A Location Aware Service to Minimize Travel Costs Using Dynamic Information

Automotive navigation systems have become common accessories in vehicles manufactured today. However, the information provided by these systems is quite limited in that many systems only provide static information. As a result, manufacturers of such systems have not been able to fully capitalize from the potential applications for mobile commerce (mcommerce) which is critically dependent on providing consumers with dynamic information. The objective of this paper is to discuss a novel method, known as Dynamic Location Cost Minimization (DLCM), which can be used with a vehicle’s navigation system to determine the optimum location to purchase gas. With the increasing cost of gas and the possibility of higher prices due to proposed gas price taxes, providing a means for consumers to minimize their costs to travel could prove to be very beneficial, and potentially help drive down prices due to increased competition. In addition, the proposed method could also be used in conjunction with mobile phones to facilitate real-time decisions for other services or purchases. Anecdotal evidence presented in this paper merits further investigation into the usability and acceptance of this technology

Decision Support for the Usage of Mobile Information Service: A Context-Aware Service Selection Approach that Considers the Effects of Context Interdependencies

In mobile business, context information is utilised to select services mostly tailored to a user’s current situation and preferences. In existing context-aware service selection approaches, a service utility is determined by comparing its non-functional properties with current context information but without considering its integration in a service composition. This may cause suboptimal selection results, as context information and thus the determined utility of a certain service are usually dependent on its preceding and succeeding services. The latter we denote as context interdependencies. In this paper, we investigate how the effects of context interdependencies can be modelled for the context-aware service selection at planning time (i.e. before starting to accomplish a service composition). To develop this approach, we use the concept of states to model context information for the selection. In our evaluation, we find that our approach leads to superior results compared to current context-aware service selection approaches