Stephen Gundry

Self organization for area coverage maximization and energy conservation in mobile ad hoc networks

Mobile Ad hoc Networks (manets) are widely used for a large number of strategic applications from military to commercial tasks including disaster area discovery, mine field clearing, and transportation systems. In realistic applications, it is not feasible to deploy mobile nodes manually or using a centralized controller. We provide a nature-inspired approach to achieve self-organization of mobile nodes over unknown terrains. In this framework, each mobile node uses a genetic algorithm as a self-distribution mechanism to decide its next speed and movement direction to obtain a uniform distribution. We present a formal analysis of the effectiveness of our genetic algorithm and introduce an inhomogeneous Markov chain model to prove its convergence. The experiment results from our simulation software and our vmware-based testbed show that our nature-inspired algorithm delivers promising results for uniform distribution of mobile nodes over unknown terrains.

Analysis of Emergent Behavior for GA-based Topology Control Mechanism for Self-Spreading Nodes in MANETs

We introduce a genetic algorithm based MANET topology control mechanism to be used in decision making process of adaptive and autonomic systems at run time. A mobile node adapts its speed and direction using limited information collected from local neighbors operating in an unknown geographical terrain. We represent the genetic operators (i.e., selection, crossover and mutation) as a dynamical system model to describe the behavior of a single node’s decision mechanism. In this dynamical system model each mobile node is viewed as a stochastic variable. We build a homogeneous Markov chain to study the convergent nature of multiple mobile nodes running our algorithm, called FGA. Each state in our chain represents a configuration of the nodes in a MANET for a given instant. The homogeneous Markov chain model of our FGA is shown to be ergodic; its convergence is demonstrated using Dobrushin’s contraction coefficients. We also observe that the nodes with longer communication ranges utilize more information about their neighborhood to make better decisions, require less movement and converge faster, whereas smaller communication ranges utilize limited information, take more time to escape local optima, and, hence, consume more energy.

Markov Chain Analysis of Self-organizing Mobile Nodes

Self-organization of autonomous mobile nodes using bio-inspired algorithms in mobile ad hoc networks (manets) has been presented in earlier work of the authors. In this paper, the convergence speed of our force-based genetic algorithm (called fga) is provided through analysis using homogeneous Markov chains. The fga is run by each mobile node as a topology control mechanism to decide a corresponding node’s next speed and movement direction so that it guides an autonomous mobile node over an unknown geographical area to obtain a uniform node distribution while only using local information. The stochastic behavior of fga, like all ga-based approaches, makes it difficult to analyze the effects that various manet characteristics have on its convergence speed. Metrically transitive homogeneous Markov chains have been used to analyze the convergence of our fga with respect to various communication ranges of mobile nodes and also the number of nodes in various scenarios. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for Markov chain model of our fga. Two different testbed platforms are presented to illustrate effectiveness of our bio-inspired algorithm in terms of area coverage.

Intracellular pH regulation by Na⁺/H⁺ exchanger-1 (NHE1) is required for growth factor-induced mammary branching morphogenesis.

Regulation of intracellular pH (pHi) and protection against cytosolic acidification is primarily a function of the ubiquitous plasma membrane Na+/H+exchanger-1 (NHE1), which uses a highly conserved process to transfer cytosolic hydrogen ions (H+) across plasma membranes in exchange for extracellular sodium ions (Na+). Growth factors, which are essential regulators of morphogenesis, have also been found to be key activators of NHE1 exchanger activity; however, the crosstalk between both has not been fully evaluated during organ development. Here we report that mammary branching morphogenesis induced by transforming growth factor-alpha (TGFα) requires PI3K-dependent NHE1-activation and subsequent pHi alkalization. Inhibiting NHE1 activity after TGFα stimulation with 10 μM of the NHE1-specific inhibitor N-Methyl-N-isobutyl Amiloride (MIA) dramatically disrupted branching morphogenesis, induced extensive proliferation, ectopic expression of the epithelial hyper-proliferative marker Keratin-6 and sustained activation of MAPK. Together these findings indicate a novel developmental signaling cascade involving TGFα>PI3K>NHE1>pHi alkalization, which leads to a permissible environment for MAPK negative feedback inhibition and thus regulated mammary branching morphogenesis.

Formal convergence analysis for bio-inspired topology control in MANETs

We present a convergence analysis of a genetic algorithm based topological control mechanism for the decision making process of evolutionary and autonomous systems that adaptively reconfigures spatial configuration in mobile ad hoc networks (MANETs). Mobile nodes adjust their speed and direction using information collected from the local neighborhood environment in unknown geography. We extend the stochastic model of the genetic operators (i.e., selection, crossover and mutation) called the dynamical system model that represents the behavior of a single nodes decision mechanism in the network viewed as a stochastic variable. We introduce an ergodic homogeneous Markov chain to analyze the convergent nature of multiple mobile nodes running our algorithm, called the Force-based Genetic Algorithm (FGA). Here, a state represents an instantaneous spatial configuration of nodes in a MANET. It is shown that the Markov chain model of our FGA is ergodic and its convergence is shown using Dobrushins contraction coefficients. It is observed that scenarios where nodes have small communication ranges compared to their movement range converge quicker than larger ones due the limited information they have of their neighborhood, making movement decisions simpler, thus conserving energy.

Convergence analysis of genetic algorithms for topology control in MANETs

We describe and verify convergence properties of our forced-based genetic algorithm (FGA) as a decentralized topology control mechanism distributed among software agents. FGA uses local information to guide autonomous mobile nodes over an unknown geographical terrain to obtain a uniform node distribution. Analyzing the convergence characteristics of FGA is difficult due to the stochastic nature of GA-based algorithms. Ergodic homogeneous Markov chains are used to describe the convergence characteristics of our FGA. In addition, simulation experiments verify the convergence of our GA-based algorithm.

Toward Genomic Based Personalized Mathematical Models for Breast Cancer Tumor Growth

Our Genomic Relevance Parameterization (GReP) model aims to explore a possible relationship between gene expression values from breast cancer patients and mathematical tumor growth modeling parameters calculated using data from clinical and preclinical measurements. We introduce two methods to relate genomic information and the tumor growth measurements. One method explores the impact of exponentiation of gene expression values, whereas the other utilizes the correlation between co-regulated genes and the growth parameters. As inputs to our GReP model, we used patient tumor volume measurements and genomic information for 74 breast cancer related genes from the I-SPY 1 TRIAL. We performed a preliminary validation of GReP model using experimental data from literature including MDA-MB-231 cell line, MDA-MB-231 cell line with CXCL12 gene over-expressed, and the MDA MB-231 sub-cell lines 1834 and 4175. Tumor growth curves generated by GReP model, for the initial exponential phase of tumor growth, closely match the pre-clinical data reported in the literature. These promising results show that it may be possible to build tools combining clinical information and genomic data to model cancerous tumor growth.

Markov chain model for differential evolution based topology control in MANETs

Mobile Ad hoc Networks (MANETs) are used for many strategic commercial and military applications where it is not feasible to use a centralized controller or manually deploy assets. They have proved useful for many practical applications, such as search and rescue, clearing mine fields, and transportation systems. We introduce a differential evolution based topological control mechanism for the decision making process of evolutionary and autonomous systems that adaptively reconfigures spatial configuration in MANETs. We present a formal analysis of the effectiveness of our topology control mechanism and introduce an inhomogeneous Markov chain model to prove its convergence. The experiment results from our simulation software show that our biologically-inspired algorithm produces encouraging results for uniform distribution of mobile nodes over unknown terrains.

Genomic based personalized chemotherapy analysis to support decision systems for breast cancer

Personalized approach to anti-cancer therapy necessitates the adaptation of standardized guidelines for chemotherapy schedules to individual cancer patients. We introduce a methodology, namely Personalized Relevance Parameterization (PReP-G), based on the genomic data of breast cancer patients to compute time course of drug efficacy on tumor progression. The pharmacodynamic (PD) parameters of transit compartmental systems are computed to quantify the drug efficacy and kinetics of cell death. We integrate the genetic information of 74 breast cancer related genes for 78 patients with clinical t-stage of 3 from the I-SPY 1 TRIAL with the tumor volume measurements from NBIA database into our PReP-G model to compute tumor growth and shrinkage parameters. The performance of the method is evaluated for the breast cancer cell lines of BT-474, MDA-MB-435 and MDA-MB-231 for a given chemotherapy, where the anti-cancer agents Doxorubicin and Cyclophosphamide are administered to animal models and the change of tumor size is measured in time. We compare our results from PReP-G model with the experimental measurements. The consistency between computed results and the volume measurements is encouraging to develop personalized tumor growth models and decision support systems based on genetic data.

Fault tolerant bio-inspired topology control mechanism for autonomous mobile node distribution in MANETs

We introduce a fault tolerant bio-inspired topolog-ical control mechanism (TCM-Y) for the evolutionary decision making process of autonomous mobile nodes that adaptively adjust their spatial configuration in MANETs. TCM-Y is based on differential evolution and maintains a user-defined minimum connectivity for each node with its near neighbors. TCM-Y, therefore, provides a topology control mechanism which is fault tolerant with regards to network connectivity that each mobile node is required to maintain. In its fitness calculations, TCM-Y uses the Yao graph structure to enforce a user-defined minimum number of neighbors while obtaining uniform network topology. The effectiveness of TCM-Y is evaluated by comparing it with our differential evolution based topology mechanism (TCM-DE) that uses virtual forces from neighbors in its fitness function. Experimental results obtained from simulation software show that TCM-Y performs well with respect to normalized area coverage, the average connectivity, and the minimum connectivity achieved by mobile nodes. Simulation experiments demonstrate that TCM-Y generates encouraging results for uniform distribution of mobile nodes over unknown terrains while maintaining a user-defined minimum connectivity between neighboring nodes.