Practical asynchronous neighbor discovery in ad hoc networks with directional antennas

Neighbor discovery is a crucial step in the initialization of wireless ad hoc networks. When directional antennas are used, this process becomes more challenging since two neighboring nodes must be in transmit and receive states, respectively, pointing their antennas to each other simultaneously. Most of the proposed neighbor discovery algorithms only consider the synchronous system and cannot work efficiently in the asynchronous environment. However, asynchronous neighbor discovery algorithms are more practical and offer many potential advantages. In this paper, we first analyze a one-way handshake-based asynchronous neighbor discovery algorithm by introducing a mathematical model named 'Problem of Coloring Balls.' Then, we extend it to a hybrid asynchronous algorithm that leads to a 24.4% decrease in the expected time of neighbor discovery. Compared with the synchronous algorithms, the asynchronous algorithms require approximately twice the time to complete the neighbor discovery process. Our proposed hybrid asynchronous algorithm performs better than both the two-way synchronous algorithm and the two-way asynchronous algorithm. We validate the practicality of our proposed asynchronous algorithms by OPNET simulations.

Adaptation of LEACH routing protocol to cognitive radio sensor networks

One of the drawbacks of LEACH protocol is the uncontrolled selection of cluster heads which, in some rounds, leads to the concentration of them in a limited area due to the randomness of the selection procedure. LEACH-C is a variant of LEACH that uses a centralized clustering algorithm and forms good clusters through sink control. According to experimental results, the IEEE 802.15.4 packets are damaged by WLAN interferences in ISM band. It seems that, sensor nodes equipped with cognitive radio capabilities can overcome this problem. In cognitive radio sensor networks (CRSN), routing must be accompanied by channel allocation. This requires spectrum management which can be devolved to cluster heads. For this networks, new duty cycle mechanisms must be designed that jointly consider neighbor discovery, and spectrum sensing/allocation. Cluster-based network architecture is a good choice for effective dynamic spectrum management. In such architecture, cluster heads have a proper spatial distribution and are optimally located all over the network. In this paper, using the physical layer information and preserving the feature of random cluster head selection in LEACH, it has been tried to both move the position of cluster heads to appropriate locations and make their quantity optimal. The simulation results show that the transferal of cluster heads to appropriate locations increases the network lifetime significantly though this comes at the price of early instability appearance. By considering the energy level in cluster head election algorithm, one can overcome the network stability issues too. However, this will move the cluster heads away from their appropriate locations. © 2012 IEEE.

New approaches to gene discovery with animal models of obesity and diabetes

DNA-based approaches to the discovery of genes contributing to the development of type 2 diabetes have not been very successful despite substantial investments of time and money. The multiple gene-gene and gene-environment interactions that influence the development of type 2 diabetes mean that DNA approaches are not the ideal tool for defining the etiology of this complex disease. Gene expression-based technologies may prove to be a more rewarding strategy to identify diabetes candidate genes. There are a number of RNA-based technologies available to identify genes that are differentially expressed in various tissues in type 2 diabetes. These include differential display polymerase chain reaction (ddPCR), suppression subtractive hybridization (SSH), and cDNA microarrays. The power of new technologies to detect differential gene expression is ideally suited to studies utilizing appropriate animal models of human disease. We have shown that the gene expression approach, in combination with an excellent animal model such as the Israeli sand rat (Psammomys obesus), can provide novel genes and pathways that may be important in the disease process and provide novel therapeutic approaches. This paper will describe a new gene discovery, beacon, a novel gene linked with energy intake. As the functional characterization of novel genes discovered in our laboratory using this approach continues, it is anticipated that we will soon be able to compile a definitive list of genes that are important in the development of obesity and type 2 diabetes.

Museum and performing arts marketing: the age of discovery

Trends in museum and performing arts marketing from 1975 to 1994 were analyzed and suggested that a third period was emerging; the data in this article confirm that claim. Among the latest arts marketing articles, there is a significantly greater focus on marketing strategy than on the other two categories--marketing as culture and marketing as tactics.

Obesity and diabetes gene discovery approaches

New treatments are currently required for the common metabolic diseases obesity and type 2 diabetes. The identification of physiological and  biochemical factors that underlie the metabolic disturbances observed in obesity and type 2 diabetes is a key step in developing better therapeutic outcomes. The discovery of new genes and pathways involved in the  pathogenesis of these diseases is critical to this process, however  identification of genes that contribute to the risk of developing these diseases represents a significant challenge as obesity and type 2 diabetes are complex diseases with many genetic and environmental causes. A number of diverse approaches have been used to discover and validate potential new targets for obesity and diabetes. To date, DNA-based approaches using candidate gene and genome-wide linkage analysis have had limited success in identifying genomic regions or genes involved in the development of these diseases. Recent advances in the ability to evaluate linkage analysis data from large family pedigrees using variance components based linkage analysis show great promise in robustly identifying genomic regions associated with the development of obesity and diabetes. RNA-based technologies such as cDNA microarrays have identified many genes differentially expressed in tissues of healthy and diseased subjects. Using a combined approach, we are endeavouring to focus attention on differentially expressed genes located in chromosomal regions previously linked with obesity and / or diabetes. Using this strategy, we have identified Beacon as a potential new target for obesity and diabetes.

Accelerating linear causal model discovery using hoeffding bounds

Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithms proposed by Dai et al. has demonstrated the ability of the Minimum Message Length (MML) principle in discovering Linear Causal Models from training data. In order to further explore ways to improve efficiency, this paper incorporates the Hoeffding Bounds into the learning process. At each step of causal discovery, if a small number of data items is enough to distinguish the better model from the rest, the computation cost will be reduced by ignoring the other data items. Experiments with data set from related benchmark models indicate that the new algorithm achieves speedup over previous work in terms of learning efficiency while preserving the discovery accuracy.

Ensembling MML causal discovery

This paper presents an ensemble MML approach for the discovery of causal models. The component learners are formed based on the MML causal induction methods. Six different ensemble causal induction algorithms are proposed. Our experiential results reveal that (1) the ensemble MML causal induction approach has achieved an improved result compared with any single learner in terms of learning accuracy and correctness; (2) Among all the ensemble causal induction algorithms examined, the weighted voting without seeding algorithm outperforms all the rest; (3) It seems that the ensembled CI algorithms could alleviate the local minimum problem. The only drawback of this method is that the time complexity is increased by δ times, where δ is the ensemble size.

Enhancing UDDI for grid service discovery by using dynamic parameters

A major problem for a grid user is the discovery of currently available services. With large number of services, it is beneficial for a user to be able to discover the services that most closely match their requirements. This report shows how to extend some concepts of UDDI such that they are suitable for dynamic parameter based discovery of grid services.

Toward self discovery for an autonomic cluster

Nondedicated clusters are currently at the forefront of the development of high performance computing systems. These clusters are relatively intolerant of hardware failures and cannot manage dynamic cluster membership efficiently. This report presents the logical design of an innovative self discovery service that provides for automated cluster management and resource discovery. The proposed service has an ability to share or recover unused computing resources, and to adapt to transient conditions autonomically, as well as the capability of providing dynamically scalable virtual computers on demand.

Further pruning for efficient association rule discovery

The Apriori algorithm’s frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence avoids these problems. It can reduce search time by using additional constraints on the search space as well as constraints on itemset frequency. However, the effectiveness of the pruning rules used during search will determine the efficiency of its search. This paper presents and analyses pruning rules for use with OPUS AR. We demonstrate that application of OPUS AR is feasible for a number of datasets for which application of the frequent itemset approach is infeasible and that the new pruning rules can reduce compute time by more than 40%.

Pattern discovery in probabilistic databases

Modeling probabilistic data is one of important issues in databases due to the fact that data is often uncertainty in real-world applications. So, it is necessary to identify potentially useful patterns in probabilistic databases. Because probabilistic data in 1NF relations is redundant, previous mining techniques don’t work well on probabilistic databases. For this reason, this paper proposes a new model for mining probabilistic databases. A partition is thus developed for preprocessing probabilistic data in a probabilistic databases. We evaluated the proposed technique, and the experimental results demonstrate that our approach is effective and efficient.

Linear causal model discovery using the MML criterion

Determining the causal structure of a domain is a key task in the area of Data Mining and Knowledge Discovery.The algorithm proposed by Wallace et al. [15] has demonstrated its strong ability in discovering Linear Causal Models from given data sets. However, some experiments showed that this algorithm experienced difficulty in discovering linear relations with small deviation, and it occasionally gives a negative message length, which should not be allowed. In this paper, a more efficient and precise MML encoding scheme is proposed to describe the model structure and the nodes in a Linear Causal Model. The estimation of different parameters is also derived. Empirical results show that the new algorithm outperformed the previous MML-based algorithm in terms of both speed and precision.