Locating the demanded content is one of the major challenges in Information-Centric Networking (ICN). This process is known as content discovery. To facilitate content discovery, in this paper we focus on Named Data Networking (NDN) and propose a novel routing scheme for content discovery, called Bloom Filter-based Routing (BFR), which is fully distributed, content oriented, and topology agnostic at the intra-domain level. In BFR, origin servers advertise their content objects using Bloom filters. We compare the performance of the proposed BFR with flooding and shortest path content discovery approaches. BFR outperforms its counterparts in terms of the average round-trip delay, while it is shown to be very robust to false positive reports from Bloom filters. Also, BFR is much more robust than shortest path routing to topology changes. BFR strongly outperforms flooding and performs almost equal with shortest path routing with respect to the normalized communication costs for data retrieval and total communication overhead for forwarding Interests. All the three approaches achieve similar mean hit distance. The signalling overhead for content advertisement in BFR is much lower than the signalling overhead for calculating shortest paths in the shortest path approach. Finally, BFR requires small storage overhead for maintaining content advertisements.
In @cite_0 , SCAN is proposed as a routing scheme for content-aware networks. The main disadvantage of SCAN is that it uses IP routing as a fall-back solution, meaning that cache routers perform both content and IP routing, i.e. , they maintain content routing tables as well as IP routing tables. Therefore, SCAN is not a fully content-oriented routing scheme.
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Since Internet routers are not aware of the contents being forwarded, the same content file is often delivered multiple times inefficiently. Similarly, users cannot exploit a nearby copy of the content of interest unless the content file is serviced by costly content delivery networks. Prior studies on the content-aware routing for efficient content delivery suffer from the scalability problem due to a large number of contents. We propose a scalable content routing, dubbed SCAN, which can exploit nearby and multiple content copies for the efficient delivery. SCAN exchanges the information of the cached contents using Bloom filter. Compared with IP routing, SCAN can offer reduced delivery latency, reduced traffic volume, and load balancing among links. X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse-view CT reconstruction. However, theoretical justification is still lacking. Inspired by the recent theory of deep convolutional framelets , the main goal of this paper is, therefore, to reveal the limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U-Net variants such as dual frame and tight frame U-Nets satisfy the so-called frame condition which makes them better for effective recovery of high frequency edges in sparse-view CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance. In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar. We have evaluated UNet++ in comparison with U-Net and wide U-Net architectures across multiple medical image segmentation tasks: nodule segmentation in the low-dose CT scans of chest, nuclei segmentation in the microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos. Our experiments demonstrate that UNet++ with deep supervision achieves an average IoU gain of 3.9 and 3.4 points over U-Net and wide U-Net, respectively. Sources of a Distributed Denial of Service (DDoS) attack can be identified by the traffic they generate using the IP traceback technique. Because of its relevance, the Probabilistic Packet Marking (PPM) schemes for IP traceback is an intensively researched field. In these schemes, routers are given the extra function of randomly selecting packets from those that go through them, to embed their address information in those selected packets. During or after the attack, the paths that were traversed by the attack traffic can be identified based on the router information in the marked packets. Since these schemes require a large number of received packets to trace an attacker successfully, they usually demand a high time and space complexity to trace many attackers as is the case in DDoS attacks. This is partly because the marking scheme allows remarking, where routers can overwrite previous marking information in a selected packet, which leads to data loss. We present the Prediction Based Scheme (PBS), which is an addition to the PPM schemes for IP tracetrack. The proposed approach consists of two parts: (a) a marking scheme, that reduces the number of packets required to trace a DoS attacker and (b) an extension to a traceback algorithm, whose main feature is to return a complete attack graph with fewer received packets than the traditional algorithm. The proposed marking scheme alleviates the problem of data loss by ensuring previous marking information is not overwritten. Additionally, the proposed traceback algorithm uses graphs built using legitimate traffic to predict the path taken by attack traffic. Results show that the marking scheme in PBS, compared to PPM, ensures that traceback is possible with about 54 as many total packets to achieve complete attack path construction, while the traceback algorithm takes about 33 as many marked packets.