A Vision for Micro and Macro Location Aware Services
The following Great Innovative Idea is from Abdeltawab Hendawi from the University of Virginia. Hendawi along with his coauthors John Stankovic from the University of Virginia, Mohamed Khalefa from the University of Alexandria in Egypt, and Harry Liu and Mohamed Ali from the University of Washington Tacoma were among the winners at the Computing Community Consortium (CCC) sponsored Blue Sky Ideas Track Competition at the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2016 (SIGSPATIAL 2016) in San Francisco, CA for their paper A Vision for Micro and Macro Location Aware Services.
The Innovative Idea
Conventional location-aware services customize search results based on users’ current location. Categories of these services include (1) service finding, e.g., “find the nearest pizza restaurant”, (2) routing, e.g., “obtain the shortest path from a user’s home to the airport”, (3) transportation, e.g., “what are the bus links to get a user from downtown to the mall”, and (4) monitoring, e.g., “alert a parent if their child school-bus deviates from its regular route”.
We envision that location-aware services need for significant improvements to suit the current era of Smart Cities and Internet of Things. The prospective smart location-aware services should be able to predict and consider users’ needs, and upcoming environmental conditions in the future as well as at the present time.
In addition, smart location-aware services will be able to collaborate with each other and work in a harmonious way. Therefore, when a conflict or a disruption occurs, an intelligent integration mechanism is required to resolve and ease the consequences. This vision can be reflected in a full-fledged system of holistic and smart location-aware services.
By applying this vision, a global optimized service will be available. Therefore, it is expected that the entire quality of location-aware services is improved, the utilization of resources, (e.g., transportation systems, fuel, time), is more efficient, and the users are well satisfied.
Our team have taken some steps to bring this vision to reality. For instance, we have worked on providing smart personalized routing system. This system integrates multiple preferences such as safety, attractions, travel time and distance in addition to the trip start time, and then provides a personalized optimal route. We also developed solutions to efficiently predict future destinations for large number of users. This research can be easily customized based on the application domain, e.g., location-aware adverting.
Our team members have wide spectrum of research backgrounds that include big data management and analysis, GIS, smart cities, sensor networks, wearable sensors, and internet of things. By looking at the location-aware services from different angles, each from his own research lenses, we were able to put this integrated vision together.