Minimizing the Energy Consumption of Wireless Sensor Network by Comparing the Performances of Maxweight and Minimum Energy Scheduling Algorithms 

2019 ◽  
Vol 3 (1) ◽  
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Minimum Energy ◽  
Scheduling Algorithms ◽  
Wireless Sensor

scholarly journals Research on the Construction of an e-Commerce Marketing System Based on the Wireless Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chi Zhang
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Minimum Energy ◽  
Cluster Head ◽  
Hardware Design ◽  
Wireless Sensor ◽  
Marketing System ◽  
Minimum Energy Consumption ◽  
Selection Of

This paper takes e-commerce as the research object, based on the combination of wireless sensor network research results, using relevant theoretical analysis tools to identify several major problems in the marketing of enterprises. Then, the internal environment conditions of developing e-commerce are comprehensively analyzed through human resources, financial resources, marketing ability, and platform building ability, and the advantages and disadvantages of the enterprise itself are presented in a three-dimensional manner to help the enterprise understand its situation. Firstly, the overall hardware structure design of this paper is analyzed, and the network marketing node hardware design is proposed as the core of the system hardware design, and the marketing node hardware design circuit diagram is given through the selection of marketing node sensors, the selection of wireless communication modules, and the selection of marketing node microprocessors. Based on the specific application of the wireless sensor network in the e-commerce marketing system, the number of cluster selection is reduced by calculating and setting the remaining energy threshold of the cluster head for the whole network. The optimal cluster head is searched for based on the density of marketing nodes in different regions and the minimum energy consumption of the cluster after the division of the region within the cluster, and the original cluster head is replaced; the density of marketing nodes in different e-commerce is different, and the optimal number of cluster heads is searched for based on the minimum energy consumption of the network. In summary, three strategies are implemented to improve the design of wireless sensor network routing, and the effectiveness of the algorithm is verified through experimental simulation. Through the analysis of e-commerce operation, the intracluster congestion control is achieved by a dual-cluster head strategy with intracluster push selection of subcluster heads; the network nonuniform hierarchy and resource scheduling strategy achieve intercluster congestion mitigation and decongestion. A minimum energy consumption multihop path tree is also proposed here, which can achieve the lowest energy consumption of marketing nodes and networks and improve the link quality relative to other transmission paths. After simulation experiments, the effectiveness and reliability of the congestion algorithm are verified. A guarantee scheme is provided for the development of e-commerce marketing strategies to help breakthroughs and developments in marketing management, and an attempt is also made to provide a template for other enterprises to follow.

A Reprograming Code Distribution Protocol for WSN Based on Hierarchy Structure

2013 ◽  
Vol 846-847 ◽  
pp. 910-913
Author(s):  
Kai Guo Qian
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Minimum Energy ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Stations ◽  
Wireless Sensor ◽  
Local Cluster ◽  
Code Distribution

A reprograming code distribution algorithm for wireless sensor network based on hierarchy structure (RCDABHS) is proposed to improve the nodes energy efficiency and extend the lifetime of wireless sensor network .The algorithm choices portion of nodes with more residual energy to form code distribution tree with minimum energy consumption cost, which completes the code distribution to all heads. Other nodes request the new code from the local cluster head nearby principle. Cluster heads were dynamically elected according to residual energy and distance from base stations to implement the entire network node load balancing, reduce the energy consumption and prolong the network life cycle effectively. The performance analysis and simulation experiments show that the new algorithm effectiveness.

scholarly journals A Heterogeneous Energy Wireless Sensor Network Clustering Protocol

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Mengjia Zeng ◽  
Xu Huang ◽  
Bo Zheng ◽  
Xiangxiang Fan
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Minimum Energy ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Network Clustering ◽  
Cluster Head Selection ◽  
Coverage Ratio ◽  
Clustering Protocol

The Low-Efficiency Adaptive Clustering Hierarchical (LEACH) protocol, a hierarchical routing protocol, has the advantage of simple implementation and can effectively balance network loads. However, to date there has been a lack of consideration for its use in heterogeneous energy network environments. To solve this problem, the Energy-Coverage Ratio Clustering Protocol (E-CRCP) is proposed, which is based on reducing the energy consumption of the system and utilizing the regional coverage ratio. First, the energy model is designed. The optimal number of clusters is determined based on the principle of “minimum energy consumption”, and the cluster head selection is based on the principle of “regional coverage maximization”. In order to balance the network load as much as possible, in the next iteration of cluster head selection, the cluster head with the lowest residual energy and the highest energy consumption is replaced to prolong the network’s life. Our simulated results demonstrate that the proposed method has some advantages in terms of longer network life, load balancing, and overall energy consumption in the environment of a heterogeneous energy wireless sensor network.

scholarly journals An Optimized Node Deployment Solution Based on a Virtual Spring Force Algorithm for Wireless Sensor Network Applications

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1817 ◽  
Author(s):  
Xiaohua Deng ◽  
Zhiyong Yu ◽  
Rongxin Tang ◽  
Xin Qian ◽  
Kai Yuan ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Large Scale ◽  
Early Stage ◽  
Minimum Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Coverage Area ◽  
Spring Force

How to effectively deploy all wireless sensors and save a system’s energy consumption is a key issue in current wireless sensor network (WSN) applications. Theoretical analysis has proven that a hexagonal structure is the best topology in the two-dimensional network, which can provide the maximum coverage area with the minimum number of sensor nodes and minimum energy consumption. Recently, many scientists presented their self-deployment strategies based on different virtual forces and discussed the corresponding efficiency via several case studies. However, according to our statistical analysis, some virtual force algorithms, e.g., virtual spring force, can still cause holes or twisted structure in a small region of the final network distribution, which cannot achieve the ideal network topology and will waste the system energy in real applications. In this paper, we first statistically analyzed the convergence and deployment effect of the virtual spring force algorithm to derive our question. Then we presented an optimized strategy that sensor deployment begins from the center of the target region by adding an external central force. At the early stage, the external force will be added to the most peripheral nodes to promote the formation of hexagonal topology and avoid covering holes or unusual structure. Finally, a series of independent simulation experiments and corresponding statistical results proved that our optimized deployment solution is very stable and effective, which can improve the energy consumption of the whole sensor network and be used in the application of a large scale WSN.

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