Safe reparametrization of component-based WSNs

Modern Wireless Sensor Networks are moving from singe-purpose custom built solutions towards multi-purpose application hosting platforms. These platforms support multiple concurrent applications managed by multiple actors. Reconfigurable component-models are a viable solution for supporting these scenarios by reducing management and development overhead while promoting software reuse. However, implicit parameter dependencies spanning component compositions make reconfiguration complex and error-prone. This paper proposes composition-safe reparametrization of components. This is accomplished by offering language annotations that allow component developers to make dependencies explicit and network protocols to resolve and enforce parameter constraints. Our approach greatly simplifies reparametrization while imposing minimal runtime overhead.status: publishe

Internet traffic classification using an ensemble of deep convolutional neural networks

Network traffic classification (NTC) has attracted considerable attention in recent years. The importance of traffic classification stems from the fact that data traffic in modern networks is extremely complex and ever-evolving in different aspects, e.g. volume, velocity and variety. The inherent security requirements of Internet-based applications also highlights further the role of traffic classification. Gaining clear insights into the network traffic for performance evaluation and network planning purposes, network behavior analysis, and network management is not a trivial task. Fortunately, NTC is a promising technique to gain valuable insights into the behavior of the network, and consequently improve the network operations. In this paper, we provide a method based on deep ensemble learning to classify the network traffic in communication systems and networks. More specifically, the proposed method combines a set of Convolutional Neural Network (CNN) models into an ensemble of classifiers. The outputs of the models are then combined to generate the final prediction. The results of performance evaluation show that the proposed method provides an average accuracy rate of 98% for the classification of traffic (e.g., FTP-DATA, MAIL, etc.) in the Cambridge Internet traffic dataset

Wireless Sensor Networks

Versatile and effective, Wireless Sensor Networks (WSNs) witness a continuous expansion of their application domains. Yet, their use is still hindered by issues such as reliability, lifetime, overall cost, design effort and multidisciplinary engineering knowledge, which often prove to be daunting for application domain experts. Several WSN design models, tools and techniques were proposed to solve these contrasting objectives, but no single comprehensive approach has emerged. With these criteria in mind we review several of the most representative ones, then we focus on two of the most effective hardware/software codesign flows. Both offer high-level design entry interfaces based on StateCharts. One allows manual module composition in a full application, and automates its mapping on a user-defined architecture for fast high-level design space exploration. The other flow automates module composition starting from the application specification and by reusing library modules. It can generate the hardware specification and the software to program and configure the WSN nodes. For these we show the typical use for the development of some representative applications, to evaluate their effectiveness