TY - GEN

T1 - Sensor network traffic load prediction with Markov random field theory

AU - Cai, Yan

AU - Yu, Limin

N1 - Publisher Copyright:
© 2015 IEEE.

PY - 2016/6/13

Y1 - 2016/6/13

N2 - Following recent advances in wireless communications and computing technology, sensor networks are widely deployed in different fields for both monitoring and control purposes. In this work, we focus on using Markov random field (MRF) theory to model traffic intensity of the three types of sensor networks. Shortest path routing is adopted in the three typical lattice network models. Then, the influences, which affect the traffic distribution dynamically in real situations, are modelled by adding the Gaussian noise to the traffic load distribution in the MATLAB simulation. Given measurements of real-Time samples of traffic, we are able to predict the traffic at each sensor node for specific network models by a MRF smoothing algorithm.

AB - Following recent advances in wireless communications and computing technology, sensor networks are widely deployed in different fields for both monitoring and control purposes. In this work, we focus on using Markov random field (MRF) theory to model traffic intensity of the three types of sensor networks. Shortest path routing is adopted in the three typical lattice network models. Then, the influences, which affect the traffic distribution dynamically in real situations, are modelled by adding the Gaussian noise to the traffic load distribution in the MATLAB simulation. Given measurements of real-Time samples of traffic, we are able to predict the traffic at each sensor node for specific network models by a MRF smoothing algorithm.

KW - Lattice sensor network

KW - Markov random field (MRF) theory

KW - Sensor network

KW - Shortest-path routing algorithm

KW - Traffic load distribution and prediction

UR - http://www.scopus.com/inward/record.url?scp=84979302805&partnerID=8YFLogxK

U2 - 10.1109/ICCSNT.2015.7490898

DO - 10.1109/ICCSNT.2015.7490898

M3 - Conference Proceeding

AN - SCOPUS:84979302805

T3 - Proceedings of 2015 4th International Conference on Computer Science and Network Technology, ICCSNT 2015

SP - 967

EP - 971

BT - Proceedings of 2015 4th International Conference on Computer Science and Network Technology, ICCSNT 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Conference on Computer Science and Network Technology, ICCSNT 2015

Y2 - 19 December 2015 through 20 December 2015

ER -