Abstract This paper addresses the energy efficiency of data collection based on a concentric chain clustering topology for wireless sensor networks (WSNs). To conserve the energy dissipation of nodes spent in data routing, the paper attempts to take advantage of the two opportunities: (a) the impact of the relative positions of wireless nodes to the base station on the energy efficiency of the routing chain within each cluster; (b) the effect of the varying‐sized chains on the selection rule of cluster heads (CHs). To establish an energy‐efficient chain to connect all the nodes in a cluster, the paper proposes aprincipal vector projectionapproach, which takes into account both the position of each node and that of the base station, to determine the order to which a node can be linked into the chain in order to reduce the energy requirement of the chain. Since the CH selection rules in the concentric chains are mutually independent, solely based on their self‐cluster sizes, the multi‐hop path passing through all the CHs will consist of longer links and thus consume a significant fraction of the total energy. Thus, in order to suppress the effect of the unequal cluster sizes on decreasing the energy efficiency of the multi‐hop path of CHs, the paper offers anaverage‐cluster‐size‐based rule(ACSB) for each cluster in order to adapt the CH selection with both the number of active nodes in the current cluster and the average value of all cluster sizes. With these two proposed schemes, an adaptive concentric chain‐based routing algorithm is proposed which enables nodes to collaboratively reduce the energy dissipation incurred in gathering sensory data. By computer simulation, the results demonstrate that the proposed algorithm performs better than other similar protocols in terms of energy saved and lifetime increased capabilities for WSNs which deploy random sensor nodes. Copyright © 2010 John Wiley & Sons, Ltd.

This paper proposes a fast block-matching algorithm that uses three fast matching error measures, besides the conventional mean-absolute error (MAE) or mean-square error (MSE). An incoming reference block in the current frame is compared to candidate blocks within the search window using multiple matching criteria. These three fast matching error measures are established on the integral projections, having the advantages of being good block features and having simple complexity in measuring matching errors. Most of the candidate blocks can be rejected only by calculating one or more of the three fast matching error measures. The time-consuming computations of MSE or MAE are performed on only a few candidate blocks that first pass all three fast matching criteria. Simulation results show that a reduction of over 86% in computations is achieved after integrating the three fast matching criteria into the full-search algorithm, while ensuring optimal accuracy.

This paper proposes a fast block-matching algorithm that uses three fast matching error measures, besides the conventional mean absolute error (MAE) or mean square error (MSE). An incoming reference block in the current frame is compared to candidate blocks within the search window using multiple matching criteria. These three fast matching error measures are established from the integral projections, taking their advantages of good representation for block features and simple complexity in measuring matching errors. Most of the candidate blocks can be rejected by calculating only one or more of the three fast matching error measures. The time-consuming computations of MSE or MAE are performed on only a few candidate blocks that first pass all the three fast matching criteria. Simulation results show a reduction of over 86% in computations is achieved after integrating the fast three matching criteria into the full-search algorithm, while ensuring the optimal accuracy.

In this paper, two efficient codebook searching algorithms for vector quantization (VQ) are presented. The first fast search algorithm utilizes the compactness property of signal energy on transform domain and the geometrical relations between the input vector and every codevector to eliminate those codevectors that have no chance to be the closest codeword of the input vector. It achieves a full search equivalent performance. As compared with other fast methods of the same kind, this algorithm requires the fewest multiplications and the least total times of distortion measurements. Then, a suboptimal searching method, which sacrifices the reconstructed signal quality to speed up the search of nearest neighbor, is presented. This algorithm performs the search process on predefined small subcodebooks instead of the whole codebook for the closest codevector. Experimental results show that this method not only needs less CPU time to encode an image but also encounters less loss of reconstructed signal quality than tree-structured VQ does.