Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
GPSR: greedy perimeter stateless routing for wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Rumor routing algorthim for sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
GHT: a geographic hash table for data-centric storage
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Geographic routing without location information
Proceedings of the 9th annual international conference on Mobile computing and networking
Matching data dissemination algorithms to application requirements
Proceedings of the 1st international conference on Embedded networked sensor systems
Combs, needles, haystacks: balancing push and pull for discovery in large-scale sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
IEEE Communications Magazine
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Data-centric design has been widely adopted in wireless sensor networks thanks to its efficiency, as PUSH and PULL are two common data dissemination algorithms for such networks. The two algorithms work well with only a few sources or a few sinks, respectively. However, when there are many sources and many sinks, both of them become inefficient. In this paper, we take advantage of these two algorithms, and propose a novel Location-Oblivious Hybrid PUSH-PULL data Diffusion (LOHD) algorithm, which suits a wide range of network settings. Different from most of the existing approaches, LOHD does not rely on any location information, as it adaptively selects an ultra-node in the middle of sources and sinks through a well-controlled flooding, and the ultra-node establishes and maintains the gradients between sources and sinks. LOHD also incorporates enhanced PUSH and PULL to deliver messages along the gradients instead of flooding. We model and analyze the algorithms and perform extensive simulations. The results show that LOHD performs much better than both PUSH and PULL, particularly when the number of sources and sinks increases. We also show that the initialization overhead well resists to such increase, and thus LOHD is highly scalable.