2,979 research outputs found
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
An Efficient Collaboration and Incentive Mechanism for Internet-of-Vehicles (IoVs) with Secured Information Exchange Based on Blockchains
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordWith the rapid development of Internet-of-Things
(IoT), mobile crowdsensing, i.e., outsourcing sensing tasks to
mobile devices or vehicles, has been proposed to address the
problem of data collection in the scenarios such as smart city.
Despite its benefits for a wide range of applications, mobile
crowdsensing lacks an efficient incentive mechanism, restricting
the development of IoT applications, especially for Internet-ofVehicles (IoV) – a typical example of IoT applications; this
is because vehicles are usually reluctant to participate these
sensing tasks. Moreover, in practice some sensing tasks may
arrive suddenly (called an emergent task) in the IoV environment,
but the resources of a single vehicle may be insufficient to
handle, and thus multi-vehicles collaboration is required. In
this case, the incentive mechanisms for the participation of
multiple vehicles and the task scheduling for their collaborations
are collectively needed. To address this important problem, we
firstly propose a new model for the scenario of two vehicles
collaboration, considering the situation of emergent appearance
of a task. In this model, for a general sensing task, we propose
a bidding mechanism to better encourage vehicles to contribute
their resources, and the tasks for those vehicles are scheduled
accordingly. Secondly, for an emergent task, a novel time-window
based method is devised to manage the tasks among vehicles
and to incent the vehicles to participate. Finally, we develop
a blockchain framework to achieve the secured information
exchange through smart contract for the proposed models in
IoV.National Key Research and Development Program of ChinaNational Natural Science Foundation of China (NSFC)Purple Mountain Laboratory: Networking, Communications and SecurityAcademician Expert Workstation of Bitvalue Technology (Hunan) Company Limite
Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity
Spatial crowdsourcing (SC) engages large worker pools for location-based
tasks, attracting growing research interest. However, prior SC task allocation
approaches exhibit limitations in computational efficiency, balanced matching,
and participation incentives. To address these challenges, we propose a
graph-based allocation framework optimized for massive heterogeneous spatial
data. The framework first clusters similar tasks and workers separately to
reduce allocation scale. Next, it constructs novel non-crossing graph
structures to model balanced adjacencies between unevenly distributed tasks and
workers. Based on the graphs, a bidirectional worker-task matching scheme is
designed to produce allocations optimized for mutual interests. Extensive
experiments on real-world datasets analyze the performance under various
parameter settings
CCAFS Program of Work and Budget 2015
The 2015 program of work and budget of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) lays out the key activities and budget in 2015
SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS
Wireless sensor networks (WSNs) are very useful in many application areas including battlefield surveillance, environment monitoring and target tracking, industrial processes and health monitoring and control. The classical WSNs are composed of large number of densely deployed sensors, where sensors are battery-powered devices with limited signal processing capabilities. In the crowdsourcing based WSNs, users who carry devices with built-in sensors are recruited as sensors. In both WSNs, the sensors send their observations regarding the target to a central node called the fusion center for final inference. With limited resources, such as limited communication bandwidth among the WSNs and limited sensor battery power, it is important to investigate algorithms which consider the trade-off between system performance and energy cost in the WSNs. The goal of this thesis is to study the sensor management problems in resource limited WSNs while performing target localization or tracking tasks.
Most research on sensor management problems in classical WSNs assumes that the number of sensors to be selected is given a priori, which is often not true in practice. Moreover, sensor network design usually involves consideration of multiple conflicting objectives, such as maximization of the lifetime of the network or the inference performance, while minimizing the cost of resources such as energy, communication or deployment costs. Thus, in this thesis, we formulate the sensor management problem in a classical resource limited WSN as a multi-objective optimization problem (MOP), whose goal is to find a set of sensor selection strategies which re- veal the trade-off between the target tracking performance and the number of selected sensors to perform the task. In this part of the thesis, we propose a novel mutual information upper bound (MIUB) based sensor selection scheme, which has low computational complexity, same as the Fisher information (FI) based sensor selection scheme, and gives estimation performance similar to the mutual information (MI) based sensor selection scheme. Without knowing the number of sensors to be selected a priori, the MOP gives a set of sensor selection strategies that reveal different trade-offs between two conflicting objectives: minimization of the number of selected sensors and minimization of the gap between the performance metric (MIUB and FI) when all the sensors transmit measurements and when only the selected sensors transmit their measurements based on the sensor selection strategy.
Crowdsourcing has been applied to sensing applications recently where users carrying devices with built-in sensors are allowed or even encouraged to contribute toward the inference tasks. Crowdsourcing based WSNs provide cost effectiveness since a dedicated sensing infrastructure is no longer needed for different inference tasks, also, such architectures allow ubiquitous coverage. Most sensing applications and systems assume voluntary participation of users. However, users consume their resources while participating in a sensing task, and they may also have concerns regarding their privacy. At the same time, the limitation on communication bandwidth requires proper management of the participating users. Thus, there is a need to design optimal mechanisms which perform selection of the sensors in an efficient manner as well as providing appropriate incentives to the users to motivate their participation. In this thesis, optimal mechanisms are designed for sensor management problems in crowdsourcing based WSNs where the fusion center (FC) con- ducts auctions by soliciting bids from the selfish sensors, which reflect how much they value their energy cost. Furthermore, the rationality and truthfulness of the sensors are guaranteed in our model. Moreover, different considerations are included in the mechanism design approaches: 1) the sensors send analog bids to the FC, 2) the sensors are only allowed to send quantized bids to the FC because of communication limitations or some privacy issues, 3) the state of charge (SOC) of the sensors affects the energy consumption of the sensors in the mechanism, and, 4) the FC and the sensors communicate in a two-sided market
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The SLIM (Social learning for the integrated management and sustainable use of water at catchment scale) Final Report
Background: SLIM stands for 'Socuak Learning for the Integrated Management and Sustainable Use of Water at Catchment Scale'. It is a multi-country research project funded by the European Commission (DG RESEARCH - 5th Framework Programme for research and technological development, 1998-2002). Its main theme is the investigation of the socio-economic aspects of the sustainable use of water. Within this theme, its main focus of interest lies in understanding the application of social learning as a conceptual framework, an operational principle, a policy instrument and a process of systemic change
Towards a global participatory platform: Democratising open data, complexity science and collective intelligence
The FuturICT project seeks to use the power of big data, analytic models grounded in complexity science, and the collective intelligence they yield for societal benefit. Accordingly, this paper argues that these new tools should not remain the preserve of restricted government, scientific or corporate élites, but be opened up for societal engagement and critique. To democratise such assets as a public good, requires a sustainable ecosystem enabling different kinds of stakeholder in society, including but not limited to, citizens and advocacy groups, school and university students, policy analysts, scientists, software developers, journalists and politicians. Our working name for envisioning a sociotechnical infrastructure capable of engaging such a wide constituency is the Global Participatory Platform (GPP). We consider what it means to develop a GPP at the different levels of data, models and deliberation, motivating a framework for different stakeholders to find their ecological niches at different levels within the system, serving the functions of (i) sensing the environment in order to pool data, (ii) mining the resulting data for patterns in order to model the past/present/future, and (iii) sharing and contesting possible interpretations of what those models might mean, and in a policy context, possible decisions. A research objective is also to apply the concepts and tools of complexity science and social science to the project's own work. We therefore conceive the global participatory platform as a resilient, epistemic ecosystem, whose design will make it capable of self-organization and adaptation to a dynamic environment, and whose structure and contributions are themselves networks of stakeholders, challenges, issues, ideas and arguments whose structure and dynamics can be modelled and analysed. Graphical abstrac
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