Software development for sensor network is made difficult by resource constrained sensor devices, distributed system complexity, communication unreliability, and high labor cost. Simulation, as a useful tool, provides an affordable way to study algorithmic problems with flexibility and controllability. However, in exchange for speed simulation often trades detail that ultimately limits its utility. In this paper, we propose a new development paradigm,  simulation-based augmented reality , in which simulation is used to enhance development on physical hardware by seamlessly integrating a running simulated network with a physical deployment in a way that is transparent to each. The advantages of such an augmented network include the ability to study a large sensor network with limited hardware and the convenience of studying a part of the physical network with simulation's debugging, profiling and tracing capabilities. We implement the  augmented reality  system based on a sensor network simulator with high fidelity and high scalability. Key to the design are "super" sensor nodes which are half virtual and half physical that interconnect simulation and physical network with fine-grained traffic forwarding and accurate time synchronization. Our results detail the overhead associated with integrating live and simulated networks and the timing accuracy between virtual and physical parts of the network. We also discuss various application scenarios for our system.
['Ye Wen', 'Wei Zhang', 'Richard Wolski', 'Navraj Chohan']
Simulation-based augmented reality for sensor network development