Claims:The scope of the invention is defined by the following claims:

Claim:
1. A layered architecture for protocol writing comprising the steps of:
a) Maximizing packet delivery ratio and for minimizing the end-to-end delay in the WSN through dynamic selection of the CHs, GW nodes and routing path.
b) Minimizing the energy consumption and maximizing the lifetime of the network through efficient selection of the CHs, GW nodes and the shortest routing path in the WSN
c) Strategic selection of the CH and GW nodes has been made using parameters, such as the residual energy of the node and angle and distance between the node and the sink
2. The layered architecture for protocol writing as claimed in claim 1, direct routing is preferred over multi hop routing to minimize energy consumption and maximizing the life time of the network.
3. The layered architecture for protocol writing as claimed in claim 1, UCS was preferred in our invention because of less electricity consumption than ECS.
4. The layered architecture for protocol writing as claimed in claim 1, CH and GW nodes are selected dynamically to maximize the packet delivery ratio and minimize the end-to-end delay of the network. , Description:Field of Invention
Multi-hop network is suitable for large-scale network where the data is sent by nodes to the sink through intermediate nodes to save energy. When the sensor network is to be deployed in unattended environment, sensors are scattered randomly and multi-hop routing becomes unavoidable despite the overheads introduced by multi-hop networks for medium access control and topology management.

Background of the Invention
Energy efficient routing can be achieved through various clustering approaches. Cluster-based routing (Nura Modi Shagari et. al., [2020], IEEE Access, vol. 8, pp. 12232-12252) minimizes energy consumption by avoiding redundant messages from various sensor nodes, and also selects the shortest path using CHs and GW nodes. Using clustering, all sensor nodes are grouped together (S. Karim et. al., [2021], IEEE Access, vol. 9, pp. 36730-36747). Each cluster has a CH that can insert and delete CMs, and the cluster's entire control relies on the CH. Clustering permits hierarchal architecture to be more scalable, minimizes the energy consumption, and improves the lifetime of the network. The various sensors deployed in the real time application field form a cluster (Z. Khan et.al., [2019] IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, pp. 3844-3859).
The node inside the cluster is called as the CM, which is able to sense, compute and communicate the information to the CH. The CH is a sensor node which collects the data from the various CMs and routes them to the sink. It receives the information from the sink and routes it to various CMs in its cluster. The CH also routes the control signals, requests and replies messages to the CM and sink. Each CH decides when to quit this role and become a CM depending on various conditions. The CH is in overall incharge of the particular cluster which performs various processes like data aggregation, scheduling and communication (J. Lin et. al., [2019], IEEE Access, vol. 7, pp. 14022-14034). A node which belongs to more than one cluster in the network becomes a GW node and is responsible for routing packets across two clusters or the CH to the sink. It is also used for the interface between the sensor networks and external networks.
The sink is a crucial data storage destination unit which gathers information from various sensor nodes through the CH and GW node and transfers the gathered information to the outside world. Clustering is an efficient technique, the various advantages of clustering are less number of sensor nodes are utilized for communication, optimized energy utilization (Chih min yu et. al., [2021], IEEE Internet of Things Journal, vol. 8, no. 8, pp. 7101-7102), versatility, reduction in communication overhead, provision of higher lifetime, higher packet delivery ratio, higher residual energy, higher energy efficiency and lower end-to-end delay.
The existing routing algorithm for the SCBMAC protocol (CN101114975A) is discussed in this section. It is one of the most useful routing techniques because it established the concept of cluster formation and assigned specialised tasks such as data processing and transmission to a selected sensor node within the cluster known as the cluster head. Hierarchical routing is one of the most widely used routing techniques because it is simple and effective (CH). Clustering also has a variety of deployment difficulties to contend with, including assuring connectivity, selecting CH and clusters, real-time operation, synchronization, data aggregation, and service level agreements (SLAs) (US20160248639A1).
The main objectives of this invention are summarized as, To minimize the energy consumption and maximize the lifetime of the network by efficiently selecting the CH and the GW node along the shortest path from the source node to the sink. To maximize the packet delivery ratio and minimize the end-to-end delay of the network by dynamically selecting the CH and GW node with the path aware clustering metric called PTRC (Predicted Transmission and Reception Count). To minimize the energy consumption and maximize the energy efficiency of the network with the mobile sink by efficiently selecting the CH and the routing path. To compare and analyse the performance of the proposed protocols in terms of residual energy, lifetime, packet delivery ratio, energy efficiency, and end-to-end delay.
Summary of the Invention
In the literature, there are two types of layer clustering models available: equal and unequal layer clustering models. When a network is partitioned into clusters of nearly the same size, this is referred to as equal clustering. When considering unequal clustering, the amount of energy used by each cluster head during a single communication cycle is taken into account. This is followed by a calculation to ensure that the total energy used by all cluster head nodes is distributed evenly across the cluster. The uneven clustering model is predicated on the assumption that all cluster heads in the system are identical to one another. This means that for cluster head nodes in layer 1, the power consumption should be the same as it is for those in higher tiers.
Brief Description of Drawings
The invention will be described in detail with reference to the exemplary embodiments shown in the figures wherein:
Figure 1 Proposed energy dissipation
Figure 2 2 Layer clustering algorithm
Figure 3 Model setup in 2 layers model.
Figure 4 Flow chart for proposed methodology
Detailed Description of the Invention
Wireless sensor networks, just like wireless ad-hoc networks are dynamic in nature due to the frequently changing wireless links and thus network connectivity. In addition, the topology of WSNs changes when the nodes die out or join the network. Further, WSNs and wireless ad-hoc networks show similarity in communication as well as WSNs communication conventionally happens in an ad-hoc manner. Sensor nodes can't survive on small and finite sources of energy because they are remotely deployed and need to communicate wirelessly. Communication consumes the most energy in WSNs compared to processing and sensing. In a centralised system, some sensor nodes must communicate over long distances, consuming more energy. Because WSN sensor nodes are densely deployed and may suffer from redundant information, it is best to process as much information locally as possible to reduce the total number of bits transmitted. Thus, sensor networks require distributed processing. By contrast, multi-hop routing consumes less energy because wireless radio transmission power is proportional to distance squared or higher order in the presence of obstacles. So, in the entire process of creation of an infrastructure, the path used to construct routes is dependent on energy considerations. As the nodes are very close to Base-station in a small-scale network, direct routing is preferred over multi hop routing.
WSN presented to the audience a mobile sink strategy that includes node scheduling as well as other features. The node scheduling technique reduced the amount of duplicate sensed data that was sent to the cluster head before it was delivered to the cluster head, resulting in a reduction in energy consumption and an extension of the life of the cluster head. The node scheduling strategy removes duplicate data from the system, which in turn cuts bandwidth utilisation even more than before. On the basis of the information received by sensing devices, sink can migrate to a site that is, on average, the shortest distance between each target, so increasing the system's lifetime even more.. It is necessary to mimic the sink speeds in order to get the best results from our model. As a result of sink's alleged endless energy, the history record is used to identify when sink first appeared on the screen.
This section describes how to simulate in a network of 400 nodes that are randomly distributed throughout a circle with a radius of 400 metres. Using this network of simulations, we can confirm the validity of the earlier models. The radius of the inner layer is estimated based on the power of the cluster head. Layer 1 clusters with sizes ranging from 6 to 14, Layer 2 clusters with sizes ranging from 6 to 4, and Layer 3 clusters with sizes ranging from 6 to 14. The number of clusters in layer 3 ranges from 6 to 14. It is assumed that there are no multiple paths to the sink. This eliminates the need to send redundant information between the layer 2 and layer 1 cluster head nodes as a result of the design. In addition, the simulation of the three-layer cluster model is completed in this section. Our two-layer model concept is depicted in Figure 3.
The electronic circuits within each WSN node are modelled in the same manner as shown in Figure 1. We created a simple radio model to show how much energy is wasted during signal reception and transmission. Transmission necessitates energy dissipation in both the amplifier and transmitter, whereas reception is radio-only. Fig 2 compares the power usage of two different layered clustering approaches. In the simulation, each node produces 1000 video packets every day, accounting for solar cell recharging and visual quality. Unequal clusters have a lower energy ratio than equal clusters. So, according to our model, the UCS uses less electricity than the ECS. As a result, reducing total power usage extends system lifetime.
4 claims & 4 Figures