TY - GEN
T1 - Advanced MDS-MAP localization algorithm with weighted clustering and heuristic ranging
AU - Wang, Jing
AU - Qiu, Xiaohe
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/24
Y1 - 2018/10/24
N2 - Inherited from classical MDS-MAP algorithm, we propose an improved MDS-MAP (HD) algorithm for large-scale wireless networks localization. We develop clustering and merging strategy to overcome massive energy requirement problem that plagues the classical MDS-MAP. To balance the energy consumption and stabilize the topology, we propose a weighted clustering scheme, which considers the degree of connectivity, residual energy and nodes density. As the original MAD-MAP method poses a limitation of merging condition, we introduce a heuristic ranging method to compensate the absent distance information and common nodes set for merging purpose. Simulation results show that the improved MDS-MAP localization algorithm has lower merging demand and higher localization accuracy, better-balanced energy consumption and stronger robustness.
AB - Inherited from classical MDS-MAP algorithm, we propose an improved MDS-MAP (HD) algorithm for large-scale wireless networks localization. We develop clustering and merging strategy to overcome massive energy requirement problem that plagues the classical MDS-MAP. To balance the energy consumption and stabilize the topology, we propose a weighted clustering scheme, which considers the degree of connectivity, residual energy and nodes density. As the original MAD-MAP method poses a limitation of merging condition, we introduce a heuristic ranging method to compensate the absent distance information and common nodes set for merging purpose. Simulation results show that the improved MDS-MAP localization algorithm has lower merging demand and higher localization accuracy, better-balanced energy consumption and stronger robustness.
KW - Clustering
KW - MDS-MAP
KW - Merging strategy
KW - Wireless sensor network
UR - http://www.scopus.com/inward/record.url?scp=85059954950&partnerID=8YFLogxK
U2 - 10.1145/3289430.3289448
DO - 10.1145/3289430.3289448
M3 - 会议稿件
AN - SCOPUS:85059954950
T3 - ACM International Conference Proceeding Series
SP - 180
EP - 185
BT - BDIOT 2018 - Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things
PB - Association for Computing Machinery
T2 - 2nd International Conference on Big Data and Internet of Things, BDIOT 2018
Y2 - 24 October 2018 through 26 October 2018
ER -
