Title :
Dynamic Estimation of Unsaturated Buffer in Context-Aware M2M WiFi Network
Author :
Yan-Bin Chen ; Guan-Yu Lin ; Hung-Yu Wei
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
Abstract :
We propose a Particle Filter framework to perform online estimation for an unsaturated buffer of the stations in the Machine to Machine (M2M) WiFi network. The dynamical variation of the traffic affects the performance severely in the M2M WiFi network. The exist researches for analyzing the unsaturated condition in the network is based on the steady-state model, whereas this proposed method is devoted to dynamically estimate the probability distribution of the packet existence in the unsaturated buffer of the stations. The estimation accuracy and effectiveness are evaluated by Root Mean Square Error. The proposed dynamic estimation is more aware of the traffic change in the varying wireless M2M WiFi network compared to the other works by the static analyzing model.
Keywords :
particle filtering (numerical methods); program diagnostics; ubiquitous computing; wireless LAN; context-aware M2M WiFi network; machine to machine WiFi network; particle filter framework; probability distribution estimation; root mean square error; static analysis model; steady-state model; unsaturated buffer dynamic estimation; Artificial neural networks; Estimation; IEEE 802.11 Standards; Markov processes; Mathematical model; Throughput; Wireless communication;
Conference_Titel :
Internet of Things (iThings), 2014 IEEE International Conference on, and Green Computing and Communications (GreenCom), IEEE and Cyber, Physical and Social Computing(CPSCom), IEEE
Print_ISBN :
978-1-4799-5967-9
DOI :
10.1109/iThings.2014.56
