Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks

The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN

Studying user behavior through a participatory sensing framework in an urban context

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe widespread use of mobile devices has given birth to participatory sensing, a data collection approach leveraging the sheer number of device users, their mobility, intelligence and device’s increasingly powerful computing and sensing capabilities. As a result, participatory sensing is able to collect various types of information at a high spatial and temporal resolution and it has many applications ranging from measuring cellular signal strength or road condition monitoring to observing the distribution of birds. However, in order to achieve better results from participatory sensing, some issues needed to be dealt with. On a high level, this thesis addressed two issues: (1) the design and development of a participatory sensing framework that allows users to flexibly create campaigns and at the same time collect different types of data and (2) the study of different aspects of the user behaviors in the context of participatory sensing. In particular, the first contribution of the thesis is the design and development of Citizense, a participatory sensing framework that facilitates flexible deployments of participatory sensing campaigns while at the same time providing intuitive interfaces for users to create sensing campaigns and collect a variety of data types. During the real-world deployments of Citizense, it has shown its effectiveness in collecting different types of urban information and subsequently received appreciation from different stakeholders. The second contribution of the thesis is the in-depth study of user behavior under the presence of different monetary incentive mechanisms and the analysis of the spatial and temporal user behavior when participants are simultaneously exposed to a large number of participatory sensing campaigns. Concerning the monetary incentive, it is observed that participants prefer fixed micro-payment to other mechanisms (i.e., lottery, variable micro-payment); their participation was increased significantly when they were given this incentive. When taking part in the participatory sensing process, participants exhibit certain spatial and temporal behaviors. They tend to primarily contribute in their free time during the working week, although the decision to respond and complete a particular participatory sensing campaign seems to be correlated to the campaign’s geographical context and/or the recency of the participants’ activities. Participants can be divided into two groups according to their behaviors: a smaller group of active participants who frequently perform participatory sensing activities and a larger group of regular participants who exhibit more intermittent behaviors

Successful Resource Seeking Strategies: An Agent Based Model of Budgetary Competition

The strategies that bureaucratic actors employ to secure resources are the result of a complex interplay between motivational states and environmental conditions. The strategies employed by bureaucrats to secure resources are now best understood as heuristics. Heuristics that may be adaptive in securing resources under some conditions may be maladaptive under different environmental circumstances (Gigerenzer 2000; 2008). This study reviews the various strategies employed by bureaucrats to secure financial resources through the lens of Downs’ typology of bureaucrats to determine the fundamental heuristics the successful strategies employ. We sought inspiration from both the extant literature and models of bureaucratic behavior within organizations beginning with Downs (1967) and continuing with the work of Bowling, Cho, and Wright (2004), and the methodological innovations afforded by agent-based modeling. By making certain basic assumptions regarding decision-making heuristics, we show a remarkable consistency between Downs, Bowling and her colleagues, and our own findings

User Guidance for Efficient Fact Checking

The Web constitutes a valuable source of information. In recent years, it fostered the construction of large-scale knowledge bases, such as Freebase, YAGO, and DBpedia. The open nature of the Web, with content potentially being generated by everyone, however, leads to inaccuracies and misinformation. Construction and maintenance of a knowledge base thus has to rely on fact checking, an assessment of the credibility of facts. Due to an inherent lack of ground truth information, such fact checking cannot be done in a purely automated manner, but requires human involvement. In this paper, we propose a comprehensive framework to guide users in the validation of facts, striving for a minimisation of the invested effort. Our framework is grounded in a novel probabilistic model that combines user input with automated credibility inference. Based thereon, we show how to guide users in fact checking by identifying the facts for which validation is most beneficial. Moreover, our framework includes techniques to reduce the manual effort invested in fact checking by determining when to stop the validation and by supporting efficient batching strategies. We further show how to handle fact checking in a streaming setting. Our experiments with three real-world datasets demonstrate the efficiency and effectiveness of our framework: A knowledge base of high quality, with a precision of above 90\%, is constructed with only a half of the validation effort required by baseline techniques

INSTITUTIONAL CHANGE IN INDIAN AGRICULTURE

Globalization, privatization and scientific advancements pose new challenges and opportunities for the development of Indian agriculture. The emerging paradigm shifts focus to creation and application of new knowledge for agricultural development and global competitiveness. To facilitate this shift and realize greater economic efficiency, a new set of responsive institutions should emerge. This volume discusses the direction of institutional change in Indian agriculture. The roles of the state, markets and collective actions are examined for evolving the knowledge-intensive agriculture. The contributed papers from a number of leading researchers cover the institutions for R&D, land and water resources, credit, marketing, trade and agro-processing.Industrial Organization, International Development,