Wireless senor networks (WSNs) are widely used for the monitoring purposes. One of the most challenges in designing these networks is minimizing the data transmission cost with accurate data recovery. Data aggregation using the theory of compressive sampling is an effec More
Wireless senor networks (WSNs) are widely used for the monitoring purposes. One of the most challenges in designing these networks is minimizing the data transmission cost with accurate data recovery. Data aggregation using the theory of compressive sampling is an effective way to reduce the cost of communication in the sink node. The existing data aggregation methods based on compressive sampling require to a large number of nodes for each measurement sample leading to inefficient energy consumption in wireless sensor network. To solve this problem, we propose a new scheme by using sparse random measurement matrix. In this scheme, the formation of routing trees with low cost and fair distribution of load on the network significantly reduces energy consumption. Toward this goal, a new algorithm called “weighted compressive data gathering (WCDG)” is suggested in which by creating weighted routing trees and using the compressive sampling, the data belong to all of nodes of each path is aggregated and then, sent to the sink node. Considering the power control ability in sensor nodes, efficient paths are selected in this algorithm. Numerical results demonstrate the efficiency of the proposed algorithm with compared to the conventional data aggregation schemes in terms of energy consumption, load balancing, and network lifetime.
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