A survey of quality of service in mobile computing environments

Chalmers, Dan; Sloman, Morris

doi:10.1109/comst.1999.5340514

Cited by 216 publications

(110 citation statements)

References 41 publications

(46 reference statements)

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“…Unfortunately, what defines quality is vague, and different views exist in different studies and from different perspectives [10,11,17,12]. The following three views are, however, most common.…”

Section: The Qos Modelmentioning

confidence: 99%

“…It is worth noting that quality management has also been considered in other research areas [9,10,17,23,24]. For example, Mecella et al [17] have designed a broker which can select the best available data from a number of different service providers based on a specified set of data quality dimensions such as accuracy, completeness, currency and consistency.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Mecella et al [17] have designed a broker which can select the best available data from a number of different service providers based on a specified set of data quality dimensions such as accuracy, completeness, currency and consistency. In [9,10], QoS management for network resources has been considered. These studies aim to identify and establish suitable quality matrices for specific services in specific application areas, so that the quality of these services can be meaningfully gauged.…”

Section: Related Workmentioning

confidence: 99%

“…It is essential, therefore, that an agent should select a service that meets not only the required capability with the lowest possible price, but also the quality requirement. Various methods for modelling, calculating and monitoring QoS have been proposed in the literature [9,10,11,12], especially for web services and multi-agent systems [13,14,15]. A common approach is to collect quality ratings from the users of a service and then aggregate them in one way or another to derive the quality of the service.…”

Section: Introductionmentioning

confidence: 99%

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A Quality of Service Management Framework Based on User Expectations

Deora

Shao

Gray

et al. 2003

Lecture Notes in Computer Science

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Abstract. The ability to gauge the quality of a service is critical if we are to achieve the service oriented computing paradigm. Many techniques have been proposed and most of them attempt to calculate the quality of a service by collecting quality ratings from the users of the service, then combining them in one way or another. We argue that collecting quality ratings alone from the users is not sufficient for deriving a reliable or accurate quality measure for a service. This is because different users often have different expectations on the quality of a service and their ratings tend to be closely related to their expectations, i.e. how their expectations are met. In this paper, we propose a quality of service management framework based on user expectations. That is, we collect expectations as well as ratings from the users of a service, then calculate the quality of the service only at the time a request for the service is made and only using the ratings that have similar expectations. We give examples to show that our approach can result in a more accurate and meaningful measure for quality of service.

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Section: The Qos Modelmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Quality of Service Management Framework Based on User Expectations

Deora

Shao

Gray

et al. 2003

Lecture Notes in Computer Science

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“…After evaluating a number of research efforts conducted in the quality of service domain relating to web services [31], mobile computing [32], mobile data collection [33], and sensor ontologies [5], we extracted the following context properties to be stored and maintained in connection with each sensor. This information helps to decide which sensor is to be used in a given situation.…”

Section: F Context Frameworkmentioning

confidence: 99%

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Perera¹,

Zaslavsky²,

Liu

et al. 2014

IEEE Sensors J.

174

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Abstract-The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICO) which will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.Index Terms-Internet of Things, context awareness, sensors, search and selection, indexing and ranking, semantic querying, quantitative reasoning, multi-dimensional data fusion.

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Principles of Mobile Computing Middleware

Mascolo

Capra

Emmerich

2004

Middleware for Communications

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These technologies have been designed and are successfully used for stationary distributed systems. However, as it will become clearer in the following, some of the requirements introduced by mobility cannot be fulfilled by these existing traditional middleware. First, the interaction primitives, such as distributed transactions, object requests or remote procedure calls, assume a stable, high bandwidth and constant connection between components. Furthermore, synchronous point-to-point communication supported by object-oriented middleware systems, such as CORBA, requires a rendez-vous between the client asking for a service, and the server delivering that service. In mobile systems, on the contrary, unreachability is not exceptional and the connection may be unstable. Moreover, it is quite likely that client and server hosts are not connected at the same time, because of voluntary disconnections (e.g., to save battery power) or forced disconnection (e.g., loss of network coverage). Disconnection is treated as an occasional fault by many traditional middleware; techniques for data-sharing and replication that have been successfully adopted in traditional systems might not, therefore, be suitable, and new methodologies need to be explored. Moreover, mobility introduces higher degrees of heterogeneity and dynamicity than traditional distributed systems. Mobile hosts might have to support different communication protocols, according to the wireless links they are exploiting; look-up operations are more elaborate than in distributed systems, because of location variability. Middleware reconfiguration becomes essential in order to adapt to highly varying context conditions. Finally, traditional middleware systems have been designed targeting devices with almost no resource limitations, especially in terms of battery power. On the contrary, even considering the improvements in the development of these technologies, resources of mobile devices will always be, by orders of magnitude, more constrained.The aim of this chapter is to give an overview of how the requirements usually associated to distributed systems are affected by physical mobility issues. We discuss how traditional middleware, and middleware built targeting mobile systems can fulfil these requirements. We provide a framework and a classification of the most relevant literature in this area, highlighting goals that have been attained and goals that need to be pursued.

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A survey of quality of service in mobile computing environments

Cited by 216 publications

References 41 publications

A Quality of Service Management Framework Based on User Expectations

A Quality of Service Management Framework Based on User Expectations

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Principles of Mobile Computing Middleware

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