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Zone Repartitioning: A Load-Balancing Mechanism for Data-Centric Storage Systems

Data‐centric storage is an efficient scheme to store and retrieve event data in sensor networks, but with the multi‐hop routing nature of sensor networks, the communication cost of the home nodes and their neighboring nodes tends to be much higher than the other nodes. These hot‐spots can adversely impact system lifetime by draining off their limited energy rapidly. In this paper, we present Zone‐Repartitioning, a load‐balancing mechanism that reduces the energy consumption of the hot‐spots by distributing their communication load while event frequency is high. The trade‐off between event storage cost and query cost makes Zone Repartitioning a competitive approach in different kinds of applications. We compare the performance of Zone Repartitioning against GHT and show that Zone Repartitioning provides better adaptability in various sensor network scenarios.