 20% of power on average in all the backbone networks considered.

Information Centric Network (ICN) has been recently studied to support future content-oriented services by designing new network architectures and protocols. In this paper, we demonstrate our implementation of a mobile router/server built on a ICN platform. This demo called MagicBox enables content caching and prefetching for personal and professional uses. Particularly, we detail how our system will be showcased in big events where Internet access and media content can be efficiently shared.

The sleep apnea is a sleep disorder characterized by cessation of respiratory flow (apnea) or a reduction in the flow (hypopnea). This disorder is often invalidating and may in some cases lead to death. During the night, symptoms can include nocturnal choking, heavy snoring, sweating, restless sleep, impotence, and witnessed apnea. As the sleep centers for apnea detection are usually overloaded and inaccessible, an automatic apnea detection algorithm for portable devices is required for in-home detection. In this paper, we propose a lightweight approach for pervasive detection of sleep apnea using Wireless Sensor Networks. The experimental results show that our proposed approach achieves good detection accuracy with low delay and low false alarm rate.

In this paper, we propose an Anomaly Detection (AD) approach for medical Wireless Sensor Networks (WSNs). This approach is able to detect abnormal changes and to cope with unreliable or maliciously injected measurements in the network, without prior knowledge of anomalous events or normal data pattern. The main objective is to reduce the false alarms triggered by abnormal measurements. In our proposed framework, each sensor applies the Exponentially Weighted Moving Average (EWMA) for one-step forecasting. To reduce the energy consumed by periodic data transmission to the Local Processing Unit (LPU), the sensor transmits only when the data point (measured, expected) falls outside the dynamically updated ellipsoidal region enclosing the normal data. The LPU exploits correlation and uses chi-square distance for spatial analysis before raising a medical alarm. We evaluate our approach on real medical data set. Experimental results through computer simulation demonstrate that our proposed approach can achieve a good detection accuracy with low false alarm rate (less than 4%).

In this paper, we propose a lightweight approach for online detection of faulty measurements by analyzing the data collected from medical wireless body area networks. The proposed framework performs sequential data analysis using a smart phone as a base station, and takes into account the constrained resources of the smart phone, such as processing power and storage capacity. The main objective is to raise alarms only when patients enter in an emergency situation, and to discard false alarms triggered by faulty measurements or ill-behaved sensors. The proposed approach is based on the Haar wavelet decomposition, nonseasonal Holt-Winters forecasting, and the Hampel filter for spatial analysis, and on for temporal analysis. Our objective is to reduce false alarms resulting from unreliable measurements and to reduce unnecessary healthcare intervention. We apply our proposed approach on real physiological dataset. Our experimental results prove the effectiveness of our approach in achieving good detection accuracy with a low false alarm rate. The simplicity and the processing speed of our proposed framework make it useful and efficient for real time diagnosis.

The aim of this paper is to propose a new approach for the detection and isolation of faulty measurements in medical wireless sensors networks. The proposed approach is based on the combination of statistical model and machine learning algorithm. We begin by collecting physiological data and then we cluster the data collected during the first few minutes using the Gaussian mixture decomposition. We use the resulted labeled data as the input for the Ant Colony algorithm to derive classification rules, which are used to detect abnormal values. Finally, we exploit the spatial correlation between monitored attributes to differentiate between faulty sensor readings and emergency situations. Our experimental results on real patient dataset show that our proposed approach achieves a high level of detection accuracy, which in turn proves the effectiveness of this approach in enhancing the reliability of medical wireless sensors networks.

To optimize energy efficiency in network, operators try to switch off as many network devices as possible. Recently, there is a trend to introduce content caches as an inherent capacity of network equipment, with the objective of improving the efficiency of content distribution and reducing network congestion. In this work, we study the impact of using in-network caches and content delivery network (CDN) cooperation on an energy-efficient routing. We formulate this problem as Energy Efficient Content Distribution. The objective is to find a feasible routing, so that the total energy consumption of the network is minimized subject to satisfying all the demands and link capacity. We exhibit the range of parameters (size of caches, popularity of content, demand intensity, etc.) for which caches are useful. Experimental results show that by placing a cache on each backbone router to store the most popular content, along with well choosing the best content provider server for each demand to a CDN, we can save a total up to 23% of power in the backbone, while 16% can be gained solely thanks to caches.

In this paper, we present our implementation and evaluation of Dynamic Adaptive Streaming over Content centric networking (DASC) which implements MPEG Dynamic Adaptive Streaming over HTTP (DASH) utilizing a Content Centric Networking (CCN) naming scheme to identify content segments in a CCN network. In particular, video segments formatted according to MPEG-DASH are available in different quality levels but instead of HTTP, CCN is used for referencing and delivery. Based on the conditions of the network, the DASC client issues interests for segments achieving the best throughput. Due to segment caching within the network, subsequent requests for the same content can be served quicker. As a result, the quality of the video a user receives progressively improves, effectively overcoming bottlenecks in the network. We present two sets of experiments to evaluate the performance of DASC showing that throughput indeed improves. However, the generated overhead is relatively large and the adaptation strategy used for DASH that assumes an end-to-end connection could be revised for the hop-by-hop architecture of CCN.

In this paper, we focus on online detection and isolation of erroneous values reported by medical wireless sensors. We propose a lightweight approach for online anomaly detection in collected data, able to raise alarms only when patients enter in emergency situation and to discard faulty measurements. The proposed approach is based on Haar wavelet decomposition and Hampel filter for spatial analysis, and on boxplot for temporal analysis. Our objective is to reduce false alarms resulted from unreliable measurements. We apply our proposed approach on real physiological data set. Our experimental results prove the effectiveness of our approach to achieve good detection accuracy with low false alarm rate.

One of the most attractive feature of IPTV is the so-called catch-up TV. It allows viewers to watch their favorite TV programs within an expanded time window. In this paper, we emphasize the challenging characteristics of catch-up TV systems that prevent known delivery systems to be used. Then, we formulate the catch-up TV as a multiple-interval graph. Based upon this modeling, we further present a Peer-Assisted Catch-Up Streaming system, namely PACUS, where a set of end users' computers assists the server for the content delivery. We show in particular how the PACUS tracker server can be efficiently implemented for catch-up TV. We demonstrate the benefits of PACUS by simulations. We especially highlight that PACUS reduces the traffic at the server side with the advantages of lightweight and self-adaptive unstructured peer-to-peer systems. Furthermore, comparing four kinds of neighborhood selection strategies implemented on the PACUS server, the network-friendly strategy is strongly recommended to efficiently alleviate the server burden, and reduce significantly the overall network traffic generated by the catch-up TV as well.

In live streaming systems (IPTV, life-stream services, etc.), an attractive feature consists in allowing users to access past portions of the stream. This is called a time-shifted streaming system. We address in this paper the design of a large-scale delivery system for a time-shifted streaming application. We highlight the challenging characteristics of time-shifted applications that prevent known delivery systems to be used. Then, we describe the turntable structure, the first structure that has been specifically designed to cope with the properties of time-shifted systems. A set of preliminary simulations confirm the interest for this structure.

Peer-to-Peer (P2P) file sharing applications use multiple TCP connections between peers to transfer data. The aggressiveness and robustness of P2P technology remarkably improve transfer efficiency and network bandwidth utilization. However, while the network bottleneck link is congested, P2P applications tend to unfairly steal bandwidth from other traditional Internet applications (Client/Server mode), which deteriorates the performance of traditional Internet applications. The paper proposes a friendlyP2P system with new application-level approaches for congestion detection and avoidance to keep fairness between P2P traffic and traditional Internet traffic. friendlyP2P, which is friendly to ISPs, namely to Internet networks and traditional Internet traffic, detects network congestion via throughput measurements and alleviates network congestion by optimization of the number of P2P connections from the viewpoint of P2P users. friendlyP2P system requires neither network node support nor TCP modification, which makes it easy to deploy. Simulation experiments demonstrate that fairness and congestion avoidance can be achieved in presence of congestion, and network bandwidth can be effectively utilized in absence of congestion with friendlyP2P technology.