Muaz A. Niazi

Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad hoc, and other complex networks

Agent-based modeling and simulation tools provide a mature platform for development of complex simulations. They however, have not been applied much in the domain of mainstream modeling and simulation of computer networks. In this article, we evaluate how and if these tools can offer any value-addition in the modeling & simulation of complex networks such as pervasive computing, large-scale peer-to-peer systems, and networks involving considerable environment and human/animal/habitat interaction. Specifically, we demonstrate the effectiveness of NetLogo - a tool that has been widely used in the area of agent-based social simulation.

A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments

In this paper, we present a novel formal agent-based simulation framework (FABS). FABS uses formal specification as a means of clear description of wireless sensor networks (WSNs) sensing a complex adaptive environment. This specification model is then used to develop an agent-based model of both the WSN as well as the environment. As proof of concept, we demonstrate the application of FABS to a boids model of self-organized flocking of animals monitored by a random deployment of proximity sensors.

Complex Adaptive Systems Modeling: A multidisciplinary Roadmap

PAC Codes07.05.Tp, 89.75.-k, 89.75.FbMathematics Subject Classification (2010)05C82, 68T42, 00A72, 92C42

Towards a Methodology for Validation of Centrality Measures in Complex Networks

Background Living systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important? Purpose The goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets. Method We take standard published network data sets while using a random network to establish a baseline. These data sets included the Zacharys Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes. Results Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.

A Unified Framework

In this section, firstly an overview of the proposed framework is presented. Next the framework is described from two different perspectives firstly in terms of study objectives of conducting a CAS research case study and the expected level of commitment. Secondly, the framework is described in correlation with the availability and access to specific data types.

Sensing Emergence in Complex Systems

We propose the Sensing of Emergent behavior in a Complex Adaptive System (SECAS), as an extension of our previous work Formal Agent-Based Simulation Framework (FABS). Using aggregated data from an array of proximity sensors, SECAS allows for the detection of complex behavior such as flocking of mobile robots or life forms. For validation, we develop an agent-based simulation model. Extensive simulation experiments using a wide range of randomly deployed sensors demonstrate the effectiveness of SECAS in the sensing of flocking.

A new hybrid agent-based modeling & simulation decision support system for breast cancer data analysis

In this paper, we present a novel technique of building hybrid decision support systems which integrates traditional decision support systems with agent based models for use in breast cancer analysis for better prediction and recommendation. Our system is based on using queries from data (converted to a standardized electronic template) to provide for simulation variables in an agent-based model. The goal is to develop an ICT tool to assist non-specialist biologist researcher users in performing analysis of large amounts of data by applying simple simulation techniques. To demonstrate the effectiveness of this novel decision support system, an extensive breast cancer data collection exercise was carried out with the support of Hospitals in a previously unexplored region. The collected data was subsequently integrated in an electronic medical record filing system for patients. We also demonstrate the application of agent based modeling and simulation techniques for building simulation models of tumor growth and treatment. Our proposed decision support system also provides a comprehensive query tool which facilitates the use of retrieved data in statistical tools2 for subsequent interpretation and analysis.

Self-organized customized content delivery architecture for ambient assisted environments

This paper gives two contributions; First, it presents an architecture for customized content delivery for Ambient Intelligent Environments. We demonstrate how physical peers made up of a Bluetooth-based network of Java-enabled mobile phones can be used to provide customized content delivery from the web without the need of a dedicated web connection per device. Secondly, we present two algorithms <b><i>S</i></b>elf-<b><i>O</i></b>rganizi<b><i>NG</i></b> random walker<b><i>S</i></b> (<b><i>SONGS</i></b>) and <b><i>p</i></b>eer-to-pee<b><i>R</i></b> self-organ<b><i>IZ</i></b>ed t<b><i>E</i></b>mporary overlay<b><i>S</i></b> (<b><i>PRIZES</i></b>), both providing mechanisms of temporary overlay formation in limited connectivity ad-hoc networks. SONGS is an extension of k-random walk algorithm whereas PRIZES is a forest-fire type flooding mechanism. We then show how adding even naive self-organization to these algorithms significantly improves the leftover queries as well as latency in terms of hop-counts.

Simulation of the research process

This paper presents first steps towards the development of a formal model of the research process. We evaluate the use of simulation as a tool for the evaluation of research strategies in nascent research organizations faced with the absence of significant data. We start by modeling the research process by using the ¿Publish or Perish¿ paradigm, a well-known criteria of evaluation of research. We demonstrate the use of this model for researchers to evaluate the effects of selection of a particular publishing venue over time. We then perform various experiments using this basic idea. By means of various visualization techniques, we see how researchers with similar publishing policies might self-organize in the form of groups. We also evaluate the effects of giving higher weights to articles in journals and see where the effects of publishing in these venues breaks even for both top as well as average acceptance rates.

Social Network Analysis of trends in the consumer electronics domain

We present a study of the trends in the consumer electronics domain using Complex Social Network Analysis (SNA) of citation data retrieved from the Thomson Reuters Web of Knowledge. Our findings include the identification of the most influential papers and journals in the domain. In addition, our analysis provides proof of the key role of IEEE Transactions on Consumer Electronics in the domain of “Consumer Electronics” in terms of both Centrality as well as frequency of articles1.