AIRCC's International Journal of Computer Science and Information Technology

Natarajan Meghanathan ¹

Affiliations
1 Department of Computer Science, Jackson State University, 1400 John Lynch St, Jackson, MS 39217, US

Abstract

In a recent work, we published the Location Prediction Based Routing (LPBR) protocol for mobile ad hoc networks (MANET). LPBR is the first such beaconless MANET protocol to simultaneously minimize the hop count of the paths as well as the routing control overhead measured in terms of the number of control messages received during an on-demand broadcast discovery. LPBR works as follows: If the minimum hop route discovered through a regular broadcast route discovery fails, instead of the source immediately launching another broadcast route discovery, the destination attempts to locally predict the global network topology based on the location and mobility information of the nodes learnt during the most recent broadcast route discovery. If the predicted path does exist in reality, the source learns the path from the destination and continues to send data packets without launching a new broadcast route discovery. The performance of LPBR has been so far studied mainly with the topology-based routing protocols that initiate on-demand route discoveries. In this paper, we compare the performance of LPBR with position-based routing protocols in which the forwarding decisions are taken independently for each data packet at every forwarding node, depending on the estimated location of the destination. Through extensive simulations, we illustrate that LPBR performs significantly better compared to the well-known position-based routing protocols and their variants with respect to several performance metrics under diverse conditions of node mobility, network density and offered traffic load.

Keywords

Location Prediction, Position-Based Routing, Simulation, Performance, Mobile Ad Hoc Networks.

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Natarajan Meghanathan ¹, Nataliya Kostyuk ², Raphael Isokpehi ², Hari Cohly ²

Affiliations
1 Department of Computer Science, Jackson State University, 1400 John Lynch St, Jackson, MS 39217, US
2 Department of Biology, Jackson State University, 1400 John Lynch St, Jackson, MS 39217, US

Abstract

The high-level contribution of this paper is the development and implementation of an algorithm to selfextract secondary keywords and their combinations (combo words) based on abstracts collected using standard primary keywords for research areas from reputed online digital libraries like IEEE Explore, PubMed Central and etc. Given a collection of N abstracts, we arbitrarily select M abstracts (M<< N; M/N as low as 0.15) and parse each of the M abstracts, word by word. Upon the first-time appearance of a word, we query the user for classifying the word into an Accept-List or non-Accept-List. The effectiveness of the training approach is evaluated by measuring the percentage of words for which the user is queried for classification when the algorithm parses through the words of each of the M abstracts. We observed that as M grows larger, the percentage of words for which the user is queried for classification reduces drastically. After the list of acceptable words is built by parsing the M abstracts, we now parse all the N abstracts, word by word, and count the frequency of appearance of each of the words in Accept-List in these N abstracts. We also construct a Combo-Accept-List comprising of all possible combinations of the single keywords in Accept-List and parse all the N abstracts, two successive words (combo word) at a time, and count the frequency of appearance of each of the combo words in the Combo-Accept-List in these N abstracts.

Keywords

Self-Extraction, Abstracts, Secondary Keywords, Combo Keywords, Frequency, Training.

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Natarajan Meghanathan ¹

Affiliations
1 Department of Electrical & Computer Engineering and Computer Science, Jackson State University, Jackson, MS, US

Abstract

Some students in the Computer Science and related majors excel very well in programming-related assignments, but not equally well in the theoretical assignments (that are not programming-based) and vice-versa. We refer to this as the "Theory vs. Programming Disparity (TPD)". In this paper, we propose a spectral bipartivity analysis-based approach to quantify the TPD metric for any student in a course based on the percentage scores (considered as decimal values in the range of 0 to 1) of the student in the course assignments (that involves both theoretical and programming-based assignments). We also propose a principal component analysis (PCA)-based approach to quantify the TPD metric for the entire class based on the percentage scores (in a scale of 0 to 100) of the students in the theoretical and programming assignments. The spectral analysis approach partitions the set of theoretical and programming assignments to two disjoint sets whose constituents are closer to each other within each set and relatively more different from each across the two sets. The TPD metric for a student is computed on the basis of the Euclidean distance between the tuples representing the actual numbers of theoretical and programming assignments vis-a-vis the number of theoretical and programming assignments in each of the two disjoint sets. The PCA-based analysis identifies the dominating principal components within the sets of theoretical and programming assignments and computes the TPD metric for the entire class as a weighted average of the correlation coefficients between the dominating principal components representing these two sets.

Keywords

Spectral Analysis, Principal Component Analysis, Correlation Coefficient, Theory vs. Programming Disparity, Eigenvector, Bipartivity.

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