Context-Aware Adaptive Opportunistic Routing in Mobile Ad-hoc and Sensor Networks

Mobile ad-hoc networks (MANETs) and wireless sensor networks (WSNs) have been attracting increasing attention for decades due to their broad civilian and military applications. Basically, a MANET or WSN is a network of nodes connected by wireless communication links. Due to the limited transmission range of the radio, many pairs of nodes in MANETs or WSNs may not be able to communicate directly, hence they need other intermediate nodes to forward packets for them. Routing in such types of networks is an important issue and it poses great challenges due to the dynamic nature of MANETs or WSNs. On the one hand, the open-air nature of wireless environments brings many difficulties when an efficient routing solution is required. The wireless channel is unreliable due to fading and interferences, which makes it impossible to maintain a quality path from a source node to a destination node. Additionally, node mobility aggravates network dynamics, which causes frequent topology changes and brings significant overheads for maintaining and recalculating paths. Furthermore, mobile devices and sensors are usually constrained by battery capacity, computing and communication resources, which impose limitations on the functionalities of routing protocols. On the other hand, the wireless medium possesses inherent unique characteristics, which can be exploited to enhance transmission reliability and routing performance. Opportunistic routing (OR) is one promising technique that takes advantage of the spatial diversity and broadcast nature of the wireless medium to improve packet forwarding reliability in multihop wireless communication. OR combats the unreliable wireless links by involving multiple neighboring nodes (forwarding candidates) to choose packet forwarders. In opportunistic routing, a source node does not require an end-to-end path to transmit packets. The packet forwarding decision is made hop-by-hop in a fully distributed fashion. Motivated by the deficiencies of existing opportunistic routing protocols in dynamic environments such as mobile ad-hoc networks or wireless sensor networks, this thesis proposes a novel context-aware adaptive opportunistic routing scheme. Our proposal selects packet forwarders by simultaneously exploiting multiple types of cross-layer context information of nodes and environments. Our approach significantly outperforms other routing protocols that rely solely on a single metric. The adaptivity feature of our proposal enables network nodes to adjust their behaviors at run-time according to network conditions. To accommodate the strict energy constraints in WSNs, this thesis integrates adaptive duty-cycling mechanism to opportunistic routing for wireless sensor nodes. Our approach dynamically adjusts the sleeping intervals of sensor nodes according to the monitored traffic load and the estimated energy consumption rate. Through the integration of duty cycling of sensor nodes and opportunistic routing, our protocol is able to provide a satisfactory balance between good routing performance and energy efficiency for WSNs.

Short-arc tracklet association for geostationary objects

Measurement association and initial orbit determination is a fundamental task when building up a database of space objects. This paper proposes an efficient and robust method to determine the orbit using the available information of two tracklets, i.e. their line-of-sights and their derivatives. The approach works with a boundary-value formulation to represent hypothesized orbital states and uses an optimization scheme to find the best fitting orbits. The method is assessed and compared to an initial-value formulation using a measurement set taken by the Zimmerwald Small Aperture Robotic Telescope of the Astronomical Institute at the University of Bern. False associations of closely spaced objects on similar orbits cannot be completely eliminated due to the short duration of the measurement arcs. However, the presented approach uses the available information optimally and the overall association performance and robustness is very promising. The boundary-value optimization takes only around 2% of computational time when compared to optimization approaches using an initial-value formulation. The full potential of the method in terms of run-time is additionally illustrated by comparing it to other published association methods.

The Moldable Inspector: a framework for domain-specific object inspection

Answering run-time questions in object-oriented systems involves reasoning about and exploring connections between multiple objects. Developer questions exercise various aspects of an object and require multiple kinds of interactions depending on the relationships between objects, the application domain and the differing developer needs. Nevertheless, traditional object inspectors, the essential tools often used to reason about objects, favor a generic view that focuses on the low-level details of the state of individual objects. This leads to an inefficient effort, increasing the time spent in the inspector. To improve the inspection process, we propose the Moldable Inspector, a novel approach for an extensible object inspector. The Moldable Inspector allows developers to look at objects using multiple interchangeable presentations and supports a workflow in which multiple levels of connecting objects can be seen together. Both these aspects can be tailored to the domain of the objects and the question at hand. We further exemplify how the proposed solution improves the inspection process, introduce a prototype implementation and discuss new directions for extending the Moldable Inspector.

The Moldable Debugger: A Framework for Developing Domain-Specific Debuggers

Debuggers are crucial tools for developing object-oriented software systems as they give developers direct access to the running systems. Nevertheless, traditional debuggers rely on generic mechanisms to explore and exhibit the execution stack and system state, while developers reason about and formulate domain-specific questions using concepts and abstractions from their application domains. This creates an abstraction gap between the debugging needs and the debugging support leading to an inefficient and error-prone debugging effort. To reduce this gap, we propose a framework for developing domain-specific debuggers called the Moldable Debugger. The Moldable Debugger is adapted to a domain by creating and combining domain-specific debugging operations with domain-specific debugging views, and adapts itself to a domain by selecting, at run time, appropriate debugging operations and views. We motivate the need for domain-specific debugging, identify a set of key requirements and show how our approach improves debugging by adapting the debugger to several domains.

Towards cheap, accurate polymorphism detection

Polymorphism, along with inheritance, is one of the most important features in object-oriented languages, but it is also one of the biggest obstacles to source code comprehension. Depending on the run-time type of the receiver of a message, any one of a number of possible methods may be invoked. Several algorithms for creating accurate call-graphs using static analysis already exist, however, they consume significant time and memory resources. We propose an approach that will combine static and dynamic analysis and yield the best possible precision with a minimal trade-off between used resources and accuracy.

Detection and quantification of 56 new psychoactive substances in whole blood and urine by LC-MS/MS

BACKGROUND New psychoactive substances (NPS) have become increasingly prevalent and are sold in internet shops as 'bath salts' or 'research chemicals' and comprehensive bioanalytical methods are needed for their detection. METHODOLOGY We developed and validated a method using LC and MS/MS to quantify 56 NPS in blood and urine, including amphetamine derivatives, 2C compounds, aminoindanes, cathinones, piperazines, tryptamines, dissociatives and others. Instrumentation included a Synergi Polar-RP column (Phenomenex) and a 3200 QTrap mass spectrometer (AB Sciex). Run time was 20 min. CONCLUSION A novel method is presented for the unambiguous identification and quantification of 56 NPS in blood and urine samples in clinical and forensic cases, e.g., intoxications or driving under the influence of drugs.

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.

A Demonstration of Mobility Prediction as a Service in Cloudified LTE Networks

Location prediction has attracted a significant amount of research effort. Being able to predict users’ movement benefits a wide range of communication systems, including location-based service/applications, mobile access control, mobile QoS provision, and resource management for mobile computation and storage management. In this demo, we present MOBaaS, which is a cloudified Mobility and Bandwidth prediction services that can be instantiated, deployed, and disposed on-demand. Mobility prediction of MOBaaS provides location predictions of a single/group user equipments (UEs) in a future moment. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operations. We demonstrate an example of real-time mobility prediction service deployment running on OpenStack platform, and the potential benefits it bring to other invoking services.

Development of a rapid column-switching LC-MS/MS method for the quantification of THCCOOH and THCCOOH-glucuronide in whole blood for assessing cannabis consumption frequency

The concentration of 11-nor-9-carboxy-Δ(9)-tetrahydrocannabinol (THCCOOH) in whole blood is used as a parameter for assessing the consumption behavior of cannabis consumers. The blood level of THCCOOH-glucuronide might provide additional information about the frequency of cannabis use. To verify this assumption, a column-switching liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the rapid and direct quantification of free and glucuronidated THCCOOH in human whole blood was newly developed. The method comprised protein precipitation, followed by injection of the processed sample onto a trapping column and subsequent gradient elution to an analytical column for separation and detection. The total LC run time was 4.5 min. Detection of the analytes was accomplished by electrospray ionization in positive ion mode and selected reaction monitoring using a triple-stage quadrupole mass spectrometer. The method was fully validated by evaluating the following parameters: linearity, lower limit of quantification, accuracy and imprecision, selectivity, extraction efficiency, matrix effect, carry-over, dilution integrity, analyte stability, and re-injection reproducibility. All acceptance criteria were analyzed and the predefined criteria met. Linearity ranged from 5.0 to 500 μg/L for both analytes. The method was successfully applied to whole blood samples from a large collective of cannabis consumers, demonstrating its applicability in the forensic field.

Dish Detection and Segmentation for Dietary Assessment on Smartphones

Diet-related chronic diseases severely affect personal and global health. However, managing or treating these diseases currently requires long training and high personal involvement to succeed. Computer vision systems could assist with the assessment of diet by detecting and recognizing different foods and their portions in images. We propose novel methods for detecting a dish in an image and segmenting its contents with and without user interaction. All methods were evaluated on a database of over 1600 manually annotated images. The dish detection scored an average of 99% accuracy with a .2s/image run time, while the automatic and semi-automatic dish segmentation methods reached average accuracies of 88% and 91% respectively, with an average run time of .5s/image, outperforming competing solutions.

The Moldable Inspector

Object inspectors are an essential category of tools that allow developers to comprehend the run-time of object-oriented systems. Traditional object inspectors favor a generic view that focuses on the low-level details of the state of single objects. Based on 16 interviews with software developers and a follow-up survey with 62 respondents we identified a need for object inspectors that support different high-level ways to visualize and explore objects, depending on both the object and the current developer need. We propose the Moldable Inspector, a novel inspector model that enables developers to adapt the inspection workflow to suit their immediate needs by making the inspection context explicit, providing multiple interchangeable domain-specific views for each object, and supporting a workflow that groups together multiple levels of connected objects. We show that the Moldable Inspector can address multiple kinds of development needs involving a wide range of objects.

GTInspector: A Moldable Domain-Aware Object Inspector

Understanding the run-time behaviour of object-oriented applications entails the comprehension of run-time objects. Traditional object inspectors favor generic views that focus on the low-level details of the state of single objects. While universally applicable, this generic approach does not take into account the varying needs of developers that could benefit from tailored views and exploration possibilities. GTInspector is a novel moldable object inspector that provides different high-level ways to visualize and explore objects, adapted to both the object and the current developer need.

Practical domain-specific debuggers using the Moldable Debugger framework

Understanding the run-time behavior of software systems can be a challenging activity. Debuggers are an essential category of tools used for this purpose as they give developers direct access to the running systems. Nevertheless, traditional debuggers rely on generic mechanisms to introspect and interact with the running systems, while developers reason about and formulate domain-specific questions using concepts and abstractions from their application domains. This mismatch creates an abstraction gap between the debugging needs and the debugging support leading to an inefficient and error-prone debugging effort, as developers need to recover concrete domain concepts using generic mechanisms. To reduce this gap, and increase the efficiency of the debugging process, we propose a framework for developing domain-specific debuggers, called the Moldable Debugger, that enables debugging at the level of the application domain. The Moldable Debugger is adapted to a domain by creating and combining domain-specific debugging operations with domain-specific debugging views, and adapts itself to a domain by selecting, at run time, appropriate debugging operations and views. To ensure the proposed model has practical applicability (i.e., can be used in practice to build real debuggers), we discuss, from both a performance and usability point of view, three implementation strategies. We further motivate the need for domain-specific debugging, identify a set of key requirements and show how our approach improves debugging by adapting the debugger to several domains.

Organ transplantation in Switzerland: impact of the new transplant law on cold ischaemia time and organ transports

On 1 July 2007 a new transplant law came into force in Switzerland. The principal item of this new law is the change from centre-oriented allocation to patient-oriented national allocation of organs. The aim of the present study is to assess the impact on cold ischaemia time (CIT) and transport requirements.

Development of two real-time PCR assays for the detection of Mycoplasma hyopneumoniae in clinical samples

In order to improve the diagnosis of enzootic pneumonia (EP) in pigs two real-time polymerase chain reaction (rtPCR) assays for the detection of Mycoplasma hyopneumoniae in bronchial swabs from lung necropsies were established and validated in parallel. As a gold standard, the current "mosaic diagnosis" was taken, including epidemiological tracing, clinical signs, macro- and histopathological lesions of the lungs and immunofluorescence. One rtPCR is targeting a repeated DNA element of the M. hyopneumoniae genome (REP assay), the other a putative ABC transporter gene (ABC assay). Both assays were shown to be specific for M. hyopneumoniae and did not cross react with other bacteria and mollicutes from pig. With material from pigs of defined EP-negative farms the two assays showed to be 100% specific. When testing lungs from pig farms with EP, the REP assay detected 50% and the ABC assay 90% of the farms as positive. Both tests together detected all positive farms. Within a positive herd the two assays tested similarly with on average over 90% of the lung samples analysed from a single farm showing positive scores. A series of samples with suspicion of EP and samples from pigs with diseases other than respiratory taken from current routine diagnostic was assayed. None of the assays showed false positive results. The sensitivities in this sample group were 50% for the REP and 70% for the ABC assays and for both assays together 85%. The two assays run in parallel are therefore a valuable tool for the improvement of the current diagnosis of EP.