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We describe the general implementation details and then we present the data gathering algorithm.
We carry out rigorous simulation experiments on the proposed algorithm and compare the results with two existing algorithms namely, tree cluster based data gathering algorithm (TCBDGA) and energy aware sink relocation (EASR).
In order to adapt to the dynamic change of network topology, we design a mobile agent based compressive data gathering algorithm (MA-Greedy algorithm), where each sensor node is uniformly visited in M measurements.
Our first data gathering algorithm is stability-oriented and it is based on the idea of finding a maximum spanning tree on a network graph whose edge weights are predicted link expiration times (LETs).
First, it is compared to another existing mobile data gathering algorithm.
Figure 15 Data gathering algorithm: timing of the sensors' transmissions for diagonal projections evaluation.
Similar(43)
The high-level contributions of this paper are the design and development of two distributed spanning tree-based data gathering algorithms for wireless mobile sensor networks and their exhaustive simulation study to investigate a complex stability-delay-throughput vs. node-network lifetime-coverage loss tradeoff that has been hitherto not explored in the literature.
Further developments of globally optimized Radon-like CS data gathering algorithms are still under investigation.
To this end, we will compare delay distortion curves for different data gathering algorithms.
Zhu et al. presented an energy-efficient data-gathering algorithm to improve the network lifetime.
A data-gathering algorithm includes some aggregation methods to minimize the data traffic.
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