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Abstract-Online network traffic measurements and analysis is critical for detecting and preventing any real-time anomalies in the network. We propose, implement, and evaluate an online, adaptive measurement platform, which utilizes real-time traffic analysis results to refine subsequent traffic measurements. Central to our solution is the concept of Multi-Resolution Tiling (MRT), a heuristic approach that performs sequential analysis of traffic data to zoom into traffic sub-regions of interest. However, MRT is sensitive to transient traffic spikes. In this paper, we propose three novel traffic streaming algorithms that overcome the limitations of MRT and can cater to varying degrees of computational and storage budgets, detection latency, and accuracy of query response. We evaluate our streaming algorithms on a highly parallel and programmable hardware as well as a traditional software based platforms. The algorithms demonstrate significant accuracy improvement over MRT in detecting anomalies consisting of synthetic hard-to-track elephant flows and global icebergs. Our proposed algorithms maintain the worst case complexities of the MRT, while incurring only a moderate increase in average resource utilization.

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Objective: To investigate the effect of pterygium morphology on recurrence with preoperative subconjunctival injection of mitomycin-C in primary pterygium surgery.

Accurate and real-time traffic measurement is becoming increasingly critical for large variety of applications including accounting, bandwidth provisioning and security analysis. Existing network measurement techniques, however, have major difficulty dealing with large number of flows in today's high-speed networks and offer limited scalability with increasing link speeds. Consequently, the current state of the art solutions have to resort to conservative sampling of the traffic stream and/or accounting for only a few frequent flows that often fail to provide accurate estimates of traffic features.In this paper, we present a novel hardware-software codesigned solution that is programmable and adaptable to runtime situations offering high-throughputs that can easily match current link-speeds. The key to our design is orthogonalization of memory lookups from traffic measurements through our query-driven measurement scheme. We have prototyped our approach on a Xilinx platform using Microblaze soft-core processors integrated with Virtex-II Pro FPGA fabric. We demonstrate the scalability of our architecture and also compare it with a recent offline (non realtime) sampling-based software alternative. The comparison shows that our architecture performs orders better in terms of speed and throughput even while being used as an offline solution.

Multi‐hop routing in vehicular ad‐hoc networks (VANETs) and wireless sensor networks has attracted significant interest of researchers in the wireless ad‐hoc networks community. Most multi‐hop routing protocols in VANET are based around the idea of choosing the next destination, which will provide the shortest‐delay to reach a destination. To ensure better monitoring and reporting of road condition information, this study proposes location‐based data forwarding through roadside sensors using k ‐shortest path routing combined with Q‐learning. Q‐learning is used for exploration of the sensing field to determine those sensors which have a higher queuing delay during peak hours as well as those which have comparatively lower delays. The use of Q‐learning for exploration (sans routing) enables faster convergence for the sensors as compared to those techniques which utilise naive Q‐learning for shortest path routing. Secondly, multi‐hop routing is being combined with source coding (Huffman and Arithmetic coding) to compress the data payload of packets. This has shown some promising results for the VANETs employing dedicated short‐range communication.