scholarly journals Density-adaptive network reprogramming protocol for wireless sensor networks

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
Sungkyu Cho ◽  
Hyojeong Shin ◽  
Seungjae Han ◽  
Hojung Cha ◽  
Rhan Ha
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Adaptive Network ◽  
Network Reprogramming

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

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
M. E. Migabo ◽  
T. O. Olwal ◽  
K. Djouani ◽  
A. M. Kurien
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Coding ◽  
Gaussian Elimination ◽  
Computational Cost ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Adaptive Network ◽  
Multiple Sinks ◽  
Gradient Based

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 GF(2S) 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.

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