Abstract-Human-carried or vehicle-mounted sensors can be exploited to collect data ubiquitously for building various sensing maps. Most of existing mobile sensing applications consider users reporting and accessing sensing data through the Internet. However, this approach cannot be applied in the scenarios with poor network coverage or expensive network access. Existing data forwarding schemes for mobile opportunistic networks are not sufficient for sensing applications as spatial-temporal correlation among sensory data has not been explored. In order to build sensing maps satisfying specific sensing quality with low delay and energy consumption, we design COUPON, a novel cooperative sensing and data forwarding framework. We first notice that cooperative sensing scheme can eliminate sampling redundancy and hence save energy. Then we design two cooperative forwarding schemes by leveraging data fusion: Epidemic Routing with Fusion (ERF) and Binary Spray-and-Wait with Fusion (BSWF). Different from previous work assuming that all packets are propagated independently, we consider that packets are spatial-temporal correlated in the forwarding process, and derive the dissemination law of correlated packets. Both the theoretic analysis and simulation results show that our cooperative forwarding schemes can achieve better tradeoff between delivery delay and transmission overhead. We also evaluate our proposed framework and schemes with real mobile traces. Extensive simulations demonstrate that the cooperative sensing scheme can reduce the number of samplings by 93 percent compared with the non-cooperative scheme; ERF can reduce the transmission overhead by 78 percent compared with Epidemic Routing (ER); BSWF can increase the delivery ratio by 16 percent, and reduce the delivery delay and transmission overhead by 5 and 32 percent respectively, compared with Binary Spray-and-Wait (BSW).
Zhao et al. REF exploited human-carried or vehicle-mounted sensors to ubiquitously collect data and build various sensing maps.
COUPON: A Cooperative Framework for Building Sensing Maps in Mobile Opportunistic Networks
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Computer Science