Self Organized Terminode Routing

We consider the problem of routing in a wide area mobile ad hoc network called Terminode Network. Routing in this network is designed with the following objectives. First, it should scale well in terms of the number of nodes and geographical coverage; second, routing should have scalable mechanisms that cope with the dynamicity in the network due to mobility; and third, nodes need to be highly collaborative and redundant, but, most of all, cannot use complex algorithms or protocols. Our routing scheme is a combination of two protocols called Terminode Local Routing (TLR) and Terminode Remote Routing (TRR). TLR is used to route packets to close destinations. TRR is used to route to remote destinations. The combination of TLR and TRR has the following features: (1) it is highly scalable because every node relies only on itself and a small number of other nodes for packet forwarding; (2) it acts and reacts well to the dynamicity of the network because as a rule multipath routing is considered; and (3) it can be implemented and run in very simple devices because the algorithms and protocols are very simple and based on high collaboration. We performed simulations of the TLR and TRR protocols using the GloMoSim simulator. The simulation results for a large, highly mobile ad hoc environment demonstrate benefits of the combination of TLR and TRR over an existing protocol that uses geographical information for packet forwarding.     

GPS-free Positioning in Mobile Ad Hoc Networks

We consider the problem of node positioning in ad hoc networks. We propose a distributed, infrastructure-free positioning algorithm that does not rely on GPS (Global Positioning System). Instead, the algorithm uses the distances between the nodes to build a relative coordinate system in which the node positions are computed in two dimensions. Despite the distance measurement errors and the motion of the nodes, the algorithm provides sufficient location information and accuracy to support basic network functions. Examples of applications where this algorithm can be used include Location Aided Routing [10] and Geodesic Packet Forwarding [2]. Another example are sensor networks, where mobility is less of a problem. The main contribution of this work is to define and compute relative positions of the nodes in an ad hoc network without using GPS. We further explain how the proposed approach can be applied to wide area ad hoc networks.     

STACRP: a secure trusted auction oriented clustering based routing protocol for MANET

In mobile ad hoc network?(MANET) nodes have a tendency to drop others’ packet to conserve its own energy. If most of the nodes in a network start to behave in this way, either a portion of the network would be isolated or total network functionality would be hampered. This behavior is known as selfishness. Therefore, selfishness mitigation and enforcing cooperation between nodes is very important to increase the availability of nodes and overall throughput and to achieve the robustness of the network. Both credit and reputation based mechanisms are used to attract nodes to forward others’ packets. In light of this, we propose a game theoretic routing model, Secure Trusted Auction oriented Clustering based Routing Protocol (STACRP), to provide trusted framework for MANET. Two auction mechanisms procurement and Dutch are used to determine the forwarding cost-per-hop for intermediate nodes. Our model is lightweight in terms of computational and communication requirements, yet powerful in terms of flexibility in managing trust between nodes of heterogeneous deployments. It manages trust locally with minimal overhead in terms of extra messages. STACRP organizes the network into 1-hop disjoint clusters and elects the most qualified and trustworthy nodes as Clusterhead. The trust is quantified with carefully chosen parameters having deep impact on network functionality. The trust model is analyzed using Markov chain and is proven as continuous time Markov chain. The security analysis of the model is analyzed to guarantee that the proposed approach achieves a secure reliable routing solution for MANETs. The proposed model have been evaluated with a set of simulations that show STACRP detects selfish nodes and enforces cooperation between nodes and achieves better throughput and packet delivery ratio with lees routing overhead compare to AODV.     

The community structure of human cellular signaling network

Living cell is highly responsive to specific chemicals in its environment, such as hormones and molecules in food or aromas. The reason is ascribed to the existence of widespread and diverse signal transduction pathways, between which crosstalks usually exist, thus constitute a complex signaling network. Evidently, knowledge of topology characteristic of this network could contribute a lot to the understanding of diverse cellular behaviors and life phenomena thus come into being. In this presentation, signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total. Computing the degree distribution, we find it is not a random network, but a scale-free network following a power-law of P(K) approximately K(-gamma), with gamma approximately equal to 2.2. Among three graph partition algorithms, the Guimera's simulated annealing method is chosen to study the details of topology structure and other properties of this cellular signaling network, as it shows the best performance. To reveal the underlying biological implications, further investigation is conducted on ad hoc community and sketch map of individual community is drawn accordingly. The involved experiment data can be found in the supplementary material.     

Adaptive Neural Network Structure Optimization Algorithm Based on Dynamic Nodes

Large-scale artificial neural networks have many redundant structures, making the network fall into the issue of local optimization and extended training time. Moreover, existing neural network topology optimization algorithms have the disadvantage of many calculations and complex network structure modeling. We propose a Dynamic Node-based neural network Structure optimization algorithm (DNS) to handle these issues. DNS consists of two steps: the generation step and the pruning step. In the generation step, the network generates hidden layers layer by layer until accuracy reaches the threshold. Then, the network uses a pruning algorithm based on Hebb’s rule or Pearson’s correlation for adaptation in the pruning step. In addition, we combine genetic algorithm to optimize DNS (GA-DNS). Experimental results show that compared with traditional neural network topology optimization algorithms, GA-DNS can generate neural networks with higher construction efficiency, lower structure complexity, and higher classification accuracy.     

Finding scalable configurations for AEDB broadcasting protocol using multi-objective evolutionary algorithms

Energy consumption is one of the main concerns in mobile ad hoc networks (or MANETs). The lifetime of its devices highly depends on the energy consumption as they rely on batteries. The adaptive enhanced distance based broadcasting algorithm, AEDB, is a message dissemination protocol for MANETs that uses cross-layer technology to highly reduce the energy consumption of devices in the process, while still providing competitive performance in terms of coverage and time. We use two different multi-objective evolutionary algorithms to optimize the protocol on three network densities, and we evaluate the scalability of the best found AEDB configurations on larger networks and different densities.     

Modeling,analysis and simulation of ant-based network routing protocols

Using the metaphor of swarm intelligence, ant-based routing protocols deploy control packets that behave like ants to discover and optimize routes between pairs of nodes. These ant-based routing protocols provide an elegant, scalable solution to the routing problem for both wired and mobile ad hoc networks. The routing problem is highly nonlinear because the control packets alter the local routing tables as they are routed through the network. We mathematically map the local rules by which the routing tables are altered to the dynamics of the entire networks. Using dynamical systems theory, we map local protocol rules to full network performance, which helps us understand the impact of protocol parameters on network performance. In this paper, we systematically derive and analyze global models for simple ant-based routing protocols using both pheromone deposition and evaporation. In particular, we develop a stochastic model by modeling the probability density of ants over the network. The model is validated by comparing equilibrium pheromone levels produced by the global analysis to results obtained from simulation studies. We use both a Matlab simulation with ideal communications and a QualNet simulation with realistic communication models. Using these analytic and computational methods, we map out a complete phase diagram of network behavior over a small multipath network. We show the existence of both stable and unstable (inaccessible) routing solutions having varying properties of efficiency and redundancy depending upon the routing parameters. Finally, we apply these techniques to a larger 50-node network and show that the design principles acquired from studying the small model network extend to larger networks.     

Traffic load monitoring and load balancing for the Internet

This paper proposes a class-based multipath routing algorithm to support Quality of Service (QoS). The algorithm is called Two-level Class-based Routing with Prediction (TCRP). Since frequently flooding routing information is very expensive for dynamic routing, the TCRP is designed to have the traffic load information monitored in one stable period as a guide to control traffic forwarding in the next stable period. The monitoring function is implemented by adopting the leaky bucket mechanism. In TCRP, the path selection function can utilize resources on multipath to achieve load balancing, increase network throughput and reduce the queuing delay. The extensive simulation is conducted to analyze the performance of the TCRP algorithm. The simulation results show that the TCRP can reduce packet drops and increase network throughput in any size network topology. This revised version was published online in July 2006 with corrections to the Cover Date.     

A Monte Carlo Study to Evaluate the Performance of Selected Tests of Homogeneity in Sparse Contingency Tables

We address the problem of tests of homogeneity in two-way contingency tables in case-control studies when the case category is subdivided into k subcategories. In this situation, we have two cells with large frequencies and 2 X k cells with frequencies that become small as k increases. We propose two ad hoc statistics in which a statistic for the sparse cells is combined with a statistic for the cells with large frequencies. We will study these tests along with the Pearson test (using a chi-square approximation) in a Monte Carlo simulation study. Two sets of null hypothesis models and two sets of alternative hypothesis models are considered. The best test for the models considered is the usual Pearson test (using an approximate chi-square distribution) although the ad hoc models are more powerful under one alternative model considered.     

Using ant-based agents for congestion control in ad-hoc wireless sensor networks

Ad-hoc wireless sensor networks suffer from problems of congestion, which lead to packet loss and excessive energy consumption. In this paper, we address the issue of congestion in these networks. We propose a new routing protocol for wireless sensor networks namely Ant-based Routing with Congestion Control (ARCC), which takes into account the congestion of the network at a given instant and proposes to reduce it and then finds the optimum paths between the source and the sink nodes. Simulation results show that ARCC performs better with respect to the throughput, the number of packets lost and the priority performance.     

Exploring the Morphospace of Communication Efficiency in Complex Networks

Graph theoretical analysis has played a key role in characterizing global features of the topology of complex networks, describing diverse systems such as protein interactions, food webs, social relations and brain connectivity. How system elements communicate with each other depends not only on the structure of the network, but also on the nature of the system''s dynamics which are constrained by the amount of knowledge and resources available for communication processes. Complementing widely used measures that capture efficiency under the assumption that communication preferentially follows shortest paths across the network (“routing”), we define analytic measures directed at characterizing network communication when signals flow in a random walk process (“diffusion”). The two dimensions of routing and diffusion efficiency define a morphospace for complex networks, with different network topologies characterized by different combinations of efficiency measures and thus occupying different regions of this space. We explore the relation of network topologies and efficiency measures by examining canonical network models, by evolving networks using a multi-objective optimization strategy, and by investigating real-world network data sets. Within the efficiency morphospace, specific aspects of network topology that differentially favor efficient communication for routing and diffusion processes are identified. Charting regions of the morphospace that are occupied by canonical, evolved or real networks allows inferences about the limits of communication efficiency imposed by connectivity and dynamics, as well as the underlying selection pressures that have shaped network topology.     

Resource Management for Ad-Hoc Wireless Networks with Cluster Organization

Boosted by technology advancements, government and commercial interest, ad-hoc wireless networks are emerging as a serious platform for distributed mission-critical applications. Guaranteeing QoS in this environment is a hard problem because several applications may share the same resources in the network, and mobile ad-hoc wireless networks (MANETs) typically exhibit high variability in network topology and communication quality. In this paper we introduce DYNAMIQUE, a resource management infrastructure for MANETs. We present a resource model for multi-application admission control that optimizes the application admission utility, defined as a combination of the QoS satisfaction ratio. A method based on external adaptation (shrinking QoS for existing applications and later QoS expansion) is introduced as a way to reduce computation complexity by reducing the search space. We designed an application admission protocol that uses a greedy heuristic to improve application utility. For this, the admission control considers network topology information from the routing layer. Specifically, the admission protocol takes benefit from a cluster network organization, as defined by ad-hoc routing protocols such as CBRP and LANMAR. Information on cluster membership and cluster head elections allows the admission protocol to minimize control signaling and to improve application quality by localizing task mapping.     

Partial AUC maximization for essential gene prediction using genetic algorithms

Identifying genes indispensable for an organism‘s life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, proteinprotein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature’s relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods. [BMB Reports 2013; 46(1): 41-46]     

Internal Node and Shortcut Based Routing with Guaranteed Delivery in Wireless Networks

Several localized position based routing algorithms for wireless networks were described recently. In greedy routing algorithm (that has close performance to the shortest path algorithm, if successful), sender or node S currently holding the message m forwards m to one of its neighbors that is the closest to destination. The algorithm fails if S does not have any neighbor that is closer to destination than S. FACE algorithm guarantees the delivery of m if the network, modeled by unit graph, is connected. GFG algorithm combines greedy and FACE algorithms. Greedy algorithm is applied as long as possible, until delivery or a failure. In case of failure, the algorithm switches to FACE algorithm until a node closer to destination than last failure node is found, at which point greedy algorithm is applied again. Past traffic does not need to be memorized at nodes. In this paper we further improve the performance of GFG algorithm, by reducing its average hop count. First we improve the FACE algorithm by adding a sooner-back procedure for earlier escape from FACE mode. Then we perform a shortcut procedure at each forwarding node S. Node S uses the local information available to calculate as many hops as possible and forwards the packet to the last known hop directly instead of forwarding it to the next hop. The second improvement is based on the concept of dominating sets. Each node in the network is classified as internal or not, based on geographic position of its neighboring nodes. The network of internal nodes defines a connected dominating set, i.e., and each node must be either internal or directly connected to an internal node. In addition, internal nodes are connected. We apply several existing definitions of internal nodes, namely the concepts of intermediate, inter-gateway and gateway nodes. We propose to run GFG routing, enhanced by shortcut procedure, on the dominating set, except possibly the first and last hops. The performance of proposed algorithms is measured by comparing its average hop count with hop count of the basic GFG algorithm and the benchmark shortest path algorithm, and very significant improvements were obtained for low degree graphs. More precisely, we obtained localized routing algorithm that guarantees delivery and has very low excess in terms of hop count compared to the shortest path algorithm. The experimental data show that the length of additional path (in excess of shortest path length) can be reduced to about half of that of existing GFG algorithm.     

Performance of Fast Routing Algorithms in Large Optical Switches Built on the Vertical Stacking of Banyan Structures

Vertical stacking of multiple optical banyan networks is a novel scheme for building banyan-based nonblocking optical switches. The resulting network, namely vertically stacked optical banyan (VSOB) network, preserves the properties of small depth and absolutely loss uniformity but loses the nice self-routing capability of banyan networks. To guarantee a high switching speed, routing in VSOB network needs special attentions so that paths can be established as fast as possible. The best known global routing algorithm for an N×N nonblocking VSOB network has the time complexity of O(NlogN), which will introduce an unacceptable long delay in path establishment for a large size optical switch. In this paper, we propose two fast routing algorithms for the VSOB network based on the idea of inputs grouping. The two algorithms, namely plane fixed routing (PFR) algorithm and partially random routing (PRR) algorithm, have the time complexities of O(logN) and O( ) respectively, and FR algorithm can actually turn a VSOB network into a self-routing one. Extensive simulation based on a network simulator indicates that for large VSOB networks our new algorithms can achieve a reasonably low blocking probability while guarantee a very high switching speed.     

Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs

Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications.     

Learning Bayesian network classifiers using ant colony optimization

Bayesian networks are knowledge representation tools that model the (in)dependency relationships among variables for probabilistic reasoning. Classification with Bayesian networks aims to compute the class with the highest probability given a case. This special kind is referred to as Bayesian network classifiers. Since learning the Bayesian network structure from a dataset can be viewed as an optimization problem, heuristic search algorithms may be applied to build high-quality networks in medium- or large-scale problems, as exhaustive search is often feasible only for small problems. In this paper, we present our new algorithm, ABC-Miner, and propose several extensions to it. ABC-Miner uses ant colony optimization for learning the structure of Bayesian network classifiers. We report extended computational results comparing the performance of our algorithm with eight other classification algorithms, namely six variations of well-known Bayesian network classifiers, cAnt-Miner for discovering classification rules and a support vector machine algorithm.