Energy Efficient and Scalable Search in Dense Wireless Sensor Networks

In this paper, we consider the problem of information discovery in a densely deployed wireless sensor network (WSN), where the initiator of search is unaware of the location of target information. We propose two protocols: increasing ray search (IRS), an energy efficient and scalable search protocol, and k-IRS, an enhanced variant of IRS. The priority of IRS is energy efficiency and sacrifices latency whereas k-IRS is configurable in terms of energy-latency trade-off and this flexibility makes it applicable to varied application scenarios. The basic principle of these protocols is to route the search packet along a set of trajectories called rays that maximizes the likelihood of discovering the target information by consuming least amount of energy. The rays are organized such that if the search packet travels along all these rays, then the entire terrain area will be covered by its transmissions while minimizing the overlap of these transmissions. In this way, only a subset of total sensor nodes transmits 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 WSN. We compare IRS and k-IRS with existing query resolution techniques for unknown target location such as expanding ring search (ERS), Random walk search, and variants of Gossip search. We show by analysis, simulation, and implementation in testbed that IRS and k-IRS are highly scalable, the cost of search (total number of transmitted bytes) is independent of node density, and it is much lower than that of existing proposals under high node density.