Abstract
A location-aware service (LAS) is an imperative topic in ambient intelligence; an LAS recommends suitable utilities to a user based on the user’s location and context. However, current LASs have several problems, and most of these services do not last. This study proposes an optimization-based approach for enhancing the sustainability of an LAS. In this paper, problems related to optimizing a LAS system are presented. The distinct nature of a LAS optimization problem in comparison with traditional optimization problems is subsequently described. Existing methods applicable to solving a LAS optimization problem are also reviewed. The advantages and disadvantages of each method are then discussed as a motive for combining multiple optimization methods in this study, as illustrated by an example. Finally, opportunities and challenges faced by researchers in this field are presented.
Highlights
A location-aware service (LAS), or location-based service, is a widely discussed topic in ambient intelligence [1,2,3]
According to Problem 3, this process can be considered a long-term optimization process, and some small-scale short-term optimization actions are undertaken at each time point for improving the LAS system
Bulk information may need to be processed, which renders the optimization model extremely large. Such data are dynamic and often incomplete [15], and this phenomenon poses a challenge to the adaptability and robustness of the optimization model
Summary
A location-aware service (LAS), or location-based service, is a widely discussed topic in ambient intelligence [1,2,3]. Relating the final action of a user to the LAS provided is a difficult task. An LAS system can resolve these problems and pursue sustainable development in the following manners (Figure 1): continuously updating the database, adding new features, and retiring uninteresting services; providing more options and flexibility; and improving the suitability for use. This involves multiple facets, and is a process that relies heavily on users’ feedback and must evolve over time. According to Problem 3, this process can be considered a long-term optimization process, and some small-scale short-term optimization actions are undertaken at each time point for improving the LAS system. Opportunities and challenges faced by researchers in this field are presented
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