Sensor Networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Decentralized erasure codes for distributed networked storage
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Growth codes: maximizing sensor network data persistence
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Differentiated Data Persistence with Priority Random Linear Codes
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Fountain Codes Based Distributed Storage Algorithms for Large-Scale Wireless Sensor Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Decentralized coding algorithms for distributed storage in wireless sensor networks
IEEE Journal on Selected Areas in Communications
Distributed flooding-based storage algorithms for large-scale wireless sensor networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Network coding for distributed storage systems
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
This work presents novel distributed data collection and storage algorithms for collaborative learning Wireless Sensor Networks (WSNs). In a large WSN, consider n sensor devices distributed randomly to acquire information and learn about a certain field. Such sensors have less power, small bandwidth, and short memory, and they might disappear from the network after certain time of operations. We propose two Distributed Data Storage Algorithms (DSAs), denoted by DSA-I and DSA-II, to solve this problem. In DSA-I, where the value of n is known for each learning sensor, we show that this algorithm is efficient in terms of the encoding/decoding operations. Furthermore, each node uses network flooding to disseminate its data throughout the network using mixing time approximately O(n). In DSA-II, it is assumed that dissemination of the data does not depend on the total number of network nodes, we show that the encoding operations take O(Cμ²), where μ is the mean degree of the network graph and C is a system parameter. Performance of these two algorithms matches the derived theoretical results. Finally, these two algorithms can be used for monitoring and measuring certain phenomenon in camp tents located in the Minna field in south-east side of Makkah.