Distributed and Parallel Databases

, Volume 29, Issue 1, pp 87-112

In-network data acquisition and replication in mobile sensor networks

  • Panayiotis AndreouAffiliated withDepartment of Computer Science, University of Cyprus
  • , Demetrios Zeinalipour-YaztiAffiliated withDepartment of Computer Science, University of Cyprus Email author 
  • , Panos K. ChrysanthisAffiliated withDepartment of Computer Science, University of Pittsburgh
  • , George SamarasAffiliated withDepartment of Computer Science, University of Cyprus

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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.


Mobile sensor networks Data management Fault tolerance