Decomposing Data-Centric Storage Query Hot-Spots in Sensor Networks

Abstract

Arising when a large percentage of queries is accessing data stored in few sensor nodes, query hot-spots reduce the quality of data (QoD) and the lifetime of the sensor network. All current in-network data-centric storage (IN-DCS) schemes fail to deal with query hot-spots resulting from skewed query loads as well as skewed sensor deployments. In this paper, we present two algorithms to locally detect and decompose query hot-spots, namely zone partitioning (ZP) and zone partial replication (ZPR). We build both algorithms on top of the DIM scheme, which has been shown to exhibit the best performance among all INDCS schemes. Experimental evaluation illustrates the efficiency of ZP/ZPR in decomposing query hot-spots while increasing QoD as well as energy savings by balancing energy consumption among sensor nodes