Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms

2011 ◽  
Vol 7 (2) ◽  
pp. 63-83 ◽  
Author(s):  
Arpit Tripathi ◽  
Pulkit Gupta ◽  
Aditya Trivedi ◽  
Rahul Kala
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Genetic Programming ◽  
Ease Of Use ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Optimal Placement ◽  
Coverage Area ◽  
Node Placement

The ease of use and re-configuration in a wireless network has played a key role in their widespread growth. The node deployment problem deals with an optimal placement strategy of the wireless nodes. This paper models a wireless sensor network, consisting of a number of nodes, and a unique sink to which all the information is transmitted using the shortest connecting path. Traditionally the systems have used Genetic Algorithms for optimal placement of the nodes that usually fail to give results in problems employing large numbers of nodes or higher areas to be covered. This paper proposes a hybrid Genetic Programming (GP) and Genetic Algorithm (GA) for solving the problem. While the GP optimizes the deployment structure, the GA is used for actual node placement as per the GP optimized structure. The GA serves as a slave and GP serves as master in this hierarchical implementation. The algorithm optimizes total coverage area, energy utilization, lifetime of the network, and the number of nodes deployed. Experimental results show that the algorithm could place the sensor nodes in a variety of scenarios. The placement was found to be better than random placement strategy as well as the Genetic Algorithm placement strategy.

Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms

2012 ◽  
pp. 125-144
Author(s):  
Arpit Tripathi ◽  
Pulkit Gupta ◽  
Aditya Trivedi ◽  
Rahul Kala
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Genetic Programming ◽  
Ease Of Use ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Optimal Placement ◽  
Coverage Area ◽  
Node Placement

The ease of use and re-configuration in a wireless network has played a key role in their widespread growth. The node deployment problem deals with an optimal placement strategy of the wireless nodes. This paper models a wireless sensor network, consisting of a number of nodes, and a unique sink to which all the information is transmitted using the shortest connecting path. Traditionally the systems have used Genetic Algorithms for optimal placement of the nodes that usually fail to give results in problems employing large numbers of nodes or higher areas to be covered. This paper proposes a hybrid Genetic Programming (GP) and Genetic Algorithm (GA) for solving the problem. While the GP optimizes the deployment structure, the GA is used for actual node placement as per the GP optimized structure. The GA serves as a slave and GP serves as master in this hierarchical implementation. The algorithm optimizes total coverage area, energy utilization, lifetime of the network, and the number of nodes deployed. Experimental results show that the algorithm could place the sensor nodes in a variety of scenarios. The placement was found to be better than random placement strategy as well as the Genetic Algorithm placement strategy.

scholarly journals FLOWER POLLINATION AND GENETIC ALGORITHM BASED OPTIMIZATION FOR NODE DEPLOYMENT IN WIRELESS SENSOR NETWORKS

2020 ◽  
Vol 5 (2) ◽  
pp. 281-293
Author(s):  
Kapil Keswani ◽  
Dr. Anand Bhaskar
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Environment ◽  
Life Time ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Field Energy ◽  
Main Concern ◽  
Node Placement ◽  
Flower Pollination

Wireless sensor network (WSN) most popular area of research where lots of work done in this field. Energy efficiency is one of the most focusing areas because life time of network is most common issue. In the WSN, the node placement is very essential part for the proper communication between the sensor nodes and base station (BS). For better communication nodes should be aware about their own or neighbor node’s location. Better optimization of resources and performance improvement are the main concern for the WSN. Optimal techniques should be utilized to place the nodes at the best possible locations for achieving the desired goal. For node placement, flower pollination optimization and genetic algorithm are useful to generate better result. BS is responsible for the communication of nodes with each other and it should be reachable to nodes. For this Region of Interest (RoI) is helpful to choose the best location. Placement of BS in the middle is suitable place for the static nodes deployment and there should be other strategy for the dynamic environment. Nodes should be connected to each other for the transmission of data from the source to BS properly. From the MATLAB simulation, it has been shown that the proposed methodology improves the network performance in terms of dead nodes, energy remaining and various packets sent to BS.

Node Placement Strategy in Wireless Sensor Network

2013 ◽  
Vol 5 (2) ◽  
pp. 18-31 ◽  
Author(s):  
Puteri Azwa Ahmad ◽  
M. Mahmuddin ◽  
Mohd Hasbullah Omar
Keyword(s):  
Sensor Networks ◽  
Region Of Interest ◽  
Target Position ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Coverage Area ◽  
Node Placement ◽  
Random Deployment ◽  
Virtual Force ◽  
Coverage And Connectivity

The performance and quality of services in wireless sensor networks (WSNs) depend on coverage and connectivity. Node placement is a fundamental issue closely related to the coverage and connectivity in sensor networks. Node placement influences the target position, coverage area, and connectivity in sensor networks. In random deployment, sensor nodes are deployed randomly in a non-invasive way. The deployment process may cause issues like coverage holes, overlapping, and connectivity failure. Enhancing coverage and connectivity are important for sensor networks to provide a reliable communication within sensing. Placing many sensor nodes in a WSN application region area is not the best solution due to cost and it results in multiple sensors used. Mobile sensor node is used as an alternative to overcome the random deployment problem. The virtual force based self node deployment is used in the mobility sensor to improve the coverage and connectivity area. Virtual Force Algorithm (VFA) approach using virtual repulsive and attractive forces is used to find the optimal node placement to minimize the problems. Simulation results proofed that a uniform deployment achieved using VFA approach with an optimal sensing range to cover the region of interest.

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 Intelligent On-Demand Connectivity Restoration for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Khalid Mahmood ◽  
Muhammad Amir Khan ◽  
Mahmood ul Hassan ◽  
Ansar Munir Shah ◽  
Shahzad Ali ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Connectivity ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
On Demand ◽  
Connectivity Restoration ◽  
Restoration Technique ◽  
Near Future

Wireless sensor networks are envisioned to play a very important role in the Internet of Things in near future and therefore the challenges associated with wireless sensor networks have attracted researchers from all around the globe. A common issue which is well studied is how to restore network connectivity in case of failure of single or multiple nodes. Energy being a scarce resource in sensor networks drives all the proposed solutions to connectivity restoration to be energy efficient. In this paper we introduce an intelligent on-demand connectivity restoration technique for wireless sensor networks to address the connectivity restoration problem, where nodes utilize their transmission range to ensure the connectivity and the replacement of failed nodes with their redundant nodes. The proposed technique helps us to keep track of system topology and can respond to node failures effectively. Thus our system can better handle the issue of node failure by introducing less overhead on sensor node, more efficient energy utilization, better coverage, and connectivity without moving the sensor nodes.

scholarly journals Genetic Algorithms for Localization in WSN

2020 ◽  
Vol 9 (5) ◽  
pp. 2397-2401
Keyword(s):  
Wireless Sensor Networks ◽  
Genetic Algorithms ◽  
Bat Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Error ◽  
Node Localization ◽  
Soft Computing Techniques ◽  
Anchor Nodes ◽  
The Mean

In wireless sensor networks, localization is a way to track the exact location of sensor nodes. Occasionally node localization may not be accurate due to the absence or limitation of anchor nodes. To reduce the mean localization error, soft computing techniques such as BAT and bacterial foraging driven bat algorithm (BDBA) are utilized in literature. For better localization with reduced error, in this paper, firefly driven bat algorithm (FDBA) is proposed, which combines the heuristic of firefly and BAT algorithms. Our proposed FDBA algorithm provides better localization in terms of error of 60% and 40 % less error as compared to BAT and BDBA algorithm, respectively.

scholarly journals An Integrated Routing Mechanism on Homogenous Wireless Sensor Networks using Genetic Algorithm

2021 ◽  
Author(s):  
Ramin Danehchin
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Hop Count ◽  
Routing Problem ◽  
Cluster Heads

Abstract Data collection on Wireless Sensor Networks (WSNs) is a significant challenge to satisfy the requirements of various applications. Providing an energy-efficient routing technique is the primary step in data collection over WSNs. The existing data collection techniques in the WSNs field struggle with the imbalance load distribution and the short lifetime of the network. This paper proposes a novel mechanism to select cluster-heads, cluster the wireless sensor nodes, and determine the optimal route from source nodes to the sink. We employ the genetic algorithm to solve the routing problem considering the hop-count of the cluster-heads to the sink, the number of each cluster member, residual energy of cluster-heads, and the number of cluster-heads connected to the sink as the fitness criteria. Our proposed mechanism uses a greedy approach to calculate the hop-count of each cluster-head to the sink for integrating the clustering and routing process on WSNs. The simulation results demonstrate that our proposed mechanism improves the energy consumption, the number of live nodes, and the lifetime of the network compared to other data collection approaches on WSNs.

scholarly journals An Exploration of Current Data Agglomeration Technique in Wireless Sensor Network

2020 ◽  
Vol 9 (5) ◽  
pp. 732-746
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Base Station ◽  
Current Data ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Communication Process ◽  
Wireless Sensor ◽  
Intermediate Node ◽  
Process Energy

Wireless sensor network plays prominently in various applications of the emerging advanced wireless technology such as smart homes, Commercial, defence sector and modern agriculture for effective communication. There are many issues and challenges involved during the communication process. Energy conservation is the major challenging matter and fascinates issue among the researchers. The reason for that, Wireless sensor network has ‘n’ number of sensor nodes to identify and recognize the data and send that data to the base station or sink through either directly or intermediate node. These nodes with poor energy create intricacy on the data rate or flow and substantially affect the lifespan of a wireless sensor network. To decrease energy utilization the sensor node has to neglect unnecessary received data from the neighbouring nodes prior to send the optimum data to the sink or another device. When a specific target is held in a particular sector, it can be identified by many sensors. To rectify such process this paper present Data agglomeration technique is one of the persuasive techniques in the neglecting unnecessary data and of improves energy efficiency and also it increases the lifetime of WSNs. The efficacious data aggregation paradigm can also decrease traffic in the network. This paper discussed various data agglomeration technique for efficient energy in WSN.

Multi-Source Hybrid Energy Harvesting Wireless Sensor Network Framework for Addressing Energy Unpredictability Issues

2021 ◽  
Vol 57 ◽  
pp. 225-241
Author(s):  
Aditi Paul ◽  
Indu Pandey
Keyword(s):  
Wireless Sensor Network ◽  
Energy Harvesting ◽  
Sensor Network ◽  
Complex Structure ◽  
Research Direction ◽  
Ease Of Use ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Functional Efficiency ◽  
Design Paradigm

Energy harvesting wireless sensor network (EH-WSN) harvests energy from the environment to supply power to the sensor nodes which apparently enhances their lifetime. However, the unpredictable nature of the resources throws challenges to the sustainability of energy supply for the continuous network operation. This creates a gap between unstable energy harvesting rates & energy requirements of the nodes of the network. The state-of-the-art algorithms proposed so far to address this problem domain are not able to bridge the gap fully to standardize the framework. Hence there is considerable scope of research to create a trade-off between EH techniques and specially designed protocols for in EH-WSN. Current study evaluates the performance and efficiency of some futuristic techniques which incorporate advanced tools and algorithms. The study aims to identify the strength and weaknesses of the proposed techniques which can emerge specific research requirement in this field. Finally, we propose a research direction towards Multi-source Hybrid EH-WSN (MHEHWSN) which is able to maximize energy availability and functional efficiency. The scope of this study is to develop a notion of a framework which eliminates the limitations of very recent techniques of EH-WSN by including multiple energy resources to extract required energy even in presence of unpredictability. However, keeping in mind the ease of use and less complex structure Multi-source hybrid EH technique requires a careful design paradigm.

Efficient Energy Conservation and Faulty Node Detection on Machine Learning-Based Wireless Sensor Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 1-20
Author(s):  
Amarasimha T. ◽  
V. Srinivasa Rao
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Communication ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Faulty Node

Wireless sensor networks are used in machine learning for data communication and classification. Sensor nodes in network suffer from low battery power, so it is necessary to reduce energy consumption. One way of decreasing energy utilization is reducing the information transmitted by an advanced machine learning process called support vector machine. Further, nodes in WSN malfunction upon the occurrence of malicious activities. To overcome these issues, energy conserving and faulty node detection WSN is proposed. SVM optimizes data to be transmitted via one-hop transmission. It sends only the extreme points of data instead of transmitting whole information. This will reduce transmitting energy and accumulate excess energy for future purpose. Moreover, malfunction nodes are identified to overcome difficulties on data processing. Since each node transmits data to nearby nodes, the misbehaving nodes are detected based on transmission speed. The experimental results show that proposed algorithm provides better results in terms of reduced energy consumption and faulty node detection.

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