Earticle

Wireless Sensor & Actuator networks take actuation decisions based on the data collected by the deployed set of sensor nodes. The method of data acquisition, leading to decision making, could be semi-automated or fully automated. In either case, the reliable delivery of information assumes critical importance since it has a direct impact on the decision making process for subsequent action by the actuator network. This paper presents in details a novel methodology “Layer Based Time Constrained Reliable Data Acquisition Mechanism” LTCRDM, which can be utilized for reliable delivery of information, sensed periodically or in response to a query, by the sensors deployed over a geographical area to a centralized sinkwhere the decision for eventual actuation is taken. Since the latency and reliability requirements in a WSAN are stringent, the mechanism detailed attempts delivery of maximum packets with minimum latency to ensure that estimation of the sensed event is accurate leading to correct decision making. The methodology ensures relatively low packet loss as compared to standard packet delivery mechanisms with latency time constraints. The algorithm for dissemination of query (LQDM) in the deployed nodes is also presented. Authors have provided detailed algorithm, results of simulation and observations using IEEE 802.15.4 PHY & MAC as underlying layers. Experimental results over a test-bed are also presented. A critical analysis of the results is presented for comparison against the standard methodologies in vogue.

In order to improve the efficiency of cloud computing task scheduling, we propose a cloud scheduling algorithm based on DAG task graph and game optimal. This method first constructed scheduling tasks of DAG task graph, and set the initial virtual machine for root and leaf nodes, then based on the optimization model of game, the effectiveness of difference between the task configuration after optimization judgment and the current task configuration until the effectiveness difference within a preset range, the experimental results show that the methods herein can achieve not only balancing scheduling between each virtual machine, but also has a faster rate scheduling.

Intensity in homogeneities cause considerable difficulties in the quantitative analysis of Magnetic Resonance (MR) images. Thus intensity in homogeneities estimation is a necessary step before quantitative analysis of MR data can be undertaken. This paper proposes a new energy minimization framework for simultaneous estimation of the intensity in homogeneities and segmentation. The intensity in homogeneities is modeled as a linear combination of a set of basis functions, and parameterized by the coefficients of the basis functions. The energy function depends on the coefficients of the basis functions, the membership ratios and the centroids of the tissues in the image. Intensity in homogeneities estimation and image segmentation are simultaneously achieved by calculating the result of minimizing this energy. Furthermore, in order to improve its robustness to noise, the membership ratios are adapted by using nonlocal information. Experimental results on both real MR images and simulated MR data show that our method can obtain more accurate results when segmenting images with bias field and noise.