QoC-based Optimization of End-to-End M-Health Data Delivery Services

Widya, Ing; Beijnum, B.-J. van; Salden, Alfons H.

doi:10.1109/iwqos.2006.250476

Cited by 22 publications

(17 citation statements)

References 17 publications

(26 reference statements)

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“…M-Evaluation will shed a light on various types of differences as well as on communication delay reduction and other operational performance advantages of adopting ASK-ASSIST. This ranking can be formally captured by inducing context-dependent hierarchies of reconfigurations following similar ideas presented in the literature [19] for quality of contextbased optimization of end-to-end mobile-health data delivery services:…”

Section: Definition 3 Let M Be a Coalition Formation Environment An mentioning

confidence: 99%

Self-organizing mobile surveillance security networks

Ferro¹,

Salden²

2007

Proceedings of the 2nd International Conference on Bio-Inspired Models of Network Information and Computing Systems

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We develop and evaluate ASK-ASSIST, a decision support system that enables automatically setting up communication networks and sharing knowledge amongst security personnel, in particular when they are confronted with a series of unexpected incidents while on patrol. Currently, team leaders of mobile security guards are still forced to assess the consequences of a series of incidents and to take decisions in a heuristic way. On the basis of incomplete and uncertain information they have to determine the security situation, who to bring in contact with whom, and which personnel or which information systems should be lined up in order to resolve the incidents as a whole. ASK-ASSIST, however, automates robust dynamic formation and coordination of the right coalitions of security personnel, such that they support human improvisation in determining the nature of incidents and taking decisions by the coalitions. These coalitions comprise security personnel, communication security infrastructures and knowledge management security infrastructures. ASK-ASSIST is based on a framework for selforganization that generates ranked lists of incident-specific critical (re)configurations of coalitions in which joint tasks are assigned to agents associated with roles, like guards or team leaders. Our main contributions are the grounding and evaluation of the framework underlying ASK-ASSIST. We instantiate, validate and test the system on the basis of real data. These data concern organizational structures reflected in the coalitions and implicit or explicit feedback provided by personnel from a private security company. The provided feedback relates to the daily operational mobile security surveillance processes including those that involve functionalities of ASK-ASSIST.

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Section: Definition 3 Let M Be a Coalition Formation Environment An mentioning

confidence: 99%

Self-organizing mobile surveillance security networks

Ferro¹,

Salden²

2007

Proceedings of the 2nd International Conference on Bio-Inspired Models of Network Information and Computing Systems

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“…information freshness and accuracy. These quality measures are denoted as a Quality of Context (QoC) and they play a significant role in QoS-information processing phase [17].…”

Section: Qos-information Modelmentioning

confidence: 99%

QoS-Predictions Service: Infrastructural Support for Proactive QoS- and Context-Aware Mobile Services (Position Paper)

Wac

Halteren

Konstantas

2006

On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops

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Abstract. Today's mobile data applications aspire to deliver services to a user anywhere -anytime while fulfilling his Quality of Service (QoS) requirements. However, the success of the service delivery heavily relies on the QoS offered by the underlying networks. As the services operate in a heterogeneous networking environment, we argue that the generic information about the networks' offered-QoS may enable an anyhow mobile service delivery based on an intelligent (proactive) selection of 'any' network available in the user's context (location and time).Towards this direction, we develop a QoS-predictions service provider, which includes functionality for the acquisition of generic offered-QoS information and which, via a multidimensional processing and history-based reasoning, will provide predictions of the expected offered-QoS in a reliable and timely manner.We acquire the generic QoS-information from distributed mobile services' components quantitatively (actively and passively) measuring the applicationlevel QoS, while the reasoning is based on statistical data mining and pattern recognition techniques.

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“…Consequently, a different technological context exists that provides the same data but with different QoD grades. These technological resource configuration chains are used to optimize the output data quality [76].…”

Section: Technical Domain Ontologymentioning

confidence: 99%

“…First, we applied the intuitive approach. As researchers, we studied the commonly used QoD dimensions in computer science and healthcare [9,25,50,76,96,97,98]. The three most commonly mentioned QoD dimensions are accuracy (or correctness), dependability (or completeness) and timeliness (or currency) [96,98].…”

Section: Approach To Select Qod Dimensionsmentioning

confidence: 99%

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Quality of clinical data aware telemedicine systems

Rubio¹

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QoC-based Optimization of End-to-End M-Health Data Delivery Services

Cited by 22 publications

References 17 publications

Self-organizing mobile surveillance security networks

Self-organizing mobile surveillance security networks

QoS-Predictions Service: Infrastructural Support for Proactive QoS- and Context-Aware Mobile Services (Position Paper)

Quality of clinical data aware telemedicine systems

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