Andreas Kliem

Security and Communication Architecture for Networked Medical Devices in Mobility-Aware eHealth Environments

Tele-medicine solutions promote cost-effective and location independent medical assistance and monitoring. However, current solutions suffer from obstacles regarding interoperability and mobility. Seamless and suitable monitoring relies on a variety of different medical device types that transfer medical data from different locations while paying attention to security and privacy issues. Emergency scenarios require on the fly network integration and data transmission ranging from domains like patients home, medical practices, ambulances and, hospitals, where each domain may correspond to a different authority. Therefore we describe a mobility aware approach allowing out-of-the-box medical device integration and authentication, and simultaneously fulfilling the typical security and privacy requirements of e-health environments.

Evaluating Adaptive Compression to Mitigate the Effects of Shared I/O in Clouds

IaaS clouds have become a promising platform for scalable distributed systems in recent years. However, while the virtualization techniques of such clouds are key to the clouds elasticity, they also result in a reduced and less predictable I/O performance compared to traditional HPC setups. Besides the regular performance degradation of virtualized I/O itself, it is also the potential loss of I/O bandwidth through co-located virtual machines that imposes considerable obstacles for porting data-intensive applications to that platform. In this paper we examine adaptive compression schemes as a means to mitigate the negative effects of shared I/O in IaaS clouds. We discuss the decision models of existing schemes and analyze their applicability in virtualized environments. Based on an evaluation using XEN, KVM, and Amazon EC2, we found that most decision metrics (like CPU utilization and I/O bandwidth) are displayed inaccurately inside virtual machines and can lead to unreasonable levels of compression. As a remedy, we present a new adaptive compression scheme for virtualized environments which solely considers the application data rate. Without requiring any calibration or training phase our adaptive compression scheme can improve the I/O throughput of virtual machines significantly as shown through experimental evaluation.

People-Centric Internet of Things—Challenges, Approach, and Enabling Technologies

Technology now offers the possibility of delivering a vast range of low-cost people-centric services to citizens. Internet of Things (IoT) supporting technologies are becoming robust, viable and cheaper. Mobile phones are increasingly more powerful and disseminated. On the other hand, social networks and virtual worlds are experiencing an exploding popularity and have millions of users. These low-cost technologies can now be used to create an Internet of People (IoP), a dynamically configurable integration platform of connected smart objects that allows enhanced, people-centric applications. As opposed to things-centric ones, IoP combines the real, sensory world with the virtual world for the benefit of people while it also enables the development of sensing applications in contexts such as e-health, sustainable mobility, social networks enhancement or fulfilling people’s special needs. This paper identifies the main challenges, a possible approach, and key enabling technologies for a people-centric society based on the Internet of Things.

The Device Driver Engine - Cloud enabled ubiquitous device integration

The increasing amount of ubiquitous devices and the ongoing convergence of Internet of Things related application domains, like Smart Homes or Ambient Assisted Living, make it difficult for device integration solutions to cover a comprehensive set of devices while paying respect to resource constraints. Moreover, device integration has to be merged with device abstraction mechanisms, in order to hide the complexity of the physical device layer from the application layer and reduce the effort required to develop useful applications. The OSGi based device integration middleware we will present, combines a modular integration approach, that allows to load and deploy required integration knowledge from Cloud repositories on demand, with a category based abstraction mechanism that simplifies application development by hiding device control logic.

The Device Cloud - Applying Cloud Computing Concepts to the Internet of Things

The pervasiveness of connected embedded devices and Internet of Things (IoT) related application domains like smart cities, e-Health or, transportation lead to an constantly increasing amount of data, compute- and storage resources surrounding us. However, currently there is a gap between data acquisition and processing, usually bridged by gateway based approaches that integrate the devices and forward the data to Clouds in order to be processed. Integration is often static and limited to a certain application domain, which the gateway is able to support. In addition, processing does not consider the compute- and storage resources already made available by the growing amount of smart devices, like smart TVs or smart phones. As a solution, we propose the Device Cloud, which can be envisioned as an application of the Cloud Computing Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) paradigms to the IoT domain. The approach will allow sharing and on demand provisioning of resources provided by the connected embedded devices surrounding us. Besides introducing an architecture draft to provide a common understanding of the overall problem, the main focus of the paper is to discuss challenges that arise and give a state of the art review of related research initiatives that are eligible to contribute to possible solutions.

Towards Container-Based Resource Management for the Internet of Things

The Internet of Things (IoT) paradigm gains momentum for vendors, developers and users. A variety of available devices and technologies promote the deployment of solutions and applications in various domains. However, the increasing amount of IoT devices leads to an increasing amount of resources made available to the users. If devices like smart phones or smart TVs are considered, this includes computing and storage resources. In order to increase the utilization of these IoT resources and reduce the amount of generated network traffic, we propose a container-based resource allocation scheme. The approach allows various applications and users to dynamically allocate resources offered by edge devices and process IoT data close to the source. The approach is evaluated regarding its feasibility in terms of performance on resource constrained IoT devices.

The Internet of Things Resource Management Challenge

Caused by the proliferation of the (IoT) and its related application domains such as Building Automation or E-Health, users face a continuously increasing amount of heterogeneous sensors and devices deployed to their environment. As a result, a large variety of protocols, data formats and physical sensing resources needs to be managed in order to gain benefit from the deployed devices. This raises the question how the resources provided by the devices can be efficiently managed and provisioned. Related concepts like on-demand provisioning, elasticity, or resource pooling and sharing are already known from the Cloud Computing domain. This paper presents the Device Cloud approach, which aims at mitigating the IoT resource management issues by applying Cloud Computing concepts to the IoT domain. Similar to the Pay-as-you-Go paradigm, the Device Cloud allows users to allocate devices from a shared resource pool on-demand. Sensors and devices are not just integrated with the Cloud by being enabled to utilize Cloud services. Instead, the physical (IoT) resources become an integral part of the Cloud resource pool and are shared and provisioned like regular Infrastructure as a Service (IaaS) Cloud resources.

Self-adaptive middleware for ubiquitous medical device integration

E-Health systems need to dynamically integrate a huge variety of medical sensors in order to provide a meaningful survey of a patients condition. Devices like smart phones or gateways usually used as integrators, often underlie resource constraints and have to cope with the mobility of the patient. Therefore it is difficult to realize an overall integration middleware, that allows to handle a sufficient amount of medical sensors and is able to quickly adapt to changing requirements (e.g. new sensors or data aggregation modules) while preserving mobility and resource constraints. We present a middleware solution for the integration of medical devices and the aggregation of resulting data streams, that is able to adapt itself to the requirements of patients and Care Delivery Operators, using a modular approach and external knowledge repositories. Knowledge in the shape of configurations and runtime pluggable software modules is used to properly integrate and handle discovered medical devices on demand.

An approach for QoS constraint networks in cloud environments

Many QoS constraints in network environments exists, beginning with hard real time networks on the field bus level, up to Differentiated Services or VLAN tagging for commodity computer networks. However, all these approaches are not applicable for cloud-based environments, where networks need to be configured dynamically and on-demand. In this paper, we present an approach for centralized and dynamic network configuration. We show that QoS constraints can be fulfilled by decoupling the control and datapath of packet forwarding hardware in Ethernet based interconnection networks, that are widely-used in data centers and cloud environments. Furthermore, we introduce our cloud middleware and explain the resulting problems raised by interconnecting virtualized embedded systems through overlay networks with QoS constraints coming from the field bus domain.

CoSeMed - cooperative and secure medical device cloud

In order to properly support patient treatment, e-health systems need to dynamically integrate heterogeneous types of medical sensors and provide access to streams of sensed medical data independent of the patients location or the movement of the respective medical devices. Treatment processes usually include several steps and medical departments, which means that sensors have to be moved between networks of different operators. The common style of vital sign monitoring basically relies on reintegrating new sensors belonging to one operator each time a patient is moved between them. Therefore we propose a novel approach, that allows to share medical devices among different operators. This means that each operator just books a medical device as long as it delivers required data and is present in the operators network, which we call the medical device cloud. Additionally an aggregation middleware that dynamically integrates medical devices by reloading device drivers and required data utilization modules is presented.