Introduction to algorithms
A tutorial on Reed-Solomon coding for fault-tolerance in RAID-like systems
Software—Practice & Experience
Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Database Systems: The Complete Book
Database Systems: The Complete Book
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Towards Sensor Database Systems
MDM '01 Proceedings of the Second International Conference on Mobile Data Management
The design of an acquisitional query processor for sensor networks
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Decentralized erasure codes for distributed networked storage
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
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Wireless sensor networks have drawn much attention due to their ability to monitor ecosystems and wildlife habitats. In such systems, the data should be intelligently collected to avoid human intervention. For this, we propose a network infrastructure in which the sensor nodes are designated as "data-generating" or "data-storage" nodes. Data-generating nodes take measurements, whereas data-storage nodes make themselves available to compute and store checksums of data received from nearby data-generating nodes.We propose a spatially-clustered architecture for our storage nodes and a coding scheme that allows a data collector to recover all original data by querying only a small random subset of storage nodes from each cluster. The size of such a subset is equal to the number of data-generating nodes that the cluster serves.When the clustering structure of the storage nodes is unknown, we show that recovering of the original data is still possible if a random subset of the right sizeof storage nodes is selected for querying. We determine this right size so as to have a successful decoding with a probability exceeding a given threshold.