Quality of information: An empirical approach

Gelenbe, Erol; Hey, Laurence

doi:10.1109/mahss.2008.4660116

Cited by 17 publications

(9 citation statements)

References 4 publications

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“…As different approaches aim at different objectives, open problems are still stimulating new research activities in emergency evacuation and rescue systems. For a WSN-based emergency evacuation system, if nodes break down or are moved by the hazards, system resilience and quality of information [52] are of concern. In addition, while most existing studies aim at an indoor system, an emergency evacuation system for outdoor and underground environments are also needed.…”

Section: Evolution Towards Greater Complexitymentioning

confidence: 99%

“…We have seen that EMSsare intrinsically distributed, so that in the future, we may see more related work on distributed control and distributed algorithms for such systems [97]. The information that is collected via sensing is often inaccurate and requires interpretation [52]. Events, such as explosions and the spread of fire, can take place at a high rate and are themselves disruptive; thus, the limitations of sensing for EMS is definitely a topic that requires much more attention.…”

Section: Areas For Future Workmentioning

confidence: 99%

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Future Research on Cyber-Physical Emergency Management Systems

Gelenbe

2013

Future Internet

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Cyber-physical systems that include human beings and vehicles in a built environment, such as a building or a city, together with sensor networks and decision support systems have attracted much attention. In emergencies, which also include mobile searchers and rescuers, the interactions among civilians and the environment become much more diverse, and the complexity of the emergency response also becomes much greater. This paper surveys current research on sensor-assisted evacuation and rescue systems and discusses the related research issues concerning communication protocols for sensor networks, as well as several other important issues, such as the integrated asynchronous control of large-scale emergency response systems, knowledge discovery for rescue and prototyping platforms. Then, we suggest directions for further research.

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Section: Evolution Towards Greater Complexitymentioning

confidence: 99%

Section: Areas For Future Workmentioning

confidence: 99%

Future Research on Cyber-Physical Emergency Management Systems

Gelenbe

2013

Future Internet

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“…Some of these may be impacted by a network, processing performed on the information and data used to derive it, and the information sources. The interested reader is referred to [1], [2], [3] for a more full discussion.…”

Section: ) Other Qoi Attributesmentioning

confidence: 99%

Quality of information functions for networked applications

Bahjat

Jiang

Cook

et al. 2012

2012 IEEE International Conference on Pervasive Computing and Communications Workshops

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In this paper we propose a framework for providing quality of information (QoI) aware networking. QoI quantifies how useful information is for an application. Its value is comprised of both intrinsic and contextual attributes related to the information. Intrinsic attributes include freshness and accuracy of the information. Contextual metrics include completeness and timeliness. To design network control algorithms, such as resource control algorithms, attributes of data must be mapped to QoI. Then, network algorithms may deliver the data in such a way that the ultimate information derived from the data is of sufficient quality for its purpose. We propose our QoI framework, and present the concept of QoI functions that capture tradeoffs between different attributes of QoI. We use optical character recognition (OCR) as an exemplar image processing application. We focus on two attributes of QoI for the OCR application: accuracy and timeliness. We show how network controls and data processing, such as error recovery and compression, operating on data, impact the QoI delivered to a recipient. We then show how reductions in the required QoI may have a drastic impact on the amount of network resources required to support a QoI-aware transaction.

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“…QoI denition in regard to monitoring environments [5] is dened as the dierence between the data that the output of the WSNs produces concerning some environment that is being monitored, and the actual events in that environment which one wishes to observe or track. The multidimensional concept [6] of QoI on application view is the collective eect of the available knowledge regarding sensor derived information that determines the degree of accuracy and condence by which those aspects of the real world (that are of interest to the user of the information) can be represented by this information.…”

Section: Quality Of Simulations: Forms and Denitionsmentioning

confidence: 99%

Simulation and Evaluation of Mixed-Mode Environments: Towards Higher Quality of Simulations

Sachidananda

Costantini

Reinl

et al. 2010

Simulation, Modeling, and Programming for Autonomous Robots

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Abstract. For rescue and surveillance scenarios, the Mixed-Mode Environments (MMEs) for data acquisition, processing, and dissemination have been proposed. Evaluation of the algorithms and protocols developed for such environments before deployment is vital. However, there is a lack of realistic testbeds for MMEs due to reasons such as high costs for their setup and maintenance. Hence, simulation platforms are usually the tool of choice when testing algorithms and protocols for MMEs.However, existing simulators are not able to fully support detailed evaluation of complex scenarios in MMEs. This is usually due to lack of highly accurate models for the simulated entities and environments. This aects the results which are obtained by using such simulators. In this paper, we highlight the need to consider the Quality of Simulations (QoSim), in particular aspects such as accuracy, validity, certainty, and acceptability. The focus of this paper is to understand the gap between real-world experiments and simulations for MMEs. The paper presents key QoSim concepts and characteristics for MMEs simulations, describing the aspects of contents of simulation, processing of simulation, and simulation outputs. Eventually, a road map for improving existing simulation environments is proposed.

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Quality of information: An empirical approach

Cited by 17 publications

References 4 publications

Future Research on Cyber-Physical Emergency Management Systems

Future Research on Cyber-Physical Emergency Management Systems

Quality of information functions for networked applications

Simulation and Evaluation of Mixed-Mode Environments: Towards Higher Quality of Simulations

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