Design Implications and Recommendations

Both literature chapters (Chapters 2 and 3) concluded with requirements, which were identified in order for a context-aware model to support context awareness in mobile applications. These requirements can be compared with the results of the evaluations conducted consisting of four experiments to identify design recommendations for a context- aware model.

The following section will first compare the extent to which the proposed model implemented as the CoPro prototype supported the requirements identified in Chapters 2 and 3. This section will then compare the requirements with the results of the evaluation experiments discussed in Section 5.6 to determine the final design recommendations for a context-aware model to support context awareness in mobile applications.

5.7.1 Support for Requirements

The requirements identified in Chapter 2 highlighted the existing issues in context awareness. The extent to which CoPro supported these issues are described in Table 5.24 for each of the existing issues identified.

Table 5.24: Extent of support for existing issues in context awareness

Design Implication Supported? Extent of Support

1. Sensor-based Context Recognition Yes CoPro successfully performed sensor-based as well as non-sensor-based context

recognition. The available inputs (i.e. sensors) on the device were detected at run- time before commencing the context recognition process.

2. Activity Recognition Yes With the use of the Google Activity Recognition API, CoPro was able to detect six different physical activities. CoPro could further determine several inferred activities by using multiple inputs. 3. Indoor Location Awareness Partially CoPro was able to detect whether the

device was indoor or outdoor. However, detecting room level precision could not be achieved as this is still a major challenge of location awareness.

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4. Automated Context Situation Prediction Yes CoPro automated the process of

determining inferred context at run time by combining several low/high-level contexts. 5. Context Ambiguity Yes Dealing with context ambiguity was

highlighted when CoPro made use of available weather data to provide values for temperature and humidity. Use of location preferences also enhanced the precision of location values.

6. Appropriate Storage Partially As the current context needed to be as fresh as possible, the context was stored within the application for further use.

Improvements could involve storing a context history on a remote server or cloud computing platform.

7. User Control and Automation Yes Context recognition processes needed to be automated as much as possible, however by allowing preferences to be set enabled a balance between user control and automation.

The requirements identified in Chapter 3 described the shortcomings in existing context- aware models. As the CoPro prototype was based on the proposed model, the extent to which CoPro supported these shortcomings are highlighted in Table 5.25 for each shortcoming identified.

Table 5.25: Extent of support for shortcomings in existing context-aware models

Design Implication Supported? Extent of Support

1. Extract High-Level Context Yes CoPro could not only extract high-level context from low-level context (raw data) but also combined this high-level context to produced inferred context.

2. Optimisation Support for Continuous Sensing and Processing

Yes Provisions for continuous sensing and processing of context were made by CoPro by using a thread pool to manage context related tasks.

3. M-health Context Partially As the proposed model focused on obtaining context with only one device, CoPro incorporated a set of health preferences. Bodily sensor readings were not detected as this would have required additional wearable devices to be used.

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4. Dimension-based Context Modelling Yes The proposed model categorized and modelled all context inputs into four key dimensions including physical, user- activity, health and user preferences. 5. Personalized Context Yes The use of user preferences allowed for

contexts such as location to be tailored (i.e. location value of "At Home").

6. Dynamic Context Yes CoPro considered both the dynamic and

static nature of context by using streaming sensor data as well as non-steaming data as input sources

5.7.2 Final Design Recommendations

The final design recommendations are discussed in this section. The results of the iterative evaluation experiments validated all of the requirements identified in Chapters 2 and 3. As a result, the evaluation results and requirements were subsequently used to derive the design recommendations. These design recommendations provide valuable insight to future researchers when designing a model to support context awareness in mobile applications.

The final design recommendations are:

 When trying to develop a model to support context awareness, the use of an existing model that is the most suitable based on the requirements can form a strong starting point. The existing model would have already solved certain issues that would possibly have to be solved again if one chose to develop a context-aware model from scratch.

 Using multiple-input sources when trying to recognize context can help alleviate issues such as when there is an absence of information (an example can be viewed in Section 5.6.3.1).

 Context is multi-dimensional and should be considered in both its static and dynamic form (i.e. steady context like gender vs. changing context such as physical activity).

 In terms of the quality of context, the objective and subjective view of the context quality needs to be considered as each evaluate different aspects of context. (i.e. perceived value versus objective value).

 Considering the limitations of the mobile computing platform is a must when designing for mobile devices. Not considering these limitations could lead to poor performance such as draining the device's battery life unnecessarily.

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 Extracting high-level abstract meanings from low-level context data can provide more useful insight about the context, instead of merely reporting the absolute values (i.e. a value of 100 lux could be better represented as "Normal" for the ambient light level).

 Tailoring produced context preferences allows for some user control over the automated context recognition process. This personalization of context can improve the adoption and acceptance rate of context-aware provisions in mobile applications.

5.8 Conclusion

This chapter addressed the evaluation phase of the DSR methodology and the fourth research question identified in Chapter 1: "How effective, reliable and capable is the proposed context-aware model and to what extent does it support context awareness in mobile applications?" The evaluation of the proposed model was noted as a core activity of the Design Cycle in DSR. Experimental evaluation methods including controlled experiments and simulation were used to demonstrate well-executed evaluation methods, as proposed by (Hevner et al. 2004). The experimental evaluations were conducted in an iterative evaluate and re-design process as this is an important characteristic of DSR. The experimental evaluation methods rigorously demonstrated the utility, quality and efficacy of CoPro.

The chapter first identified evaluation techniques, which could be used to evaluate CoPro. The goals of the evaluation were then described. A suitable evaluation technique was then selected and motivated based on the evaluation goals. The experimental design was then described in detail, which included the evaluation objectives, evaluation metrics, evaluation instruments and evaluation procedure.

The analysis of results as well as the design improvements for each repeated experiment were presented. The utility results (Section 5.6.1) highlighted that CoPro was effective in not only obtaining initial context but also at detecting changes in the current context and tailoring context where applicable with preferences. The quality results (Section 5.6.2) demonstrated that the quality of context produced by CoPro was of high-quality and reliable. The efficacy results showed that CoPro dealt with the limitations of the mobile computing platform by making provisions for limited battery power, limited processing power and absence of information.

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Lastly, design implications and design recommendations were discussed to conclude the chapter. CoPro supported all the design implications that were identified from Chapters 2 and 3. Based on these design implications and the results obtained from the evaluation experiments, design recommendations were made for the benefit of future researchers in this area of research.

The final chapter, Chapter 6, concludes this dissertation by providing an overview of the research conducted. The final chapter will highlight the contributions of this research conducted using a DSR methodology in order to add value to the DSR knowledge base as highlighted in Section 1.4.3. Future work for continuation of this research will also be presented in the final chapter. The concluding chapter will complete the DSR process by communicating the findings of this research to the appropriate audience.

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Chapter 6: Conclusions

6.1 Introduction

The main objective of this research was to develop a context-aware model to support the context awareness needs of mobile applications. The Design Science Research methodology was used during this development. Implementation of the context-aware model as a prototype formed part of the main objective. The prototype, named CoPro, was implemented for the purpose of assessing the feasibility of the proposed context-aware model in order for it to be considered a viable DSR artefact. Other deliverables of this research included the context- aware model, the evaluation of the prototype and the final design recommendations highlighted in Chapter 5.

This chapter concludes this dissertation by presenting the conclusions and contributions of the research. Conclusions will be inferred about whether the model can facilitate context awareness in mobile applications using multiple inputs. Contributions of this research in terms of theory and practice will be identified. The initial goals of the research will be reviewed to determine whether the research met its planned objectives. A summary of the problems and limitations encountered in conducting this research will be discussed. Lastly, future research will be identified to conclude the chapter.