Building a context rich interface to low level sensor data

Sensor networks play an important role in our modern information society. These networks are used for a variety of activities in different domains, including traffic monitoring, environmental analysis, transport and personal health. In general, systems generate data in their own format with little or no associated semantics. As a result, data must be managed individually and significant human effort is required to analyze data and develop ad-hoc applications for different end-user requirements. The research presented here proposes a holistic and comprehensive approach to significantly reduce the human effort in analyzing networks of sensors. The goal is to facilitate any form of sensor network, enabling users to combine related semantics with sensor data, and facilitate the end-user transformation of data necessary to provide more complex query expressions, and thus meet the analytical requirements

Distributed Adaptation Techniques for Connected Vehicles

In this PhD dissertation, we propose distributed adaptation mechanisms for connected vehicles to deal with the connectivity challenges. To understand the system behavior of the solutions for connected vehicles, we first need to characterize the operational environment. Therefore, we devised a large scale fading model for various link types, including point-to-point vehicular communications and multi-hop connected vehicles. We explored two small scale fading models to define the characteristics of multi-hop connected vehicles. Taking our research into multi-hop connected vehicles one step further, we propose selective information relaying to avoid message congestion due to redundant messages received by the relay vehicle. Results show that the proposed mechanism reduces messaging load by up to 75% without sacrificing environmental awareness. Once we define the channel characteristics, we propose a distributed congestion control algorithm to solve the messaging overhead on the channels as the next research interest of this dissertation. We propose a combined transmit power and message rate adaptation for connected vehicles. The proposed algorithm increases the environmental awareness and achieves the application requirements by considering highly dynamic network characteristics. Both power and rate adaptation mechanisms are performed jointly to avoid one result affecting the other negatively. Results prove that the proposed algorithm can increase awareness by 20% while keeping the channel load and interference at almost the same level as well as improve the average message rate by 18%. As the last step of this dissertation, distributed cooperative dynamic spectrum access technique is proposed to solve the channel overhead and the limited resources issues. The adaptive energy detection threshold, which is used to decide whether the channel is busy, is optimized in this work by using a computationally efficient numerical approach. Each vehicle evaluates the available channels by voting on the information received from one-hop neighbors. An interdisciplinary approach referred to as entropy-based weighting is used for defining the neighbor credibility. Once the vehicle accesses the channel, we propose a decision mechanism for channel switching that is inspired by the optimal flower selection process employed by bumblebees foraging. Experimental results show that by using the proposed distributed cooperative spectrum sensing mechanism, spectrum detection error converges to zero

Understanding the bi-directional relationship between analytical processes and interactive visualization systems

Interactive visualizations leverage the human visual and reasoning systems to increase the scale of information with which we can effectively work, therefore improving our ability to explore and analyze large amounts of data. Interactive visualizations are often designed with target domains in mind, such as analyzing unstructured textual information, which is a main thrust in this dissertation. Since each domain has its own existing procedures of analyzing data, a good start to a well-designed interactive visualization system is to understand the domain experts' workflow and analysis processes. This dissertation recasts the importance of understanding domain users' analysis processes and incorporating such understanding into the design of interactive visualization systems. To meet this aim, I first introduce considerations guiding the gathering of general and domain-specific analysis processes in text analytics. Two interactive visualization systems are designed by following the considerations. The first system is Parallel-Topics, a visual analytics system supporting analysis of large collections of documents by extracting semantically meaningful topics. Based on lessons learned from Parallel-Topics, this dissertation further presents a general visual text analysis framework, I-Si, to present meaningful topical summaries and temporal patterns, with the capability to handle large-scale textual information. Both systems have been evaluated by expert users and deemed successful in addressing domain analysis needs. The second contribution lies in preserving domain users' analysis process while using interactive visualizations. Our research suggests the preservation could serve multiple purposes. On the one hand, it could further improve the current system. On the other hand, users often need help in recalling and revisiting their complex and sometimes iterative analysis process with an interactive visualization system. This dissertation introduces multiple types of evidences available for capturing a user's analysis process within an interactive visualization and analyzes cost/benefit ratios of the capturing methods. It concludes that tracking interaction sequences is the most un-intrusive and feasible way to capture part of a user's analysis process. To validate this claim, a user study is presented to theoretically analyze the relationship between interactions and problem-solving processes. The results indicate that constraining the way a user interacts with a mathematical puzzle does have an effect on the problemsolving process. As later evidenced in an evaluative study, a fair amount of high-level analysis can be recovered through merely analyzing interaction logs

Location-based Marketing: the academic framework

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Over the last several years one could observe revolution in location-based technologies and geospatial information. Location awareness of mobile devices resulted in development of Location-Based Services (LBS) that are realization of that revolution in the most personal and contextual way. The ability to reach consumers in the highly targeted manner based on spatio-temporal criteria, attracted marketers from the early beginning of LBS creating field called Location-Based Marketing. Today decreasing prices of smartphones and wireless internet, as well as integration of location-aware mobile solutions and social media is leading to new possibilities and opportunities. The academic and professional interests of the author made him noticed that although the industry has challenged a significant development, there is lack of publications that would put an academic framework on that progress. The research has fulfilled this gap by extensive investigation of the current state of the art of Location-Based Marketing and its foundations - Location Based Services. The dissertation provides academic framework by comprehensive analysis of the Location-Based Marketing from LBS and marketing perspective. Further the thesis is addressing the issue of significant discrepancy between theoretical concepts of measurable Location-Based Social Media data and the actual data than can be legally accessed and used for marketing analysis purposes by investigation a case study of Location-Based Social Network - Foursqaure and Location-Based Analytics platform VenueLabs