On the scalability of expanding ring search for dense wireless sensor networks

• Authors: Kiran K. Rachuri;C. Siva Ram Murthy
• Venue: Journal of Parallel and Distributed Computing
• Year: 2010

Expanding Ring Search (ERS) is a prominent technique used for information discovery in multi-hop networks where the initiator of the search is unaware of any of the @c locations of the target information. ERS reduces the overhead of the search by successively searching for a larger number of hops starting from the location of search initiator. Even though ERS reduces the overhead of the search compared to flooding, it still incurs a very high cost which makes it unsuitable especially for energy constrained networks like Wireless Sensor Networks (WSNs). Moreover, the cost of search (number of transmitted bytes) using ERS increases with node density, which limits its scalability in densely deployed WSNs. In this paper, we apply the principles of area coverage to ERS and propose a new protocol called Coverage based Expanding Ring Search (CERS) for energy efficient and scalable search in WSNs. CERS is configurable in terms of energy-latency trade-off which enables it applicable to varied application scenarios. The basic principle of CERS is to route the search packet along a set of ring based trajectories that minimizes the number of messages transmitted to find the target information. The transmissions are performed such that only a subset of total sensor nodes transmit the search packet to cover the entire terrain area while others listen. We believe that query resolution based on the principles of area coverage provides a new dimension for conquering the scale of dense WSNs. We compare CERS with existing query resolution techniques for unknown target location such as ERS, Random walk search, and Gossip search. We prove by both analysis and simulation that CERS is highly scalable, the cost of search is independent of node density, the energy consumed is much lower than that of the existing search techniques, and CERS always finds the nearest replica of the target information under high node density.