In-network data acquisition and replication in mobile sensor networks

  • Authors:

  • Panayiotis Andreou;Demetrios Zeinalipour-Yazti;Panos K. Chrysanthis;George Samaras

  • Affiliations:

  • Department of Computer Science, University of Cyprus, Nicosia, Cyprus 1678;Department of Computer Science, University of Cyprus, Nicosia, Cyprus 1678;Department of Computer Science, University of Pittsburgh, Pittsburgh, USA 5213-4034;Department of Computer Science, University of Cyprus, Nicosia, Cyprus 1678

  • Venue:
  • Distributed and Parallel Databases
  • Year:
  • 2011

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Abstract

This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting and aggregating spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in mobile environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter, in order to minimize energy consumption, while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the nodes laying on the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a low communication complexity. For storage and fault-tolerance we devise the Data Replication Algorithm (DRA), a voting-based replication scheme that enables the exact retrieval of values from the network in cases of failures. We also extend DRA with a spatio-temporal in-network aggregation scheme based on minimum bounding rectangles to form the Hierarchical-DRA (HDRA) algorithm, which enables the approximate retrieval of events from the network. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates. In particular, we found that when failures across all nodes are less than 60%, our framework can recover over 80% of detected values exactly.