Scalable Data Processing for Community Sensing Applications

Duarte, Sérgio; Navalho, David; Ferreira, Heitor; Preguiça, Nuno

doi:10.1007/s11036-012-0424-9

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Arnau leverages mobile GPUs in a power-efficient way by using Parallel Frame Rendering (PFR) techniques to effectively split the workload among battery-constrained devices. This novel approach is tailored towards a specific graphics-oriented application but can be effectively reused in a number of different GPGPU computing [59] scenarios [31,50].…”

Section: (Trusted) Distributed Mobile Computingmentioning

confidence: 99%

Trusted, Heterogeneous, and Autonomic Mobile Cloud

Lombardi

Pietro

2016

Secure System Design and Trustable Computing

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Offloading computing to distributed and possibly mobile nodes is increasingly popular thanks to the convenience and availability of cloud resources. However, trusted mobile computing is not presently viable due to a number of issues in both the mobile platform architectures and in the cloud service implementations. The complexity of such systems potentially exposes them to malicious and/or selfish behavior. This chapter describes the state-of-the-art research on theoretical advancements and practical implementations of trusted computing on a mobile cloud. Further, mobile distributed cloud computing security and reliability issues are introduced. Discussed solutions feature different levels of resiliency against malicious and misbehaving nodes.

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Section: (Trusted) Distributed Mobile Computingmentioning

confidence: 99%

Trusted, Heterogeneous, and Autonomic Mobile Cloud

Lombardi

Pietro

2016

Secure System Design and Trustable Computing

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“…A full discussion about how this kind of data can be used to acquire an effective feedback on physical activity and how it can be used for global healthcare is provided in 18 . The evaluation of different architectures for continuous processing of GPS data is provided 19 . Some studies 16 17 provide general statistics regarding sports activity data sets.…”

Section: Related Workmentioning

confidence: 99%

Stream Processing of Healthcare Sensor Data: Studying User Traces to Identify Challenges from a Big Data Perspective

Cortés

Bonnaire

Marin

et al. 2015

Procedia Computer Science

134

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The Internet of Things (IoT) generates massive streams of data which call for ever more efficient real time processing. Designing and implementing a big data service for the real time processing of such data requires an extensive knowledge of both input load and data distribution in order to provide a service which can cope with the workload. In this context, we study in this paper the challenges inherent to the real time processing of massive data flows from the IoT. We provide a detailed analysis of traces gathered from a well-known healthcare sport-oriented application in order to illustrate our conclusions from a big data perspective.

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“…server or communication links) represents a serious restriction to address personal security evaluation, because the system should be available when required. Duarte et al go a step forward in the decentralization of the system architecture for participatory sensing and propose the use of mobile units acting as an intermediary between servers and the sensors, which eventually can support asynchronous communication among the network components. Finally, Ochoa and Santos go a step even further proposing a human‐centric wireless sensor network, which includes all the components of its predecessors, but also witness units that act as a repository of information for users located in a particular area.…”

Section: Related Workmentioning

confidence: 99%

Supporting personal security using participatory sensing

Carreño

Gutiérrez

Ochoa

et al. 2014

Concurrency and Computation

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Artículo de publicación ISIPersonal security is an open problem in large cities. After several attempts to reduce violence and crime, there seems to be an agreement that preventive actions are the best way to address this problem. Trying to help deal with that challenge, this paper proposes a mobile collaborative application, named Personal Guardian, which is used by civilians while walking in urban areas. The application is focused on crime prevention and it implements participatory sensing to help people be aware of the risks that appear to exist in a certain place at a certain time. Based on that information, citizens can take appropriate and on-time preventive actions. The system is supported by a human-centric wireless sensor network, and it is complementary to the security solutions already used by public and private organizations. The system architecture and its main components are described, and the main requirements and design decisions are also discussed. A preliminary evaluation of the solution was conducted to determine its strengths and weaknesses in terms of quality of service. The obtained results indicate that the information feeding process is more relevant for end-users than the unattended delivery of awareness information about their personal security. In addition, this former capability does not require to be adjusted to the end-users' context.Fondecyt Project (Chile) 1120207 Ph.D. Scholarship Program of Conicyt Chile CONICYT-PCHA/DoctoradoNacional/2013-2113007

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Scalable Data Processing for Community Sensing Applications

Cited by 5 publications

References 25 publications

Trusted, Heterogeneous, and Autonomic Mobile Cloud

Trusted, Heterogeneous, and Autonomic Mobile Cloud

Stream Processing of Healthcare Sensor Data: Studying User Traces to Identify Challenges from a Big Data Perspective

Supporting personal security using participatory sensing

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