Assessing the role of dispersed floralresources for managed bees in providingsupporting ecosystem services for croppollination

Most pollination ecosystem services studies have focussed on wild pollinators and their dependence on natural floral resources adjacent to crop fields. However, managed pollinators depend on a mixture of floral resources that are spatially separated from the crop field. Here, we consider the supporting role these resources play as an ecosystem services provider to quantify the use and availability of floral resources, and to estimate their relative contribution to support pollination services of managed honeybees. Beekeepers supplying pollination services to the Western Cape deciduous fruit industry were interviewed to obtain information on their use of floral resources. For 120 apiary sites, we also analysed floral resources within a two km radius of each site based on geographic data. The relative availability of floral resources at sites was compared to regional availability. The relative contribution of floral resources-types to sustain managed honeybees was estimated. Beekeepers showed a strong preference for eucalypts and canola. Beekeepers selectively placed more hives at sites with eucalypt and canola and less with natural vegetation. However, at the landscape-scale, eucalypt was the least available resource, whereas natural vegetation was most common. Based on analysis of apiary sites, we estimated that 700,818 ha of natural vegetation, 73,910 ha of canola fields, and 10,485 ha of eucalypt are used to support the managed honeybee industry in the Western Cape. Whereas the Cape managed honeybee system uses a bee native to the region, alien plant species appear disproportionately important among the floral resources being exploited. We suggest that an integrated approach, including evidence from interview and landscape data, and fine-scale biological data is needed to study floral resources supporting managed honeybees

Trajectory Privacy Preservation and Lightweight Blockchain Techniques for Mobility-Centric IoT

Various research efforts have been undertaken to solve the problem of trajectory privacy preservation in the Internet of Things (IoT) of resource-constrained mobile devices. Most attempts at resolving the problem have focused on the centralized model of IoT, which either impose high delay or fail against a privacy-invading attack with long-term trajectory observation. These proposed solutions also fail to guarantee location privacy for trajectories with both geo-tagged and non-geo-tagged data, since they are designed for geo-tagged trajectories only. While a few blockchain-based techniques have been suggested for preserving trajectory privacy in decentralized model of IoT, they require large storage capacity on resource-constrained devices and can only provide conditional privacy when a set of authorities governs the blockchain. This dissertation addresses these challenges to develop efficient trajectory privacy-preservation and lightweight blockchain techniques for mobility-centric IoT. We develop a pruning-based technique by quantifying the relationship between trajectory privacy and delay for real-time geo-tagged queries. This technique yields higher trajectory privacy with a reduced delay than contemporary techniques while preventing a long-term observation attack. We extend our study with the consideration of the presence of non-geo-tagged data in a trajectory. We design an attack model to show the spatiotemporal correlation between the geo-tagged and non-geo-tagged data which undermines the privacy guarantee of existing techniques. In response, we propose a methodology that considers the spatial distribution of the data in trajectory privacy-preservation and improves existing solutions, in privacy and usability. With respect to blockchain, we design and implement one of the first blockchain storage management techniques utilizing the mobility of the devices. This technique reduces the required storage space of a blockchain and makes it lightweight for resource-constrained mobile devices. To address the trajectory privacy challenges in an authority-based blockchain under the short-range communication constraints of the devices, we introduce a silence-based one of the first technique to establish a balance between trajectory privacy and blockchain utility. The designed trajectory privacy- preservation techniques we established are light- weight and do not require an intermediary to guarantee trajectory privacy, thereby providing practical and efficient solution for different mobility-centric IoT, such as mobile crowdsensing and Internet of Vehicles

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

4Sensing - decentralized processing for participatory sensing data

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática.Participatory sensing is a new application paradigm, stemming from both technical and social drives, which is currently gaining momentum as a research domain. It leverages the growing adoption of mobile phones equipped with sensors, such as camera, GPS and accelerometer, enabling users to collect and aggregate data, covering a wide area without incurring in the costs associated with a large-scale sensor network. Related research in participatory sensing usually proposes an architecture based on a centralized back-end. Centralized solutions raise a set of issues. On one side, there is the implications of having a centralized repository hosting privacy sensitive information. On the other side, this centralized model has financial costs that can discourage grassroots initiatives. This dissertation focuses on the data management aspects of a decentralized infrastructure for the support of participatory sensing applications, leveraging the body of work on participatory sensing and related areas, such as wireless and internet-wide sensor networks, peer-to-peer data management and stream processing. It proposes a framework covering a common set of data management requirements - from data acquisition, to processing, storage and querying - with the goal of lowering the barrier for the development and deployment of applications. Alternative architectural approaches - RTree, QTree and NTree - are proposed and evaluated experimentally in the context of a case-study application - SpeedSense - supporting the monitoring and prediction of traffic conditions, through the collection of speed and location samples in an urban setting, using GPS equipped mobile phones

Privacy in trajectory micro-data publishing : a survey

We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research.Comment: Accepted for publication at Transactions for Data Privac