Supporting historic queries in sensor networks with flash storage

  • Authors:

  • Adam Dou;Song Lin;Vana Kalogeraki;Dimitrios Gunopulos

  • Affiliations:

  • Google, Mountain View, CA, United States;Google, Mountain View, CA, United States;Department of Informatics, Athens University of Economics and Business, Athens, Greece;Department of Informatics and Telecommunications, University of Athens, Athens, Greece

  • Venue:
  • Information Systems
  • Year:
  • 2014

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Abstract

Many recent sensor devices are being equipped with flash memories due to their unique advantages: non-volatile storage, small size, shock-resistance, fast read access and power efficiency. The ability of storing large amounts of data in sensor devices necessitates the need for efficient indexing structures to locate required information. The challenge with flash memories is that they are unsuitable for maintaining dynamic data structures because of their specific read, write and wear constraints; this combined with very limited data memory on sensor devices prohibits the direct application of most existing indexing methods. In this paper we propose a suite of index structures and algorithms which permit us to efficiently support several types of historical online queries on flash-equipped sensor devices: temporally constrained aggregate queries, historical online sampling queries and pattern matching queries. We have implemented our methods using nesC and have run extensive experiments in TOSSIM, the simulation environment of TinyOS. Our experimental evaluation using trace-driven real world data sets demonstrates the efficiency of our indexing algorithms.