scholarly journals An area coverage algorithm for wireless sensor networks based on differential evolution

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879673 ◽  
Author(s):  
Ning-ning Qin ◽  
Jia-le Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Differential Evolution ◽  
High Energy ◽  
Area Coverage ◽  
Wireless Sensor ◽  
Compensation Strategy ◽  
Coverage Ratio ◽  
Area Of Interest ◽  
Binary Differential Evolution

Lifetime requirements and coverage demands are emphasized in wireless sensor networks. An area coverage algorithm based on differential evolution is developed in this study to obtain a given coverage ratio [Formula: see text]. The proposed algorithm maximizes the lifetime of wireless sensor networks to monitor the area of interest. To this end, we translate continuous area coverage into classical discrete point coverage, so that the optimization process can be realized by wireless sensor networks. Based on maintaining the ε-coverage performance, area coverage algorithm based on differential evolution takes the minimal energy as optimization objective. In area coverage algorithm based on differential evolution, binary differential evolution is redeveloped to search for an improved node subset and thus meet the coverage demand. Taking into account that the results of binary differential evolution are depended on the initial value, the resulting individual is not an absolutely perfect node subset. A compensation strategy is provided to avoid unbalanced energy consumption for the obtained node subset by introducing the positive and negative utility ratios. Under the helps of those ratios and compensation strategy, the resulting node subset can be added additional nodes to remedy insufficient coverage, and redundancy active nodes can be pushed into sleep state. Furthermore, balance and residual energy are considered in area coverage algorithm based on differential evolution, which can expand the scope of population exploration and accelerate convergence. Experimental results show that area coverage algorithm based on differential evolution possesses high energy and computation efficiencies and provides 90% network coverage.

Intelligent Water Drop Based Coverage-Connectivity and Lifespan Maximization Protocol for Wireless Sensor Networks

2019 ◽  
Vol 13 (3) ◽  
pp. 261-273
Author(s):  
R. Sharma ◽  
D.K. Lobiyal
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Water Drop ◽  
Relay Node ◽  
Residual Energy ◽  
Base Station ◽  
Energy Utilization ◽  
Wireless Sensor ◽  
Coverage Ratio ◽  
Area Of Interest

Background: A significant issue of consideration in wireless sensor networks is to reduce the energy utilization while preserving the required coverage and connectivity of an area of interest. We have revised all patents relating to preserving of energy in sensor motes of the wireless sensor networks. Methods: We proposed a novel; Intelligent Water Drop based coverage-connectivity and lifespan protocol which minimizes energy consumption of the network. In this routing protocol, sensors are partitioned into the connected first layer and connected successive layer sets and a scheduling mechanism has been used to activate and deactivate sensors. Multi-hoping is used to transmit packets from sensors to the Base Station and sensor with maximum residual energy has been selected as the next hop. Power wastage has been avoided by removing duplicate information through a common relay node. Results: We have derived the expected number of sensors required to cover an area of interest and our protocol gives a long life to the network. A theorem has been provided to validate the results for different communication ranges of sensors. Conclusion: The protocol has been compared with other protocols and it proved better than other protocols in terms of the lifespan and the coverage ratio of the area. Results approve that our protocol reduces the problem of energy holes and maintains the connectivity of the network.

Deterministic Deployment for Wireless Image Sensor Nodes

2014 ◽  
Vol 8 (1) ◽  
pp. 668-674
Author(s):  
Junguo Zhang ◽  
Yutong Lei ◽  
Fantao Lin ◽  
Chen Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Visual Information ◽  
Image Sensor ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Coverage Area ◽  
Effective Coverage ◽  
Area Of Interest ◽  
Key Issues

Wireless sensor networks composed of camera enabled source nodes can provide visual information of an area of interest, potentially enriching monitoring applications. The node deployment is one of the key issues in the application of wireless sensor networks. In this paper, we take the effective coverage and connectivity as the evaluation indices to analyze the effect of the perceivable angle and the ratio of communication radius and sensing radius for the deterministic circular deployment. Experimental results demonstrate that the effective coverage area of the triangle deployment is the largest when using the same number of nodes. When the nodes are deployed in the same monitoring area in the premise of ensuring connectivity, rhombus deployment is optimal when √2 < rc / rs < √3 . The research results of this paper provide an important reference for the deployment of the image sensor networks with the given parameters.

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.

scholarly journals Overlay Virtualized Wireless Sensor Networks for Application in Industrial Internet of Things: A Review

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3215 ◽  
Author(s):  
Malvin Nkomo ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz ◽  
Saurabh Sinha ◽  
Adeiza. J. Onumanyi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Smart City ◽  
Overlay Networks ◽  
Network Virtualization ◽  
Wireless Sensor ◽  
Industrial Internet Of Things ◽  
Area Of Interest ◽  
Industrial Internet

In recent times, Wireless Sensor Networks (WSNs) are broadly applied in the Industrial Internet of Things (IIoT) in order to enhance the productivity and efficiency of existing and prospective manufacturing industries. In particular, an area of interest that concerns the use of WSNs in IIoT is the concept of sensor network virtualization and overlay networks. Both network virtualization and overlay networks are considered contemporary because they provide the capacity to create services and applications at the edge of existing virtual networks without changing the underlying infrastructure. This capability makes both network virtualization and overlay network services highly beneficial, particularly for the dynamic needs of IIoT based applications such as in smart industry applications, smart city, and smart home applications. Consequently, the study of both WSN virtualization and overlay networks has become highly patronized in the literature, leading to the growth and maturity of the research area. In line with this growth, this paper provides a review of the development made thus far concerning virtualized sensor networks, with emphasis on the application of overlay networks in IIoT. Principally, the process of virtualization in WSN is discussed along with its importance in IIoT applications. Different challenges in WSN are also presented along with possible solutions given by the use of virtualized WSNs. Further details are also presented concerning the use of overlay networks as the next step to supporting virtualization in shared sensor networks. Our discussion closes with an exposition of the existing challenges in the use of virtualized WSN for IIoT applications. In general, because overlay networks will be contributory to the future development and advancement of smart industrial and smart city applications, this review may be considered by researchers as a reference point for those particularly interested in the study of this growing field.

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.

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