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.

Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art

2020 ◽  
pp. 1580-1600
Author(s):  
Subhendu Kumar Pani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Sensor Devices ◽  
Huge Variety

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art

2016 ◽  
pp. 441-461
Author(s):  
Subhendu Kumar Pani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Sensor Devices ◽  
Huge Variety

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.

scholarly journals Proficient QoS-Based Target Coverage Problem in Wireless Sensor Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 74315-74325 ◽  
Author(s):  
Manju ◽  
Samayveer Singh ◽  
Sandeep Kumar ◽  
Anand Nayyar ◽  
Fadi Al-Turjman ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Coverage Problem ◽  
Target Coverage Problem

scholarly journals Simulated Annealing Application to Maximum Lifetime Coverage Problem in Wireless Sensor Networks

10.29007/gl61 ◽  
2018 ◽  
Author(s):  
Antonina Tretyakova ◽  
Franciszek Seredynski
Keyword(s):  
Wireless Sensor Networks ◽  
Simulated Annealing ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Maximization Problem ◽  
Main Task ◽  
Coverage Problem ◽  
Wsn Lifetime ◽  
Efficient Performance ◽  
Hard Problems

Energy optimization problem in Wireless Sensor Networks (WSN) is a backbone of efficient performance of sensor network consisting of small devices with limited and non-recovering battery. WSN lifetime maximization problem under assumption of that the coverage is main task of the network is known as Maximal lifetime coverage problem (MLCP).This problem belongs to a class of NP-hard problems. In this paper we propose a novel simulated annealing (SA) algorithm to solve MLCP. The proposed algorithm is studied for high dense WSN instances under different parameter setup.

scholarly journals A Novel Bi-Tuning SSO Algorithm for Optimizing the Budget-Limited Sensing Coverage Problem in Wireless Sensor Networks

2021 ◽  
Vol 11 (21) ◽  
pp. 10197
Author(s):  
Wenbo Zhu ◽  
Chia-Ling Huang ◽  
Wei-Chang Yeh ◽  
Yunzhi Jiang ◽  
Shi-Yi Tan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Smart Grids ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Swarm Optimization ◽  
Coverage Ratio ◽  
Sensing Coverage ◽  
Limited Sensing ◽  
Novel Concept

The wireless sensor network (WSN) plays an essential role in various practical smart applications, e.g., smart grids, smart factories, Internet of Things, and smart homes, etc. WSNs are comprised and embedded wireless smart sensors. With advanced developments in wireless sensor networks research, sensors have been rapidly used in various fields. In the meantime, the WSN performance depends on the coverage ratio of the sensors being used. However, the coverage of sensors generally relates to their cost, which usually has a limit. Hence, a new bi-tuning simplified swarm optimization (SSO) is proposed that is based on the SSO to solve such a budget-limited WSN sensing coverage problem to maximize the number of coverage areas to improve the performance of WSNs. The proposed bi-tuning SSO enhances SSO by integrating the novel concept to tune both the SSO parameters and SSO update mechanism simultaneously. The performance and applicability of the proposed bi-tuning SSO using seven different parameter settings are demonstrated through an experiment involving nine WSN tests ranging from 20, 100, to 300 sensors. The proposed bi-tuning SSO outperforms two state-of-the-art algorithms: genetic algorithm (GA) and particle swarm optimization (PSO), and can efficiently accomplish the goals of this work.

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