Sebastian Zöller

CBFR: Bloom filter routing with gradual forgetting for tree-structured wireless sensor networks with mobile nodes

In tree-structured data collection sensor networks, packets are routed towards a sink node by iteratively choosing a nodes immediate parent node as the next hop. It is however beyond the scope of these routing protocols to transfer messages along the reverse path, i.e., from the sink to individual nodes in the network. In this paper, we present CBFR, a novel routing scheme that builds upon collection protocols to enable efficient point-to-point communication. We propose the use of space-efficient data structures known as Bloom filters to efficiently store routing tables on the networked devices. In particular, each node in the collection tree stores the addresses of its direct and indirect child nodes in its local Bloom filter. A packet is forwarded down-tree only if the nodes local filter indicates the presence of the packets destination address among the nodes descendants. In order to cater for the presence of mobile nodes, we apply the concept of counting Bloom filters to allow for the removal of elements from the filter by means of gradual forgetting. The effectiveness of our approach in achieving both high delivery rates and low overhead is demonstrated by means of simulations and experiments.

Scoresheet-based event relevance determination for energy efficiency in wireless sensor networks

As wireless sensor nodes are mostly battery-powered, energy-efficient operation is a necessity to use their confined energy budget optimally. This is especially true in the logistics domain, where timely and accurate monitoring of containers is required, while the cost pressure is high. Thus, besides the need for energy efficiency, wireless sensor network deployments in logistics require cost efficiency as well. As data transmission represents the most expensive operation in terms of energy consumption and monetary costs, we present a concept for the local determination of transmission relevance in this paper. By omitting irrelevant events from transmission, the amount of data to transmit is effectively reduced. Our approach employs concepts from the business economics sector and is based on the use of scoresheets, which evaluate information on a wireless sensor node to decide whether they are “worth” transmitting or not. Thus, a scoresheet-based approach provides a viable solution for local filtering to realize energy- and cost-efficient operation of a wireless sensor network while maintaining the benefits of data fidelity and real-time event notifications.

Complex service provisioning in collaborative cloud markets

Todays cloud consumers gain a high level of flexibility by using externally provided cloud-based services. However, they have no means for requesting combined services from different clouds or for enforcing an individual quality level. Laying the foundation for market-based cloud collaborations including the negotiation of individual quality parameters is an important aspect for future cloud computing. Cloud consumers, especially enterprises are then able to request complex services with consumerdriven quality guarantees according to their individual needs and are not concerned with the problem on how to make the different components work together. In this paper, we present an approach for collaborative complex service provisioning in cloud computing and an evaluation of selected mechanisms for the negotiation of quality parameters in such a collaborative market-based scenario.

Distributed data filtering in logistics wireless sensor networks based on transmission relevance

Energy-efficient operation is mandatory in wireless sensor networks due to the limited energy budget of sensor nodes. Considering the potential application of wireless sensor networks in logistics, cost efficiency is another major requirement due to high cost pressure. To save on data transmissions within such sensor deployments, which account for the major part of energy consumption and monetary costs, we develop a method for data filtering based on an in-network determination of transmission relevance of sensor data. Our approach explicitly incorporates interdependencies between wireless sensor nodes and their measurements and data transmissions. It contributes to efficiency in wireless sensor networks by filtering out irrelevant data and enables a subsequent reduction of unnecessary transmissions from a network-wide view, while being able to still offer real-time data provision with sufficient data fidelity to stakeholders. The benefits of our approach are indicated by preliminary evaluation results.

A concept for vehicle internet connectivity for non-safety applications

Internet access to multimedia content in vehicles today is only possible via cellular networks which offer insufficient bandwidth. By using additional V2X technology in a hybrid manner, vehicles can benefit from additional bandwidth to receive enhanced internet connectivity. We introduce a holistic concept that pursues the vision of an optimal use of available access networks in a vehicular environment combined with a managed resource utilization in a user-centric way to result in maximum user experience and economic efficiency. The resulting internet connection introduces further opportunities for value-added services that maximize Quality of Experience and allows further personalization.

Data filtering for wireless sensor networks using forecasting and value of information

Energy constitutes a scarce resource in wireless sensor networks, making energy-efficient operation mandatory. Data transmission has been identified as one of the most energy consuming operations. Consequently, different approaches to reduce data transmissions have been proposed, like data filtering. Recently, the value of information of sensor data has been identified for data filtering, explicitly incorporating application-specific and context-dependent information needs. The filtering is done according to the benefit a data transmission would induce at the recipient. We propose an on-mote filtering approach, which relies on local multi-step assessment of sensor data with forecasting and assessing value of information. We apply our approach to logistics transport processes and evaluate it concerning number of data transmissions and energy efficiency. Our simulation results showed that with our approach the number of data transmissions and the energy consumption can be reduced by over 25% to over 60%, while simultaneously accounting for user-specific information desires.

A Tale of Millis and Nanos: Time Measurements in Virtual and Physical Machines

Cloud computing makes large infrastructure capacities available to users in a flexible and affordable fashion, which is of specific interest to scientists for conducting experiments. Unfortunately, our past research has provided first indications that virtual machines – the most popular type of cloud-based infrastructure – have substantial deficits with respect to time measurements, which are an important tool for researchers. In this paper, we provide a detailed analysis on the accuracy of time measurements based on various machine configurations. They cover influence factors such as machine type, virtualization solution, and programming language. The results indicate that not the use of virtualization as such, but the potentially uncontrollable utilization of the physical host is a decisive factor for the accuracy of time measurements. Different virtualization solutions and programming languages play an inferior role. Our findings, along with the publicly released tool TiMeAcE.KOM, can provide a valuable decision support for researchers in the selection and configuration of cloud-based experimental infrastructures.

A concept for cross-layer optimization of wireless sensor networks in the logistics domain by exploiting business knowledge

Energy is limited in wireless sensor networks due to mostly battery-operated wireless sensor nodes. Consequently, an efficient energy usage significantly enhances the lifetime of a wireless sensor network. In this paper, we propose a cross-layer optimization concept that exploits business knowledge in the logistics domain to adapt communication by business relevancy. A corresponding information value is assessed for each detected event. Energy and communication costs can be saved by dropping or postponing the transmission of less relevant event information, without negative impact on the business and application level.

Where is that car parked? A wireless sensor network-based approach to detect car positions

The global trend of increased urbanization makes space rare in city environments in general and for parking in particular. In addition, cars become bigger and often use more than one parking space. As a result neighboring parking spaces can be affected by a parked car. So, a basically free parking space might be too narrow for an arriving car depending on the arriving cars size. Therefore, means to detect car positions on parking spaces in a fine granular way are required to detect such situations and avoid inefficient parking space searches. Wireless sensor networks provide the possibility to sense the exact occupation of a parking space and potential influences on neighboring parking spaces. However, current solutions focus only on the detection if a parking space is occupied or not. In our work, we present a sensor deployment and a machine learning-based approach able to provide the mentioned more fine-granular detection level. We have conducted an extensive real-world evaluation of our solution, in particular considering different characteristics of todays car bodies. In our tests, our approach achieved an accuracy of more than 98%.

Integrating Smart Objects as Data Basis in Virtual Enterprise Collaborations

Small and Medium Enterprises of the manufacturing domain have to cope with a highly competitive market today. To establish flexible and efficient collaborations with partners in such an environment, new collaboration concepts and corresponding IT architectures are required, such as Virtual Manufacturing Enterprises. Therefore, we provide in this paper an overview of a generic IT architecture for realizing collaborations within Virtual Manufacturing Enterprises. However, besides an adequate IT architecture, a sound and up-to-date data basis is an essential necessity for inter-company collaborations to be successful. Smart Objects constitute a promising technology to gather and transmit a huge diversity of different process-relevant data in real time and can thus act as valuable data source in order to achieve such a comprehensive and up-to-date data basis. In consequence, we describe in this paper how Smart Object technology can be employed and integrated in our architecture for Virtual Manufacturing Enterprises in order to enable efficient data provisioning in such collaboration scenarios.