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Hadia, Sarman K.
- PSO Algorithm Based Optimization of Network Coverage in Wireless Sensor Network
Abstract Views :83 |
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Authors
Affiliations
1 V.T Patel Department of Electronics & Communication, Charotar University of Science and Technology, Changa, Gujarat, IN
1 V.T Patel Department of Electronics & Communication, Charotar University of Science and Technology, Changa, Gujarat, IN
Source
Wireless Communication, Vol 4, No 15 (2012), Pagination: 884-887Abstract
Wireless sensor network has the self-monitoring functionality which is also an intelligent network system. It consists of sensor nodes which are low-cost, low-power and small size. They communicate with each other to perform sensing and data processing. Network coverage has an important role in the system’s lifetime. In this paper particle swarm optimization algorithm was used to optimize the network coverage. MATLAB was used as a tool to apply the algorithm. The probability sensing model is used to optimize area coverage of wireless sensor network in this paper. Coverage problem also arises because of the randomly deployed sensors. The sensors need to be placed in a position such that the sensing capability of the network is fully utilized to get maximum coverage. This paper also shows that optimal positioned sensor nodes gives good result compared to randomly deployed sensor nodes.Keywords
Wireless Sensor Network (WSN), Bird Flocking, Particle Swarm Optimization (PSO), Sensing Model, Area Coverage.
- Comparative Study of PSO and ABC Algorithms for Finding Base-Station Locations in Two-Tiered Wireless Sensor Networks
Abstract Views :121 |
PDF Views:5
In this work both PSO and ABC algorithms are applied to find nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results show the performance comparison of the proposed PSO & ABC approaches. The proposed algorithms can thus help finding good BS locations to reduce power consumption and maximize network lifetime in two-tiered wireless sensor networks.
Authors
Affiliations
1 V.T. Patel Department of Electronics & Communication Engineering, C.S. Patel Institute of Technology, Changa, IN
2 Marwadi College of Engineering & Technology, Rajkot, IN
1 V.T. Patel Department of Electronics & Communication Engineering, C.S. Patel Institute of Technology, Changa, IN
2 Marwadi College of Engineering & Technology, Rajkot, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 8 (2012), Pagination: 466-470Abstract
Recently, several modern heuristic algorithms have been developed for solving combinatorial and optimization problems. These algorithms can be classified into different groups depending on the criteria being considered, such as population based, iterative based, stochastic, deterministic, etc. Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Particle swarm optimization (PSO) is a popular multidimensional optimization technique which models social behavior of a flock of birds & Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm.In this work both PSO and ABC algorithms are applied to find nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results show the performance comparison of the proposed PSO & ABC approaches. The proposed algorithms can thus help finding good BS locations to reduce power consumption and maximize network lifetime in two-tiered wireless sensor networks.