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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.

Distributed data collection and storage algorithms for collaborative learning vision sensor devices with applications to pilgrimage