The Minimum Cost Sensor Placement Problem for Directional Wireless Sensor Networks

2008 ◽  
Author(s):  
Yahya Osais ◽  
Marc St-Hilaire ◽  
F. Richard Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Placement ◽  
Minimum Cost ◽  
Wireless Sensor ◽  
Placement Problem

Non Uniform Grid Based Cost Minimization and Routing in Wireless Sensor Networks

2012 ◽  
Vol 2 (1) ◽  
pp. 16-28
Author(s):  
Tata Jagannadha Swamy ◽  
Jayant Vaibhav Srivastava ◽  
Garimella Ramamurthy
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Location ◽  
Cost Minimization ◽  
Sensor Placement ◽  
Real Life ◽  
Minimum Cost ◽  
Wireless Sensor ◽  
Uniform Grid ◽  
Technological Advances

Recent technological advances have facilitated the widespread use of wireless sensor networks in many applications. In real life situations we have to cover or monitor a lot of points/places on plane. Sensor’s range is proportional to their cost, as high cost sensors have higher ranges. In this paper the authors developed a new algorithm for sensor placement for target location with cost minimization and coverage to non-uniform plane. Sensor placement for target location implies that they are given different type of sensors with different cost and range for given points on plane, which are to be covered with minimum cost. Then the authors discuss how information can be passed from one node to another.

An Intelligent Sensor Placement Method to Reach a High Coverage in Wireless Sensor Networks

2013 ◽  
pp. 168-183
Author(s):  
Shirin Khezri ◽  
Karim Faez ◽  
Amjad Osmani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Placement ◽  
New Method ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Grid Points ◽  
Adequate Coverage

Adequate coverage is one of the main problems for Sensor Networks. The effectiveness of distributed wireless sensor networks highly depends on the sensor deployment scheme. Optimizing the sensor deployment provides sufficient sensor coverage and saves cost of sensors for locating in grid points. This article applies the modified binary particle swarm optimization algorithm for solving the sensor placement in distributed sensor networks. PSO is an inherent continuous algorithm, and the discrete PSO is proposed to be adapted to discrete binary space. In the distributed sensor networks, the sensor placement is an NP-complete problem for arbitrary sensor fields. One of the most important issues in the research fields, the proposed algorithms will solve this problem by considering two factors: the complete coverage and the minimum costs. The proposed method on sensors surrounding is examined in different area. The results not only confirm the successes of using the new method in sensor placement, also they show that the new method is more efficiently compared to other methods like Simulated Annealing(SA), PBIL and LAEDA.

Exploring Coverage within Wireless Sensor Networks through Evolutionary Computations

2010 ◽  
pp. 191-200
Author(s):  
Sami Habib
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Search Space ◽  
Service Area ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Evolutionary Computations ◽  
Placement Problem ◽  
Sensor Devices ◽  
Computationally Intensive

The evolutionary search approach has demonstrated its effectiveness in many real world applications, such as the coverage problem in wireless sensor networks. It is to place sensor devices in a service area so that the entire service area is covered. We have modeled the coverage problem as two sub-problems: floorplan and placement. The floorplan problem is to partition the service area into well-defined geometric cells, where the placement problem is to assign the sensor devices into a set of cells. Even though the search space has been transformed from continuous into discrete, the complexity of the coverage problem is computationally intensive. The objective function is to maximize the coverage of the service area while not exceeding a given budget. The merged optimization problem has been coded into the genetic algorithm (GA) and the experimental results reveal the versatility of GA to adapt and find a good solution in a short time.

Sign in / Sign up

Export Citation Format

Share Document