Abstract
Energy is a major factor in designing wireless sensor networks (WSNs). In order to extend the network lifetime, researchers should consider energy consumption in routing protocols of WSNs. Routing will serve to facilitate a number of sensors on the technology of WSNs to identify the optimal path and manage energy consumption saving at the time of transmitting data. Current Wireless Sensor Networks efficiency system uses node selection as the main parameter without applying path finding routing. It will not complete the optimization of energy. This research was designed to address the problem of energy optimization by using fuzzy-improved heuristic A-Star. A new algorithm named improved heuristic A-Star was developed from previous A-Star algorithm. The result of fuzzy-improved heuristic A-Star indicated node sensor to sink destination saved 0.3698 joule energy dissipation which resulted in longer lifetime.
Highlights
A sensor node has limited memory, energy, and resources requiring hierarchical settings using clustering so as to lead energy efficiency
This cluster head collects data from sensor nodes and sends information to the base station, it can be said that the cluster head serves as a bridge between the sensor nodes and the base station and sometimes between cluster heads for the multihop case
It was suggested to improve the lifetime of the network by two times compared to using LEACH and SEP with multi-hop transmission and to modify the technique of choosing cluster head which could extend the lifetime of Wireless Sensor Networks
Summary
A sensor node has limited memory, energy, and resources requiring hierarchical settings using clustering so as to lead energy efficiency. In this research[3], the approach of fuzzy logic is employed for choosing cluster head Wireless Sensor Networks. The present research attempts to compare Fuzzy Sugeno and Fuzzy Mamdani in terms of efficiency in extending the lifetime of Wireless Sensor Networks. This research attempted to optimize PEGASIS and LEACH as well as fuzzy which only provide the result of sensor node selection. The algorithm consumes more energy because the farthest node is selected to be the cluster head[6]. This research attempted to develop A-Star algorithm from heuristic value assisted by fuzzy algorithm to fill in the weight value of sensor node which had been calculated by fuzzy.
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