Filtering Context

Context information that is gathered such as raw data after sensing is solitary, unstable and inaccurate. This context information is usually unfiltered and ambiguous, for example accelerometer data, GPS coordinates and vague text (Antila et al. 2011). Unfiltered context information contains a lot of noise and is not meaningful to users, especially low-level unfiltered data (Antila et al. 2011; Huang et al. 2011). Noise in context information represents random unwanted fluctuations in the measured context values. Context information that is unfiltered can also cause an error known as drift, which occurs when the actual values slowly increase or decrease from their true values over time. Another error that can occur with unfiltered data is called offset, whereby the initial sensing does not start at a zero point when it is meant to (Milette & Stroud 2012). Thus it is essential to use filters and match conditions to perform functions such as noise screening in order to effectively use the acquired sensed data.

One method to filter the errors that occur in context information is by using low-pass and high-pass filters. Even though sensors in mobile devices are continually improving, in many instances mobile applications may rely on some type of smoothing or averaging, known as low-pass filtering. Low-pass filtering passes slowly varying changes by filtering out high- frequency noise. In contrast, high-pass filtering emphasizes the higher-frequency and ignores the slow varying changes, which helps deal with offset and drift errors. Using both a low-pass and a high pass filter may be useful to highlight a specific frequency and ignore unwanted lower and higher frequencies. The use of a high-pass filter with a low-pass filter is known as a bandpass filter. Using a bandpass filter would first involve applying the high-pass filter and then the low-pass filter (Milette & Stroud 2012).

Another method to deal with the errors contained in the context information is by using Quality of Context metrics (Zheng, Wang & Kerong 2012).

21 Quality of Context (QoC) can be defined as:

"...any information that describes the quality of information that is used as context information. Thus, QoC refers to information and not to the process nor the hardware component that possibly provides the information" (Buchholz, Küpper & Schiffers 2003).

Quality of Context (QoC) is a measurable metric that provides information about the quality of context, which can assist in resolving uncertain and conflicting situations about context information. Context-aware applications can therefore benefit from using practical QoC metrics that are aligned with the requirements of the applications in terms of collecting, processing and provisioning of context information (Manzoor, Truong & Dustdar 2010). For example, using QoC metrics can help eliminate unwanted context data (e.g. sensor data) that does not meet the minimum quality levels. These quality levels can be explicitly set in the form of QoC thresholds or by comparing the quality of new data to previous data. The quality levels will ensure that only high-quality context information that meet the quality requirements are produced. As a result, the QoC metrics will also improve the context-aware reasoning and decision making of the context-aware application (Filho, Miron, Satoh, Gensel & Martin 2010; Manzoor et al. 2010).

QoC information can be implicitly gathered from mobile devices in pervasive environments. Implicitly sensing to provide context from a mobile device is a core activity in making a system context-aware (i.e. mobile application), which according to Mostefaoui, Pasquier- Rocha and Brezillon (2004), and Gray and Salber (2010) is far more complicated than explicit input to the system.

QoC information deteriorates during the process of sensing to provide context, as the QoC can be affected by the shortcomings of the sensors and the environment of a particular measurement. As a result, QoC information can be ambiguous, inaccurate and incomplete (Dey & Abowd 1999). Context-aware systems can suffer from poor performance without being able to identify the actual problem, if there is insufficient information about QoC (Manzoor et al. 2010). Existing context-aware applications rarely consider QoC information (Baldauf, Dustdar & Rosenberg 2007). Context-aware applications also need to make additional effort to deal with the uncertainty of context information (Ranganathan, Al- Muhtadi & Campbell 2004).

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Manzoor et al. (2010) highlighted that existing definitions of QoC only consider it as an objective quality measure and completely disregard the multi-facetted nature of QoC in terms of being both objective and subjective. Considering both the objective and subjective natures of the QoC will assist in identifying the true quality of the context information. QoC information that is independent of the consumer's requirements is seen as the objective view of QoC. On the other hand, QoC information that is determined or derived using the context consumer's requirements is considered as the subjective view of QoC (Manzoor, Truong & Dustdar 2008; Manzoor et al. 2010).

The lack of context-aware applications that evaluate QoC metrics and provide them with the context information to context consumers was also emphasized by Manzoor et al. (2010). QoC metrics can enrich context information, which would improve the capabilities of context-aware applications to successfully use the context information to adapt to the changing situations in mobile computing environments (Manzoor et al. 2008).

QoC metrics can be used to identify the quality of context information from several different perspectives, such as the degree to which the context is considered fresh. QoC metrics can be measured as a decimal number with values ranging between [0..1], as quality is relative and typically matched against certain standards. A minimum value of 0 indicates that the QoC metric is in complete non-compliance to the quality requirements. A maximum value of 1, however, indicates complete compliance of the QoC metric with the quality requirements (Manzoor et al. 2010).

Considering the quality of the context information is an important step towards using context information effectively and achieving the capability of context awareness. Buchholz et al. (2003), Zheng et al. (2012) and Manzoor et al. (2010) show that the most important QoC metrics are the following:

 Freshness: Indicates validity of the context information in terms of the objective view of timeliness.

 Up-to-dateness: Indicates validity of the context information in terms of the subjective view of timeliness.

 Reliability: Indicates the extent to which context can be considered credible.

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 Confidence Interval: Indicates the confidence in the context produced.

 Significance: Indicates the subjective importance of the context produced based on the context consumer's requirements.

Extracting simple semantic information from the sensor data and discarding the unwanted information is another vital step, which is also one of the aims of using filters (Huang et al. 2011). The key to extracting clear and meaningful semantic context is to develop relationships between context elements, for example the relationship between lighting conditions and temperature conditions.

Information from multiple input sources is often undesirable and sometimes even contradictory (Huang et al. 2011). For example, attempting to obtain the temperature from weather web services and the temperature sensor could yield unexpected and unmatched results. Context fusion enables one to maximize the effectiveness of inconsistent information from a variety of sources based on specific knowledge and rules. This knowledge and rules are needed to avoid incorrect decisions being made by the system.

Context elements that are related to or combined with other contextual elements have a greater impact and directly influence the high-level context information used by applications for the end-user. Context information whether filtered or unfiltered, needs to be stored effectively for further processing and accessibility (Huang et al. 2011).