Scalability is one of the most important features that future wireless sensor networks (WSNs) should provide, and clustering is widely considered as a viable approach for high scalability. In the cluster-based architecture, the cluster heads play a key role in relaying messages between the sensor nodes and the sink. While the cluster heads are involved in both intra-cluster and intercluster communication, the latter typically requires transmission over much longer distance than the former. In this paper, we consider a scenario in which each cluster head is equipped with dual radios: IEEE 802.15.4 and IEEE 802.11 for intra-cluster and inter-cluster communication, respectively. IEEE 802.11 links between the cluster heads and the sink provide a high capacity backbone for large-scale WSNs. IEEE 802.15.4 and IEEE 802.11 share a lot of similarities including CSMA/CA MAC. Their operating spectrum also overlaps at the 2.4 GHz ISM band, and this may cause interference. We first experimentally measure how severe the interference can be, when two radios are concurrently used in a WSN. We, then, propose an interference mitigation solution which relies on adaptive aggregation of packets and adaptive transmission scheduling. Through prototyping and experimental evaluation, we show that the proposed scheme significantly reduces the interferences between the two types of radios.
At present, block-transform coding is probably the most popular approach for image compression. However, for the sake of its implementation, an image is partitioned into spatially adjoining blocks which are processed independently without considering inter-block correlation. So, this approach inescapably causes an annoying defect called a blocking artifact. In this letter, in order to reduce a blocking artifact appearing in block-coded images, a new quantization constraint set based on the theory of projection onto convex set (POCS) is proposed. This set can efficiently complement the drawbacks of the projection onto the other constraint sets, particularly the smoothness constraint set. Experimental results, using the proposed quantization constraint set as a substitute for the conventional quantization constraint set, show that the postprocessed images not only converge at a fast rate but also obtain better performance in both objective and subjective quality. Moreover, we know that the postprocessed images maintain the clearness of the decoded image before postprocessing.Index Terms-Adaptive quantization constraint set, blocking artifact, convergence rate, postprocessing, POCS.
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