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Storage Management Issues for Sensor Networks

This paper discusses the critical storage management issues faced by sensor networks, particularly in scenarios of network partitioning where storage is scarce. It highlights how naive protocols can lead to information loss and inefficient storage practices. The authors propose the need for efficient protocols capable of managing data storage within the network. Key goals include enhancing storage time, improving fault tolerance and reliability, ensuring scalability, and minimizing protocol overhead while maximizing energy efficiency. The document evaluates various candidate protocols such as Local Storage, Deterministic Broadcast, Probabilistic Broadcast, and Clustering, comparing their performance based on storage and fault tolerance.

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Storage Management Issues for Sensor Networks

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  1. Storage Management Issues for Sensor Networks Sameer Tilak (SUNY Binghamton) Wendi Heinzelman (Univ. of Rochester) Nael Abu-Ghazaleh (SUNY Binghamton)

  2. Network Partitioning

  3. Problem • For tiny sensors storage is a scarce resource • Naïve protocols can lead to loss of information or inefficient storage • Need efficient storage management protocol • Un-partitioned network • If delays can be tolerated instead of reporting to the base station, data can be aggregated and stored in the network to increase energy efficiency

  4. Protocol Design Goals • Higher storage-time • Low average storage/sensor • Fault-tolerance, high reliability • Scalable • Low protocol overhead • High energy efficiency

  5. Candidate Protocols • Local Storage • Deterministic Broadcast (Bcast) • Probabilistic Broadcast (PBcast) • Clustering

  6. Local Storage = storage/sensor • No communication • Each sensor stores its own data locally, low fault-tolerance • No spatial aggregation

  7. Broadcast (Bcast) = storage/sensor • All sensors store data • All sensors broadcast data to neighbors, high fault-tolerance • Spatial aggregation possible

  8. PBcast = storage/sensor • Sensors probabilistically send/store data, high fault-tolerance • Spatial aggregation possible

  9. Clustering Round 1 (time = 0) Round 2 (time = 20) • Only CH stores data • Rotate CH • Distributed storage, medium fault-tolerance • Spatial aggregation possible Round 1 (time = 40)

  10. Protocol Comparison

  11. Clustering: Scales well, low storage • Bcast: Scaling problem, high storage • Pbcast,Local: Scales well

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