2685835
Energy Consumption of Swarm Intelligence inspired Routing Algorithms in MANETs
Swarm Intelligence (SI) inspired routing algorithms have become a research focus in recent years due to their self-organizing nature, which is very suitable to the routing problems in Mobile Ad hoc Networks (MANETs). The network information can be collected and updated in a decentralized and dynamic way through the localized cooperation among these swarm agents. In this paper, we, for the first time, provide a comprehensive comparison about current SI inspired routing algorithms in MANETs in the aspects of network metrics and simulation environment. Then, we make a detailed study about the energy consumption of the SI inspired routing problem in MANETs. Based on this study, we present the selection criterion of the intermediate nodes so as to be energy efficient. Experimental results are provided with reasonable verification.
2598908
A Swarm Intelligence inspired Autonomic Routing Scenario in Ubiquitous Sensor Networks
Autonomic computing has attracted large amount of attention as a novel computing paradigm in the past few years. In this paper, we explore the inherent accordance between autonomic computing and swarm intelligence. Then, we propose a swarm intelligence inspired autonomic routing scenario with a targeting application area in ubiquitous sensor network. This scenario covers most of the characteristics of autonomic computing. The working flow and steps of our SI inspired autonomic routing scenario are explained in detail together with some preliminary simulation results, such as the power consumption, delivery ratio etc.
6516220
Resource-Constrained Workflow Modeling
Business processes are often constrained by resource availability. This is particularly true in an emergency response system where large quantity of resources, including emergency responders, ambulances, fire trucks, medications, food, clothing, etc., are required. In our previous work, we introduced WIFA model to meet the needs of incident command systems workflow modeling and analysis. In this paper, we extend WIFA to take resources into account when modeling and enacting workflows. A formal resource-constrained workflow model is defined and the state transitions rules are established to support workflow analysis. An example is given to illustrate the use of the model.
8287323
A recurrent neural network for solving linear projection equations
Linear projection equations arise in many optimization problems and have important applications in science and engineering. In this paper, we present a recurrent neural network for solving linear projection equations in real time. The proposed neural network has two layers and is amenable to parallel implementation with simple hardware. In the theoretical aspect, we prove that the proposed neural network can converge globally to the solution set of the problem when the matrix involved in the problem is positive semidefinite and can converge exponentially to a unique solution when the matrix is positive definite. In addition, we analyze the stability of the related dynamic system in detail. As an application, we show that the proposed neural network can be used directly to solve linear and convex quadratic programming problems and linear complementary problems with positive semidefinite matrices. The validity and transient behavior of the neural network are demonstrated by using three numerical examples.
16978334
Evaluating a class of distance-mapping algorithms for data mining and clustering
A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudoEuclidean space in such a way that the distances among objects are approximately preserved. Distance mapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuristics that combine the two algorithms in different ways. Experimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations may be done in minutes.
14219803
A new method to measure the semantic similarity of GO terms
MOTIVATION
Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources.


RESULTS
To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery.


AVAILABILITY
The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME.


SUPPLEMENTARY INFORMATION
http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.
18868983
Mobility-Pattern Based Localization Update Algorithms for Mobile Wireless Sensor Networks
In mobile wireless sensor networks, sensors move in the monitored area at any direction and at any speed. Unlike many other networking hosts, sensor nodes do not have global addresses. Very often they are identified by using a location-based addressing scheme. Therefore, it is important to have the knowledge of the sensor location indicating where the data came from. In this paper, we design three mobility-pattern based localization update algorithms. Specifically, we divide sensor movements into three states, Pause, Linear, and Random. Each state adopts different localization update algorithms. Analytical and simulation results are provided to study the localization cost and location accuracy of the proposed localization-update algorithm in different mobility patterns. The analysis to these results indicates that the localization cost is minimized and the location accuracy is improved.