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Today, wireless sensor networks (WSNs) is thriving successfully in development and innovation because of the massive growth it sees in the wireless communications or embedded systems. Certain issues that the WSNs faced were synchronization, power consumption, routing, limitation of bandwidth, and dynamic topology. Dynamic topology (also known as dyntopo) is a dynamic tessellation sculpting method that allows you to add and delete details on the fly, whereas standard sculpting just changes the contour of a mesh. This enables the creation of complicated shapes from a simple mesh. Most of the energy that was consumed at the time of data communication was among the nodes of the WSN. Data transfer frequency among nodes could be reduced by means of clustering and in-network data aggregation (Visu et al, 2020) The requirements of the application, its usage of relative energy, and determination of the method of data aggregation had to be chosen. Any progress was conducted by the Cluster Heads (CHs) and data aggregators.
Clustering of sensor nodes can be a very effective technique in the conservation of energy. During clustering, the whole network is grouped into clusters. Every cluster will have one leader node called the CH. The CH is responsible for collecting local data from sensor nodes within the clusters (Divakar et al, 2020), aggregating data, and forwarding them to the Base Station (BS) either directly or by means of other CHs. Low-energy adaptive clustering hierarchy (LEACH) refers to a kind of cluster-based routing protocol that makes use of cluster formation. The LEACH chooses some sensor nodes like the CH that rotates the role to distribute a load of energy among the sensors within the network. There were some more drawbacks that were related with this protocol. They are single-hop routing which is used in every node that transmits the sink and CH directly. These CHs are randomly chosen and thus have a possibility where all CHs are concentrated within the same area. The main idea of dynamic clustering can be used, resulting in additional overheads owing to advertisements and CH changes. Thus, the LEACH is not well-suited for large networks (Sharma & Verma 2013).
A way of lowering the consumption of energy of a clustering node was by making use of multi-hop communication. For this type of communication, there are many nodes required will be a path between a CH and BS. The CHs were chosen on the basis of the remainder of energy and their distance from the BS. The algorithm further proposes two different multi-hoping criteria, which is the distance that is based on load balancing and multi-hopping for optimizing the CH selection. For the first criterion, the algorithm will select the shortest path to transmit and avoid unwanted data overheads. In the case of the second criterion, an optimum path is selected in accordance with the traffic load for the intermediate node (Kirsan, 2020).