Network Lifetime Extension Evaluation of Energy Harvesting and Clustering Approaches in WSNs

Author(s):  
Alexandros Zachariadis ◽  
Konstantinos Oikonomou ◽  
Georgios Tsoumanis
Author(s):  
Chi-Tung Chen ◽  
Sirin Tekinay ◽  
Cem Saraydar ◽  
Hsing-Chung Chen ◽  
Ming-Yuan Hsieh ◽  
...  

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Chiwoo Cho ◽  
Kyung-Joon Park ◽  
Hyuk Lim

In wireless sensor networks powered by battery-limited energy harvesting, sensor nodes that have relatively more energy can help other sensor nodes reduce their energy consumption by compressing the sensing data packets in order to consequently extend the network lifetime. In this article, we consider a data compression technique that can shorten the data packet itself to reduce the energies consumed for packet transmission and reception and to eventually increase the entire network lifetime. First, we present an energy consumption model, in which the energy consumption at each sensor node is derived. We then propose a data compression algorithm that determines the compression level at each sensor node to decrease the total energy consumption depending on the average energy level of neighboring sensor nodes while maximizing the lifetime of multihop wireless sensor networks with energy harvesting. Numerical simulations show that the proposed algorithm achieves a reduced average energy consumption while extending the entire network lifetime.


Author(s):  
Mohammed Mehdi Saleh ◽  
Ruslan Saad Abdulrahman ◽  
Aymen Jaber Salman

Wireless sensor networks are regarded as the most essential components of contemporary technologies since they are in charge of sensing and monitoring processes, which are the primary functions of these technologies. Because these nodes rely on an unchangeable battery and are randomly deployed in the environment, node energy management is the most essential issue to consider when designing algorithms to enhance the network's life. Clustering is a wireless sensor network (WSN) routing technique that has been implemented in order to extend network lifetime. Also, it is trendy to increase the energy levels of the node battery by utilizing various energy harvesting techniques in order to extend the network lifetime. In this paper, a new energy-aware clustering algorithm (EHEARA) has been proposed. The proposed algorithm is based on a dynamic clustering function and adopts a solar energy harvesting scheme in order to improve network lifetime. Furthermore, the active-sleep mechanism was used to distribute node activity and balance communication among nodes within clusters and cluster heads with the base station. The proposed algorithm is simulated using matrix laboratory (MATLAB), and the results show that it outperforms the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC), and stable election protocol (SEP) algorithms in terms of network lifetime, energy consumption, and network throughput.


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