NETQOS policy management architecture for flexible QOS provisioning in Future Internet

This paper is focussed on the NETQOS architecture for automated QoS policy provisioning, which can be used in Future Internet scenarios by the different actors (i.e. network operators, service providers, and users) for flexible QoS configuration over combinations of mobile, fixed, sensor and broadcast networks. The NETQOS policy management architecture opens the possibility to specify QoS policies on a "business" level using ontology descriptions and policy management interfaces, which are specific to the actors. The business level policy specifications are translated by the NETQOS system into intermediate and operational QoS policies for automated QoS configuration at the managed heterogeneous network and transport entities. NETQOS allows QoS policy specification and dependency analysis considering Service Level Agreements (SLAs) between the actors, as well as automated policy provisioning and adaptation. The interaction of the NETQOS components is based on a common po licy repository. The particular focus of the paper is aimed to discuss ontology and actor oriented QoS policy specification and configuration for heterogeneous networks, as well as NETQOS QoS policy management interfaces at business level and automated translation of business QoS policies to intermediate and operational policy level

Prototyping and Evaluation of Sensor Data Integration in Cloud Platforms

The SFI Smart Ocean centre has initiated a long-running project which consists of developing a wireless and autonomous marine observation system for monitoring of underwater environments and structures. The increasing popularity of integrating the Internet of Things (IoT) with Cloud Computing has led to promising infrastructures that could realize Smart Ocean's goals. The project will utilize underwater wireless sensor networks (UWSNs) for collecting data in the marine environments and develop a cloud-based platform for retrieving, processing, and storing all the sensor data. Currently, the project is in its early stages and the collaborating partners are researching approaches and technologies that can potentially be utilized. This thesis contributes to the centre's ongoing research, focusing on the aspect of how sensor data can be integrated into three different cloud platforms: Microsoft Azure, Amazon Web Services, and the Google Cloud Platform. The goals were to develop prototypes that could successfully send data to the chosen cloud platforms and evaluate their applicability in context of the Smart Ocean project. In order to determine the most suitable option, each platform was evaluated based on set of defined criteria, focusing on their sensor data integration capabilities. The thesis has also investigated the cloud platforms' supported protocol bindings, as well as several candidate technologies for metadata standards and compared them in surveys. Our evaluation results shows that all three cloud platforms handle sensor data integration in very similar ways, offering a set of cloud services relevant for creating diverse IoT solutions. However, the Google Cloud Platform ranks at the bottom due to the lack of IoT focus on their platform, with less service options, features, and capabilities compared to the other two. Both Microsoft Azure and Amazon Web Services rank very close to each other, as they provide many of the same sensor data integration capabilities, making them the most applicable options.Masteroppgave i Programutvikling samarbeid med HVLPROG399MAMN-PRO

Managed ecosystems of networked objects

Small embedded devices such as sensors and actuators will become the cornerstone of the Future Internet. To this end, generic, open and secure communication and service platforms are needed in order to be able to exploit the new business opportunities these devices bring. In this paper, we evaluate the current efforts to integrate sensors and actuators into the Internet and identify the limitations at the level of cooperation of these Internet-connected objects and the possible intelligence at the end points. As a solution, we propose the concept of Managed Ecosystem of Networked Objects, which aims to create a smart network architecture for groups of Internet-connected objects by combining network virtualization and clean-slate end-to-end protocol design. The concept maps to many real-life scenarios and should empower application developers to use sensor data in an easy and natural way. At the same time, the concept introduces many new challenging research problems, but their realization could offer a meaningful contribution to the realization of the Internet of Things

Task allocation in group of nodes in the IoT: A consensus approach

The realization of the Internet of Things (IoT) paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. In order for objects to dynamically cooperate to IoT applications' execution, they need to make their resources available in a flexible way. However, available resources such as electrical energy, memory, processing, and object capability to perform a given task, are often limited. Therefore, resource allocation that ensures the fulfilment of network requirements is a critical challenge. In this paper, we propose a distributed optimization protocol based on consensus algorithm, to solve the problem of resource allocation and management in IoT heterogeneous networks. The proposed protocol is robust against links or nodes failures, so it's adaptive in dynamic scenarios where the network topology changes in runtime. We consider an IoT scenario where nodes involved in the same IoT task need to adjust their task frequency and buffer occupancy. We demonstrate that, using the proposed protocol, the network converges to a solution where resources are homogeneously allocated among nodes. Performance evaluation of experiments in simulation mode and in real scenarios show that the algorithm converges with a percentage error of about±5% with respect to the optimal allocation obtainable with a centralized approach

A semantic context management framework on mobile device

We present a semantic context management framework named ContextTorrent, which can make various types of context information be semantically searchable and sharable among local and remote context-aware applications. We implement this framework on the Google Android platform with its elegant application support. An open source RDF parser has been extended to effectively get RDF triples from files or over the network. Three embedded database systems were evaluated for storing ontology represented contexts in the resource-constrained mobile devices. We use the FOAF ontology schema and a synthetic data set of up to 2500 records to evaluate the context query and storage performance. Ordinary context queries can be replied instantaneously.published_or_final_versionThe 6th IEEE International Conference on Embedded Software and Systems (ICESS 2009), Zhejiang, China, 25-27 May 2009. In Proceedings of the 6th ICESS, 2009, p. 331-33