Development of incremental strategies for wireless sensor network

Abstract

Adaptive filter plays an important role in the field of digital signal processing and wireless communication. It incorporates LMS algorithm in real time environment because of its low computational complexity and simplicity. The LMS algorithm encompasses RLS (recursive least square), GN (Gaussian Newton), LMF (least mean fourth) and XE-NLMF algorithms, which provides faster convergence rate and low steady state error when compared to LMS. The adaptive distributed strategy is based on the incremental mode of co-operation between different nodes, which are distributed in the geographical area. These nodes perform local computation and share the result with the predefined nodes. The resulting algorithm is distributed, co-operative and able to respond to the real time change in environment. By using incremental method, algorithms such as RLS,GN, DCT-LMS and DFT-LMS produces faster convergence and better steady state performance than that of the LMS when simulated in the presence of Gaussian noise. Higher Order error algorithm like LMF, XE-NLMF and variable XE-NLMF algorithm produce better convergence and steady state performance under Gaussian and non-Gaussian noise. A spatial-temporal energy conservation argument is used to evaluate the steady state performance of the entire network. A topology named as CLMS (convex LMS) was presented which combined the effect of both fast and accurate filtering at the same time. Initially CLMS have parallel independent connection, the proposed topology consists of series convex connection of adaptive filters, which achieves similar result with reduced time of operation. Computer simulations corroborate the results.