Qualitative representation of positional information
Artificial Intelligence
Qualitative Representation of Spatial Knowledge
Qualitative Representation of Spatial Knowledge
Using Orientation Information for Qualitative Spatial Reasoning
Proceedings of the International Conference GIS - From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning on Theories and Methods of Spatio-Temporal Reasoning in Geographic Space
Integrated spatial reasoning in geographic information systems: combining topology and direction
Integrated spatial reasoning in geographic information systems: combining topology and direction
A relative positioning system for co-located mobile devices
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
Towards Collective Spatial Awareness Using Binary Relations
ICAS '07 Proceedings of the Third International Conference on Autonomic and Autonomous Systems
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
A framework for utilizing qualitative spatial relations between networked embedded systems
Pervasive and Mobile Computing
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An increasing number of computationally enhanced objects is distributed around us in physical space, which are equipped - or at least can be provided -- with sensors for measuring spatial contexts like position, direction and acceleration. We consider spatial relationships between them, which can basically be acquired by a pairwise comparison of their spatial contexts, as crucial information for a variety of applications. If such objects do have wireless communication capabilities, they will be able to build up an ad-hoc network and exchange their spatial contexts among each other. However, processing detailed sensor information and routing it through the network lowers their battery lifetime or even may exceed the capabilities of embedded systems with limited resources. Thus, we present a novel and efficient approach for inferring and distributing spatial contexts in multi-hop networks, which builds upon qualitative spatial representation and reasoning techniques. Simulation results show its behavior with respect to common network topologies.