- NS2: This simulator doesn't provide WSNs' simulations, in its original form. Different patches although are available for simulating WSNs, e.g., NS2-MIUN, NRL, Mannasim.
- OMNet++: This simulator is comparatively easy to learn. Moreover, it provides drag-n-drop based GUI for designing network. For simulating WSNs, it has components Castalia, MF, MiXiM.
Monday, February 22, 2010
Wireless Sensor Networks Simulation Environments
I have been searching for simulators appropriate for Wireless Sensor Networks, and found following simulators reasonable enough for simulating WSNs:
Thursday, October 22, 2009
Reseach Paper in IEEE ICET Conference
I got my 1st research paper accepted in IEEE ICET 5th conference and also included in the proceedings of the conference. The conference is held on 19-20 October, 2009 in Pakistan.
Abstract
Recent studies have shown that Evolutionary Algorithms have had reasonable success at providing solutions to those problems that fall in NP-Complete class of algorithms. Ant Colony Optimization (ACO) algorithm is one of the promising field of evolutionary algorithms that gave acceptable solutions to Travelling Salesperson Problem and various Network Routing Optimization problems in polynomial time. These classic computer science problems belong to a NP-Complete class of problems that is amongst some of the most interesting in mathematics, including the Sudoku Puzzle Problem. People have tried to automate solving Sudoku Puzzle Problem using brute force, tabu search. Given the success of ACO algorithm with problems within NP-Complete class of problems, it would be interesting to see how it handles this puzzle. A novel technique is presented as modification to standard ACO algorithm. Moreover, we will compare performance matrix (quality of solution and time complexity) of ACO algorithm with other techniques presented in the past to solve the Sudoku puzzle.
Abstract
Recent studies have shown that Evolutionary Algorithms have had reasonable success at providing solutions to those problems that fall in NP-Complete class of algorithms. Ant Colony Optimization (ACO) algorithm is one of the promising field of evolutionary algorithms that gave acceptable solutions to Travelling Salesperson Problem and various Network Routing Optimization problems in polynomial time. These classic computer science problems belong to a NP-Complete class of problems that is amongst some of the most interesting in mathematics, including the Sudoku Puzzle Problem. People have tried to automate solving Sudoku Puzzle Problem using brute force, tabu search. Given the success of ACO algorithm with problems within NP-Complete class of problems, it would be interesting to see how it handles this puzzle. A novel technique is presented as modification to standard ACO algorithm. Moreover, we will compare performance matrix (quality of solution and time complexity) of ACO algorithm with other techniques presented in the past to solve the Sudoku puzzle.
Sunday, April 12, 2009
Artificial Intelligence
I have implemented some algorithms of Artificial Intelligence like:
- Unification
- Depth-First Search
- Breadth-First Search
- Limited Depth-First Search
- Heuristic Search
- Learning (specialized) using Semantic Nets
- Production System
- Hebb's Rule-AND Function
- Perceptron-AND Function
- Adaline-AND Function
- Hebb Rule for Pattern Association
- Hetero-Associative Neural Networks
- Auto-Associative Neural Networks
- Discrete Hopfield Networks
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