The TOMO-ETNA experiment: an imaging active campaign at Mt. Etna volcano. Context, main objectives, working-plans and involved research projects

The TOMO-ETNA experiment was devised to image of the crust underlying the volcanic edifice and, possibly, its plumbing system by using passive and active refraction/reflection seismic methods. This experiment included activities both on-land and offshore with the main objective of obtaining a new high-resolution seismic tomography to improve the knowledge of the crustal structures existing beneath the Etna volcano and northeast Sicily up to Aeolian Islands. The TOMO ETNA experiment was divided in two phases. The first phase started on June 15, 2014 and finalized on July 24, 2014, with the withdrawal of two removable seismic networks (a Short Period Network and a Broadband network composed by 80 and 20 stations respectively) deployed at Etna volcano and surrounding areas. During this first phase the oceanographic research vessel “Sarmiento de Gamboa” and the hydro-oceanographic vessel “Galatea” performed the offshore activities, which includes the deployment of ocean bottom seismometers (OBS), air-gun shooting for Wide Angle Seismic refraction (WAS), Multi-Channel Seismic (MCS) reflection surveys, magnetic surveys and ROV (Remotely Operated Vehicle) dives. This phase finished with the recovery of the short period seismic network. In the second phase the Broadband seismic network remained operative until October 28, 2014, and the R/V “Aegaeo” performed additional MCS surveys during November 19-27, 2014. Overall, the information deriving from TOMO-ETNA experiment could provide the answer to many uncertainties that have arisen while exploiting the large amount of data provided by the cutting-edge monitoring systems of Etna volcano and seismogenic area of eastern Sicily.

Mathematical Programming Models for Influence Maximization on Social Networks

In this dissertation, we apply mathematical programming techniques (i.e., integer programming and polyhedral combinatorics) to develop exact approaches for influence maximization on social networks. We study four combinatorial optimization problems that deal with maximizing influence at minimum cost over a social network. To our knowl- edge, all previous work to date involving influence maximization problems has focused on heuristics and approximation. We start with the following viral marketing problem that has attracted a significant amount of interest from the computer science literature. Given a social network, find a target set of customers to seed with a product. Then, a cascade will be caused by these initial adopters and other people start to adopt this product due to the influence they re- ceive from earlier adopters. The idea is to find the minimum cost that results in the entire network adopting the product. We first study a problem called the Weighted Target Set Selection (WTSS) Prob- lem. In the WTSS problem, the diffusion can take place over as many time periods as needed and a free product is given out to the individuals in the target set. Restricting the number of time periods that the diffusion takes place over to be one, we obtain a problem called the Positive Influence Dominating Set (PIDS) problem. Next, incorporating partial incentives, we consider a problem called the Least Cost Influence Problem (LCIP). The fourth problem studied is the One Time Period Least Cost Influence Problem (1TPLCIP) which is identical to the LCIP except that we restrict the number of time periods that the diffusion takes place over to be one. We apply a common research paradigm to each of these four problems. First, we work on special graphs: trees and cycles. Based on the insights we obtain from special graphs, we develop efficient methods for general graphs. On trees, first, we propose a polynomial time algorithm. More importantly, we present a tight and compact extended formulation. We also project the extended formulation onto the space of the natural vari- ables that gives the polytope on trees. Next, building upon the result for trees---we derive the polytope on cycles for the WTSS problem; as well as a polynomial time algorithm on cycles. This leads to our contribution on general graphs. For the WTSS problem and the LCIP, using the observation that the influence propagation network must be a directed acyclic graph (DAG), the strong formulation for trees can be embedded into a formulation on general graphs. We use this to design and implement a branch-and-cut approach for the WTSS problem and the LCIP. In our computational study, we are able to obtain high quality solutions for random graph instances with up to 10,000 nodes and 20,000 edges (40,000 arcs) within a reasonable amount of time.

Pillars and Elements to Develop an Open Business Model for Innovation Networks

Part 9: Innovation Networks

Improving robustness of rolling stock circulations in rapid transit networks

The rolling stock circulation depends on two different problems: the rolling stock assignment and the train routing problems, which up to now have been solved sequentially. We propose a new approach to obtain better and more robust circulations of the rolling stock train units, solving the rolling stock assignment while accounting for the train routing problem. Here robustness means that difficult shunting operations are selectively penalized and propagated delays together with the need for human resources are minimized. This new integrated approach provides a huge model. Then, we solve the integrated model using Benders decomposition, where the main decision is the rolling stock assignment and the train routing is in the second level. For computational reasons we propose a heuristic based on Benders decomposition. Computational experiments show how the current solution operated by RENFE (the main Spanish train operator) can be improved: more robust and efficient solutions are obtained

An Innovation Model for Collaborative Networks of SOA-Based Software Providers

Part 5: Service Orientation in Collaborative Networks

Determinants of engagement in virtual social networks in Singapore

The use of virtual social networks (VSNs) has been prevalent among consumers worldwide. Numerous studies have investigated various aspects of VSNs. However, these studies have mainly focused on students and young adults as they were early adopters of these innovative networks. A search of the literature revealed there has been a paucity of research on adult consumers’ use of VSNs. This research study addressed this gap in the literature by examining the determinants of engagement in VSNs among adult consumers in Singapore. The objectives of this study are to empirically investigate the determinants of engagement in VSNs and to offer theoretical insights into consumers’ preference and usage of VSNs. This study tapped upon several theories developed in the discipline of technology and innovation adoption. These were Roger’s Diffusion of Innovation, Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), Conceptual Framework of Individual Innovation Adoption by Frambach and Schillewaert (2002), Enhanced Model of Innovation Adoption by Talukder (2011), Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and the Information Systems (IS) Success Model. The proposed research model, named the Media Usage Model (MUM), is a framework rooted in innovation diffusion and IS theories. The MUM distilled the essence of these established models and thus provides an updated, lucid explanation of engagement in VSNs. A cross-sectional, online social survey was conducted to collect quantitative data to examine the validity of the proposed research model. Multivariate data analysis was carried out on a data set comprising 806 usable responses by utilizing SPSS, and for structural equation modeling AMOS and SmartPLS. The results indicate that consumer attitude towards VSNs is significantly and positively influenced by: three individual factors – hedonic motivation, incentives and experience; two system characteristics – system quality and information quality; and one social factor – social bonding. Consumer demographics were found to influence people’s attitudes towards VSNs. In addition, consumer experience and attitude towards VSNs significantly and positively influence their usage of VSNs. The empirical data supported the proposed research model, explaining 80% of variance in attitude towards VSNs and 45% of variance in usage of VSNs. Therefore, the MUM achieves a definite contribution to theoretical knowledge of consumer engagement in VSNs by deepening and broadening our appreciation of the intricacies related to use of VSNs in Singapore. This study’s findings have implications for customer service management, services marketing and consumer behavior. These findings also have strategic implications for maximizing efficient utilization and effective management of VSNs by businesses and operators. The contributions of this research are: firstly, shifting the boundaries of technology or innovation adoption theories from research on employees to consumers as well as the boundaries of Internet usage or adoption research from students to adults, which is also known as empirical generalization; secondly, highlighting the issues associated with lack of significance of social factors in adoption research; and thirdly, augmenting information systems research by integrating important antecedents for success in information systems.

Intrusion Detection Systems in SDN-based Self-Healing PMU Networks

Nowadays, Power grids are critical infrastructures on which everything else relies, and their correct behavior is of the highest priority. New smart devices are being deployed to be able to manage and control power grids more efficiently and avoid instability. However, the deployment of such smart devices like Phasor Measurement Units (PMU) and Phasor Data Concentrators (PDC), open new opportunities for cyber attackers to exploit network vulnerabilities. If a PDC is compromised, all data coming from PMUs to that PDC is lost, reducing network observability. Our approach to solve this problem is to develop an Intrusion detection System (IDS) in a Software-defined network (SDN). allowing the IDS system to detect compromised devices and use that information as an input for a self-healing SDN controller, which redirects the data of the PMUs to a new, uncompromised PDC, maintaining the maximum possible network observability at every moment. During this research, we have successfully implemented Self-healing in an example network with an SDN controller based on Ryu controller. We have also assessed intrinsic vulnerabilities of Wide Area Management Systems (WAMS) and SCADA networks, and developed some rules for the Intrusion Detection system which specifically protect vulnerabilities of these networks. The integration of the IDS and the SDN controller was also successful. \\To achieve this goal, the first steps will be to implement an existing Self-healing SDN controller and assess intrinsic vulnerabilities of Wide Area Measurement Systems (WAMS) and SCADA networks. After that, we will integrate the Ryu controller with Snort, and create the Snort rules that are specific for SCADA or WAMS systems and protocols.

Methodological and Technological Support for the Coordination of Smart Networks of SMEs

Part 2: Behaviour and Coordination

Evolution of developmental gene regulatory networks in echinoids

Developmental gene regulatory networks (dGRNs) are assemblages of regulatory genes that direct embryonic development of animal body plans and their morpho-logical structures. dGRNs exhibit recursively-wired circuitry that is encoded in the genome and executed during development. Alteration to the regulatory architecture of dGRNs causes variation in developmental programs both during the development of an individual organism and during the evolution of an individual lineage. The ex-planatory power of these networks is best exemplified by the global dGRN directing early development of the euechinoid sea urchin Strongylocentrotus purpuratus. This network consists of numerous regulatory genes engaging in hundreds of genomic regulatory transactions that collectively direct the delineation of early embryonic domains and the specification of cell lineages. Research on closely-related euechi-noid sea urchins, e.g. Lytechinus variegatus and Paracentrotus lividus, has revealed marked conservation of dGRN architecture in echinoid development, suggesting little appreciable alteration has occurred since their divergence in evolution at least 90 million years ago (mya).

We sought to test whether this observation extends to all sea urchins (echinoids) and undertook a systematic analysis of over 50 regulatory genes in the cidaroid sea urchin Eucidaris tribuloides, surveing their regulatory activity and function in a sea urchin that diverged from euechinoid sea urchins at least 268 mya. Our results revealed extensive alterations have occurred to all levels of echinoid dGRN archi-tecture since the cidaroid-euechinoid divergence. Alterations to mesodermal sub-circuits were particularly striking, including functional di˙erences in specification of non-skeletogenic mesenchyme (NSM), skeletogenic mesenchyme (SM), and en-domesodermal segregation. Specification of endomesodermal embryonic domains revealed that, while their underlying network circuitry had clearly diverged, regu-latory states established in pregastrular embryos of these two groups are strikingly similar. Analyses of E. tribuloides specification leading to the estab-lishment of dorsal-ventral (aboral-oral) larval polarity indicated that regulation of regulatory genes expressed in mesodermal embryonic domains had incurred significantly more alterations than those expressed in endodermal and ectodermal domains. Taken together, this study highlights the ability of dGRN architecture to buffer extensive alterations in the evolution and early development of echinoids and adds further support to the notion that alterations can occur at all levels of dGRN architecture and all stages of embryonic development.

The intellectual structure of research in hospitality management: A literature review using bibliometric methods of the journal International Journal of Hospitality Management

The purpose of this study is to analyze the existing literature on hospitality management from all the research papers published in The International Journal of Hospitality Management (IJHM) between 2008 and 2014. The authors apply bibliometric methods – in particular, author citation and co-citation analyses (ACA) – to identify the main research lines within this scientific field; in other words, its ‘intellectual structure’. Social network analysis (SNA) is also used to perform a visualization of this structure. The results of the analysis allow us to define the different research lines or fronts which shape the intellectual structure of research on hospitality management.

SECURITY, PRIVACY AND APPLICATIONS IN VEHICULAR AD HOC NETWORKS

With wireless vehicular communications, Vehicular Ad Hoc Networks (VANETs) enable numerous applications to enhance traffic safety, traffic efficiency, and driving experience. However, VANETs also impose severe security and privacy challenges which need to be thoroughly investigated. In this dissertation, we enhance the security, privacy, and applications of VANETs, by 1) designing application-driven security and privacy solutions for VANETs, and 2) designing appealing VANET applications with proper security and privacy assurance. First, the security and privacy challenges of VANETs with most application significance are identified and thoroughly investigated. With both theoretical novelty and realistic considerations, these security and privacy schemes are especially appealing to VANETs. Specifically, multi-hop communications in VANETs suffer from packet dropping, packet tampering, and communication failures which have not been satisfyingly tackled in literature. Thus, a lightweight reliable and faithful data packet relaying framework (LEAPER) is proposed to ensure reliable and trustworthy multi-hop communications by enhancing the cooperation of neighboring nodes. Message verification, including both content and signature verification, generally is computation-extensive and incurs severe scalability issues to each node. The resource-aware message verification (RAMV) scheme is proposed to ensure resource-aware, secure, and application-friendly message verification in VANETs. On the other hand, to make VANETs acceptable to the privacy-sensitive users, the identity and location privacy of each node should be properly protected. To this end, a joint privacy and reputation assurance (JPRA) scheme is proposed to synergistically support privacy protection and reputation management by reconciling their inherent conflicting requirements. Besides, the privacy implications of short-time certificates are thoroughly investigated in a short-time certificates-based privacy protection (STCP2) scheme, to make privacy protection in VANETs feasible with short-time certificates. Secondly, three novel solutions, namely VANET-based ambient ad dissemination (VAAD), general-purpose automatic survey (GPAS), and VehicleView, are proposed to support the appealing value-added applications based on VANETs. These solutions all follow practical application models, and an incentive-centered architecture is proposed for each solution to balance the conflicting requirements of the involved entities. Besides, the critical security and privacy challenges of these applications are investigated and addressed with novel solutions. Thus, with proper security and privacy assurance, these solutions show great application significance and economic potentials to VANETs. Thus, by enhancing the security, privacy, and applications of VANETs, this dissertation fills the gap between the existing theoretic research and the realistic implementation of VANETs, facilitating the realistic deployment of VANETs.

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Leveraging Symbiotic Relationships for Emulation of Computer Networks

The lack of analytical models that can accurately describe large-scale networked systems makes empirical experimentation indispensable for understanding complex behaviors. Research on network testbeds for testing network protocols and distributed services, including physical, emulated, and federated testbeds, has made steady progress. Although the success of these testbeds is undeniable, they fail to provide: 1) scalability, for handling large-scale networks with hundreds or thousands of hosts and routers organized in different scenarios, 2) flexibility, for testing new protocols or applications in diverse settings, and 3) inter-operability, for combining simulated and real network entities in experiments. This dissertation tackles these issues in three different dimensions. First, we present SVEET, a system that enables inter-operability between real and simulated hosts. In order to increase the scalability of networks under study, SVEET enables time-dilated synchronization between real hosts and the discrete-event simulator. Realistic TCP congestion control algorithms are implemented in the simulator to allow seamless interactions between real and simulated hosts. SVEET is validated via extensive experiments and its capabilities are assessed through case studies involving real applications. Second, we present PrimoGENI, a system that allows a distributed discrete-event simulator, running in real-time, to interact with real network entities in a federated environment. PrimoGENI greatly enhances the flexibility of network experiments, through which a great variety of network conditions can be reproduced to examine what-if questions. Furthermore, PrimoGENI performs resource management functions, on behalf of the user, for instantiating network experiments on shared infrastructures. Finally, to further increase the scalability of network testbeds to handle large-scale high-capacity networks, we present a novel symbiotic simulation approach. We present SymbioSim, a testbed for large-scale network experimentation where a high-performance simulation system closely cooperates with an emulation system in a mutually beneficial way. On the one hand, the simulation system benefits from incorporating the traffic metadata from real applications in the emulation system to reproduce the realistic traffic conditions. On the other hand, the emulation system benefits from receiving the continuous updates from the simulation system to calibrate the traffic between real applications. Specific techniques that support the symbiotic approach include: 1) a model downscaling scheme that can significantly reduce the complexity of the large-scale simulation model, resulting in an efficient emulation system for modulating the high-capacity network traffic between real applications; 2) a queuing network model for the downscaled emulation system to accurately represent the network effects of the simulated traffic; and 3) techniques for reducing the synchronization overhead between the simulation and emulation systems.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.