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Journal of Computer Networks and Communications
Volume 2017, Article ID 5301462, 10 pages
Research Article

Cooperative and Adaptive Network Coding for Gradient Based Routing in Wireless Sensor Networks with Multiple Sinks

1F’SATI, Tshwane University of Technology (TUT), Pretoria 0001, South Africa
2LISSI, University of Paris Est Créteil (UPEC), Paris, France

Correspondence should be addressed to M. E. Migabo; moc.liamg@leunamme.obagim

Received 29 March 2017; Accepted 10 August 2017; Published 9 October 2017

Academic Editor: Liansheng Tan

Copyright © 2017 M. E. Migabo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Despite its low computational cost, the Gradient Based Routing (GBR) broadcast of interest messages in Wireless Sensor Networks (WSNs) causes significant packets duplications and unnecessary packets transmissions. This results in energy wastage, traffic load imbalance, high network traffic, and low throughput. Thanks to the emergence of fast and powerful processors, the development of efficient network coding strategies is expected to enable efficient packets aggregations and reduce packets retransmissions. For multiple sinks WSNs, the challenge consists of efficiently selecting a suitable network coding scheme. This article proposes a Cooperative and Adaptive Network Coding for GBR (CoAdNC-GBR) technique which considers the network density as dynamically defined by the average number of neighbouring nodes, to efficiently aggregate interest messages. The aggregation is performed by means of linear combinations of random coefficients of a finite Galois Field of variable size at each node and the decoding is performed by means of Gaussian elimination. The obtained results reveal that, by exploiting the cooperation of the multiple sinks, the CoAdNC-GBR not only improves the transmission reliability of links and lowers the number of transmissions and the propagation latency, but also enhances the energy efficiency of the network when compared to the GBR-network coding (GBR-NC) techniques.