TY - GEN
T1 - A multi-stage graph approach for efficient clustering in self-organized wireless sensor networks
AU - Karpate, Abhishek
AU - Ali, Hesham H.
PY - 2015
Y1 - 2015
N2 - With the rapid increase in applications utilizing the current advancements of wireless sensor networks, a number of problems related to self-organization, energy-awareness and network organizations have attracted many researchers in the field. Various groups have proposed grouping the sensors into clusters and design communication routes in two levels as a way to improve communication cost and better organize networks of large sensors. In this paper, we propose a new approach to cluster wireless sensors and identify cluster heads using multi-stage graph algorithms. The approach takes advantage of the optimally associated with finding matching solutions in multi-stage graph networks. The proposed solution is designed to accommodate networks with different sizes and levels of density. We tested the algorithm using different types of networks and measure the quality of the key parameters as compared to those obtained by traditional greedy heuristics. Obtained results show that the multi-stage graph approach produces better network organization and better cluster head selection which leads to be more efficient self-organized networks.
AB - With the rapid increase in applications utilizing the current advancements of wireless sensor networks, a number of problems related to self-organization, energy-awareness and network organizations have attracted many researchers in the field. Various groups have proposed grouping the sensors into clusters and design communication routes in two levels as a way to improve communication cost and better organize networks of large sensors. In this paper, we propose a new approach to cluster wireless sensors and identify cluster heads using multi-stage graph algorithms. The approach takes advantage of the optimally associated with finding matching solutions in multi-stage graph networks. The proposed solution is designed to accommodate networks with different sizes and levels of density. We tested the algorithm using different types of networks and measure the quality of the key parameters as compared to those obtained by traditional greedy heuristics. Obtained results show that the multi-stage graph approach produces better network organization and better cluster head selection which leads to be more efficient self-organized networks.
KW - Clustering
KW - Energy-aware solutions
KW - Graph modeling
KW - Multi-stage graph algorithms
KW - Self-organized networks
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=84938771870&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84938771870&partnerID=8YFLogxK
U2 - 10.5220/0005244700560062
DO - 10.5220/0005244700560062
M3 - Conference contribution
AN - SCOPUS:84938771870
T3 - SENSORNETS 2015 - 4th International Conference on Sensor Networks, Proceedings
SP - 56
EP - 62
BT - SENSORNETS 2015 - 4th International Conference on Sensor Networks, Proceedings
A2 - Benavente-Peces, Cesar
A2 - Plainchault, Patrick
A2 - Postolache, Octavian
PB - SciTePress
T2 - 4th International Conference on Sensor Networks, SENSORNETS 2015
Y2 - 11 February 2015 through 13 February 2015
ER -