A broad variety of location dependent services will become feasible in the near future due to the use of the Global Position System (GPS), which provides location information (latitude, longitude and possibly height) and global timing to mobile users. Routing is a problem of sending a message from a source to a destination. Geocasting is the problem of sending a message to all nodes located within a region (e.g. circle or square). Recently, several localized GPS based routing and geocasting protocols for a mobile ad hoc network were reported in literature. In directional (DIR) routing and geocasting methods, node A (the source or intermediate node) transmits a message m to all neighbors located between the two tangents from A to the region that could contain the destination. It was shown that memoryless directional methods may create loops in routing process.In two other proposed methods (proven to be loop-free), geographic distance (GEDIR) or most forward progress within radius (MFR) routing, node A forwards the message to its neighbor who is closest to destination, or has greatest progress toward destination (respectively). In this paper, we propose a general algorithm (based on an unified framework for both routing and geocasting problems), in which message is forwarded to exactly those neighbors which may be best choices for a possible position of destination (using the appropriate criterion). We then propose and discuss new V-GEDIR and CH-MFR methods and define R-DIR, modified version of existing directional methods. In V-GEDIR method, these neighbors are determined by intersecting the Voronoi diagram of neighbors with the circle (or rectangle) of possible positions of destination, while the portion of the convex hull of neighboring nodes is analogously used in the CH-MFR method. Routing and geocasting algorithms differ only inside the circle/rectangle. We propose memoryless and past traffic memorization variants of each scheme. The proposed methods may be also used for the destination search phase allowing the application of different routing schemes after the exact position of destination is discovered. Memoryless V-GEDIR and CH-MFR algorithms are loop free, and have smaller flooding rate (with similar success rate) compared to directional method. Simulations, involving the proposed and some known algorithms, are in progress and confirm our expectations.
In this paper, we propose a general algorithm (based on an unified framework for both routing and geocasting problems), in which message is forwarded to exactly those neighbors which may be best choices for a possible position of destination (using the appropriate criterion). We then propose and discuss new VD‐GREEDY and CH‐MFR methods and define R‐DIR, modified version of existing directional methods. In VD‐GREEDY method, these neighbors are determined by intersecting the Voronoi diagram of neighbors with the circle (or rectangle) of possible positions of destination, while the portion of the convex hull of neighboring nodes is analogously used in the CH‐MFR method. Routing and geocasting algorithms differ only inside the circle/rectangle. The proposed methods may be also used for the destination search phase allowing the application of different routing schemes after the exact position of destination is discovered. VD‐GREEDY and CH‐MFR algorithms are loop free, and have smaller flooding rate (with similar success rate) compared to directional method. We proposed to use dominating set concept to reduce flooding ratio significantly, with a marginal impact on success rate and hop count. Simulations, involving the proposed and some known algorithms, are performed for two basic scenarios, one for geocasting and reactive routing, and the other for proactive routing, and both showed that our methods have higher success rate and lower flooding rate compared to existing methods. Copyright © 2006 John Wiley & Sons, Ltd.
The Attappady Black goat is a native goat breed of Kerala in India and is mainly known for its valuable meat and skin. In this work, a comparative study of connectionist network [also known as artificial neural network (ANN)] and multiple regression is made to predict the body weight from body measurements in Attappady Black goats. A multilayer feed forward network with backpropagation of error learning mechanism was used to predict the body weight. Data collected from 824 Attappady Black goats in the age group of 0-12 months consisting of 370 males and 454 females were used for the study. The whole data set was partitioned into two data sets, namely training data set comprising of 75 per cent data (277 and 340 records in males and females, respectively) to build the neural network model and test data set comprising of 25 per cent (93 and 114 records in males and females, respectively) to test the model. Three different morphometric measurements viz. chest girth, body length and height at withers were used as input variables, and body weight was considered as output variable. Multiple regression analysis (MRA) was also done using the same training and testing data sets. The prediction efficiency of both models was compared using the R 2 value and root mean square error (RMSE). The correlation coefficients between the actual and predicted body weights in case of ANN were found to be positive and highly significant and ranged from 90.27 to 93.69%. The low value of RMSE and high value of R 2 in case of connectionist network (RMSE: male-1.9005, female-1.8434; R 2 : male-87.34, female-85.70) in comparison with MRA model (RMSE: male-2.0798, female-2.0836; R 2 : male-84.84, female-81.74) show that connectionist network model is a better tool to predict body weight in goats than MRA.
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