Virgil Dobrota

Bio: Virgil Dobrota is an academic researcher from Technical University of Cluj-Napoca. The author has contributed to research in topics: Cloud computing & The Internet. The author has an hindex of 7, co-authored 67 publications receiving 383 citations.

Evaluation of constrained application protocol for wireless sensor networks

Abstract: IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) and IPv6 Routing Protocol for Low-power and Lossy networks (RPL) have accelerated the integration of Wireless Sensor Networks (WSNs) and smart objects with the Internet. At the same time, the development of the Constrained Application Protocol (CoAP) has made it possible to provide resource constrained devices with web service functionalities. CoAP is an HTTP like web transfer protocol able to extend the REpresentational State Transfer (REST) architecture to LoWPANs. The major difference between CoAP and HTTP is the different transport layer protocol (UDP instead of TCP) and the header compression which makes the packet size significantly smaller. This work provides an evaluation of CoAP compared to HTTP. The performance is evaluated in terms of mote's energy consumption and response time. The aim of the paper is to demonstrate, with a quantitative and qualitative analysis, that CoAP is more suited to REST based WSNs compared to HTTP. The results have been obtained by means of simulation as well as tests on real sensor motes.

REST Enabled Wireless Sensor Networks for Seamless Integration with Web Applications

Abstract: The use of Internet Protocol (IP) technology in Wireless Sensor Network (WSN) is a key prerequisite for the realization of the Internet of Things (IoT) vision. The IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) standard enables the use of IPv6 in networks of constrained devices. 6LowPAN enables the use of Service Oriented Architectures (SOAs) in WSN. The Internet Engineering Task Force (IETF) has defined the Constrained Application Protocol (CoAP), a web transfer protocol which provides several Hypertext Transfer Protocol (HTTP) functionalities, re-designed for constrained embedded devices. CoAP allows WSN applications to be built on top of Representational State Transfer (REST) architectures. This considerably eases the IoT application development and facilitates the integration of constrained devices with the Web. This work describes the prototype design and development of a Web platform which integrates a REST/CoAP WSN with a REST/HTTP Web application and allows a user to visualize WSN measurements in the Web browser. Since the WSN Web integration relies on a non-transparent gateway/server, we also show how to provide transparent cross-protocol resource access by means of an HTTP-CoAP proxy. The paper describes the major building blocks, functionalities and the implementation approach.

Evaluation of network traffic prediction based on neural networks with multi-task learning and multiresolution decomposition

Abstract: Network traffic exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper compares predictions produced by different types of neural networks (NN) with forecasts from statistical time series models (ARMA, ARAR, HW). The novelty of our approach is to predict aggregated Ethernet traffic with NNs employing multiresolution learning (MRL) which is based on wavelet decomposition. In addition, we introduce a new NN training paradigm, namely the combination of multi-task learning with MRL. The experimental results show that nonlinear prediction based on NNs is better suited for traffic prediction purposes than linear forecasting models. Moreover, MRL helps to exploit the correlation structures at lower resolutions of the traffic trace and improves the generalization capability of NNs.

SDN-Based Network Slicing Mechanism for a Scalable 4G/5G Core Network: A Kubernetes Approach.

Abstract: Managing the large volumes of IoT and M2M traffic requires the evaluation of the scalability and reliability for all the components in the end-to-end system. This includes connectivity, mobile network functions, and application or services receiving and processing the data from end devices. Firstly, this paper discusses the design of a containerized IoT and M2M application and the mechanisms for delivering automated scalability and high availability when deploying it in: (1) the edge using balenaCloud; (2) the Amazon Web Services cloud with EC2 instances; and (3) the dedicated Amazon Web Services IoT service. The experiments showed that there are no significant differences between edge and cloud deployments regarding resource consumption. Secondly, the solutions for scaling the 4G/5G network functions and mobile backhaul that provide the connectivity between devices and IoT/M2M applications are analyzed. In this case, the scalability and high availability of the 4G/5G components are provided by Kubernetes. The experiments showed that our proposed scaling algorithm for network slicing managed with SDN guarantees the necessary radio and network resources for end-to-end high availability.

Cross-layer QoS and its application in congestion control

Abstract: The paper proposes a preliminary design of cross-layer quality of service applied to congestion control in future Internet. This is an alternative solution to QoS-aware routing, whenever the infrastructure operator cannot add new resources, and/or when re-routing is not possible. Dedicated software, running in each node, collects a list of local parameters such as available transfer rate and one-way delay between all neighbors. Then this real-time status information is distributed to all the in-network management enabled nodes that are allowed to be reached. Due to the statistics regarding individual link traffic, a minimal network coding scheme, triggered by cross-layer quality of service, is temporarily activated. This system presents an enhanced distributed routing that preserves the performances of the running services, despite the congestion which cannot be eliminated.

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

Abstract: This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.

Internet of Things: Architectures, Protocols, and Applications

Abstract: The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Abstract: Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.