Judith Kelner

Mires: a publish/subscribe middleware for sensor networks

A wireless sensor network (WSN) consists of a large number of small devices with computational power, wireless communication and sensing capability. These networks have been developed for a wide range of applications, such as habitat monitoring, object tracking, precision agriculture, building monitoring and military systems. Meanwhile, middleware systems have also been proposed in to facilitate both the development of these applications and provide common application services. The development of middleware for sensor networks, however, places new challenges on middleware developers due to the low availability of resources and processing capacity of the sensor nodes. In this context, this paper presents the design and implementation of a middleware for WSN named Mires. Mires incorporates characteristics of message-oriented middleware by allowing applications communicate in a publish/subscribe way. In order to illustrate the proposed middleware, we have also developed an environment-monitoring application and a data aggregation service.

Evaluation of security mechanisms in wireless sensor networks

Ad hoc and wireless sensor networks have recently emerged as successful technologies in a number of application domains. The need to build security services into them remains however a considerable challenge as the hardware used often shows serious processing and energy limitations. This work evaluates the impact of a number of security mechanism on sensor nodes and the network as a whole. Hence a number of actual measurements were undertaken in a real sensor platform in order to accurately establish energy consumption for various encryption algorithms; as well the baseline scenario obtained when none of these is used. Measurements have shown that integrity code length added to application messages using some cryptography algorithms and MAC (message authentication code) is acceptable for a sensor node with 128 kB of ROM memory and 4 kB of RAM (MICA2). We also were able to check that power consumption of the encryption process does not in itself cause representative impact, since it is in the micro-joules range.

Open Source Cloud Computing Platforms

With the popularization of cloud computing, several enterprises and open-source communities have developed their own cloud solutions. A number of factors weigh on user selection, as each one has peculiar characteristics and may target different usage scenarios. Considering such challenge, this paper focuses on giving the reader an understanding of some major existing open cloud computing solutions – XCP, Eucalyptus and Open Nebula. Hopefully, a deep comparison of such solutions can leverage the cloud computing research area providing a good starting point to research groups and interested readers.

Better network traffic identification through the independent combination of techniques

Traffic identification is currently an important challenge for network management and dimensioning. In recent years, some new algorithms and the different uses of known techniques have been proposed, yet the results are so far limited in scope and frequently disappointing. Furthermore, existing results cannot be directly compared, since networks and traffic profiles differ significantly among collected traces. When submitted to an analysis, considering different networks, data granularities and baselines, most algorithms perform well in one or two scenarios. However, no algorithm has proven better than the others in the majority of the scenarios. Summarizing four years of research in traffic identification, this work shows that the identification abilities of algorithms vary for different situations and proposes a new methodology for traffic identification through the combination of any set of algorithms for traffic identification. Four different combination mechanisms (and many variations) are validated against four different network scenarios that are commonly used in the literature. Combination shows promising results, mainly because it revealed to be robust against bias towards any scenario, which happens in previous identification algorithms.

Exploiting the Small-World Effect to Increase Connectivity in Wireless Ad Hoc Networks

This paper investigates how the small world concept can be applied in the context of wireless ad hoc networks. Different from wireless ad hoc networks, small world networks have small characteristic path lengths and are highly clustered. This path length reduction is caused by long-range edges between randomly selected nodes. However, in a wireless ad hoc network there are no such long-range connections. Then, we propose to use a fraction of nodes in the network equipped with two radios with different transmission ranges in order to introduce the long-range shortcuts. We analyze the system from a percolation perspective and show that a small fraction of these “special nodes” can improve connectivity in a significant way. We also study the effects of the special nodes on the process of information diffusion and on network robustness.

Photorealistic rendering for Augmented Reality: A global illumination and BRDF solution

This paper presents a solution for the photorealistic rendering of synthetic objects into dynamic real scenes, in Augmented Reality applications. In order to achieve this goal, an Image Based Lighting approach is used, where environment maps with different levels of glossiness are generated for each virtual object in the scene at every frame. Due to this, illumination effects, such as color bleeding and specular reflections, can be simulated for virtual objects in a consistent way. A unifying sampling method for the spherical harmonics transformation pass is also used. It is independent of map format and does not need to apply different weights for each sample. The developed technique is combined with an extended version of Lafortune Spatial BRDF, featuring Fresnel effect and an innovative tangent rotation parameterization. The solution is evaluated in various Augmented Reality case studies, where other features like shadowing and lens effects are also exploited.

Massively Parallel Nearest Neighbor Queries for Dynamic Point Clouds on the GPU

We introduce a parallel algorithm to solve approximate and exact nearest neighbor queries on the GPU, exploiting its massively parallel processing power. Both data structure construction and nearest neighbor queries are performed on the GPU, avoiding memory copies from system memory to device memory. This algorithm achieves real-time performance, enabling its usage in dynamic scenarios, by minimizing the sorting comparisons needed for a large K value. The underlying data structure for spatial subdivision handles 3D points and is based on grid spatial hashing. Users can specify the grid size interactively. Comparisons were done with other nearest neighbor algorithms implemented on both CPU and GPU. Our approach clearly surpasses CPU implementations regarding processing time, while it presents a competitive solution to GPU ones. Real-time results were obtained with ANN searches (K = 10) for data sets up to 163K points and the potential of our algorithm is demonstrated through a point-based rendering application.

A stratified traffic sampling methodology for seeing the big picture

This work explores the use of statistical techniques, namely stratified sampling and cluster analysis, as powerful tools for deriving traffic properties at the flow level. Our results show that the adequate selection of samples leads to significant improvements allowing further important statistical analysis. Although stratified sampling is a well-known technique, the way we classify the data prior to sampling is innovative and deserves special attention. We evaluate two partitioning clustering methods, namely clustering large applications (CLARA) and K-means, and validate their outcomes by using them as thresholds for stratified sampling. We show that using flow sizes to divide the population we can obtain accurate estimates for both size and flow durations. The presented sampling and clustering classification techniques achieve data reduction levels higher than that of existing methods, on the order of 0.1% while maintaining good accuracy for the estimates of the sum, mean and variance for both flow duration and sizes.

Approaches to resource reservation for migrating real-time sessions in future mobile wireless networks

This paper presents two novel frameworks for session admission control and resource reservation in the context of next generation mobile and cellular networks. We also devised a special scheme that avoids per-user reservation signaling overhead in order to meet scalability requirements needed for next generation multi-access networks. The first proposal, Distributed Call Admission Control with Aggregate Resource Reservation (VR), uses mobility prediction based on mobile positioning system location information and takes into account the expected bandwidth to be used by calls handing off to and from neighboring cells within a configurable estimation time window. In conjunction, a novel concept called virtual reservation has been devised to prevent per-user reservation. Our second proposal, Local Call Admission Control and Time Series-based Resource Reservation, takes into account the expected bandwidth to be used by calls handed off from neighboring cells based only on local information stored into the current cell a user is seeking admission to. To this end, we suggest the use of two time series-based models for predicting handoff load: the Trigg and Leach (TL), which is an adaptive exponential smoothing technique, and Autoregressive Integrated Moving Average (ARIMA) that uses the Box and Jenkins methodology. It is worth to emphasize that the use of bandwidth prediction based on ARIMA technique still exist for wireless networks. The novelty of our approach is to build an adaptive framework based on ARIMA technique that takes into account the measured handoff dropping probability in order to tuning the prediction time window size so increasing the prediction accuracy. The proposed schemes are compared through simulations with the fixed guard channel (GC) and other optimized dynamic reservation-based proposals present in the literature. The results show that our schemes outperform many others and that the simpler local proposal based on TL can grant nearly similar levels of handoff dropping probability as compared to those from more the complex distributed approach.

Characterizing signature sets for testing DPI systems

Deep Packet Inspection (DPI) is a crucial tool that enables more accurate network management and traffic profiling. Its high computational demands have been pushing networking researchers to study how to improve DPI technologies. However, in this quest for better performance, little effort has been devoted to making fair performance comparisons among DPIs. Even worse, most studies show performance gains by applying DPI techniques using specific, and sometimes small, signature sets. Consequently, one DPI engine may report a certain maximum throughput while another one may report a similar performance but may have used a significantly smaller signature set. This fact makes it clear that the research community needs a well-defined framework for testing new DPI systems. This paper proposes a new framework for characterizing signature sets for evaluating DPI systems and provides an in-depth analysis of signature sets from application, protocol, and intrusion detection systems.