Mobility and Bandwidth Prediction as a Service in Virtualized LTE Systems

Recently telecommunication industry benefits from infrastructure sharing, one of the most fundamental enablers of cloud computing, leading to emergence of the Mobile Virtual Network Operator (MVNO) concept. The most momentous intents by this approach are the support of on-demand provisioning and elasticity of virtualized mobile network components, based on data traffic load. To realize it, during operation and management procedures, the virtualized services need be triggered in order to scale-up/down or scale-out/in an instance. In this paper we propose an architecture called MOBaaS (Mobility and Bandwidth Availability Prediction as a Service), comprising two algorithms in order to predict user(s) mobility and network link bandwidth availability, that can be implemented in cloud based mobile network structure and can be used as a support service by any other virtualized mobile network services. MOBaaS can provide prediction information in order to generate required triggers for on-demand deploying, provisioning, disposing of virtualized network components. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operation, as well. Through the preliminary experiments with the prototype implementation on the OpenStack platform, we evaluated and confirmed the feasibility and the effectiveness of the prediction algorithms and the proposed architecture.

An Application-Layer RESTful Sleepy Nodes Implementation For Internet of Things Systems

Two of the main issues in wireless industrial Internet of Things applications are interoperability and network lifetime. In this work we extend a semantic interoperability platform and introduce an application-layer sleepy nodes protocol that can leverage on information stored in semantic repositories. We propose an integration platform for managing the sleep states and an application layer protocol based upon the Constraint Application Layer protocol. We evaluate our system on windowing based task allocation strategies, aiming for lower overall energy consumption that results in higher network lifetime.

Enabling knowledge creation through outsiders: towards a push model of open innovation

Open innovation is increasingly being adopted in business and describes a situation in which firms exchange ideas and knowledge with external participants, such as customers, suppliers, partner firms, and universities. This article extends the concept of open innovation with a push model of open innovation: knowledge is voluntarily created outside a firm by individuals and organisations who proceed to push knowledge into a firm’s open innovation project. For empirical analysis, we examine source code and newsgroup data on the Eclipse Development Platform. We find that outsiders invest as much in the firm’s project as the founding firm itself. Based on the insights from Eclipse, we develop four propositions: ‘preemptive generosity’ of a firm, ‘continuous commitment’, ‘adaptive governance structure’, and ‘low entry barrier’ are contexts that enable the push model of open innovation.

A multi-level stakeholder approach to sustainable land management

Soils provide us with over 90% of all human food, livestock feed, fibre and fuel on Earth. Soils, however, have more than just productive functions. The key challenge in coming years will be to address the diverse and potentially conflicting demands now being made by human societies and other forms of life, while ensuring that future generations have the same potential to use soils and land of comparable quality. In a multi-level stakeholder approach, down-to-earth action will have to be supplemented with measures at various levels, from households to communities, and from national policies to international conventions. Knowledge systems, both indigenous and scientific, and related research and learning processes must play a central role. Ongoing action can be enhanced through a critical assessment of the impact of past achievements, and through better cooperation between people and institutions.

An Exploratory Study of Data Quality Management Practices in the ERP Software Systems Context

Quality data are not only relevant for successful Data Warehousing or Business Intelligence applications; they are also a precondition for efficient and effective use of Enterprise Resource Planning (ERP) systems. ERP professionals in all kinds of businesses are concerned with data quality issues, as a survey, conducted by the Institute of Information Systems at the University of Bern, has shown. This paper demonstrates, by using results of this survey, why data quality problems in modern ERP systems can occur and suggests how ERP researchers and practitioners can handle issues around the quality of data in an ERP software Environment.

Comparative Study of Digital Rights Management Systems for Music and Text Files

Digital Rights Management Systems (DRMS) are seen by content providers as the appropriate tool to, on the one hand, fight piracy and, on the other hand, monetize their assets. Although these systems claim to be very powerful and include multiple protection technologies, there is a lack of understanding about how such systems are currently being implemented and used by content providers. The aim of this paper is twofold. First, it provides a theoretical basis through which we present shortly the seven core protection technologies of a DRMS. Second, this paper provides empirical evidence that the seven protection technologies outlined in the first section of this paper are the most commonly used technologies. It further evaluates to what extent these technologies are being used within the music and print industry. It concludes that the three main Technologies are encryption, password, and payment systems. However, there are some industry differences: the number of protection technologies used, the requirements for a DRMS, the required investment, or the perceived success of DRMS in fighting piracy.

The Application of Digital Rights Management Systems in the Music Industry - An Empirical Investigation

Technology advances in hardware, software and IP-networks such as the Internet or peer-to-peer file sharing systems are threatening the music business. The result has been an increasing amount of illegal copies available on-line as well as off-line. With the emergence of digital rights management systems (DRMS), the music industry seems to have found the appropriate tool to simultaneously fight piracy and to monetize their assets. Although these systems are very powerful and include multiple technologies to prevent piracy, it is as of yet unknown to what extent such systems are currently being used by content providers. We provide empirical analyses, results, and conclusions related to digital rights management systems and the protection of digital content in the music industry. It shows that most content providers are protecting their digital content through a variety of technologies such as passwords or encryption. However, each protection technology has its own specific goal, and not all prevent piracy. The majority of the respondents are satisfied with their current protection but want to reinforce it for the future, due to fear of increasing piracy. Surprisingly, although encryption is seen as the core DRM technology, only few companies are currently using it. Finally, half of the respondents do not believe in the success of DRMS and their ability to reduce piracy.

Rethinking ERP-Outsourcing Decisions for Leveraging Technological and Preserving Business Knowledge

During the selection, implementation and stabilization phases, as well as the operations and optimization phase of an ERP system (ERP-lifecycle), numerous companies consider to utilize the support of an external service provider. This paper analyses how different categories of knowledge influence the sourcing decision of crucial tasks within the ERP lifecycle. Based on a review of the IS outsourcing literature, essential knowledge-related determinants for the IS outsourcing decision are presented and aggregated in a structural model. It will be hypothesized that internal deficits in technological knowledge in comparison to external vendors as well as the specificity of the synthesis of special technological and specific business knowledge have a profound impact on the outsourcing decision. Then, a classification framework will be developed which facilitates the assignment of various tasks within the ERP lifecycle to their respective knowledge categories and knowledge carriers which might be internal or external stakeholders. The configuaration task will be used as an example to illustrate how the structural model and the classification framework may be applied to evaluate the outsourcing of tasks within the ERP lifecycle.

Fine-grained indoor positioning and tracking systems

Indoor positioning has attracted considerable attention for decades due to the increasing demands for location based services. In the past years, although numerous methods have been proposed for indoor positioning, it is still challenging to find a convincing solution that combines high positioning accuracy and ease of deployment. Radio-based indoor positioning has emerged as a dominant method due to its ubiquitousness, especially for WiFi. RSSI (Received Signal Strength Indicator) has been investigated in the area of indoor positioning for decades. However, it is prone to multipath propagation and hence fingerprinting has become the most commonly used method for indoor positioning using RSSI. The drawback of fingerprinting is that it requires intensive labour efforts to calibrate the radio map prior to experiments, which makes the deployment of the positioning system very time consuming. Using time information as another way for radio-based indoor positioning is challenged by time synchronization among anchor nodes and timestamp accuracy. Besides radio-based positioning methods, intensive research has been conducted to make use of inertial sensors for indoor tracking due to the fast developments of smartphones. However, these methods are normally prone to accumulative errors and might not be available for some applications, such as passive positioning. This thesis focuses on network-based indoor positioning and tracking systems, mainly for passive positioning, which does not require the participation of targets in the positioning process. To achieve high positioning accuracy, we work on some information of radio signals from physical-layer processing, such as timestamps and channel information. The contributions in this thesis can be divided into two parts: time-based positioning and channel information based positioning. First, for time-based indoor positioning (especially for narrow-band signals), we address challenges for compensating synchronization offsets among anchor nodes, designing timestamps with high resolution, and developing accurate positioning methods. Second, we work on range-based positioning methods with channel information to passively locate and track WiFi targets. Targeting less efforts for deployment, we work on range-based methods, which require much less calibration efforts than fingerprinting. By designing some novel enhanced methods for both ranging and positioning (including trilateration for stationary targets and particle filter for mobile targets), we are able to locate WiFi targets with high accuracy solely relying on radio signals and our proposed enhanced particle filter significantly outperforms the other commonly used range-based positioning algorithms, e.g., a traditional particle filter, extended Kalman filter and trilateration algorithms. In addition to using radio signals for passive positioning, we propose a second enhanced particle filter for active positioning to fuse inertial sensor and channel information to track indoor targets, which achieves higher tracking accuracy than tracking methods solely relying on either radio signals or inertial sensors.

Enhanced Knowledge Management by Synchronizing Mind Maps and Fuzzy Cognitive Maps

This paper presents a conceptual approach to enhance knowledge management by synchronizing mind maps and fuzzy cognitive maps. The use of mind maps allows taking advantage of human creativity, while the application of fuzzy cognitive maps enables to store information expressed in natural language. By applying cognitive computing, it makes possible to gather and extract relevant information out of a data pool. Therefore, this approach is supposed to give a framework that enhances knowledge management. To demonstrate the potential of this framework, a use case concerning the development of a smart city app is presented.