Alex Delis

GPS-Free node localization in mobile wireless sensor networks

An important problem in mobile ad-hoc wireless sensor networks is the localization of individual nodes, i.e., each nodes awareness of its position relative to the network. In this paper, we introduce a variant of this problem (directional localization) where each node must be aware of both its position and orientation relative to the network. This variant is especially relevant for the applications in which mobile nodes in a sensor network are required to move in a collaborative manner. Using global positioning systems for localization in large scale sensor networks is not cost effective and may be impractical in enclosed spaces. On the other hand, a set of pre-existing anchors with globally known positions may not always be available. To address these issues, in this work we propose an algorithm for directional node localization based on relative motion of neighboring nodes in an ad-hoc sensor network without an infrastructure of global positioning systems (GPS), anchor points, or even mobile seeds with known locations. Through simulation studies, we demonstrate that our algorithm scales well for large numbers of nodes and provides convergent localization over time, even with errors introduced by motion actuators and distance measurements. Furthermore, based on our localization algorithm, we introduce mechanisms to preserve network formation during directed mobility in mobile sensor networks. Our simulations confirm that, in a number of realistic scenarios, our algorithm provides for a mobile sensor network that is stable over time irrespective of speed, while using only constant storage per neighbor.

Flexible use of cloud resources through profit maximization and price discrimination

Modern frameworks, such as Hadoop, combined with abundance of computing resources from the cloud, offer a significant opportunity to address long standing challenges in distributed processing. Infrastructure-as-a-Service clouds reduce the investment cost of renting a large data center while distributed processing frameworks are capable of efficiently harvesting the rented physical resources. Yet, the performance users get out of these resources varies greatly because the cloud hardware is shared by all users. The value for money cloud consumers achieve renders resource sharing policies a key player in both cloud performance and user satisfaction. In this paper, we employ microeconomics to direct the allotment of cloud resources for consumption in highly scalable master-worker virtual infrastructures. Our approach is developed on two premises: the cloud-consumer always has a budget and cloud physical resources are limited. Using our approach, the cloud administration is able to maximize per-user financial profit. We show that there is an equilibrium point at which our method achieves resource sharing proportional to each users budget. Ultimately, this approach allows us to answer the question of how many resources a consumer should request from the seemingly endless pool provided by the cloud.

Reaching Available Public Parking Spaces in Urban Environments Using Ad Hoc Networking

A fundamental application in vehicular ad-hoc networks (VANETs) is the discovery of available parking spaces as vehicles navigate through urban road networks. Vehicles are now capable of finding such parking spots using their on-board sensing and computational infrastructure and then they can disseminate this information for use by other members of the travelling community in the geographic vicinity. In this context, we examine the problem of locating an available parking space for a vehicle entering an urban network of roads. Upon its entry, a vehicle has to determine the best way to visit parking spots reported to be free. In deciding this, the vehicle has to consider the time required to reach each candidate position, its distance from the final destination should the driver walk, and of course, the probability that the spot(s) will be still-free once the vehicle shows up at location. We formulate the question at hand as a Time-Varying Travelling Salesman problem and we propose an approach for computing the route that a vehicle must traverse in order to visit all parking spaces known to be available. Our method takes into account the limited computational resources of vehicles and attempts to find the best feasible trip. This is done in conjunction with a cost function that estimates the probability to find a space filled. In order to ascertain the effectiveness of our proposal, we compare it with a best-first approach and examine computational overheads. We also investigate how close to optimal results our approach comes.

Web Information Systems Engineering -- WISE 2012, 13th International Conference, Paphos, Cyprus, November 28-30, 2012 Proceedings

This book constitutes the proceedings of the 13th International Conference on Web Information Systems Engineering, WISE 2012, held in Paphos, Cyprus, in November 2012. The 44 full papers, 13 short papers, 9 demonstrations papers and 9 challenge papers were carefully reviewed and selected from 194 submissions. The papers cover various topics in the field of Web Information Systems Engineering

An analysis of errors in a reuse-oriented development environment

Component reuse is widely considered vital for obtaining significant improvement in development productivity. However, as an organization adopts a reuse-oriented development process, the nature of the problems in development is likely to change. In this article, we use a measurement-based approach to better understand and evaluate an evolving reuse process. More specifically, we study the effects of reuse across seven projects in narrow domain from a single development organization. An analysis of the errors that occur in new and reused components across all phases of system development provides insight into the factors influencing the reuse process. We found significant differences between errors associated with new and various types of reused components in terms of the types of errors committed. In addition, we identified differences when errors are introduced and the effect that the errors have on the development process.

VM : placement in non-Homogeneous Iaas-clouds

Infrastructure-as-a-Service (IaaS) cloud providers often combine different hardware components in an attempt to form a single infrastructure. This single infrastructure hides any underlying heterogeneity and complexity of the physical layer. Given a non-homogeneous hardware infrastructure, assigning VMs to physical machines (PMs) becomes a particularly challenging task. VM placement decisions have to take into account the operational conditions of the cloud (e.g., current PM load) and load balancing prospects through VM migrations. In this work, we propose a service realizing a two-phase VM-to-PM placement scheme. In the first phase, we identify a promising group of PMs, termed cohort, among the many choices that might be available; such a cohort hosts the virtual infrastructure of the user request. In the second phase, we determine the final VM-to-PM mapping considering all low-level constraints arising from the particular user requests and special characteristics of the selected cohort. Our evaluation shows that in large non-homogeneous physical infrastructures, we significantly reduce the VM placement plan production time and improve plan quality.

Malware characteristics and threats on the internet ecosystem

Malware encyclopedias now play a vital role in disseminating information about security threats. Coupled with categorization and generalization capabilities, such encyclopedias might help better defend against both isolated and clustered specimens.In this paper, we present Malware Evaluator, a classification framework that treats malware categorization as a supervised learning task, builds learning models with both support vector machines and decision trees and finally, visualizes classifications with self-organizing maps. Malware Evaluator refrains from using readily available taxonomic features to produce species classifications. Instead, we generate attributes of malware strains via a tokenization process and select the attributes used according to their projected information gain. We also deploy word stemming and stopword removal techniques to reduce dimensions of the feature space. In contrast to existing approaches, Malware Evaluator defines its taxonomic features based on the behavior of species throughout their life-cycle, allowing it to discover properties that previously might have gone unobserved. The learning and generalization capabilities of the framework also help detect and categorize zero-day attacks. Our prototype helps establish that malicious strains improve their penetration rate through multiple propagation channels as well as compact code footprints; moreover, they attempt to evade detection by resorting to code polymorphism and information encryption. Malware Evaluator also reveals that breeds in the categories of Trojan, Infector, Backdoor, and Worm significantly contribute to the malware population and impose critical risks on the Internet ecosystem.

Progressive and approximate techniques in ray-tracing-based radio wave propagation prediction models

Progressive and approximate techniques are proposed here for ray-tracing systems used to predict radio propagation. In a progressive prediction system, intermediate prediction results are fed back to users continuously. As more raypaths are processed, the accuracy of prediction results improves progressively. We consider how to construct a progressive system that satisfies the requirements of continuous observability and controllability as well as faithfulness and fairness. Adding a workload estimator to such a progressive prediction system allows termination of the computation when a desired accuracy (mean and standard deviation of the error) is achieved without knowing the final result that would be obtained if the prediction system runs to completion. The sample generator is at the core of the progressive prediction system and serves to cluster and prioritize raypaths according to their expected contributions to prediction results. Two types of progressive approaches, source-group-raypath-permute and raypath-interleave, are proposed. The workload estimator determines the number of raypaths to be processed to achieve the specified requirement on prediction accuracy. Two approximate models are described that adjust the workload dynamically during the prediction process. Our experiments show that the proposed progressive and approximate methods provide flexible mechanisms to trade prediction accuracy for prediction time in a relatively fine granularity.

Exploiting Network-Topology Awareness for VM Placement in IaaS Clouds

In contemporary IaaS configurations, resources are distributed to users primarily through the assignment of virtual machines (VMs) to physical nodes (PMs). This resource allocation is typically done in a way that does not consider user preferences and is unaware of the underlying network layout. The latter is of key significance as cost of the clouds internal network does not grow linearly to the size of the physical infrastructure. In this paper, we focus on IaaS clouds built on the highly fault-tolerant and scalable PortLand networks. We examine how the performance of the could can benefit from VM placement algorithms that exploit user-provided hints regarding the features of sought VM interconnections within a virtual infrastructure. We propose and evaluate two such VM placement algorithms: the first seeks to rapidly place the required VMs as closely as possible on the PortLand network starting with the most demanding virtual link and by following a greedy approach. The second approach identifies promising neighborhoods of PMs for deploying the virtual infrastructure sought. Both methods try to reduce the network utilization of the physical layer while taking advantage of the PortLand layout. Moreover, we seek to minimize the time expended for the placement decision regardless of the size of the infrastructure. Our experimentation shows that our methods outperform the traditional methods (first-fit) in respect to network usage. Our greedy approach reduces the network traffic routed through the top-level core-switches in the PortLand topology by up to 75%. The second approach attains an additional 20% improvement.

Radio-wave propagation prediction using ray-tracing techniques on a network of workstations (NOW)

Ray-tracing based radio wave propagation prediction models play an important role in the design of contemporary wireless networks as they may now take into account diverse physical phenomena including reflections, diffractions, and diffuse scattering. However, such models are computationally expensive even for moderately complex geographic environments. In this paper, we propose a computational framework that functions on a network of workstations (NOW) and helps speed up the lengthy prediction process. In ray-tracing based radio propagation prediction models, orders of diffractions are usually processed in a stage-by-stage fashion. In addition, various source points (transmitters, diffraction corners, or diffuse scattering points) and different ray-paths require different processing times. To address these widely varying needs, we propose a combination of the phase-parallel and manager/workers paradigms as the underpinning framework. The phase-parallel component is used to coordinate different computation stages, while the manager/workers paradigm is used to balance workloads among nodes within each stage. The original computation is partitioned into multiple small tasks based on either raypath-level or source-point-level granularity. Dynamic load-balancing scheduling schemes are employed to allocate the resulting tasks to the workers. We also address issues regarding main memory consumption, intermediate data assembly, and final prediction generation. We implement our proposed computational model on a NOW configuration by using the message passing interface (MPI) standard. Our experiments with real and synthetic building and terrain databases show that, when no constraint is imposed on the main memory consumption, the proposed prediction model performs very well and achieves nearly linear speedups under various workload. When main memory consumption is a concern, our model still delivers very promising performance rates provided that the complexity of the involved computation is high, so that the extra computation and communication overhead introduced by the proposed model do not dominate the original computation. The accuracy of prediction results and the achievable speedup rates can be significantly improved when 3D building and terrain databases are used and/or diffuse scattering effect is taken into account.