Assessing traditional knowledge on forest uses to understand forest ecosystem dynamics

Forest ecosystems worldwide are heavily influenced by human activities. Information on these human activities are key for understanding ecosystem dynamics, especially as some of these human activities have long-term consequences, i.e. legacy effects. We assessed the diversity of forest uses across the Swiss Alps and the respective traditional forest-related knowledge (TFRK) by conducting 56 oral history interviews in five regions. As TFRK tends to be underrepresented in written records, oral history proves to be a very valuable approach. We classified the information gathered in 61 specific forest use practices, characterized by activity, product and use, in order to gain a comprehensive picture of the regional variability but also cross-regional ubiquity of certain forest uses. Based on these results we are able to name the ecologically most relevant forest uses which were most likely, or potentially occurring in Central European forests. We suggest a simple, systematic historical assessment of these relevant forest uses to be applied in all studies on forest ecosystems. This procedure will enable scientists to better evaluate to what degree human activities had an impact on forest ecosystem dynamics in their study area.

Intellectual Property and Traditional Cultural Expressions in a Digital Environment

In the face of increasing globalisation, and a collision between global communication systems and local traditions, this book offers innovative trans-disciplinary analyses of the value of traditional cultural expressions (TCE) and suggests appropriate protection mechanisms for them. It combines approaches from history, philosophy, anthropology, sociology and law, and charts previously untravelled paths for developing new policy tools and legal designs that go beyond conventional copyright models. Its authors extend their reflections to a consideration of the specific features of the digital environment, which, despite enhancing the risks of misappropriation of traditional knowledge and creativity, may equally offer new opportunities for revitalising indigenous peoples' values and provide for the sustainability of TCE.This book will appeal to scholars interested in multidisciplinary analyses of the fragmentation of international law in the field of intellectual property and traditional cultural expressions. It will also be valuable reading for those working on broader governance and human rights issues.

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.

A Smart TCP Acknowledgment Approach for Multihop Wireless Networks

Reliable data transfer is one of the most difficult tasks to be accomplished in multihop wireless networks. Traditional transport protocols like TCP face severe performance degradation over multihop networks given the noisy nature of wireless media as well as unstable connectivity conditions in place. The success of TCP in wired networks motivates its extension to wireless networks. A crucial challenge faced by TCP over these networks is how to operate smoothly with the 802.11 wireless MAC protocol which also implements a retransmission mechanism at link level in addition to short RTS/CTS control frames for avoiding collisions. These features render TCP acknowledgments (ACK) transmission quite costly. Data and ACK packets cause similar medium access overheads despite the much smaller size of the ACKs. In this paper, we further evaluate our dynamic adaptive strategy for reducing ACK-induced overhead and consequent collisions. Our approach resembles the sender side's congestion control. The receiver is self-adaptive by delaying more ACKs under nonconstrained channels and less otherwise. This improves not only throughput but also power consumption. Simulation evaluations exhibit significant improvement in several scenarios

Improving Management of Distributed Services Using Correlations and Predictions in SLA-Driven Cloud Computing Systems

Recent advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing environmental conditions and number of users, application performance might suffer, leading to Service Level Agreement (SLA) violations and inefficient use of hardware resources. We introduce a system for controlling the complexity of scaling applications composed of multiple services using mechanisms based on fulfillment of SLAs. We present how service monitoring information can be used in conjunction with service level objectives, predictions, and correlations between performance indicators for optimizing the allocation of services belonging to distributed applications. We validate our models using experiments and simulations involving a distributed enterprise information system. We show how discovering correlations between application performance indicators can be used as a basis for creating refined service level objectives, which can then be used for scaling the application and improving the overall application's performance under similar conditions.

Towards a Moldable Debugger

The debugger is an essential tool in any programming environment, as it helps developers understand the dynamic behaviour of software systems. However, traditional debuggers fail in answering domain-specific questions, as the semantics of what they show and do are fixed. In this paper we introduce our work towards a moldable debugger which, unlike traditional debuggers, both adapts itself and can be adapted to a particular debugging context. Thus, it allows developers to answer their questions by using concepts from their own application domains.

Software Engineering for Self-Adaptive Systems: A Second Research Roadmap

The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Geo-Locating the Knowledge Transfer in StackOverflow

By analyzing the transactions in Stack Overflow we can get a glimpse of the way in which the different geographical regions in the world contribute to the knowledge market represented by the website. In this paper we aggregate the knowledge transfer from the level of the users to the level of geographical regions and learn that Europe and North America are the principal and virtually equal contributors; Asia comes as a distant third, mainly represented by India; and Oceania contributes less than Asia but more than South America and Africa together.

Integration of WSNs into enterprise systems based on semantic physical business entities

Internet of Things based systems are anticipated to gain widespread use in industrial applications. Standardization efforts, like 6L0WPAN and the Constrained Application Protocol (CoAP) have made the integration of wireless sensor nodes possible using Internet technology and web-like access to data (RESTful service access). While there are still some open issues, the interoperability problem in the lower layers can now be considered solved from an enterprise software vendors' point of view. One possible next step towards integration of real-world objects into enterprise systems and solving the corresponding interoperability problems at higher levels is to use semantic web technologies. We introduce an abstraction of real-world objects, called Semantic Physical Business Entities (SPBE), using Linked Data principles. We show that this abstraction nicely fits into enterprise systems, as SPBEs allow a business object centric view on real-world objects, instead of a pure device centric view. The interdependencies between how currently services in an enterprise system are used and how this can be done in a semantic real-world aware enterprise system are outlined, arguing for the need of semantic services and semantic knowledge repositories. We introduce a lightweight query language, which we use to perform a quantitative analysis of our approach to demonstrate its feasibility.