Abstract
In wireless sensor networks that consist of a number of power constrained sensor nodes, the foremost challenges are the limited energy and system lifetime. Therefore, designing efficient routing protocols, which prolong the network lifetime, is one of the most critical issues. This paper evaluated several clustering algorithms, namely: Highest Degree Clustering Algorithm (HDCA), and Lowest Identifier Clustering Algorithm (LIDCA) under three metrics: throughput, Packets Delivered Ratio Factor (PDR) and network lifetime. One of the most important challenges facing Mobile Ad hoc Networks is saving energy that led to a longer network lifetime, which is why we proposed a new clustering algorithm that is considered to be more efficient under network lifetime, and it compared to the clustering algorithms mentioned above. Our experiment occurrences showed that the proposed clustering algorithm supplied a relatively better network lifetime and a more efficient energy distribution for the nodes.
Highlights
Self-designing structures of haphazardly moving nodes set up Mobile Ad-hoc Networks (MANETs) in which moving nodes function as mobile terminals, just as directing stations [1]
Three scenarios will be explained so as to obtain the simulation results when implementing each of Highest Degree Clustering Algorithm (HDCA), Lowest Identifier Clustering Algorithm (LIDCA), and our proposed algorithm
Clustering algorithms make the mobile ad-hoc network work efficiently, as specific nodes called a cluster head node are assigned to be responsible for transmission operations like gathering data and sending them to the goal node (Base Station Node)
Summary
Self-designing structures of haphazardly moving nodes set up Mobile Ad-hoc Networks (MANETs) in which moving nodes function as mobile terminals, just as directing stations [1]. A weight value is calculated for each node depending on certain metrics, such as the speed, degree, and energy of nodes This algorithm chooses the minimum weighted node as cluster head [11]. 1.3 (HDCA): As this algorithm utilizes area data for cluster arrangement, it selects the cluster head from the highest degree node in an area. It is a connectivity-based clustering algorithm, and the degree of a node depends on its distance from others, taking the node with the highest degree. Whenever progressively many local nodes are associated with the cluster node, the highest degree node increments, after which that particular node turns into the cluster head of that cluster [11],[12]
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