Wireless Sensor Networks (WSNs) are a new technology foreseen to be used increasingly in the near future due to their data acquisition and data processing abilities. Security for WSNs is an area that needs to be considered in order to protect the functionality of these networks, the data they convey and the location of their members. The security models and protocols used in wired and other networks are not suited to WSNs because of their severe resource constraints, especially concerning energy. In this article, we propose a centralized intrusion detection scheme based on Support Vector Machines (SVMs) and sliding windows. We find that our system can detect black hole attacks and selective forwarding attacks with high accuracy without depleting the nodes of their energy.

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Almost every routing protocol in mobile ad hoc networks (MANETs) depends on a broadcast scheme to disseminate routing information. For this reason, creating an efficient broadcast scheme is important and a large variety of approaches have been proposed. Among them, multipoint relay (MPR) is one of the distributed broadcast schemes which is efficient and simple. Based on the MPR concept, many broadcast schemes have been proposed, which generally focus on different performance issues. In this article we present a comprehensive survey of MPR-based broadcast schemes, classified into three categories based on their objectives. Different heuristics are described, and the evaluation of their performances is provided in light of their costs. Advantages and limitations of different broadcast schemes are also highlighted.

Positron Emission Tomography (PET) is a medical imaging procedure that shows the physiological function of an organ or tissue. The role of PET during the past decade has evolved rapidly in the detection of lung tumors but the research on quantitative evaluation of PET images is still in its infancy. PET commonly involves scanning the patient after administration of a radioactive analogue of glucose called fluoro deoxy-glucose (FDG). Tumor cells metabolise more glucose than most normal cells. In PET lung images the heart is often visible and because of its constant pumping of blood it requires more glucose and hence both the tumor and the heart appear brighter than the rest in the PET image. In this paper we present a novel segmentation scheme for detecting the tumor alone in lung PET images using standard uptake values (SUV) and connected component analysis. We perform the segmentation in two steps. In coarse segmentation, a non linear scaling of SUV values is performed and then a threshold is chosen adaptively to convert the gray image into the binary image. Fine segmentation is performed on the coarse segmented data in order to narrow down the region of interest using connected component labeling. To our knowledge no one has used connected component analysis for segmenting PET images. We compare our proposed scheme with several commonly used medical image segmentation techniques like threshold, sobel edge detector, laplacian of gaussian (LoG) edge detector, region growing and SUV based segmentation (applied only to PET as SUV is specific to PET). One of the problems in lung tumor detection is the presence of the heart in the image which accumulates activity and often gets recognized as a hot spot (a probable tumor). All the other segmentation schemes detected both the heart and the tumor as hot spots while our segmentation scheme detected the tumor alone as the hot spot. The preliminary study of the proposed scheme has yielded very promising results and will be studied for more lung tumor detection scenarios in future.

Flooding methods have been widely used by routing protocols to disseminate route information and control messages in wireless ad hoc and sensor networks both. However, most of the efficient flooding approaches proposed in the research literature are designed for ad hoc networks only, and they might not be suitable for wireless sensor networks due to their higher node densities, constrained energy and memory resources in sensor nodes. In this paper, we present a distributed lowcost flooding algorithm that is designed particularly for wireless sensor networks. We prove that our new algorithm has O(n+∆) time and O(n) signaling message complexity, where n is the total number of nodes in a network and ∆ is the maximum node degree. Moreover, we show that our low-cost algorithm has a constant approximation ratio, and its signaling message size is also bounded by a constant. Our simulation study demonstrates that our new algorithm not only generates fewer forwarding nodes, but it also uses much less number of signaling messages and has significantly smaller signaling message size than other algorithms published in the literature.

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Lung cancer is one of the most lethal form of cancer worldwide. The tumor present in the lungs is not static and changes its shape and position during each breathing cycle. In order to segment the tumor, the physicians manually outline the tumor on each slice. Slice by slice manual segmentation is prone to errors and causes physician fatigue. A semi-automatic method to segment and track the tumor in all the frames of PET data is proposed in this paper. The tumor is segmented from each slice of the first frame using wavelet features and support vector machine classifier. This segmented tumor, after validated by the experts is used in initialization of the contour for segmentation of the tumor in subsequent frames by the level set method. Another important contribution of this paper is setting up tumor volume obtained from the first frame as the termination condition for the level set method. The results obtained from the proposed methodology are very promising and eliminates the need for manual tumor segmentation. Our proposed technique also maintains consistent segmentation and the results obtained are not dependent on the operator as is the case in manual segmentation.